From khoury-dean at northeastern.edu Tue Jan 15 14:28:38 2019 From: khoury-dean at northeastern.edu (Khoury Dean's Office) Date: Tue, 15 Jan 2019 19:28:38 +0000 Subject: [Colloq] Colloq. Talk: Data-Driven Genomic Computing: Making Sense of the Signals from the Genome & Extraction of Evolving Knowledge from Social Media Message-ID: Date: January 28, 2019 Time: Stefano Ceri - 12:00 - 12:30pm & Marco Brambilla - 12:30 - 1:00pm Location: 366 West Village H Talk #1 - Stefano Ceri Title: Data-Driven Genomic Computing: Making Sense of the Signals from the Genome Abstract Genomic computing is a new science focused on understanding the functioning of the genome, as a premise to fundamental discoveries in biology and medicine. Next Generation Sequencing (NGS) allows the production of the entire human genome sequence at a cost of about 1000 US $; many algorithms exist for the extraction of genome features, or "signals", including peaks (enriched regions), variants, or gene expression (intensity of transcription activity). The missing gap is a system supporting data integration and exploration, giving a "biological meaning" to all the available information; such a system can be used, e.g., for better understanding cancer or how environment influences cancer development. The GeCo Project (Data-Driven Genomic Computing, ERC Advanced Grant, 2016-2021) has the objective or revisiting genomic computing through the lens of basic data management, through models, languages, and instruments, focusing on genomic data integration. Starting from an abstract model, we developed a system that can be used to query processed data produced by several large Genomic Consortia, including Encode and TCGA; the system employs internally the Spark engine, and prototypes can already be accessed from Polimi, from Cineca (Italian supercomputing center) and from the Broad Institute in Cambridge. During the five-years of the ERC project, the system will be enriched with data analysis tools and environments and will be made increasingly efficient. Among the objectives of the project, the creation of an "open source" repository of public data, available to biological and clinical research through queries, web services and search interfaces. Biography Stefano Ceri is professor of Database Systems at the Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB) of Politecnico di Milano. His research work covers four decades (1978-2018) and has been generally concerned with extending database technologies in order to incorporate new features: distribution, object-orientation, rules, streaming data; with the advent of the Web, his research has been targeted towards the engineering of Web-based applications and to search systems. More recently he turned to genomic computing. He authored over 350 publications (H-index 75) and authored or edited 15 books in English. He is the recipient of two ERC Advanced Grants: "Search Computing (SeCo)" (2008-2013), focused upon the rank-aware integration of search engines in order to support multi-domain queries and "Data-Centered Genomic Computing (GeCo)" (2016-2021), focused upon new abstractions for querying and integrating genomic datasets. He is the recipient of the ACM-SIGMOD "Edward T. Codd Innovation Award" (New York, June 26, 2013), an ACM Fellow and a member of Academia Europaea. Talk #2 - Marco Brambilla Title: Extraction of Evolving Knowledge from Social Media Abstract Knowledge in the world continuously evolves. Ontologies that aim at formalizing this knowledge are largely incomplete, especially regarding data belonging to the so-called long tail. On the other side, informal sources such has social media are typically very up to date with respect to facts, events and relations between real-world entities. We propose a method for discovering emerging knowledge by extracting it from social content. Once initialized by domain experts, the method is capable of finding relevant entities by means of a mixed syntactic-semantic method. The method uses seeds, i.e. prototypes of emerging entities provided by experts, for generating candidates; then, it associates candidates to feature vectors built by using terms occurring in their social content and ranks the candidates by using their distance from the centroid of seeds, returning the top candidates. Our method can run iteratively, using the results as new seeds. The talk will describe the different extraction techniques, the advantages obtained by combining them, and the results of the experiments performed with the different methods. Biography Marco Brambilla is associate professor at Politecnico di Milano. His research interests include data science, domain specific modeling languages and design patterns, crowdsourcing, social media monitoring, and big data analysis. He has been visiting researcher at CISCO, San Jos?, and University of California, San Diego. He has been visiting professor at Dauphine University, Paris. He is co-founder of the startups Fluxedo, focusing on social media analysis and Social engagement, and WebRatio, devoted to software modeling tools for Web, Mobile and Business Process based software applications. He is author of various international books and research articles in journals and conferences, with over 200 papers. He was awarded various best paper prizes and gave keynotes and speeches at many conferences and organizations. He runs research projects on data science and industrial projects on data-driven innovation and big data. He is the main author of the OMG standard IFML. Related Papers Extracting Emerging Knowledge from Social Media. WWW 2017 https://dl.acm.org/citation.cfm?id=3052697 Iterative Knowledge Extraction from Social Networks. WWW Comp. 2018 https://dl.acm.org/citation.cfm?id=3191578 ccis-dean at northeastern.edu Jan Belmonte/Laura Schumann Executive Assistants to Dean Carla Brodley Khoury College of Computer and Information Sciences Northeastern University West Village H, Suite 202 Office Tel: 617.373.5204 Jan's cell: 339.927.1649 Laura's cell: 407.619.2974 From khoury-dean at northeastern.edu Mon Feb 4 16:21:48 2019 From: khoury-dean at northeastern.edu (Khoury Dean's Office) Date: Mon, 4 Feb 2019 21:21:48 +0000 Subject: [Colloq] Seminar: Frans Oliehoek, Beyond local Nash Equilibria for Generative Adversarial Networks on 2/6 Message-ID: What: Seminar: Frans Oliehoek, Beyond local Nash Equilibria for Generative Adversarial Networks Where: ISEC 655 When: Wednesday, 2/6 10-11 Who: Frans Oliehoek, TU Delft Title: Beyond local Nash Equilibria for Generative Adversarial Networks Generative Adversarial Networks (GANs) are a framework in which two neural networks compete with each other: the generator (G) tries to trick the classifier (C) into classifying its generated fake data as true. GANs hold great promise for the development of accurate generative models for complex distributions, and have formed the basis of new approaches to learn from demonstrations (e.g., GAIL). As such, they clearly showcase the potential of multiagent learning methods to make impact on a large variety of machine learning tasks. However, save for some special cases, most current training methods for GANs are at best guaranteed to converge to a ??local Nash equilibrium?? (LNE). Such LNEs, however, can be arbitrarily far from an actual ('global') Nash equilibrium (NE). In this talk, I will cover some recent work which proposes to model GANs explicitly as games in mixed strategies, thereby ensuring that every LNE is an NE. With this formulation, we propose a solution method that is proven to monotonically converge to a /resource-bounded/ Nash equilibrium (RB-NE): by increasing computational resources we can find better solutions. We empirically demonstrate that our method is less prone to typical GAN problems such as mode collapse, and produces solutions that are less exploitable than those produced by GANs, and more closely resemble theoretical predictions about NEs. I will also show some limitations of our current solution technique and discuss ideas to tackle these in the future. Bio: Dr. Frans A. Oliehoek (1981) is Associate Professor at Delft University of Technology. He received his Ph.D. in Computer Science (2010) and M.Sc. Artificial Intelligence (2005) both from the University of Amsterdam (UvA). He subsequently did postdocs at MIT (2010-2012), Maastricht University (MU, 2012-2013), and UvA (2014-2017), and took up roles as assistant professor (MU), Lecturer and Senior Lecturer at the University of Liverpool (2014-2018). Frans?? research interests lie in the intersection of machine learning, AI and game theory. He is considered an expert in the field of decision making under uncertainty, with emphasis on multiagent systems. He organized several workshops on topics such as Multiagent Sequential Decision Making Under Uncertainty and Multiagent Reinforcement Learning. He received the best PC-member award at AAMAS 2012, and was awarded a number of research grants, including a prestigious ??1.5M ERC Starting Grant for his project ?INFLUENCE: Influence-based Decision-making in Uncertain Environments? which started February 2018. From khoury-academicaffairs at northeastern.edu Fri Mar 22 08:40:21 2019 From: khoury-academicaffairs at northeastern.edu (Javed A. Aslam, Khoury Academic Affairs) Date: Fri, 22 Mar 2019 12:40:21 +0000 Subject: [Colloq] Distinguished Lecture Talk Today: Fri Mar 22 @ 11:45 am in 310 Behrakis Message-ID: Title: ?Towards Visually Interactive Neural Probabilistic Models? Speaker: Hanspeter Pfister Date/Time: Friday, March 22, 2019/ 11:45 am ? 1:00 pm Location: Behrakis Center 310 Abstract: Deep learning methods have been a tremendously effective approach to problems in computer vision and natural language processing. However, these black-box models can be dif?cult to deploy in practice as they are known to make unpredictable mistakes that can be hard to analyze and correct. In this talk, I will present collaborative research to develop visually interactive interfaces for probabilistic deep learning models, with the goal of allowing users to examine and correct black-box models through visualizations and interactive inputs. Through co-design of models and visual interfaces we will take the necessary next steps for model interpretability. Achieving this aim requires active investigation into developing new deep learning models and analysis techniques, and integrating them within interactive visualization frameworks. Bio: Hanspeter Pfister is the An Wang Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences and an affiliate faculty member of the Center for Brain Science. His research in visual computing lies at the intersection of visualization, computer graphics, and computer vision and spans a wide range of topics, including biomedical visualization, image and video analysis, 3D fabrication, and visual analytics in data science. Pfister has a PhD in computer science from the State University of New York at Stony Brook and an MS in electrical engineering from ETH Zurich, Switzerland. From 2013 to 2017 he was director of the Institute for Applied Computational Science. Before joining Harvard, he worked for over a decade at Mitsubishi Electric Research Laboratories, where he was associate director and senior research scientist. He was the chief architect of VolumePro, Mitsubishi Electric?s award-winning real-time volume rendering graphics card, for which he received the Mitsubishi Electric President?s Award in 2000. Pfister is the recipient of the 2010 IEEE Visualization Technical Achievement Award, the 2009 IEEE Meritorious Service Award, and the 2009 Petra T. Shattuck Excellence in Teaching Award. Pfister was elected as chair and is currently a director of the IEEE Visualization and Graphics Technical Committee. Website: https://www.seas.harvard.edu/directory/pfister -------------- next part -------------- A non-text attachment was scrubbed... Name: Pfister Flyer.pdf Type: application/pdf Size: 165624 bytes Desc: Pfister Flyer.pdf URL: From khoury-academicaffairs at northeastern.edu Fri Apr 5 10:28:00 2019 From: khoury-academicaffairs at northeastern.edu (Javed A. Aslam, Khoury Academic Affairs) Date: Fri, 5 Apr 2019 14:28:00 +0000 Subject: [Colloq] Colloquium Speaker: Mon April 8 @ 2 pm in 366 WVH/ Bogdan Nicolae "Data Management and Resilience Techniques at Large Scale" Message-ID: Speaker: Bogdan Nicolae Date: Monday, April 8, 2019 Time: 2:00 pm - 3:30 pm Location: 366 WVH Title: "Data Management and Resilience Techniques at Large Scale" Abstract: This talk introduces a series of data management and resilience techniques at large scale. It is structured in three parts. First, it focuses on distributed versioning techniques that keep track of the history of changes for massive data sets, which can be used both to revisit previous states and to optimize concurrent read/write operations. These techniques form the core of the BlobSeer project. Second, it introduces an approach to perform scalable, on-demand broadcast of massive data sets using an adaptive peer-to-peer collaboration strategy, which was adopted by the HPCDS project. Third, it focuses on checkpoint-restart techniques at extreme scale: multi-level checkpointing, adaptive asynchronous post-processing, etc. These techniques form the core of the VeloC project. Bio: Bogdan Nicolae is a Computer Scientist at Argonne National Laboratory, USA. In the past, he held appointments at Huawei Research Germany and IBM Research Ireland. He specializes in scalable storage, data management and fault tolerance for large scale distributed systems, with a focus on high performance architectures cloud computing. He holds a PhD from University of Rennes 1, France and a Dipl. Eng. degree from Politehnica University Bucharest, Romania. He is interested by and authored numerous papers in the areas of scalable I/O, storage elasticity and virtualization, data and metadata decentralization and availability, multi-versioning, data-intensive and big data analytics, checkpoint-restart, live migration. From khoury-academicaffairs at northeastern.edu Mon Apr 8 09:35:15 2019 From: khoury-academicaffairs at northeastern.edu (Javed A. Aslam, Khoury Academic Affairs) Date: Mon, 8 Apr 2019 13:35:15 +0000 Subject: [Colloq] Colloquium Speaker: TODAY, Mon April 8 @ 2 pm in 366 WVH/ Bogdan Nicolae "Data Management and Resilience Techniques at Large Scale" Message-ID: Colloquium Talk: Today, Monday, April 8th @ 2 pm in 366 WVH/ Bogdan Nicolae "Data Management and Resilience Techniques at Large Scale" Speaker: Bogdan Nicolae Date: Monday, April 8, 2019 Time: 2:00 pm - 3:30 pm Location: 366 WVH Title: "Data Management and Resilience Techniques at Large Scale" Abstract: This talk introduces a series of data management and resilience techniques at large scale. It is structured in three parts. First, it focuses on distributed versioning techniques that keep track of the history of changes for massive data sets, which can be used both to revisit previous states and to optimize concurrent read/write operations. These techniques form the core of the BlobSeer project. Second, it introduces an approach to perform scalable, on-demand broadcast of massive data sets using an adaptive peer-to-peer collaboration strategy, which was adopted by the HPCDS project. Third, it focuses on checkpoint-restart techniques at extreme scale: multi-level checkpointing, adaptive asynchronous post-processing, etc. These techniques form the core of the VeloC project. Bio: Bogdan Nicolae is a Computer Scientist at Argonne National Laboratory, USA. In the past, he held appointments at Huawei Research Germany and IBM Research Ireland. He specializes in scalable storage, data management and fault tolerance for large scale distributed systems, with a focus on high performance architectures cloud computing. He holds a PhD from University of Rennes 1, France and a Dipl. Eng. degree from Politehnica University Bucharest, Romania. He is interested by and authored numerous papers in the areas of scalable I/O, storage elasticity and virtualization, data and metadata decentralization and availability, multi-versioning, data-intensive and big data analytics, checkpoint-restart, live migration. From khoury-academicaffairs at northeastern.edu Fri Apr 12 12:34:51 2019 From: khoury-academicaffairs at northeastern.edu (Javed A. Aslam, Khoury Academic Affairs) Date: Fri, 12 Apr 2019 16:34:51 +0000 Subject: [Colloq] =?windows-1252?q?=3D=3FWindows-1252=3FQ=3FColloquium=5FT?= =?windows-1252?q?alk=3A=5FWednesday=2C_=5FApril=5F24th=5F=40=5F1=3A00=5FP?= =?windows-1252?q?M=5Fin=5FISEC=3F=3D__Auditorium/_Victor_Lesser_=93Coordi?= =?windows-1252?q?nating_Multi-Agent_Reinforcement_Learners=94?= Message-ID: Colloquium Talk: Wednesday, April 24th @ 1:00 PM in ISEC Auditorium (room 102)/ Victor Lesser ?Coordinating Multi-Agent Reinforcement Learners? Speaker: Victor Lesser Date: Wednesday, April 24, 2019 Time: 1:00-2:30 pm Location: ISEC Auditorium Title: ?Coordinating Multi-Agent Reinforcement Learners? Abstract: Multi-agent reinforcement learning (MARL) provides an attractive, scalable, and approximate approach for agents to learn coordination policies and adapt their behavior to the dynamics of the uncertain and evolving environment. However, for most large-scale applications involving hundreds of agents, current MARL techniques are inadequate. MARL may converge slowly, converge to inferior equilibria, or even diverge in realistic settings. There are no known distributed approaches that guarantee convergence without either very constraining assumptions about the learning environment and the knowledge at each agent or intractable amounts of computation and communication. These assumptions do not hold in most realistic applications. In this lecture, I will overview my group?s work in melding multi-agent coordination technology with more complex single agent reinforcement learning for scaling MARL to large agent networks. This discussion will include the use of non-local multi-level supervisory control to coordinate and guide the agents? learning process, the use of approximate DCOP algorithms for peer-to-peer learning coordination, the use of conflict resolution detection to dynamically expand the policy space of an agent so as to incorporate additional non-local information, and more recently the use of incremental and on-line transfer learning. This is joint work with, Sherief Abdallah, Bruno Castro da Silva, Dan Garant and Chongjie Zhang together with Anita Raja and Shanjun Cheng from University of North Carolina Charlotte, and Xiangbin Zhu, Zhejiang Normal University, China. Bio: Victor Lesser received the Ph.D. degree in Computer Science from Stanford University, Stanford, CA, 1973. He is an Emeritus Distinguished Professor of Computer Science and Founding Director of the Multi-Agent Systems Laboratory in the College of Information and Computer Sciences at the University of Massachusetts, Amherst. His major research focus is on the control and organization of complex AI systems. He has pioneered work in the development of the blackboard architecture and its control structure, approximate processing for use in control and real-time AI, self-aware control, and a wide variety of techniques for the coordination of and negotiation among multiple agents. He was the system architect for first fully developed blackboard architecture (HEARSAY-II), when he was a research computer scientist at CMU from 1972 thru 1976, and is considered one of the founders of the Multi-Agent field starting with his early work in 1978. He has also made contributions in the areas of machine learning, signal understanding, diagnostics, plan recognition, and computer-supported cooperative work. He has worked in application areas such as sensor networks for vehicle tracking and weather monitoring, speech and sound understanding, information gathering on the internet, peer-to-peer information retrieval, intelligent user interfaces, distributed task allocation and scheduling, and virtual agent enterprises. In terms of statistics, he has published over 500 papers, graduated 36 PhD students, and based on Google Scholar his citation count is over 27000, h-index is 82 and i10-index is 295. A number of his former students (Professors Bo An of NTU, Edmund Durfee of University of Michigan and Tuomas Sandholm of CMU) are internationally recognized AI scholars in the highest tier of their age cohorts. Professor Lesser's research accomplishments have been recognized by many major awards over the years. He received the IJCAI-09 Award for Research Excellence, the most prestigious award in AI. He is also a Founding Fellow of AAAI and an IEEE Fellow. He was General Chair of the first international conference on Multi-Agent Systems (ICMAS) in 1995, and Founding President of the International Foundation of Autonomous Agents and Multi-Agent Systems (IFAAMAS). In 2007, to honor his contributions to the field of multi-agent systems, IFAAMAS established the ?Victor Lesser Distinguished Dissertation Award.? He also received a Special Recognition Award for his foundational research in generalized coordination technologies from the Information Processing Technology Office at DARPA. From khoury-academicaffairs at northeastern.edu Mon Apr 22 14:40:36 2019 From: khoury-academicaffairs at northeastern.edu (Javed A. Aslam, Khoury Academic Affairs) Date: Mon, 22 Apr 2019 18:40:36 +0000 Subject: [Colloq] =?windows-1252?q?=3D=3FWindows-1252=3FQ=3FColloquium=5FT?= =?windows-1252?q?alk=3A=5FWednesday=2C_=5FApril=5F24th=5F=40=5F1=3A00=5FP?= =?windows-1252?q?M=5Fin=5FISEC=3F=3D__Auditorium/_Victor_Lesser_=93Coordi?= =?windows-1252?q?nating_Multi-Agent_Reinforcement_Learners=94?= In-Reply-To: References: Message-ID: Colloquium Talk: Wednesday, April 24th @ 1:00 PM in ISEC Auditorium (room 102)/ Victor Lesser ?Coordinating Multi-Agent Reinforcement Learners? Speaker: Victor Lesser Date: Wednesday, April 24, 2019 Time: 1:00-2:30 pm Location: ISEC Auditorium Title: ?Coordinating Multi-Agent Reinforcement Learners? Abstract: Multi-agent reinforcement learning (MARL) provides an attractive, scalable, and approximate approach for agents to learn coordination policies and adapt their behavior to the dynamics of the uncertain and evolving environment. However, for most large-scale applications involving hundreds of agents, current MARL techniques are inadequate. MARL may converge slowly, converge to inferior equilibria, or even diverge in realistic settings. There are no known distributed approaches that guarantee convergence without either very constraining assumptions about the learning environment and the knowledge at each agent or intractable amounts of computation and communication. These assumptions do not hold in most realistic applications. In this lecture, I will overview my group?s work in melding multi-agent coordination technology with more complex single agent reinforcement learning for scaling MARL to large agent networks. This discussion will include the use of non-local multi-level supervisory control to coordinate and guide the agents? learning process, the use of approximate DCOP algorithms for peer-to-peer learning coordination, the use of conflict resolution detection to dynamically expand the policy space of an agent so as to incorporate additional non-local information, and more recently the use of incremental and on-line transfer learning. This is joint work with, Sherief Abdallah, Bruno Castro da Silva, Dan Garant and Chongjie Zhang together with Anita Raja and Shanjun Cheng from University of North Carolina Charlotte, and Xiangbin Zhu, Zhejiang Normal University, China. Bio: Victor Lesser received the Ph.D. degree in Computer Science from Stanford University, Stanford, CA, 1973. He is an Emeritus Distinguished Professor of Computer Science and Founding Director of the Multi-Agent Systems Laboratory in the College of Information and Computer Sciences at the University of Massachusetts, Amherst. His major research focus is on the control and organization of complex AI systems. He has pioneered work in the development of the blackboard architecture and its control structure, approximate processing for use in control and real-time AI, self-aware control, and a wide variety of techniques for the coordination of and negotiation among multiple agents. He was the system architect for first fully developed blackboard architecture (HEARSAY-II), when he was a research computer scientist at CMU from 1972 thru 1976, and is considered one of the founders of the Multi-Agent field starting with his early work in 1978. He has also made contributions in the areas of machine learning, signal understanding, diagnostics, plan recognition, and computer-supported cooperative work. He has worked in application areas such as sensor networks for vehicle tracking and weather monitoring, speech and sound understanding, information gathering on the internet, peer-to-peer information retrieval, intelligent user interfaces, distributed task allocation and scheduling, and virtual agent enterprises. In terms of statistics, he has published over 500 papers, graduated 36 PhD students, and based on Google Scholar his citation count is over 27000, h-index is 82 and i10-index is 295. A number of his former students (Professors Bo An of NTU, Edmund Durfee of University of Michigan and Tuomas Sandholm of CMU) are internationally recognized AI scholars in the highest tier of their age cohorts. Professor Lesser's research accomplishments have been recognized by many major awards over the years. He received the IJCAI-09 Award for Research Excellence, the most prestigious award in AI. He is also a Founding Fellow of AAAI and an IEEE Fellow. He was General Chair of the first international conference on Multi-Agent Systems (ICMAS) in 1995, and Founding President of the International Foundation of Autonomous Agents and Multi-Agent Systems (IFAAMAS). In 2007, to honor his contributions to the field of multi-agent systems, IFAAMAS established the ?Victor Lesser Distinguished Dissertation Award.? He also received a Special Recognition Award for his foundational research in generalized coordination technologies from the Information Processing Technology Office at DARPA. Faculty Host: Chris Amato From khoury-academicaffairs at northeastern.edu Wed Apr 24 09:06:00 2019 From: khoury-academicaffairs at northeastern.edu (Javed A. Aslam, Khoury Academic Affairs) Date: Wed, 24 Apr 2019 13:06:00 +0000 Subject: [Colloq] =?windows-1252?q?=3D=3FWindows-1252=3FQ=3FColloquium=5FT?= =?windows-1252?q?alk=3A=5FToday=2C_=5FWednesday=2C_=5FApril=5F24th=5F=40?= =?windows-1252?q?=5F1=3A00=5FPM=5F=3F=3D_in_ISEC_Auditorium/_Victor_Lesse?= =?windows-1252?q?r_=93Coordinating_Multi-Agent_Reinforcement_Learners=94?= Message-ID: Colloquium Talk: Wednesday, April 24th @ 1:00 PM in ISEC Auditorium (room 102)/ Victor Lesser ?Coordinating Multi-Agent Reinforcement Learners? Speaker: Victor Lesser Date: Wednesday, April 24, 2019 Time: 1:00-2:30 pm Location: ISEC Auditorium Title: ?Coordinating Multi-Agent Reinforcement Learners? Abstract: Multi-agent reinforcement learning (MARL) provides an attractive, scalable, and approximate approach for agents to learn coordination policies and adapt their behavior to the dynamics of the uncertain and evolving environment. However, for most large-scale applications involving hundreds of agents, current MARL techniques are inadequate. MARL may converge slowly, converge to inferior equilibria, or even diverge in realistic settings. There are no known distributed approaches that guarantee convergence without either very constraining assumptions about the learning environment and the knowledge at each agent or intractable amounts of computation and communication. These assumptions do not hold in most realistic applications. In this lecture, I will overview my group?s work in melding multi-agent coordination technology with more complex single agent reinforcement learning for scaling MARL to large agent networks. This discussion will include the use of non-local multi-level supervisory control to coordinate and guide the agents? learning process, the use of approximate DCOP algorithms for peer-to-peer learning coordination, the use of conflict resolution detection to dynamically expand the policy space of an agent so as to incorporate additional non-local information, and more recently the use of incremental and on-line transfer learning. This is joint work with, Sherief Abdallah, Bruno Castro da Silva, Dan Garant and Chongjie Zhang together with Anita Raja and Shanjun Cheng from University of North Carolina Charlotte, and Xiangbin Zhu, Zhejiang Normal University, China. Bio: Victor Lesser received the Ph.D. degree in Computer Science from Stanford University, Stanford, CA, 1973. He is an Emeritus Distinguished Professor of Computer Science and Founding Director of the Multi-Agent Systems Laboratory in the College of Information and Computer Sciences at the University of Massachusetts, Amherst. His major research focus is on the control and organization of complex AI systems. He has pioneered work in the development of the blackboard architecture and its control structure, approximate processing for use in control and real-time AI, self-aware control, and a wide variety of techniques for the coordination of and negotiation among multiple agents. He was the system architect for first fully developed blackboard architecture (HEARSAY-II), when he was a research computer scientist at CMU from 1972 thru 1976, and is considered one of the founders of the Multi-Agent field starting with his early work in 1978. He has also made contributions in the areas of machine learning, signal understanding, diagnostics, plan recognition, and computer-supported cooperative work. He has worked in application areas such as sensor networks for vehicle tracking and weather monitoring, speech and sound understanding, information gathering on the internet, peer-to-peer information retrieval, intelligent user interfaces, distributed task allocation and scheduling, and virtual agent enterprises. In terms of statistics, he has published over 500 papers, graduated 36 PhD students, and based on Google Scholar his citation count is over 27000, h-index is 82 and i10-index is 295. A number of his former students (Professors Bo An of NTU, Edmund Durfee of University of Michigan and Tuomas Sandholm of CMU) are internationally recognized AI scholars in the highest tier of their age cohorts. Professor Lesser's research accomplishments have been recognized by many major awards over the years. He received the IJCAI-09 Award for Research Excellence, the most prestigious award in AI. He is also a Founding Fellow of AAAI and an IEEE Fellow. He was General Chair of the first international conference on Multi-Agent Systems (ICMAS) in 1995, and Founding President of the International Foundation of Autonomous Agents and Multi-Agent Systems (IFAAMAS). In 2007, to honor his contributions to the field of multi-agent systems, IFAAMAS established the ?Victor Lesser Distinguished Dissertation Award.? He also received a Special Recognition Award for his foundational research in generalized coordination technologies from the Information Processing Technology Office at DARPA. Faculty Host: Chris Amato From khoury-academicaffairs at northeastern.edu Wed Apr 24 10:29:03 2019 From: khoury-academicaffairs at northeastern.edu (Javed A. Aslam, Khoury Academic Affairs) Date: Wed, 24 Apr 2019 14:29:03 +0000 Subject: [Colloq] =?windows-1252?q?=3D=3FWindows-1252=3FQ=3FTIME=5FCORRECT?= =?windows-1252?q?ION=3A=5FColloquium=5FTalk=3A=5FToday=2C_=5FWednesday=2C?= =?windows-1252?q?_=5FApri=3F=3D_l_24th_=40_1=3A15_PM_in_ISEC_Auditorium/_?= =?windows-1252?q?Victor_Lesser_=93Coordinating_Multi-Agent_Reinforcement_?= =?windows-1252?q?Learners=94?= Message-ID: Colloquium Talk: Wednesday, April 24th @ 1:00 PM in ISEC Auditorium (room 102)/ Victor Lesser ?Coordinating Multi-Agent Reinforcement Learners? Speaker: Victor Lesser Date: Wednesday, April 24, 2019 Time: 1:15-2:30 pm Location: ISEC Auditorium Title: ?Coordinating Multi-Agent Reinforcement Learners? Abstract: Multi-agent reinforcement learning (MARL) provides an attractive, scalable, and approximate approach for agents to learn coordination policies and adapt their behavior to the dynamics of the uncertain and evolving environment. However, for most large-scale applications involving hundreds of agents, current MARL techniques are inadequate. MARL may converge slowly, converge to inferior equilibria, or even diverge in realistic settings. There are no known distributed approaches that guarantee convergence without either very constraining assumptions about the learning environment and the knowledge at each agent or intractable amounts of computation and communication. These assumptions do not hold in most realistic applications. In this lecture, I will overview my group?s work in melding multi-agent coordination technology with more complex single agent reinforcement learning for scaling MARL to large agent networks. This discussion will include the use of non-local multi-level supervisory control to coordinate and guide the agents? learning process, the use of approximate DCOP algorithms for peer-to-peer learning coordination, the use of conflict resolution detection to dynamically expand the policy space of an agent so as to incorporate additional non-local information, and more recently the use of incremental and on-line transfer learning. This is joint work with, Sherief Abdallah, Bruno Castro da Silva, Dan Garant and Chongjie Zhang together with Anita Raja and Shanjun Cheng from University of North Carolina Charlotte, and Xiangbin Zhu, Zhejiang Normal University, China. Bio: Victor Lesser received the Ph.D. degree in Computer Science from Stanford University, Stanford, CA, 1973. He is an Emeritus Distinguished Professor of Computer Science and Founding Director of the Multi-Agent Systems Laboratory in the College of Information and Computer Sciences at the University of Massachusetts, Amherst. His major research focus is on the control and organization of complex AI systems. He has pioneered work in the development of the blackboard architecture and its control structure, approximate processing for use in control and real-time AI, self-aware control, and a wide variety of techniques for the coordination of and negotiation among multiple agents. He was the system architect for first fully developed blackboard architecture (HEARSAY-II), when he was a research computer scientist at CMU from 1972 thru 1976, and is considered one of the founders of the Multi-Agent field starting with his early work in 1978. He has also made contributions in the areas of machine learning, signal understanding, diagnostics, plan recognition, and computer-supported cooperative work. He has worked in application areas such as sensor networks for vehicle tracking and weather monitoring, speech and sound understanding, information gathering on the internet, peer-to-peer information retrieval, intelligent user interfaces, distributed task allocation and scheduling, and virtual agent enterprises. In terms of statistics, he has published over 500 papers, graduated 36 PhD students, and based on Google Scholar his citation count is over 27000, h-index is 82 and i10-index is 295. A number of his former students (Professors Bo An of NTU, Edmund Durfee of University of Michigan and Tuomas Sandholm of CMU) are internationally recognized AI scholars in the highest tier of their age cohorts. Professor Lesser's research accomplishments have been recognized by many major awards over the years. He received the IJCAI-09 Award for Research Excellence, the most prestigious award in AI. He is also a Founding Fellow of AAAI and an IEEE Fellow. He was General Chair of the first international conference on Multi-Agent Systems (ICMAS) in 1995, and Founding President of the International Foundation of Autonomous Agents and Multi-Agent Systems (IFAAMAS). In 2007, to honor his contributions to the field of multi-agent systems, IFAAMAS established the ?Victor Lesser Distinguished Dissertation Award.? He also received a Special Recognition Award for his foundational research in generalized coordination technologies from the Information Processing Technology Office at DARPA. Faculty Host: Chris Amato From khoury-academicaffairs at northeastern.edu Wed Jun 19 10:35:55 2019 From: khoury-academicaffairs at northeastern.edu (Khoury Academic Affairs) Date: Wed, 19 Jun 2019 14:35:55 +0000 Subject: [Colloq] Colloquium Talk: Wednesday, June 26 @ 2 pm in ISEC 655/ Zhenkai Liang "Scaling up Binary Analysis via Knowledge-oriented Techniques" Message-ID: Colloquium Talk: Wednesday, June 26 @ 2 pm, 655 ISEC/ Zhenkai Liang " Scaling up Binary Analysis via Knowledge-oriented Techniques" Speaker: Zhenkai Liang Date: Wednesday, June 26, 2019 Time: 2:00 pm Location: 655 ISEC Title: "Scaling up Binary Analysis via Knowledge-oriented Techniques" Abstract: Binary analysis is a fundamental technique in software and system security. It has a wide range of applications, such as vulnerability discovery, attack response, malware analysis, and software testing and debugging. Due to the lack of high-level semantics and complex program behaviors, it is challenging for binary analysis solutions to scale up to large binaries in practice. Existing solutions are often driven by specific tasks, where the practical time limit hinders comprehensively understanding of binaries. Furthermore, it is also difficult to integrate the knowledge generated across different solutions. In this talk, we discuss our research in scaling up binary analysis in a knowledge-oriented manner. We believe knowledge abstraction is the key to scale up binary analysis, where binary analysis solutions generate understandings that can be shared and reused in other solution. Our investigation includes techniques for knowledge extraction, tools for knowledge integration, and platforms for knowledge accumulations and sharing. The accumulated knowledge not only allows broader and deeper analysis into binaries. It also enables emerging data-driven and learning techniques to be effectively adopted in binary analysis solutions. In this talk, I will also share our experience and reflection in system security education. Bio: Zhenkai Liang is an Associate Professor of the School of Computing, National University of Singapore. His main research interests are in system and software security, web security, mobile security, and program analysis. He is also the Co-Lead PI of National Cybersecurity R&D Lab in Singapore. He has served as the technical program committee members of many system security conferences, including the ACM Conference on Computer and Communications Security (CCS), USENIX Security Symposium and the Network and Distributed System Security Symposium (NDSS), as well as a member of NDSS Steering Group. As a co-author, he received the Best Paper Award in ICECCS 2014, the Best Paper Award in W2SP 2014, the ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2009, the Best Paper Award at USENIX Security Symposium 2007, and the Outstanding Paper Award at ACSAC 2003. He also won the Annual Teaching Excellence Award of National University of Singapore in 2014 and 2015. He received his Ph.D. degree in Computer Science from Stony Brook University in 2006, and B.S. degrees in Computer Science and Economics from Peking University in 1999. Faculty Host: Long Lu From khoury-academicaffairs at northeastern.edu Mon Jun 24 13:44:45 2019 From: khoury-academicaffairs at northeastern.edu (Khoury Academic Affairs) Date: Mon, 24 Jun 2019 17:44:45 +0000 Subject: [Colloq] Reminder: Colloquium Talk: Wednesday, June 26 @ 2 pm in ISEC 655/ Zhenkai Liang "Scaling up Binary Analysis via Knowledge-oriented Techniques" Message-ID: Colloquium Talk: Wednesday, June 26 @ 2 pm, 655 ISEC/ Zhenkai Liang " Scaling up Binary Analysis via Knowledge-oriented Techniques" Speaker: Zhenkai Liang Date: Wednesday, June 26, 2019 Time: 2:00 pm Location: 655 ISEC Title: "Scaling up Binary Analysis via Knowledge-oriented Techniques" Abstract: Binary analysis is a fundamental technique in software and system security. It has a wide range of applications, such as vulnerability discovery, attack response, malware analysis, and software testing and debugging. Due to the lack of high-level semantics and complex program behaviors, it is challenging for binary analysis solutions to scale up to large binaries in practice. Existing solutions are often driven by specific tasks, where the practical time limit hinders comprehensively understanding of binaries. Furthermore, it is also difficult to integrate the knowledge generated across different solutions. In this talk, we discuss our research in scaling up binary analysis in a knowledge-oriented manner. We believe knowledge abstraction is the key to scale up binary analysis, where binary analysis solutions generate understandings that can be shared and reused in other solution. Our investigation includes techniques for knowledge extraction, tools for knowledge integration, and platforms for knowledge accumulations and sharing. The accumulated knowledge not only allows broader and deeper analysis into binaries. It also enables emerging data-driven and learning techniques to be effectively adopted in binary analysis solutions. In this talk, I will also share our experience and reflection in system security education. Bio: Zhenkai Liang is an Associate Professor of the School of Computing, National University of Singapore. His main research interests are in system and software security, web security, mobile security, and program analysis. He is also the Co-Lead PI of National Cybersecurity R&D Lab in Singapore. He has served as the technical program committee members of many system security conferences, including the ACM Conference on Computer and Communications Security (CCS), USENIX Security Symposium and the Network and Distributed System Security Symposium (NDSS), as well as a member of NDSS Steering Group. As a co-author, he received the Best Paper Award in ICECCS 2014, the Best Paper Award in W2SP 2014, the ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2009, the Best Paper Award at USENIX Security Symposium 2007, and the Outstanding Paper Award at ACSAC 2003. He also won the Annual Teaching Excellence Award of National University of Singapore in 2014 and 2015. He received his Ph.D. degree in Computer Science from Stony Brook University in 2006, and B.S. degrees in Computer Science and Economics from Peking University in 1999. Faculty Host: Long Lu From khoury-academicaffairs at northeastern.edu Wed Jun 26 09:16:54 2019 From: khoury-academicaffairs at northeastern.edu (Khoury Academic Affairs) Date: Wed, 26 Jun 2019 13:16:54 +0000 Subject: [Colloq] Today: Colloquium Talk, Wednesday, June 26 @ 2 pm in ISEC 655/ Zhenkai Liang "Scaling up Binary Analysis via Knowledge-oriented Techniques" Message-ID: Colloquium Talk: Wednesday, June 26 @ 2 pm, 655 ISEC/ Zhenkai Liang " Scaling up Binary Analysis via Knowledge-oriented Techniques" Speaker: Zhenkai Liang Date: Wednesday, June 26, 2019 Time: 2:00 pm Location: 655 ISEC Title: "Scaling up Binary Analysis via Knowledge-oriented Techniques" Abstract: Binary analysis is a fundamental technique in software and system security. It has a wide range of applications, such as vulnerability discovery, attack response, malware analysis, and software testing and debugging. Due to the lack of high-level semantics and complex program behaviors, it is challenging for binary analysis solutions to scale up to large binaries in practice. Existing solutions are often driven by specific tasks, where the practical time limit hinders comprehensively understanding of binaries. Furthermore, it is also difficult to integrate the knowledge generated across different solutions. In this talk, we discuss our research in scaling up binary analysis in a knowledge-oriented manner. We believe knowledge abstraction is the key to scale up binary analysis, where binary analysis solutions generate understandings that can be shared and reused in other solution. Our investigation includes techniques for knowledge extraction, tools for knowledge integration, and platforms for knowledge accumulations and sharing. The accumulated knowledge not only allows broader and deeper analysis into binaries. It also enables emerging data-driven and learning techniques to be effectively adopted in binary analysis solutions. In this talk, I will also share our experience and reflection in system security education. Bio: Zhenkai Liang is an Associate Professor of the School of Computing, National University of Singapore. His main research interests are in system and software security, web security, mobile security, and program analysis. He is also the Co-Lead PI of National Cybersecurity R&D Lab in Singapore. He has served as the technical program committee members of many system security conferences, including the ACM Conference on Computer and Communications Security (CCS), USENIX Security Symposium and the Network and Distributed System Security Symposium (NDSS), as well as a member of NDSS Steering Group. As a co-author, he received the Best Paper Award in ICECCS 2014, the Best Paper Award in W2SP 2014, the ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2009, the Best Paper Award at USENIX Security Symposium 2007, and the Outstanding Paper Award at ACSAC 2003. He also won the Annual Teaching Excellence Award of National University of Singapore in 2014 and 2015. He received his Ph.D. degree in Computer Science from Stony Brook University in 2006, and B.S. degrees in Computer Science and Economics from Peking University in 1999. Faculty Host: Long Lu From khoury-academicaffairs at northeastern.edu Tue Aug 27 11:00:27 2019 From: khoury-academicaffairs at northeastern.edu (Khoury Academic Affairs) Date: Tue, 27 Aug 2019 15:00:27 +0000 Subject: [Colloq] Colloquium Talk, Wednesday, September 25 @ 2:00 pm in ISEC 655/ Weidong Cui "Triaging and Debugging Failures in Deployed Software by Reverse Execution" Message-ID: Colloquium Talk: Wednesday, September 25 @ 2 pm, 655 ISEC/ Weidong Cui "Triaging and Debugging Failures in Deployed Software by Reverse Execution" Speaker: Dr. Weidong Cui Date: Wednesday, September 25, 2019 Time: 2:00 pm - 3:00 pm Location: 655 ISEC Title: "Triaging and Debugging Failures in Deployed Software by Reverse Execution" Abstract: Many software providers operate crash reporting services to automatically collect failures in deployed software from millions of customers. Triaging and debugging such failures is critical because they impact real users and customers. However, it is notoriously hard in practice because developers have to rely on limited information such as memory dumps. In this talk, I will present two systems we built at Microsoft Research to address the challenges in triaging and debugging failures in deployed software. Both systems were deployed inside Microsoft as a major solution for triaging and debugging software failures. I will also share our experiences in developing and deploying these solutions in practice. First, I will present RETracer, the first system to triage software failures based on program semantics reconstructed from memory dumps. RETracer is designed to meet the requirements of large-scale crash reporting services. It performs binary-level backward taint analysis without a recorded execution trace to understand how functions on the stack contribute to the failure. When comparing it with the previous crash triaging tool used by Microsoft, we find that RETracer eliminates two thirds of triage errors based on a manual analysis of 140 bugs fixed in Microsoft Windows and Office Second, I will present REPT, a practical system that enables reverse debugging of failures in deployed software. REPT reconstructs the execution history with high fidelity by combining online lightweight hardware tracing of a program's control flow with offline binary analysis that recovers its data flow. It is seemingly impossible to recover data values thousands of instructions before the failure due to information loss and concurrent execution. REPT tackles these challenges by iteratively performing forward and backward execution with error correction and constructing a partial execution order with timestamps logged by hardware. When evaluating it on 16 real-world bugs, we find that REPT can recover data values accurately (93% on average) and efficiently (less than 20 seconds) for these bugs, and enables effective reverse debugging for 14 of them. Bio: Weidong Cui is a Senior Principal Research Manager managing the Systems Security and Privacy Research group in the Microsoft Research Redmond lab. Weidong enjoys building real-world systems to tackle hard problems. His current passion is on ensuring Microsoft Azure is the most secure cloud. Weidong and his team built REPT, the first lightweight record and replay solution that is widely deployed to enable reverse debugging of software failures. Weidong led the development of RETracer, a technology that improves the triaging accuracy of access violations significantly. Weidong and his collaborators introduced controlled-channel attacks that can steal rich information from secure enclaves. Weidong led the development of KOP, a Windows kernel rootkit detection system that still represents the state-of-the-art after many years. Weidong is also known for his early work on automatic protocol reverse engineering. Weidong received his Ph.D. and M.S. degrees from UC Berkeley, and his M.E. and B.E. degrees from Tsinghua University. Faculty Host: Long Lu From khoury-academicaffairs at northeastern.edu Mon Sep 23 09:11:15 2019 From: khoury-academicaffairs at northeastern.edu (Khoury Academic Affairs) Date: Mon, 23 Sep 2019 13:11:15 +0000 Subject: [Colloq] Reminder: Colloquium Talk, Wednesday, September 25 @ 2:00 pm in ISEC 655/ Weidong Cui "Triaging and Debugging Failures in Deployed Software by Reverse Execution" Message-ID: Colloquium Talk: Wednesday, September 25 @ 2 pm, 655 ISEC/ Weidong Cui "Triaging and Debugging Failures in Deployed Software by Reverse Execution" Speaker: Dr. Weidong Cui Date: Wednesday, September 25, 2019 Time: 2:00 pm - 3:00 pm Location: 655 ISEC Title: "Triaging and Debugging Failures in Deployed Software by Reverse Execution" Abstract: Many software providers operate crash reporting services to automatically collect failures in deployed software from millions of customers. Triaging and debugging such failures is critical because they impact real users and customers. However, it is notoriously hard in practice because developers have to rely on limited information such as memory dumps. In this talk, I will present two systems we built at Microsoft Research to address the challenges in triaging and debugging failures in deployed software. Both systems were deployed inside Microsoft as a major solution for triaging and debugging software failures. I will also share our experiences in developing and deploying these solutions in practice. First, I will present RETracer, the first system to triage software failures based on program semantics reconstructed from memory dumps. RETracer is designed to meet the requirements of large-scale crash reporting services. It performs binary-level backward taint analysis without a recorded execution trace to understand how functions on the stack contribute to the failure. When comparing it with the previous crash triaging tool used by Microsoft, we find that RETracer eliminates two thirds of triage errors based on a manual analysis of 140 bugs fixed in Microsoft Windows and Office Second, I will present REPT, a practical system that enables reverse debugging of failures in deployed software. REPT reconstructs the execution history with high fidelity by combining online lightweight hardware tracing of a program's control flow with offline binary analysis that recovers its data flow. It is seemingly impossible to recover data values thousands of instructions before the failure due to information loss and concurrent execution. REPT tackles these challenges by iteratively performing forward and backward execution with error correction and constructing a partial execution order with timestamps logged by hardware. When evaluating it on 16 real-world bugs, we find that REPT can recover data values accurately (93% on average) and efficiently (less than 20 seconds) for these bugs, and enables effective reverse debugging for 14 of them. Bio: Weidong Cui is a Senior Principal Research Manager managing the Systems Security and Privacy Research group in the Microsoft Research Redmond lab. Weidong enjoys building real-world systems to tackle hard problems. His current passion is on ensuring Microsoft Azure is the most secure cloud. Weidong and his team built REPT, the first lightweight record and replay solution that is widely deployed to enable reverse debugging of software failures. Weidong led the development of RETracer, a technology that improves the triaging accuracy of access violations significantly. Weidong and his collaborators introduced controlled-channel attacks that can steal rich information from secure enclaves. Weidong led the development of KOP, a Windows kernel rootkit detection system that still represents the state-of-the-art after many years. Weidong is also known for his early work on automatic protocol reverse engineering. Weidong received his Ph.D. and M.S. degrees from UC Berkeley, and his M.E. and B.E. degrees from Tsinghua University. Faculty Host: Long Lu From khoury-academicaffairs at northeastern.edu Wed Sep 25 09:21:07 2019 From: khoury-academicaffairs at northeastern.edu (Khoury Academic Affairs) Date: Wed, 25 Sep 2019 13:21:07 +0000 Subject: [Colloq] Reminder: Colloquium Talk today: Wednesday, September 25 @ 2:00 pm in ISEC 655/ Weidong Cui "Triaging and Debugging Failures in Deployed Software by Reverse Execution" Message-ID: Colloquium Talk today: Wednesday, September 25 @ 2 pm, 655 ISEC/ Weidong Cui "Triaging and Debugging Failures in Deployed Software by Reverse Execution" Speaker: Dr. Weidong Cui Date: Wednesday, September 25, 2019 Time: 2:00 pm - 3:00 pm Location: 655 ISEC Title: "Triaging and Debugging Failures in Deployed Software by Reverse Execution" Abstract: Many software providers operate crash reporting services to automatically collect failures in deployed software from millions of customers. Triaging and debugging such failures is critical because they impact real users and customers. However, it is notoriously hard in practice because developers have to rely on limited information such as memory dumps. In this talk, I will present two systems we built at Microsoft Research to address the challenges in triaging and debugging failures in deployed software. Both systems were deployed inside Microsoft as a major solution for triaging and debugging software failures. I will also share our experiences in developing and deploying these solutions in practice. First, I will present RETracer, the first system to triage software failures based on program semantics reconstructed from memory dumps. RETracer is designed to meet the requirements of large-scale crash reporting services. It performs binary-level backward taint analysis without a recorded execution trace to understand how functions on the stack contribute to the failure. When comparing it with the previous crash triaging tool used by Microsoft, we find that RETracer eliminates two thirds of triage errors based on a manual analysis of 140 bugs fixed in Microsoft Windows and Office Second, I will present REPT, a practical system that enables reverse debugging of failures in deployed software. REPT reconstructs the execution history with high fidelity by combining online lightweight hardware tracing of a program's control flow with offline binary analysis that recovers its data flow. It is seemingly impossible to recover data values thousands of instructions before the failure due to information loss and concurrent execution. REPT tackles these challenges by iteratively performing forward and backward execution with error correction and constructing a partial execution order with timestamps logged by hardware. When evaluating it on 16 real-world bugs, we find that REPT can recover data values accurately (93% on average) and efficiently (less than 20 seconds) for these bugs, and enables effective reverse debugging for 14 of them. Bio: Weidong Cui is a Senior Principal Research Manager managing the Systems Security and Privacy Research group in the Microsoft Research Redmond lab. Weidong enjoys building real-world systems to tackle hard problems. His current passion is on ensuring Microsoft Azure is the most secure cloud. Weidong and his team built REPT, the first lightweight record and replay solution that is widely deployed to enable reverse debugging of software failures. Weidong led the development of RETracer, a technology that improves the triaging accuracy of access violations significantly. Weidong and his collaborators introduced controlled-channel attacks that can steal rich information from secure enclaves. Weidong led the development of KOP, a Windows kernel rootkit detection system that still represents the state-of-the-art after many years. Weidong is also known for his early work on automatic protocol reverse engineering. Weidong received his Ph.D. and M.S. degrees from UC Berkeley, and his M.E. and B.E. degrees from Tsinghua University. Faculty Host: Long Lu From khoury-academicaffairs at northeastern.edu Tue Oct 1 16:41:20 2019 From: khoury-academicaffairs at northeastern.edu (Khoury Academic Affairs) Date: Tue, 1 Oct 2019 20:41:20 +0000 Subject: [Colloq] =?windows-1252?q?=3D=3FWindows-1252=3FQ=3FCraig=5FZilles?= =?windows-1252?q?=5FEducation=5FTalk=3A=5F=3D93Effective=5FSecure=2C_=5Fa?= =?windows-1252?q?nd=5FEf=3F=3D_ficient_Summative_Assessment_using_a_Compu?= =?windows-1252?q?ter-based_Testing_Facility=94_sponsored_by_the_Departmen?= =?windows-1252?q?t_of_Electrical_and_Computer_Engineering=2C_Tues=2E_Oct_?= =?windows-1252?q?8th_=40_10_am_in_136_ISEC?= Message-ID: Craig Zilles Education Talk, sponsored by the Department of Electrical and Computer Engineering Speaker: Assistant Professor, Craig Zilles, University of Illinois at Urbana- Champaign Date: Tuesday, October 8, 2019 Time: 10:00 am ? 11:00 am Location: 136 ISEC Title: ?Effective Secure, and Efficient Summative Assessment using a Computer-based Testing Facility? Abstract: Exams are a widely used method for summative assessment in college education, especially in introductory courses. However, at many universities, introductory courses are large (e.g., 200+ students). Running traditional pencil and-paper exams at this scale presents management challenges that include requesting space, printing exams, proctoring, timely grading, and handling conflict exams. These practical concerns often have more influence on how assessment is performed than pedagogical concerns. In this talk, we'll discuss an effective, secure, and efficient alternative to traditional pencil-and-paper exams. At the College of Engineering at the University of Illinois, we've been running a Computer-Based Testing Facility (CBTF) for more than four years now and have been running at scale (30+ courses, 50,000+ exams/semester) for the past couple years. The CBTF is a proctored, "locked-down" computer lab that is operated as a service to courses. The CBTF has changed how we teach, leading to improved student learning and enabling the introduction of more project and group work in large classes, because graduate TAs are freed from routine proctoring and grading. The goal of the CBTF is to make assessment with exams better for everyone involved--students, faculty, and course staff. Four concepts are key to achieving this goal. First, by running the exams on computers, we can write complex, authentic (e.g., numeric, programming, graphical, design) questions that are auto-gradable, allowing us to test a broad set of learning objectives with minimal grading time and providing students with immediate feedback. Second, we write question generators that use randomness to produce a collection of problems, allowing us to give each student different questions and permitting the problem generators to be used semester after semester. Third, because each student has a unique exam, we allow students to schedule their exams at a time convenient to them within a specified day range, providing students flexibility and avoiding the need to manage conflict exams. Finally, because exam scheduling and proctoring is completely handled by the CBTF, once faculty have their exam content, it is no more effort to run more frequent, smaller exams, which reduces anxiety for some students, as well as offering second-chance exams to reduce failure rates by allowing struggling students an opportunity to review and demonstrate mastery of concepts that they missed on an exam. In this talk, I'll discuss the basic operation of our CBTF and the key components that make it work. I'll present findings on aggregate student behavior in the CBTF and data on increased learning gains and reduced failure rates in specific courses. I'll discuss our mechanisms and policies for maintaining security, supporting testing accommodations, and minimizing faculty disruption. Finally, I'll present findings from surveys of faculty and students and discuss the cost of operating the CBTF and how it compares to traditional exams and online services. Bio: Craig Zilles is an Associate Professor in the Computer Science department at the University of Illinois at Urbana-Champaign. His current research focuses on applying computing and data analytics to education. Previously, his research focused on the interaction between compilers and computer architecture, and he developed the first algorithm that allowed rendering arbitrary three-dimensional polygonal shapes for haptic interfaces (force-feedback human-computer interfaces). Faculty Host: Professor David Kaeli, Department of Electrical and Computer Engineering kaeli at ece.neu.edu -------------- next part -------------- A non-text attachment was scrubbed... Name: Craig Zilles Flyer.pdf Type: application/pdf Size: 389938 bytes Desc: Craig Zilles Flyer.pdf URL: From khoury-academicaffairs at northeastern.edu Tue Oct 15 16:32:15 2019 From: khoury-academicaffairs at northeastern.edu (Khoury Academic Affairs) Date: Tue, 15 Oct 2019 20:32:15 +0000 Subject: [Colloq] =?windows-1252?q?=3D=3FWindows-1252=3FQ=3FColloquium=5FT?= =?windows-1252?q?alk=3A=5FFriday=2C_=5FOctober=5F18=5F=40=5F11=5Fam=2C_?= =?windows-1252?q?=5F366=5FWVH/=5FI-=3F=3D_Ting_Angelia_Lee_=93Advances_in?= =?windows-1252?q?_Determinacy_Race_Detection_for_Task-Parallel_Code=94?= Message-ID: Colloquium Talk: Friday, October 18 @ 11 am, 366 WVH/ I-Ting Angelia Lee ?Advances in Determinacy Race Detection for Task-Parallel Code? Speaker: I-Ting Angelina Lee, Washington University in St. Louis Title: ?Advances in Determinacy Race Detection for Task-Parallel Code? Time: Friday, October 18, 11 AM Place: 366 WVH Abstract: The widespread deployment of multicore platforms --- from personal computers to mobile devices to hardware for rent on the cloud --- has made it critical to develop simple approaches to programming them. In this talk, I will discuss the progress that we made in addressing some of the challenges that arise in multicore programming. I will focus on task parallelism, a programming model designed to simplify the job of writing parallel code that can utilize the multicore hardware efficiently. With task parallelism, the programmer expresses the logical parallelism of the computation using high-level parallel control constructs, and lets the the underlying runtime system automates the necessary scheduling and synchronizations. Even with task parallelism, writing correct and efficient parallel code can still be challenging. One of the challenges is to deal with determinacy races, which occur when logically parallel parts of the computation access the same memory location and at least one of the accesses is a write. Determinacy races are generally bugs in the program since they lead to non-deterministic program behavior --- different schedules of the program can lead to different results. Moreover, they are difficult to detect and debug, since a race may manifest itself in one run but not in another. In this talk, I will discuss our work on supporting efficient determinacy race detection for task-parallel code. I will also briefly discuss how this work fits into my overall research agenda to simplify multicore programming. I take the approach of tackling the problem from multiple perspectives: designing programming models, developing system support, and building an ecosystem of productivity tool supports around the model. I will use our work on determinacy race detection as an example to illustrate that working from multiple perspectives can be synergistic and lead to results that are difficult to obtain otherwise. If time permits, I will also briefly discuss some of my other ongoing work. Bio: I-Ting Angelina Lee is an assistant professor in the Computer Science and Engineering department in Washington University in St. Louis. Her research agenda is to make multicore programming accessible for everyone, so that every programmer, particularly the non-experts, can rapidly develop high performance software that takes advantage of commodity multicore hardware. To that end, she is interested in many aspects of multicore computing, including designing programming models and linguistic constructs to simplify multicore programming, developing runtime mechanisms and scheduling algorithms to enable parallel code to execute efficiently, and building productivity tools to aid debugging and performance engineering of parallel code. She obtained her Ph.D. from Massachusetts Institute of Technology under the supervision of Professor Charles Leiserson. Host: Rajmohan Rajaraman From khoury-academicaffairs at northeastern.edu Fri Oct 18 08:48:06 2019 From: khoury-academicaffairs at northeastern.edu (Khoury Academic Affairs) Date: Fri, 18 Oct 2019 12:48:06 +0000 Subject: [Colloq] =?windows-1252?q?=3D=3FWindows-1252=3FQ=3FReminder=3A=5F?= =?windows-1252?q?Colloquium=5FTalk=5Ftoday=3A=5FFriday=2C_=5FOctober=5F18?= =?windows-1252?q?=5F=40=5F11=3F=3D__am=2C_366_WVH/_I-Ting_Angelia_Lee_=93?= =?windows-1252?q?Advances_in_Determinacy_Race_Detection_for_Task-Parallel?= =?windows-1252?q?_Code=94?= Message-ID: Colloquium Talk: Friday, October 18 @ 11 am, 366 WVH/ I-Ting Angelia Lee ?Advances in Determinacy Race Detection for Task-Parallel Code? Speaker: I-Ting Angelina Lee, Washington University in St. Louis Title: ?Advances in Determinacy Race Detection for Task-Parallel Code? Time: Friday, October 18, 11 AM Place: 366 WVH Abstract: The widespread deployment of multicore platforms --- from personal computers to mobile devices to hardware for rent on the cloud --- has made it critical to develop simple approaches to programming them. In this talk, I will discuss the progress that we made in addressing some of the challenges that arise in multicore programming. I will focus on task parallelism, a programming model designed to simplify the job of writing parallel code that can utilize the multicore hardware efficiently. With task parallelism, the programmer expresses the logical parallelism of the computation using high-level parallel control constructs, and lets the the underlying runtime system automates the necessary scheduling and synchronizations. Even with task parallelism, writing correct and efficient parallel code can still be challenging. One of the challenges is to deal with determinacy races, which occur when logically parallel parts of the computation access the same memory location and at least one of the accesses is a write. Determinacy races are generally bugs in the program since they lead to non-deterministic program behavior --- different schedules of the program can lead to different results. Moreover, they are difficult to detect and debug, since a race may manifest itself in one run but not in another. In this talk, I will discuss our work on supporting efficient determinacy race detection for task-parallel code. I will also briefly discuss how this work fits into my overall research agenda to simplify multicore programming. I take the approach of tackling the problem from multiple perspectives: designing programming models, developing system support, and building an ecosystem of productivity tool supports around the model. I will use our work on determinacy race detection as an example to illustrate that working from multiple perspectives can be synergistic and lead to results that are difficult to obtain otherwise. If time permits, I will also briefly discuss some of my other ongoing work. Bio: I-Ting Angelina Lee is an assistant professor in the Computer Science and Engineering department in Washington University in St. Louis. Her research agenda is to make multicore programming accessible for everyone, so that every programmer, particularly the non-experts, can rapidly develop high performance software that takes advantage of commodity multicore hardware. To that end, she is interested in many aspects of multicore computing, including designing programming models and linguistic constructs to simplify multicore programming, developing runtime mechanisms and scheduling algorithms to enable parallel code to execute efficiently, and building productivity tools to aid debugging and performance engineering of parallel code. She obtained her Ph.D. from Massachusetts Institute of Technology under the supervision of Professor Charles Leiserson. Host: Rajmohan Rajaraman From khoury-academicaffairs at northeastern.edu Tue Dec 3 16:43:45 2019 From: khoury-academicaffairs at northeastern.edu (Khoury Academic Affairs) Date: Tue, 3 Dec 2019 21:43:45 +0000 Subject: [Colloq] Colloquium Talk: Friday, December 6 @ 11 am in 366 WVH: Mayur Thakur "A Tale of 3 Data Types: Surveillance Algorithms over Text, Graph, and Numeric Data/ Faculty Host: Ravi Sundaram Message-ID: Colloquium Talk: Friday, December 6 @ 11 am in 366 WVH: Mayur Thakur "A Tale of 3 Data Types: Surveillance Algorithms over Text, Graph, and Numeric Data Date: Friday, December 6, 2019 Speaker: Mayur Thakur Talk Title: "A Tale of 3 Data Types: Surveillance Algorithms over Text, Graph, and Numeric Data" Time: 11:00 am - 11:50 am Location: 366 WVH Abstract: Each day petabytes of financial data flow through the pipes of large financial institutions such as Goldman Sachs. These include billions of market events, millions of trades, emails, and deals. From billions of little pieces of data, algorithms mine patterns and find a relatively tiny number of risky events such as insider trading, market manipulation, and leakage of confidential information. In this talk we will we will show that building robust surveillances requires solving, at scale, some well-studied problems in distributed systems, search engines, NLP, and graph algorithms. For example, non-parametric outlier detection and k-nearest neighbor techniques are crucial in detecting money laundering. Bio: Mayur Thakur is the head of Surveillance Engineering in the Global Compliance Division. He joined Goldman Sachs as a managing director in 2014. In the past 5 years, he has led the development of a big data surveillance engineering platform, which now handles petabytes of data and runs a variety of surveillances. In his capacity as the head of surveillance engineering, Mayur has been responsible for building a team of engineers who have skills in big data, modeling, and finance. Prior to joining the firm, Mayur worked at Google, where he designed search algorithms for more than seven years. Previously, he was an assistant professor of computer science at the University of Missouri. Faculty host: Ravi Sundaram From khoury-academicaffairs at northeastern.edu Fri Dec 6 08:38:44 2019 From: khoury-academicaffairs at northeastern.edu (Khoury Academic Affairs) Date: Fri, 6 Dec 2019 13:38:44 +0000 Subject: [Colloq] CANCELED: Colloquium Talk: Friday, December 6 @ 11 am: Mayur Thakur/ Faculty Host: Ravi Sundaram Message-ID: The talk originally scheduled for today at 11 am has been canceled. From: Khoury Academic Affairs Sent: Tuesday, December 3, 2019 4:44 PM To: Khoury Academic Affairs Subject: Colloquium Talk: Friday, December 6 @ 11 am in 366 WVH: Mayur Thakur "A Tale of 3 Data Types: Surveillance Algorithms over Text, Graph, and Numeric Data/ Faculty Host: Ravi Sundaram Colloquium Talk: Friday, December 6 @ 11 am in 366 WVH: Mayur Thakur "A Tale of 3 Data Types: Surveillance Algorithms over Text, Graph, and Numeric Data Date: Friday, December 6, 2019 Speaker: Mayur Thakur Talk Title: "A Tale of 3 Data Types: Surveillance Algorithms over Text, Graph, and Numeric Data" Time: 11:00 am - 11:50 am Location: 366 WVH Abstract: Each day petabytes of financial data flow through the pipes of large financial institutions such as Goldman Sachs. These include billions of market events, millions of trades, emails, and deals. From billions of little pieces of data, algorithms mine patterns and find a relatively tiny number of risky events such as insider trading, market manipulation, and leakage of confidential information. In this talk we will we will show that building robust surveillances requires solving, at scale, some well-studied problems in distributed systems, search engines, NLP, and graph algorithms. For example, non-parametric outlier detection and k-nearest neighbor techniques are crucial in detecting money laundering. Bio: Mayur Thakur is the head of Surveillance Engineering in the Global Compliance Division. He joined Goldman Sachs as a managing director in 2014. In the past 5 years, he has led the development of a big data surveillance engineering platform, which now handles petabytes of data and runs a variety of surveillances. In his capacity as the head of surveillance engineering, Mayur has been responsible for building a team of engineers who have skills in big data, modeling, and finance. Prior to joining the firm, Mayur worked at Google, where he designed search algorithms for more than seven years. Previously, he was an assistant professor of computer science at the University of Missouri. Faculty host: Ravi Sundaram From khoury-academicaffairs at northeastern.edu Fri Dec 6 16:33:26 2019 From: khoury-academicaffairs at northeastern.edu (Khoury Academic Affairs) Date: Fri, 6 Dec 2019 21:33:26 +0000 Subject: [Colloq] Colloquium Talk: Wednesday, December 11 am in 655 ISEC: Guy Rosman "Uncertainty-aware Representations Robotics"/ Faculty Host: Rob Platt Message-ID: Colloquium Talk: Wed. Dec 11 @ 10 am in 655 ISEC: Guy Rosman "Uncertainty-aware Representations Robotics" Date: Wednesday, December 11, 2019 Speaker: Guy Rosman, Toyota Research Institute Talk Title: "Uncertainty-aware Representations Robotics" Time: 10:00 - 11:00 am Location: 655 ISEC Abstract: For machines to excel in autonomous driving, surgery, and other robotics applications, they must capture what they sense and act on. Such a good robotic situational awareness requires us to handle uncertainty when we represent the world. In this talk I demonstrate several novel representations for handling uncertainty and their use in robotic applications. In this talk I demonstrate several novel representations for handling uncertainty and their use in three robotic applications: (1) 3D sensing for robotic assembly, (2) autonomous driving, and (3) video analysis of surgery. Bio: Guy Rosman is a research scientist at Toyota Research Institute (TRI) in Cambridge, where he explores uncertainty and risk in human driver behavior modeling. During his postdoc at MIT/CSAIL, he received the Technion-MIT post-doctoral Fellowship and worked with the Distributed Robotics Lab and the Sensing, Learning and Inference group. His research interests include inference and machine learning techniques in robotics, autonomous driving, and computer vision, as well as 3D sensing and AI-assisted surgery. His past works explore priors in structure and motion estimation from visual sensors, and the role of different optimization and inference techniques in computer vision. He obtained in 2004 his BSc Summa Cum Laude, in 2008 MSc Cum Laude, and in 2013 PhD at the Technion, in the Computer Science Department. Prior to TRI, he has worked at several companies, including IBM research, RAFAEL Ltd., Medicvision, and Invision Biometrics (now Intel Realsense). He was recently a Best Paper Award finalist at ICRA'18 and ICRA'19, is the recipient of an MIT-Technion Postdoctoral Fellowship (2013), an Intel PhD award (2013), the Faculty Excellence Scholarship (2011, 2012) and the Jacobs-Qualcomm Scholarship (2011). Faculty Host: Rob Platt From khoury-academicaffairs at northeastern.edu Wed Dec 11 08:57:21 2019 From: khoury-academicaffairs at northeastern.edu (Khoury Academic Affairs) Date: Wed, 11 Dec 2019 13:57:21 +0000 Subject: [Colloq] Colloquium TODAY at 10 AM: Wednesday, December 11 @ 10 am in 655 ISEC: Guy Rosman "Uncertainty-aware Representations Robotics"/Faculty Host: Rob Platt Message-ID: Colloquium: Wed. Dec 11 @ 10 am in 655 ISEC: Guy Rosman "Uncertainty-aware Representations Robotics" Date: Wednesday, December 11, 2019 Speaker: Guy Rosman, Toyota Research Institute Talk Title: "Uncertainty-aware Representations Robotics" Time: 10:00 - 11:00 am Location: 655 ISEC Abstract: For machines to excel in autonomous driving, surgery, and other robotics applications, they must capture what they sense and act on. Such a good robotic situational awareness requires us to handle uncertainty when we represent the world. In this talk I demonstrate several novel representations for handling uncertainty and their use in robotic applications. In this talk I demonstrate several novel representations for handling uncertainty and their use in three robotic applications: (1) 3D sensing for robotic assembly, (2) autonomous driving, and (3) video analysis of surgery. Bio: Guy Rosman is a research scientist at Toyota Research Institute (TRI) in Cambridge, where he explores uncertainty and risk in human driver behavior modeling. During his postdoc at MIT/CSAIL, he received the Technion-MIT post-doctoral Fellowship and worked with the Distributed Robotics Lab and the Sensing, Learning and Inference group. His research interests include inference and machine learning techniques in robotics, autonomous driving, and computer vision, as well as 3D sensing and AI-assisted surgery. His past works explore priors in structure and motion estimation from visual sensors, and the role of different optimization and inference techniques in computer vision. He obtained in 2004 his BSc Summa Cum Laude, in 2008 MSc Cum Laude, and in 2013 PhD at the Technion, in the Computer Science Department. Prior to TRI, he has worked at several companies, including IBM research, RAFAEL Ltd., Medicvision, and Invision Biometrics (now Intel Realsense). He was recently a Best Paper Award finalist at ICRA'18 and ICRA'19, is the recipient of an MIT-Technion Postdoctoral Fellowship (2013), an Intel PhD award (2013), the Faculty Excellence Scholarship (2011, 2012) and the Jacobs-Qualcomm Scholarship (2011). Faculty Host: Rob Platt From khoury-academicaffairs at northeastern.edu Mon Dec 23 10:04:10 2019 From: khoury-academicaffairs at northeastern.edu (Khoury Academic Affairs) Date: Mon, 23 Dec 2019 15:04:10 +0000 Subject: [Colloq] Visiting Research Speaker: Mon. Jan 13 @ 1:30 pm in 655 ISEC: Mathy Vanhoef "Dragonblood: Attacking the Dragonfly Handshake of WPA3 and EAP-pwd"/ Faculty Host: Aanjhan Ranganathan Message-ID: Visiting Research Speaker: Mon. Jan. 13 @ 1:30 pm in 655 ISEC: Mathy Vanhoef "Dragonblood: Attacking the Dragonfly Handshake of WPA3 and EAP-pwd" Date: Monday, January 13, 2019 Time: 1:30 - 2:30 pm Speaker: Mathy Vanhoef Talk Title: "Dragonblood: Attacking the Dragonfly Handshake of WPA3 and EAP-pwd" Location: 655 ISEC Abstract: In this talk, we show that the Dragonfly handshake of WPA3 and EAP-pwd is affected by several design and implementations flaws. Most prominently, we present side-channel leaks that allow an adversary to perform brute-force attacks on the password. Additionally, we present invalid curve attacks against all EAP-pwd and one WPA3 implementation. These implementation-specific attacks enable an adversary to bypass authentication. Finally, we briefly discuss countermeasures that have been incorporated into the Wi-Fi standard. Bio: Mathy Vanhoef is a postdoctoral researcher at New York University Abu Dhabi. He is most well-known for his KRACK attack against WPA2 and the RC4 NOMORE attack against RC4. His research interest lies in computer security with a focus on network security, wireless security (e.g. Wi-Fi), network protocols, and applied cryptography. Currently, his research is about analyzing security protocols to automatically discover (logical) implementation vulnerabilities. Faculty host: Aanjhan Ranganathan