- talks - lists.cs.princeton.edu

CS Colloquium Speaker: Tri Dao TODAY at 12:30pm
by Emily C. Lawrence 02 Mar '23

02 Mar '23

CS Colloquium Speaker Speaker: Tri Dao, Stanford University Date: Thursday, March 2 Time: 12:30pm EST Location: CS 105 Host: Jia Deng Event page: https://www.cs.princeton.edu/events/26343 Title: Hardware-aware Algorithms for Efficient Machine Learning Abstract: Machine learning (ML) models training will continue to grow to consume more cycles, their inference will proliferate on more kinds of devices, and their capabilities will be used on more domains. Some goals central to this future are to make ML models efficient so they remain practical to train and deploy, and to unlock new application domains with new capabilities. We describe some recent developments in hardware-aware algorithms to improve the efficiency-quality tradeoff of ML models and equip them with long context. In the first half, we focus on structured sparsity, a natural approach to mitigate the extensive compute and memory cost of large ML models. We describe a line of work on learnable fast transforms which, thanks to their expressiveness and efficiency, yields some of the first sparse training methods to speed up large models in wall-clock time (2x) without compromising their quality. In the second half, we focus on efficient Transformer training and inference for long sequences. We describe FlashAttention, a fast and memory-efficient algorithm to compute attention with no approximation. By careful accounting of reads/writes between different levels of memory hierarchy, FlashAttention is 2-4x faster and uses 10-20x less memory compared to the best existing attention implementations, allowing us to train higher-quality Transformers with 8x longer context. FlashAttention is now widely used in some of the largest research labs and companies, in just 6 months after its release. We conclude with some exciting directions in ML and systems, such as software-hardware co-design, structured sparsity for scientific AI, and long context for new AI workflows and modalities. Bio: Tri Dao is a PhD student in Computer Science at Stanford, co-advised by Christopher Ré and Stefano Ermon. He works at the interface of machine learning and systems, and his research interests include sequence models with long-range memory and structured matrices for compact deep learning models. His work has received the ICML 2022 Outstanding paper runner-up award.

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ECE Seminar - Thierry Tambe, TODAY at 4:30pm
by Emily C. Lawrence 02 Mar '23

02 Mar '23

ECE SEMINAR Speaker: Thierry Tambe, Harvard University Title: Architecting High Performance Silicon Systems for Accurate, Adaptive, and Efficient On-Chip Deep Learning Day: March 2, 2023 Time: 4:30pm Location: B205 Engineering Quadrangle Host: Sharad Malik Abstract: The unabated pursuit of omniscient and omnipotent AI is levying hefty latency, memory, and energy taxes at all computing scales. My research is building a heterogeneity of self-adaptive solutions co-optimized across the algorithm, architecture, and silicon stack to generate breakthrough advances in arithmetic performance, compute density and efficiency for on-chip machine learning, and natural language processing (NLP) in particular. In the algorithm front, I will discuss best paper award-winning work on developing a novel floating-point based data type, AdaptivFloat, which enables resilient quantized AI computations; and is particularly suitable for NLP networks with large parameter distribution. Then, I will describe a 16nm chip prototype that adopts AdaptivFloat in the acceleration of noise-robust AI speech and machine translation tasks - and whose fidelity to the front-end application is verified via a formal hardware/software compiler interface. Towards the goal of lowering the prohibitive energy cost of inferencing large language models on TinyML devices, I will describe a principled algorithm-hardware co-design solution, validated in a 12nm chip tapeout, that accelerates Transformer workloads by tailoring the accelerator's latency and energy expenditures according to the complexity of the input query it processes. Finally, I will conclude with some of my current research efforts on further pushing the on-chip energy-efficiency frontiers by leveraging specialized non-conventional dynamic memory structures for ML training -- and recently prototyped in a 16nm chip tapeout. Bio: Thierry Tambe is a final year Electrical Engineering PhD candidate at Harvard University advised by Prof. Gu-Yeon Wei and Prof. David Brooks. His current research interests focus on designing energy-efficient and high-performance algorithms, hardware accelerators and systems for machine learning and natural language processing in particular. He also bears a keen interest in agile SoC design methodologies. Prior to debuting his doctoral studies, Thierry was an engineer at Intel in Hillsboro, Oregon, USA designing various mixed-signal architectures for high-bandwidth memory and peripheral interfaces on Xeon and Xeon-Phi HPC SoCs. He received a B.S. (2010) and M.Eng. (2012) in Electrical Engineering from Texas A&M University. Thierry Tambe is a recipient of the Best Paper Award at the 2020 ACM/IEEE Design Automation Conference, a 2021 NVIDIA Graduate PhD Fellowship, and a 2022 IEEE SSCS Predoctoral Achievement Award.

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CS Colloquium Speaker: Stephanie Wang TODAY at 12:30pm
by Emily C. Lawrence 01 Mar '23

01 Mar '23

CS Colloquium Speaker Speaker: Stephanie Wang, University of California, Berkeley Date: Wednesday, March 1 Time: 12:30pm EST Location: CS 105 Host: Wyatt Lloyd Event page: https://www.cs.princeton.edu/events/26342 Title: The Design of a General-Purpose Distributed Execution System Abstract: Scaling applications with distributed execution has become the norm. With the rise of big data and machine learning, more and more developers must build applications that involve complex and data-intensive distributed processing. In this talk, I will discuss the design of a general-purpose distributed execution system that can serve as a common platform for such applications. Such a system offers two key benefits: (1) common system functionality such as distributed resource management can be shared across different application domains, and (2) by building on the same platform, applications across domains can easily interoperate. First, I will introduce the distributed futures interface, a powerful yet expressive distributed programming abstraction for remote execution and memory. Second, I will introduce ownership, an architecture for distributed futures systems that simultaneously provides horizontal scalability, low latency, and fault tolerance. Finally, I will present Exoshuffle, a large-scale shuffle system that builds on distributed futures and ownership to match the speed and reliability of specialized data processing frameworks while using an order of magnitude less code. These works have reached a broad audience through Ray, an open-source distributed futures system for Python that has more than 23,000 GitHub stars and that has been used to train ChatGPT and to break the world record for CloudSort. Bio: Stephanie Wang is a final-year PhD student at UC Berkeley, advised by Professor Ion Stoica. She is interested in distributed systems, with current focus on problems in cloud computing and fault tolerance. She is a co-creator and committer of the popular open-source project Ray for distributed Python. Stephanie has received the UC Berkeley Chancellor’s Fellowship, a Distinguished Artifact Award at SOSP’19, and was selected for Rising Stars in EECS in 2021.

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CITP Distinguished Lecture Series - Alessandro Acquisti, TODAY at 4:30pm
by Emily C. Lawrence 01 Mar '23

01 Mar '23

CITP Distinguished Lecture Series Speaker: Alessandro Acquisti Date: Wednesday, March 1 Time: 4:30 p.m. Location: Friend Center Convocation room Host: CITP Event page: [ https://citp.princeton.edu/event/citp-distinguished-lecture-acquisti/ | https://citp.princeton.edu/event/citp-distinguished-lecture-acquisti/ ] Title: Who Benefits from the Data Economy? Abstract: In the public debate around privacy and the data economy, several claims have been made concerning the benefits that multiple stakeholders may accrue from the collection and analysis of consumer data. How many of those claims are empirically validated by independent research? We will review prior work and present a series of ongoing studies that aim at understanding and estimating how the economic value extracted from consumer data is being allocated to different stakeholders, and the way privacy protection may influence those allocations. Bio: Alessandro Acquisti is the Trustees Professor of Information Technology and Public Policy at the Heinz College, Carnegie Mellon University. He is an Andrew Carnegie Fellow (inaugural class), the director of the PeeX (Privacy Economics Experiments) lab at CMU, a member of the steering committee of CMU’s Center for Behavioral and Decision Research (CBDR), and the Faculty Chair of the Heinz College’s Master of Science in Information Security Policy & Management (MISPM) program. He is the current Chair of CMU Institutional Review Board (IRB). Previously, for four years he was the Faculty Director of the CMU Digital Transformation and Innovation Center sponsored by PwC (where he managed a multi-million dollar budget to fund CMU research in areas including analytics, security, and public policy), and the PwC William W. Cooper Professor of Risk and Regulatory Innovation. He has been a member of the Board of Regents of the U.S. National Library of Medicine (NLM), and a member of the U.S. National Academies’ Committee on public response to alerts and warnings using social media. Acquisti investigates the economics and behavioral economics of privacy and personal data. While completing his Ph.D. thesis at UC Berkeley, he started contributing to the revival of the research on the economics of privacy. Upon joining Carnegie Mellon, he spearheaded the application of behavioral economics to the study of privacy and security decision making, and the investigation of privacy and disclosure behavior in online social media through the mining and analysis of public profile data. His research in these fields has kept evolving, including the investigation of nudging and behavioral interventions in the privacy domain, the study of value creation and surplus allocation in the data economy, and the examination of the economic impact of novel privacy-preserving analytics. He has published and disseminated interdisciplinary research in leading journals across diverse fields (including Science, Proceedings of the National Academy of Science, Management Science, Journal of Economic Literature, Journal of Consumer Research, Journal of Marketing Research, Marketing Science, Information Systems Research, ACM Transactions, and so forth), as well as through conference proceedings (including USENIX, CHI, CSCW, ACM EC, PETS, SOUPS, and so forth), edited books, and numerous keynotes. Acquisti has been the recipient of the PET Award for Outstanding Research in Privacy Enhancing Technologies, the IBM Best Academic Privacy Faculty Award, the IEEE Cybersecurity Award for Innovation, the Heinz College School of Information’s Teaching Excellence Award, and numerous Best Paper awards (including best papers awards across journals in the information technology and information systems fields such as Management Science, MISQ, and Information Systems Research). He has held editorial positions across several journals and conference committees, including Information Systems Research (Senior Editor) and Management Science (Associate Editor). His research has been supported by grants from the National Science Foundation, the MacArthur Foundation, the Sloan Foundation, DARPA, the Department of Homeland Security, the National Security Agency, the Transcoop Foundation, Microsoft, and Google. Security education research and tools he developed with colleagues at Carnegie Mellon University were deployed in the CMU start-up Wombat Security Technologies, Inc, which was later acquired by Proofpoint. He has testified before the U.S. Senate and House committees on issues related to privacy policy and consumer behavior, and has been frequently invited to consult on privacy policy issues by government bodies including the White House’s Office of Science and Technology Policy (OSTP) and the Council of Economic Advisers (CEA), the Federal Trade Commission (FTC), and the National Telecommunications and Information Administration (NTIA), and the European Commission. Acquisti’s findings have been featured in national and international media outlets, including the Economist, the New Yorker, the New York Times and New York Times Magazine, the Wall Street Journal, the Washington Post, the Financial Times, Wired.com, NPR, CNN, and 60 Minutes. His TED talks on privacy and human behavior have been viewed online over 1.5 million times. His 2009 study on the predictability of Social Security numbers was featured in the “Year in Ideas” issue of the NYT Magazine (the SSNs assignment scheme was changed by the US Social Security Administration in 2011). Connecting his research and musical interests, Alessandro has collaborated as technologist on an interactive musical opera on privacy and surveillance inspired by his own research on social media, face recognition, and inference of sensitive information from public data. The opera, titled “Looking at You,” premiered at New York West Village’s HERE in 2019, and was recently awarded a grant by the Sloan Foundation to go on tour across the United States in 2023. He holds a Ph.D. from UC Berkeley, and Master’s degrees from UC Berkeley, the London School of Economics, and Trinity College Dublin. He has held visiting positions at the Universities of Rome, Paris, and Freiburg (visiting professor); Harvard University (visiting scholar); University of Chicago (visiting fellow); Microsoft Research (visiting researcher); and Google (visiting scientist).

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Thea Sommerschield Lecture - today at 4:30pm
by Emily C. Lawrence 27 Feb '23

27 Feb '23

"Inscribed Networks : AI for ancient Mediterranean epigraphies ". Thea Sommerschield Ca' Foscari University of Venice February 27, 2023 | 4:30pm Friend Center 006 and via Zoom This is a hybrid event. [ https://princeton.zoom.us/meeting/register/tJctduivqzgrGdWT0_LE98TAhJ697BIf… | Click here to register for the Zoom link ]

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CS Colloquium Speaker: Milijana Surbatovich TODAY at 12:30pm
by Emily C. Lawrence 27 Feb '23

27 Feb '23

CS Colloquium Speaker Speaker: Milijana Surbatovich, Carnegie Mellon University Date: Monday, February 27 Time: 12:30pm EST Location: CS 105 Host: Amit Levy Event page: https://www.cs.princeton.edu/events/26341 Title: Designing Formally Correct Intermittent Systems Abstract: "Extreme edge computing" is an emerging computing paradigm targeting application domains like medical wearables, disaster-monitoring tiny satellites, or smart infrastructure. This paradigm brings sophisticated sensing and data processing into an embedded device's deployment environment, enabling computing in environments that are too harsh, inaccessible, or dense to support frequent communication with a central server. Batteryless, energy harvesting devices (EHDs) are key to enabling extreme edge computing; instead of using batteries, which may be too costly or even impossible to replace, they can operate solely off energy collected from their environment. However, harvested energy is typically too weak to power a device continuously, causing frequent, arbitrary power failures that break traditional software and make correct programming difficult. Given the high assurance requirements of the envisioned application domains, EHDs must execute software without bugs that could render the device inoperable or leak sensitive information. While researchers have developed intermittent systems to support programming EHDs, they rely on informal, undefined correctness notions that preclude proving such necessary correctness and security properties. My research lays the foundation for designing formally correct intermittent systems that provide correctness guarantees. In this talk, I show how existing correctness notions are insufficient, leading to unaddressed bugs. I then present the first formal model of intermittent execution, along with correctness definitions for important memory consistency and timing properties. I use these definitions to design and implement both the language abstractions that programmers can use to specify their desired properties and the enforcement mechanisms that uphold them. Finally, I discuss my future research directions in intermittent system security and leveraging formal methods for full-stack correctness reasoning. Bio: Milijana Surbatovich is a PhD Candidate in the Electrical and Computer Engineering Department at Carnegie Mellon University, co-advised by Professors Brandon Lucia and Limin Jia. Her research interests are in applied formal methods, programming languages, and systems for intermittent computing and non-traditional computing platforms broadly. She is excited by research problems that require reasoning about correctness and security across the architecture, system, and language stack. She was awarded CMU's CyLab Presidential Fellowship in 2021 and was selected as a 2022 Rising Star in EECS. Previously, she received an MS in ECE from CMU in 2020 and a BS in Computer Science from the University of Rochester in 2017.

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Tuesday Feb 28 - Danica Kragic - Princeton Robotics Seminar
by Emily C. Lawrence 24 Feb '23

24 Feb '23

Speaker: Danica Kragic, Professor in Computer Science, Royal Institute of Technology, KTH Date: Tuesday Feb 28, 2023 Time: 4:25pm EST Location: Computer Science 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Learning perception, action and interaction Abstract: All day long, our fingers touch, grasp and move objects in various media such as air, water, oil. We do this almost effortlessly - it feels like we do not spend time planning and reflecting over what our hands and fingers do or how the continuous integration of various sensory modalities such as vision, touch, proprioception, hearing help us to outperform any other biological system in the variety of the interaction tasks that we can execute. Largely overlooked, and perhaps most fascinating is the ease with which we perform these interactions resulting in a belief that these are also easy to accomplish in artificial systems such as robots. However, there are still no robots that can easily hand-wash dishes, button a shirt or peel a potato. Our claim is that this is fundamentally a problem of appropriate representation or parameterization. When interacting with objects, the robot needs to consider geometric, topological, and physical properties of objects. This can be done either explicitly, by modeling and representing these properties, or implicitly, by learning them from data. The main objective of our work is to create new informative and compact representations of deformable objects that incorporate both analytical and learning-based approaches and encode geometric, topological, and physical information about the robot, the object, and the environment. We do this in the context of challenging multimodal, bimanual object interaction tasks. The focus will be on physical interaction with deformable and soft objects. Bio: Danica Kragic is a Professor at the School of Computer Science and Communication at the Royal Institute of Technology, KTH. She received MSc in Mechanical Engineering from the Technical University of Rijeka, Croatia in 1995 and PhD in Computer Science from KTH in 2001. She has been a visiting researcher at Columbia University, Johns Hopkins University and INRIA Rennes. She is the Director of the Centre for Autonomous Systems. Danica received the 2007 IEEE Robotics and Automation Society Early Academic Career Award. She is a member of the Royal Swedish Academy of Sciences, Royal Swedish Academy of Engineering Sciences and Founding member of Young Academy of Sweden. She holds a Honorary Doctorate from the Lappeenranta University of Technology. Her research is in the area of robotics, computer vision and machine learning.

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CS Department Colloquium Series: week of Feb 27-Mar 3
by Emily C. Lawrence 24 Feb '23

24 Feb '23

The CS Department Colloquium Series kicks off next week. Announcements will be sent each Friday with the following week's talks. And the full calendar can be viewed here: [ https://www.cs.princeton.edu/general/newsevents/events | https://www.cs.princeton.edu/general/newsevents/events ] Speaker: Milijana Surbatovich, Carnegie Mellon University Date: Monday, February 27 Time: 12:30pm EST Location: CS 105 Host: Amit Levy Event page: https://www.cs.princeton.edu/events/26341 Title: Designing Formally Correct Intermittent Systems Abstract: "Extreme edge computing" is an emerging computing paradigm targeting application domains like medical wearables, disaster-monitoring tiny satellites, or smart infrastructure. This paradigm brings sophisticated sensing and data processing into an embedded device's deployment environment, enabling computing in environments that are too harsh, inaccessible, or dense to support frequent communication with a central server. Batteryless, energy harvesting devices (EHDs) are key to enabling extreme edge computing; instead of using batteries, which may be too costly or even impossible to replace, they can operate solely off energy collected from their environment. However, harvested energy is typically too weak to power a device continuously, causing frequent, arbitrary power failures that break traditional software and make correct programming difficult. Given the high assurance requirements of the envisioned application domains, EHDs must execute software without bugs that could render the device inoperable or leak sensitive information. While researchers have developed intermittent systems to support programming EHDs, they rely on informal, undefined correctness notions that preclude proving such necessary correctness and security properties. My research lays the foundation for designing formally correct intermittent systems that provide correctness guarantees. In this talk, I show how existing correctness notions are insufficient, leading to unaddressed bugs. I then present the first formal model of intermittent execution, along with correctness definitions for important memory consistency and timing properties. I use these definitions to design and implement both the language abstractions that programmers can use to specify their desired properties and the enforcement mechanisms that uphold them. Finally, I discuss my future research directions in intermittent system security and leveraging formal methods for full-stack correctness reasoning. Bio: Milijana Surbatovich is a PhD Candidate in the Electrical and Computer Engineering Department at Carnegie Mellon University, co-advised by Professors Brandon Lucia and Limin Jia. Her research interests are in applied formal methods, programming languages, and systems for intermittent computing and non-traditional computing platforms broadly. She is excited by research problems that require reasoning about correctness and security across the architecture, system, and language stack. She was awarded CMU's CyLab Presidential Fellowship in 2021 and was selected as a 2022 Rising Star in EECS. Previously, she received an MS in ECE from CMU in 2020 and a BS in Computer Science from the University of Rochester in 2017. Speaker: Stephanie Wang, University of California, Berkeley Date: Wednesday, March 1 Time: 12:30pm EST Location: CS 105 Host: Wyatt Lloyd Event page: https://www.cs.princeton.edu/events/26342 Title: The Design of a General-Purpose Distributed Execution System Abstract: Scaling applications with distributed execution has become the norm. With the rise of big data and machine learning, more and more developers must build applications that involve complex and data-intensive distributed processing. In this talk, I will discuss the design of a general-purpose distributed execution system that can serve as a common platform for such applications. Such a system offers two key benefits: (1) common system functionality such as distributed resource management can be shared across different application domains, and (2) by building on the same platform, applications across domains can easily interoperate. First, I will introduce the distributed futures interface, a powerful yet expressive distributed programming abstraction for remote execution and memory. Second, I will introduce ownership, an architecture for distributed futures systems that simultaneously provides horizontal scalability, low latency, and fault tolerance. Finally, I will present Exoshuffle, a large-scale shuffle system that builds on distributed futures and ownership to match the speed and reliability of specialized data processing frameworks while using an order of magnitude less code. These works have reached a broad audience through Ray, an open-source distributed futures system for Python that has more than 23,000 GitHub stars and that has been used to train ChatGPT and to break the world record for CloudSort. Bio: Stephanie Wang is a final-year PhD student at UC Berkeley, advised by Professor Ion Stoica. She is interested in distributed systems, with current focus on problems in cloud computing and fault tolerance. She is a co-creator and committer of the popular open-source project Ray for distributed Python. Stephanie has received the UC Berkeley Chancellor’s Fellowship, a Distinguished Artifact Award at SOSP’19, and was selected for Rising Stars in EECS in 2021. Speaker: Tri Dao, Stanford University Date: Thursday, March 2 Time: 12:30pm EST Location: CS 105 Host: Jia Deng Event page: https://www.cs.princeton.edu/events/26343 Title: Hardware-aware Algorithms for Efficient Machine Learning Abstract: Machine learning (ML) models training will continue to grow to consume more cycles, their inference will proliferate on more kinds of devices, and their capabilities will be used on more domains. Some goals central to this future are to make ML models efficient so they remain practical to train and deploy, and to unlock new application domains with new capabilities. We describe some recent developments in hardware-aware algorithms to improve the efficiency-quality tradeoff of ML models and equip them with long context. In the first half, we focus on structured sparsity, a natural approach to mitigate the extensive compute and memory cost of large ML models. We describe a line of work on learnable fast transforms which, thanks to their expressiveness and efficiency, yields some of the first sparse training methods to speed up large models in wall-clock time (2x) without compromising their quality. In the second half, we focus on efficient Transformer training and inference for long sequences. We describe FlashAttention, a fast and memory-efficient algorithm to compute attention with no approximation. By careful accounting of reads/writes between different levels of memory hierarchy, FlashAttention is 2-4x faster and uses 10-20x less memory compared to the best existing attention implementations, allowing us to train higher-quality Transformers with 8x longer context. FlashAttention is now widely used in some of the largest research labs and companies, in just 6 months after its release. We conclude with some exciting directions in ML and systems, such as software-hardware co-design, structured sparsity for scientific AI, and long context for new AI workflows and modalities. Bio: Tri Dao is a PhD student in Computer Science at Stanford, co-advised by Christopher Ré and Stefano Ermon. He works at the interface of machine learning and systems, and his research interests include sequence models with long-range memory and structured matrices for compact deep learning models. His work has received the ICML 2022 Outstanding paper runner-up award.

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ECE Seminar 3/2/2023 - Thierry Tambe, Harvard University
by Emily C. Lawrence 23 Feb '23

23 Feb '23

ECE SEMINAR Speaker: Thierry Tambe, Harvard University Title: Architecting High Performance Silicon Systems for Accurate, Adaptive, and Efficient On-Chip Deep Learning Day: March 2, 2023 Time: 4:30pm Location: B205 Engineering Quadrangle Host: Sharad Malik Abstract: The unabated pursuit of omniscient and omnipotent AI is levying hefty latency, memory, and energy taxes at all computing scales. My research is building a heterogeneity of self-adaptive solutions co-optimized across the algorithm, architecture, and silicon stack to generate breakthrough advances in arithmetic performance, compute density and efficiency for on-chip machine learning, and natural language processing (NLP) in particular. In the algorithm front, I will discuss best paper award-winning work on developing a novel floating-point based data type, AdaptivFloat, which enables resilient quantized AI computations; and is particularly suitable for NLP networks with large parameter distribution. Then, I will describe a 16nm chip prototype that adopts AdaptivFloat in the acceleration of noise-robust AI speech and machine translation tasks - and whose fidelity to the front-end application is verified via a formal hardware/software compiler interface. Towards the goal of lowering the prohibitive energy cost of inferencing large language models on TinyML devices, I will describe a principled algorithm-hardware co-design solution, validated in a 12nm chip tapeout, that accelerates Transformer workloads by tailoring the accelerator's latency and energy expenditures according to the complexity of the input query it processes. Finally, I will conclude with some of my current research efforts on further pushing the on-chip energy-efficiency frontiers by leveraging specialized non-conventional dynamic memory structures for ML training -- and recently prototyped in a 16nm chip tapeout. Bio: Thierry Tambe is a final year Electrical Engineering PhD candidate at Harvard University advised by Prof. Gu-Yeon Wei and Prof. David Brooks. His current research interests focus on designing energy-efficient and high-performance algorithms, hardware accelerators and systems for machine learning and natural language processing in particular. He also bears a keen interest in agile SoC design methodologies. Prior to debuting his doctoral studies, Thierry was an engineer at Intel in Hillsboro, Oregon, USA designing various mixed-signal architectures for high-bandwidth memory and peripheral interfaces on Xeon and Xeon-Phi HPC SoCs. He received a B.S. (2010) and M.Eng. (2012) in Electrical Engineering from Texas A&M University. Thierry Tambe is a recipient of the Best Paper Award at the 2020 ACM/IEEE Design Automation Conference, a 2021 NVIDIA Graduate PhD Fellowship, and a 2022 IEEE SSCS Predoctoral Achievement Award.

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CITP Distinguished Lecture Series: Thomas Ristenpart happening NOW
by Emily C. Lawrence 22 Feb '23

22 Feb '23

CITP Distinguished Lecture Series: Thomas Ristenpart – Mitigating Technology Abuse in Intimate Partner Violence and Encrypted Messaging Date: Wednesday, February 22, 2023 Time: 4:30 pm - 6:00 pm Location: Friend Center Convocation Room [ https://forms.gle/igzdYph1aLCTHpXLA | Please register here ] to attend in person. Co-sponsored by the Department of Computer Science and the Department of Electrical and Computer Engineering Computer security is traditionally about the protection of technology, whereas trust and safety efforts focus on preventing technology abuse from harming people. In this talk, we’ll explore the interplay between security and tech abuse, and make the case that trust and safety represents an important frontier for computer security researchers. To do so, we will draw on examples from two lines of my recent work. First, an overview our work on technology abuse in the context of intimate partner violence (IPV) will be presented. IPV is a widespread social ill affecting about one in four women and one in ten men at some point in their lives. Via interviews with survivors and professionals, online measurement studies, and reverse engineering of malicious tools, our research has provided the most granular view to date of technology abuse in IPV contexts. This has helped educate our efforts on intervention design, most notably in the form of what we call clinical computer security: direct, expert assistance to help survivors navigate technology abuse. Our work led to establishing the Clinic to End Tech Abuse, which has so far worked to help hundreds of survivors of IPV in New York City. Second, we’ll discuss how basic security tools like encrypted messaging need to be adapted in light of tech abuse. Here we find a fundamental tension between the desire for messaging service providers to help moderate malicious content and the confidentiality goals of encryption, which prevent the platform from seeing content. How we end up reconceptualizing and redesigning basic cryptographic tools to more securely support abuse mitigation will be presented. The talk will include content on abuse, including discussion of physical, sexual, and emotional violence. Bio: Thomas Ristenpart is an associate professor at Cornell Tech and a member of the computer science department at Cornell University. Before joining Cornell Tech in May, 2015, he spent four and a half years as an assistant professor at the University of Wisconsin-Madison. He completed his Ph.D. at UC San Diego in 2010. His research spans a wide range of computer security topics, with recent focuses including digital privacy and safety in intimate partner violence, anti-abuse mitigations for encrypted messaging systems, improvements to authentication mechanisms including passwords, and topics in applied and theoretical cryptography. His work is routinely featured in the media and has been recognized by a number of distinguished paper awards, two ACM CCS test-of-time awards, an Advocate of New York City award, an NSF CAREER Award, and a Sloan Research Fellowship. To request accommodations for a disability please contact Jean Butcher, butcher(a)princeton.edu , at least one week prior to the event. The video will be posted to the CITP website, the CITP YouTube channel and the Princeton University Media Central channel. The livestream will be available here: [ https://mediacentrallive.princeton.edu/ | https://mediacentrallive.princeton.edu/ ]

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