- talks - lists.cs.princeton.edu

Colloquium Speaker Sara Achour, Tues. Feb 18 at 12:30pm
by Emily C. Lawrence 12 Feb '20

12 Feb '20

CS Colloquium Speaker Sara Achour, Massachusetts Institute of Technology Tuesday, February 18 - 12:30pm Computer Science - Room 105 Host: Jennifer Rexford [ https://www.cs.princeton.edu/events/25917 | https://www.cs.princeton.edu/events/25917 ] Title: New Compilation Techniques for Reconfigurable Analog Devices Reconfigurable analog devices are a powerful new computing substrate especially appropriate for executing dynamical systems in an energy efficient manner. These devices leverage the physical behavior of transistors to directly implement computation. Under this paradigm, voltages and currents within the device implement continuously evolving variables in the computation. In this talk, I discuss compilation techniques for automatically configuring such devices to execute dynamical systems. I present Legno , the first compilation system that automatically targets a real reconfigurable analog device of this class. Legno synthesizes analog circuits from parametric and specialized analog blocks and accounts for analog noise, quantization error, operating range limitations, and manufacturing variations within the device. I evaluate Legno on applications from the biology, physics, and controls domains. The results demonstrate that these applications execute with acceptable error while consuming microjoules of energy. Bio: Sara Achour is a PhD candidate at the Computer Science and Artificial Intelligence Laboratory at Massachusetts Institute of Technology (CSAIL MIT) and a NSF Fellowship recipient. Her research focuses on new techniques and tools, specifically new programming languages, compilers, and runtime systems, that enable end users to easily develop computations that exploit the potential of emerging nontraditional computing platforms.

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Colloquium Speaker: Kevin Ellis TODAY at 12:30pm
by Emily C. Lawrence 11 Feb '20

11 Feb '20

CS Colloquium Speaker Kevin Ellis, Massachusetts Institute of Technology Tuesday, February 11 - 12:30pm Computer Science - Room 105 Host: Tom Griffiths [ https://www.cs.princeton.edu/events/25918 | https://www.cs.princeton.edu/events/25918 ] Title: Building Machines that Discover Generalizable, Interpretable Knowledge Humans can learn to solve an endless range of problems: building, drawing, designing, coding, and cooking, to name a few, and need relatively modest amounts of experience to acquire any one new individual skill. Machines which can similarly master a diverse span of problems are surely far off. Here, however, I will argue that program induction--an emerging AI technique--will play a role in building this more human-like AI. Program induction systems represent knowledge as programs, and learn by synthesizing code. Across three case studies in vision, natural language, and learning-to-learn, this talk will present program induction systems that take a step toward machines that can: acquire new knowledge from modest amounts of experience; strongly generalize that knowledge to extrapolate beyond their training; learn to represent their knowledge in an interpretable format; and are applicable to a broad spread of problems, from drawing pictures to discovering equations. Driving these developments is a new neuro-symbolic algorithm for Bayesian program synthesis. This algorithm integrates maturing program synthesis technologies with several complementary AI traditions (symbolic, probabilistic, and neural). Building a human-like machine learner is a very distant, long-term goal for the field. In the near-term program induction comes with a roadmap of practical problems to push, such as language learning, scene understanding, and programming-by-examples, which this talk explores. But it's worth keeping these long-term goals in mind as well. Bio: Kevin Ellis works across artificial intelligence, program synthesis, and machine learning. He develops learning algorithms that teach machines to write code, and applies these algorithms to problems in artificial intelligence. His work has appeared in machine learning venues ( NeurIPS , ICLR, IJCAI) and cognitive science venues ( CogSci , TOPICS). He has collaborated with researchers at Harvard, Brown, McGill, Siemens, and MIT, where he is a final-year graduate student advised by Josh Tenenbaum and Armando Solar- Lezama .

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Reminder: Colloquium Speaker Kevin Ellis tomorrow at 12:30pm
by Emily C. Lawrence 10 Feb '20

10 Feb '20

CS Colloquium Speaker Kevin Ellis, Massachusetts Institute of Technology Tuesday, February 11 - 12:30pm Computer Science - Room 105 Host: Tom Griffiths [ https://www.cs.princeton.edu/events/25918 | https://www.cs.princeton.edu/events/25918 ] Title: Building Machines that Discover Generalizable, Interpretable Knowledge Humans can learn to solve an endless range of problems: building, drawing, designing, coding, and cooking, to name a few, and need relatively modest amounts of experience to acquire any one new individual skill. Machines which can similarly master a diverse span of problems are surely far off. Here, however, I will argue that program induction--an emerging AI technique--will play a role in building this more human-like AI. Program induction systems represent knowledge as programs, and learn by synthesizing code. Across three case studies in vision, natural language, and learning-to-learn, this talk will present program induction systems that take a step toward machines that can: acquire new knowledge from modest amounts of experience; strongly generalize that knowledge to extrapolate beyond their training; learn to represent their knowledge in an interpretable format; and are applicable to a broad spread of problems, from drawing pictures to discovering equations. Driving these developments is a new neuro-symbolic algorithm for Bayesian program synthesis. This algorithm integrates maturing program synthesis technologies with several complementary AI traditions (symbolic, probabilistic, and neural). Building a human-like machine learner is a very distant, long-term goal for the field. In the near-term program induction comes with a roadmap of practical problems to push, such as language learning, scene understanding, and programming-by-examples, which this talk explores. But it's worth keeping these long-term goals in mind as well. Bio: Kevin Ellis works across artificial intelligence, program synthesis, and machine learning. He develops learning algorithms that teach machines to write code, and applies these algorithms to problems in artificial intelligence. His work has appeared in machine learning venues ( NeurIPS , ICLR, IJCAI) and cognitive science venues ( CogSci , TOPICS). He has collaborated with researchers at Harvard, Brown, McGill, Siemens, and MIT, where he is a final-year graduate student advised by Josh Tenenbaum and Armando Solar- Lezama .

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Colloquium Speaker Kira Radinsky, TODAY at 12:30pm
by Emily C. Lawrence 10 Feb '20

10 Feb '20

CS Colloquium Speaker Dr. Kira Radinsky, Diagnostic Robotics Monday, February 10 - 12:30pm Computer Science - Room 105 Host: Elad Hazan [ https://www.cs.princeton.edu/events/25906 | https://www.cs.princeton.edu/events/25906 ] Learning to predict the future in healthcare Our world faces increasingly complex challenges: we destabilized the climate, haven’t beaten all diseases, and haven’t spread the values of democracy and freedom to large parts of the globe, where violence and riots reign supreme. The world must be fixed in our generation - everyone would agree. But in order to take action, build a plan, we need to see the complete picture, and empower decision makers with tools to make those changes. This decade, we have finally reached a critical amount of data to facilitate the creation of such tools. My work is inspired by Mark Twain’s quote, who once said: “The past does not repeat itself, but it rhymes." Although future events have unique circumstances, they typically follow familiar past patterns. Over the past few years, I devoted my life to development of prediction techniques. My system inferred that Cholera outbreaks in land-locked areas are more likely to occur following storms, especially when preceded by a long drought. Another inference is that genocide events tend to occur following events where local opinion makers describe minority groups as pests. These types of patterns are composed of several abstractions, over variable-term temporal extents and selected from a large number of possible causes. The algorithms I developed deal with the complexity of discovering such patterns. Large-scale digital histories, social and real-time media, and human web behavior are harvested and augmented with human knowledge mined from the web to afford real-time estimations of likelihoods of future events. Most recently, these algorithms have accurately predicted the first Cholera outbreak reported in Cuba in fifty years. These types of actionable predictions, that enable preventative measures, have drawn the attention of a UN genocide-prevention organization and the Gates foundations and illustrate the vast potential for real impact on the state of humanity. In the last few years I have been focusing on applying similar techniques for the healthcare and Pharma, leveraging large amount of data obtained from both medical records, EMR and other medical research results data in a quest to create an AI system for automated medical research and breakthroughs. Bio: Dr. Kira Radinsky is the chairman and CTO of Diagnostic Robotics, where the most advanced technologies in the field of artificial intelligence are harnessed to make healthcare better, cheaper, and more widely available. Dr. Radinsky has founded SalesPredict , acquired by eBay in 2016 and served as eBay Chief Scientist (IL). She gained international recognition for her work at the Technion and Microsoft Research for developing predictive algorithms that recognized the early warning signs of globally impactful events, as disease epidemics and political unrests. In 2013, she was named one of MIT Technology Review’s 35 Young Innovators Under 35 and in 2015 Forbes included her as "30 Under 30 Rising Stars in Enterprise Tech”. She is a frequent presenter at global tech and industry conferences, including TEDx , Wired, Strata Data Science, Techcrunch and publishes in HBR. Radinsky also serves as a board member in Israel Security Authorities and technology board of HSBC bank. She also holds a visiting professor position at the Technion focusing on the application of predictive data mining in medicine.

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Reminder: Colloquium Speaker Kira Radinsky, tomorrow at 12:30pm
by Emily C. Lawrence 09 Feb '20

09 Feb '20

CS Colloquium Speaker Dr. Kira Radinsky, Diagnostic Robotics Monday, February 10 - 12:30pm Computer Science - Room 105 Host: Elad Hazan [ https://www.cs.princeton.edu/events/25906 | https://www.cs.princeton.edu/events/25906 ] Learning to predict the future in healthcare Our world faces increasingly complex challenges: we destabilized the climate, haven’t beaten all diseases, and haven’t spread the values of democracy and freedom to large parts of the globe, where violence and riots reign supreme. The world must be fixed in our generation - everyone would agree. But in order to take action, build a plan, we need to see the complete picture, and empower decision makers with tools to make those changes. This decade, we have finally reached a critical amount of data to facilitate the creation of such tools. My work is inspired by Mark Twain’s quote, who once said: “The past does not repeat itself, but it rhymes." Although future events have unique circumstances, they typically follow familiar past patterns. Over the past few years, I devoted my life to development of prediction techniques. My system inferred that Cholera outbreaks in land-locked areas are more likely to occur following storms, especially when preceded by a long drought. Another inference is that genocide events tend to occur following events where local opinion makers describe minority groups as pests. These types of patterns are composed of several abstractions, over variable-term temporal extents and selected from a large number of possible causes. The algorithms I developed deal with the complexity of discovering such patterns. Large-scale digital histories, social and real-time media, and human web behavior are harvested and augmented with human knowledge mined from the web to afford real-time estimations of likelihoods of future events. Most recently, these algorithms have accurately predicted the first Cholera outbreak reported in Cuba in fifty years. These types of actionable predictions, that enable preventative measures, have drawn the attention of a UN genocide-prevention organization and the Gates foundations and illustrate the vast potential for real impact on the state of humanity. In the last few years I have been focusing on applying similar techniques for the healthcare and Pharma, leveraging large amount of data obtained from both medical records, EMR and other medical research results data in a quest to create an AI system for automated medical research and breakthroughs. Bio: Dr. Kira Radinsky is the chairman and CTO of Diagnostic Robotics, where the most advanced technologies in the field of artificial intelligence are harnessed to make healthcare better, cheaper, and more widely available. Dr. Radinsky has founded SalesPredict , acquired by eBay in 2016 and served as eBay Chief Scientist (IL). She gained international recognition for her work at the Technion and Microsoft Research for developing predictive algorithms that recognized the early warning signs of globally impactful events, as disease epidemics and political unrests. In 2013, she was named one of MIT Technology Review’s 35 Young Innovators Under 35 and in 2015 Forbes included her as "30 Under 30 Rising Stars in Enterprise Tech”. She is a frequent presenter at global tech and industry conferences, including TEDx , Wired, Strata Data Science, Techcrunch and publishes in HBR. Radinsky also serves as a board member in Israel Security Authorities and technology board of HSBC bank. She also holds a visiting professor position at the Technion focusing on the application of predictive data mining in medicine.

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Nick Giannarakis will present his Pre FPO "An Intermediate Language for Network Verification" on Friday, February 14, 2020 at 2pm in CS 302
by Nicki Mahler 07 Feb '20

07 Feb '20

Nick Giannarakis will present his Pre FPO "An Intermediate Language for Network Verification" on Friday, February 14, 2020 at 2pm in CS 302. The members of his committee are as follows: David Walker (adviser), Aarti Gupta (reader), Ratul Mahajan (UW, reader), Andrew Appel and Jennifer Rexford (examiners). Everyone is invited to attend his talk. The talk title and abstract follow below. Title: An Intermediate Language for Network Verification Abstract: In recent years network misconfiguration-induced outages have become rampant both in frequency and impact. Misconfigurations often occur in the control plane, a distributed system responsible for exchanging routing information among routers. To configure routing policies, operators have to issue per-device configurations in low-level languages, while accounting for interactions with external networks, as well as potential device or link failures. For large networks --- which may involve millions of lines of configuration spread across thousands of devices --- this is a challenging task. To aid operators, researchers have developed a range of static analysis tools to establish correctness properties of network control planes. However, developing and maintaining such tools is an enormous undertaking because of the complexity of configuration languages and the plethora of features the protocols pack. Inspired by prior work on intermediate languages for verification such as Boogie and Why3, I will describe the design and implementation of NV, an intermediate language for verification of routing protocols and their configurations. NV was designed to strike a balance between expressiveness, tractability and ease of use. I will explain how to leverage NV's design to develop efficient implementations (often outperforming the current state-of-the-art by an order of magnitude) of network analyses such as simulation and SMT-based verification. Beyond these standard techniques, NV also facilitates the rapid development of new analyses; I will describe the key insights behind a highly scalable fault tolerance analysis, and present its implementation in the NV language. Finally, I will present a new approach to network compression, implemented on top of NV, and suitable for reasoning about fault-tolerance properties.

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Colloquium Speaker Noam Slonim, TODAY at 12:30pm
by Emily C. Lawrence 07 Feb '20

07 Feb '20

CS Colloquium Speaker Noam Slonim , IBM Haifa Research Lab Friday, February 7 - 12:30pm Computer Science - Room 105 Host: Yoram Singer and Karthik Narasimhan [ https://www.cs.princeton.edu/events/25915 | https://www.cs.princeton.edu/events/25915 ] Title: Project Debater - How persuasive can a computer be? Project Debater is the first AI system that can meaningfully debate a human opponent. The system, an IBM Grand Challenge, is designed to build coherent, convincing speeches on its own, as well as provide rebuttals to the opponent's main arguments. In February 2019, Project Debater competed against Harish Natarajan, who holds the world record for most debate victories, in an event held in San Francisco that was broadcasted live world-wide. In this talk I will tell the story of Project Debater, from conception to a climatic final event, describe its underlying technology, and discuss how it can be leveraged for advancing decision making and critical thinking. Bio: Noam Slonim is a Distinguished Engineer at IBM Research AI. He received his doctorate from the Interdisciplinary Center for Neural Computation at the Hebrew University and held a post-doc position at the Genomics Institute at Princeton University. During his PhD, Noam received the best paper award in UAI and ECIR, and the best presentation award at SIGIR. Noam joined the IBM Haifa Research Lab in 2007, and in 2011 he proposed to develop Project Debater. He has been serving as the Principal Investigator of the project since then. Noam published around 60 peer reviewed articles, focusing on the last few years on advancing the emerging field of Computational Argumentation. Finally, Noam used to have a secondary career as a TV script writer. Coincidentally, or not, in a sitcom he co-created back in 1998, the last episode was focused on competitive debates.

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Reminder: Colloquium Speaker Noam Slonim, tomorrow at 12:30pm
by Emily C. Lawrence 06 Feb '20

06 Feb '20

CS Colloquium Speaker Noam Slonim , IBM Haifa Research Lab Friday, February 7 - 12:30pm Computer Science - Room 105 Host: Yoram Singer and Karthik Narasimhan [ https://www.cs.princeton.edu/events/25915 | https://www.cs.princeton.edu/events/25915 ] Title: Project Debater - How persuasive can a computer be? Project Debater is the first AI system that can meaningfully debate a human opponent. The system, an IBM Grand Challenge, is designed to build coherent, convincing speeches on its own, as well as provide rebuttals to the opponent's main arguments. In February 2019, Project Debater competed against Harish Natarajan, who holds the world record for most debate victories, in an event held in San Francisco that was broadcasted live world-wide. In this talk I will tell the story of Project Debater, from conception to a climatic final event, describe its underlying technology, and discuss how it can be leveraged for advancing decision making and critical thinking. Bio: Noam Slonim is a Distinguished Engineer at IBM Research AI. He received his doctorate from the Interdisciplinary Center for Neural Computation at the Hebrew University and held a post-doc position at the Genomics Institute at Princeton University. During his PhD, Noam received the best paper award in UAI and ECIR, and the best presentation award at SIGIR. Noam joined the IBM Haifa Research Lab in 2007, and in 2011 he proposed to develop Project Debater. He has been serving as the Principal Investigator of the project since then. Noam published around 60 peer reviewed articles, focusing on the last few years on advancing the emerging field of Computational Argumentation. Finally, Noam used to have a secondary career as a TV script writer. Coincidentally, or not, in a sitcom he co-created back in 1998, the last episode was focused on competitive debates.

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Colloquium Speaker: Kevin Ellis, Tuesday, Feb 11 at 12:30pm
by Emily C. Lawrence 05 Feb '20

05 Feb '20

CS Colloquium Speaker Kevin Ellis, Massachusetts Institute of Technology Tuesday, February 11 - 12:30pm Computer Science - Room 105 Host: Tom Griffiths [ https://www.cs.princeton.edu/events/25918 | https://www.cs.princeton.edu/events/25918 ] Title: Building Machines that Discover Generalizable, Interpretable Knowledge Humans can learn to solve an endless range of problems: building, drawing, designing, coding, and cooking, to name a few, and need relatively modest amounts of experience to acquire any one new individual skill. Machines which can similarly master a diverse span of problems are surely far off. Here, however, I will argue that program induction--an emerging AI technique--will play a role in building this more human-like AI. Program induction systems represent knowledge as programs, and learn by synthesizing code. Across three case studies in vision, natural language, and learning-to-learn, this talk will present program induction systems that take a step toward machines that can: acquire new knowledge from modest amounts of experience; strongly generalize that knowledge to extrapolate beyond their training; learn to represent their knowledge in an interpretable format; and are applicable to a broad spread of problems, from drawing pictures to discovering equations. Driving these developments is a new neuro-symbolic algorithm for Bayesian program synthesis. This algorithm integrates maturing program synthesis technologies with several complementary AI traditions (symbolic, probabilistic, and neural). Building a human-like machine learner is a very distant, long-term goal for the field. In the near-term program induction comes with a roadmap of practical problems to push, such as language learning, scene understanding, and programming-by-examples, which this talk explores. But it's worth keeping these long-term goals in mind as well. Bio: Kevin Ellis works across artificial intelligence, program synthesis, and machine learning. He develops learning algorithms that teach machines to write code, and applies these algorithms to problems in artificial intelligence. His work has appeared in machine learning venues ( NeurIPS , ICLR, IJCAI) and cognitive science venues ( CogSci , TOPICS). He has collaborated with researchers at Harvard, Brown, McGill, Siemens, and MIT, where he is a final-year graduate student advised by Josh Tenenbaum and Armando Solar- Lezama .

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TODAY at 4:30pm in B205 - Elaine Shi, Cornell University
by Emily Lawrence 04 Feb '20

04 Feb '20

Speaker: Elaine Shi, Cornell University Title: Practical Oblivious Computation Day: Tuesday, February 4, 2020 Time: 4:30 pm Room: B205 Engineering Quadrangle Host: Prateek Mittal Abstract: Oblivious computation refers to the ability to compute on "encrypted" data, such that neither intermediate results nor the program's runtime behavior reveal anything about secret inputs. Oblivious computation can enable privacy-preserving data mining for sensitive data (e.g., genomic or financial data), and allow businesses and individuals to monetize their data without compromising their privacy. In this talk, I will present a novel, binary-tree based paradigm for constructing Oblivious RAM (ORAM) schemes. ORAM is a generic and powerful primitive that is central to realizing oblivious computation based on either trusted hardware or cryptographic secure computation. Our new ORAM constructions not only solve a thirty-year open theoretical challenge, but also allow ORAM to evolve from a theoretical primitive to a practical building block. Specifically, we made it possible, for the first time, to implement ORAM atop secure processors as well as secure multi-party computation. Additionally, I will present programming language techniques that not only accelerate oblivious computation, but also make it accessible to real-life programmers who are not security experts. Based on these algorithmic and programming language advances, we have built new platforms for oblivious computation and developed various demo applications such as common data structures, data mining, graph algorithms, and streaming algorithms. Evaluation results suggest that our work has led to four to five orders of magnitude performance improvement for moderately large data sizes, and has enabled a dramatic reduction in application development effort. Bio: Elaine Shi is an Associate Professor in Cornell University. Her research interests include security, cryptography, distributed systems, foundations of blockchains, and language-based security. She is a recipient of the Packard Fellowship, the Sloan Fellowship, the ONR YIP award, the NSF CAREER award, the NSA Best Scientific Security paper, and various other best-paper and research awards. Elaine obtained her Ph.D. from Carnegie Mellon University. Before joining Cornell, she was an Assistant Professor in UMD. This seminar is supported by Korhammer Lecture Series Funds

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