March 2023 - talks - lists.cs.princeton.edu

CS Department Colloquium Series: week of Mar 27-31
by Emily C. Lawrence 21 Apr '23

21 Apr '23

Here are next week's CS Department Colloquium Series talks. As always, you can find the full calendar of events here: https://www.cs.princeton.edu/general/newsevents/events Speaker: Adam Fisch, Massachusetts Institute of Technology Date: Monday, March 27 Time: 12:30pm EST Location: CS 105 Host: Karthik Narasimhan Event page: https://www.cs.princeton.edu/events/26363 Title: Towards Efficient and Reliable Machine Learning for Natural Language Processing (and Beyond) Abstract: In this talk, I will introduce work on fundamental techniques for building and deploying effective natural language processing (NLP) systems that are also efficient and reliable. Specifically, I will address three interconnected challenges for modern machine learning in NLP: how to quickly adapt foundation models to new tasks with limited data, how to dynamically reconfigure large architectures for more efficient computation, and how to develop powerful theoretical tools for rigorous, yet practical, uncertainty quantification. To conclude, I will highlight a number of my future research directions, as well as extensions to interesting applications beyond natural language. Bio: Adam Fisch is a PhD candidate at MIT working with Regina Barzilay and Tommi Jaakkola, and a recipient of an NSF Graduate Research Fellowship. His research centers around principled methods for efficient and reliable machine learning systems that work effectively in realistic scenarios, and has appeared in top-tier venues such as *ACL, ICLR, ICML, and NeurIPS. Adam also served as a co-instructor for the tutorial on Uncertainty Estimation for NLP at COLING 2022, and as a co-organizer of the Machine Reading for Question Answering workshops at EMNLP 2019 and 2021. Prior to MIT, Adam was a research engineer at Meta (Facebook) AI Research for two years, and studied mechanical engineering as an undergraduate at Princeton University. Speaker: Aviral Kumar, University of California, Berkeley Date: Tuesday, March 28 Time: 12:30pm EST Location: CS 105 Host: Jia Deng Event page: https://www.cs.princeton.edu/events/26377 Title: Reinforcement Learning from Static Datasets: Algorithms, Analysis and Applications Abstract: Typically, reinforcement learning (RL) methods rely on trial-and-error interaction with the environment from scratch to discover effective behaviors. While this sort of paradigm has the potential to discover good strategies, this paradigm also inhibits RL methods from collecting enough experience or training data in real-world problems where active interaction is expensive (e.g., in drug design) or dangerous (e.g., for robots operating around humans). My work develops approaches to alleviate this limitation: how can we learn policies to effectively make decisions entirely from previously-collected, static datasets in an offline manner? In this talk, I will discuss challenges that appear in this kind of offline reinforcement learning (offline RL) and develop algorithms and techniques to address these challenges. I will then discuss how my approaches for offline RL and decision-making have enabled us to make progress in real-world problems such as hardware accelerator design, robotic manipulation, and computational chemistry. Finally, I will discuss how we can enable offline RL methods to benefit from generalization capabilities offered by large and expressive models, similar to supervised learning. Bio: Aviral Kumar is a final year Ph.D. student at UC Berkeley. His research focuses on developing effective and reliable approaches for (sequential) decision-making. Towards this goal, he focuses on designing reinforcement learning techniques to static datasets and on understanding and applying these methods in practice. Before his Ph.D., Aviral obtained his B.Tech. in Computer Science from IIT Bombay in India. He is a recipient of the C.V. & Daulat Ramamoorthy Distinguished Research Award, given to 1 PhD student in EECS at Berkeley for outstanding contributions to a new area of research in computer science, Facebook Ph.D. Fellowship in Machine Learning and Apple Scholars in AI/ML Ph.D. Fellowship.

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Decenter Seminar: Joachim Matthias Neu, Monday, March 27 at 3:30
by Emily C. Lawrence 07 Apr '23

07 Apr '23

Decenter Seminar Speaker: Joachim Matthias Neu, Stanford University Day: Monday, March 27, 2023 Time: 3:30-4:30pm Location: CS 105 Host: Pramod Viswanath Title: Internet-Scale Consensus In The Blockchain Era Abstract: Blockchains have ignited interest in Internet-scale consensus as a fundamental building block for decentralized applications and services, which in turn promise more egalitarian access and improved robustness to faults and abuse. While consensus has been studied in distributed systems for decades, Internet-scale consensus requires a fundamental rethinking of models, desiderata, and protocols. Participants are no longer a handful of computers in one company's data centers, but numerous mistrustful entities distributed across the Internet. I will discuss two examples of challenges and solutions for this new setting: (1) Ethereum, the second largest blockchain, aims to strengthen consensus liveness under open participation where parties come and go at will, and to strengthen safety to enable accountability in case of any safety violation. However, we show that no traditional single-ledger protocol can satisfy both strengthened properties simultaneously. To resolve this dilemma, we develop the multi-ledger consensus paradigm that is now the security design-specification for Ethereum. A by-product of this work are attacks on Ethereum's consensus protocol that prompted design changes. (2) Traditional network models do not capture rate constraints on communication and processing, leaving popular "provably secure" protocols vulnerable to attack. We show via a new queuing-based model how to schedule message handling securely. Our policies are simple enough to be forward-deployed at Internet service providers via a system that can also protect traffic of applications beyond blockchains. Bio: Joachim Neu is a PhD candidate at Stanford University with David Tse. His research focuses on the science and engineering of Internet-scale consensus as a fundamental building block for decentralized systems, using tools from distributed systems, applied probability, networking and communications, and applied cryptography. While a Masters student at Technical University of Munich, he worked in information and coding theory. Joachim has received the Protocol Labs PhD Fellowship and the Stanford Graduate Fellowship.

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Quantum Seminar - Gokul Subramanian Ravi: Thursday, April 6 at 4:30pm
by Emily C. Lawrence 31 Mar '23

31 Mar '23

Speaker: Gokul Subramanian Ravi, University of Chicago Date: Thursday, April 6 Time: 4:30pm EST Location: Engineering Quadrangle B205 Host: Hakan Türeci Event page: https://www.cs.princeton.edu/events/26361 Title: A Hybrid Computing Ecosystem for Practical Quantum Advantage Abstract: As quantum computing transforms from lab curiosity to technical reality, we must unlock its full potential to enable meaningful benefits on real-world applications with imperfect quantum technology. Achieving this vision requires computer architects to play a key role, leveraging classical computing principles to build and facilitate a hybrid computing ecosystem for practical quantum advantage. First, I will introduce my four research thrusts toward building this hybrid ecosystem: Classical Application Transformation, Adaptive Noise Mitigation, Scalable Error Correction and Efficient Resource Management. Second, from the Classical Application Transformation thrust, I will present "CAFQA: A classical simulation bootstrap for variational quantum algorithms", which enables accurate classical initialization for VQAs by searching efficiently through the classically simulable portion of the quantum space with Bayesian Optimization. CAFQA recovers as much as 99.99% of the accuracy lost in prior state-of-the-art classical initialization, with mean improvements of 56x. Third, from the Scalable Error Correction thrust, I will present "Clique: Better than worst-case decoding for quantum error correction", which proposes the Clique QEC decoder for cryogenic quantum systems. Clique is a lightweight cryo-decoder for decoding and correcting common trivial errors, so that only the rare complex errors are handled outside the cryo-refrigerator. Clique eliminates 90-99+% of the cryo-refrigerator I/O decoding bandwidth, while supporting more than a million physical qubits. Finally, I will conclude with an overview of other prior and ongoing work, along with my future research vision toward practical quantum advantage. Bio: Gokul Subramanian Ravi is a 2020 NSF CI Fellows postdoctoral scholar at the University of Chicago, mentored by Prof. Fred Chong. His research targets quantum computing architecture and systems, primarily on themes at the intersection of quantum and classical computing. He received his PhD in computer architecture from UW-Madison in 2020 and was advised by Prof. Mikko Lipasti. He was awarded the 2020 Best ECE Dissertation Award from UW-Madison and named a 2019 Rising Star in Computer Architecture. His quantum and classical computing research have resulted in publications at top computer architecture, systems, and engineering venues (such as ASPLOS, ISCA, MICRO, HPCA, TACO, ISLPED, QCE, IISWC), as well as two granted and three pending patents. His co-authored work was recognized as the Best Paper at HPCA 2022 and as a 2023 IEEE Micro Top Picks Honorable Mention.

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ECE Seminar: Sneha Goenka, TODAY at 4:30pm
by Emily C. Lawrence 29 Mar '23

29 Mar '23

ECE SEMINAR Speaker: Sneha Goenka, Stanford University Title: Biology needs Computer Architects: Keeping up with genomic-scale data Day: Wednesday, March 29, 2023 Time: 4:30 PM Location: B205 Engineering Quadrangle Host: Sharad Malik Abstract: The landscape of computing has undergone a significant transformation with the death of Dennard scaling and the slowing of Moore's law: applications now drive innovations in computer systems architecture. At the same time, the advent of high throughput, low cost sequencing technology has revolutionized genomics. The massive amount of data generated in genomics has uncovered acute compute challenges in biological inference due to limitations of software on traditional multi-core systems. To address this challenge, my research employs a hardware-software-algorithm co-design approach to significantly improve computational performance in key areas of comparative and clinical genomics. In this talk, I will present systems that accelerate pipelines in both these domains of genomics. First, I will present Darwin-WGA (FPGA/ASIC) and SegAlign (GPU) for cross-species whole genome alignment where co-design has yielded orders of magnitude increase in speed. Additionally, there are gains in accuracy while modifying the algorithm to improve the underlying hardware implementation. Next, I will talk about the ultra-rapid nanopore whole genome sequencing pipeline that can deliver a genetic diagnosis in under 8 hours, making it the fastest pipeline to date. The scalable, cloud-based distributed infrastructure overcomes system bottlenecks to enable near real-time computation and improved variant identification. This pipeline has been deployed in critical care units in Stanford hospitals and applied to 13 patients. Finally, I will share my vision for continued advancements in computer systems applied to emerging fields such as pangenomics, single cell genomics, and long read clinical sequencing. Bio: Sneha Goenka is a Ph.D. candidate in the Electrical Engineering Department at Stanford University where she is advised by Prof. Mark Horowitz. Her research centers on designing efficient computer systems for advancing genomic pipelines for clinical and research applications, with a focus on improving speed and cost. She is a 2023 Forbes 30 Under 30 Honoree in the Science category, 2022 NVIDIA Graduate Fellow, and 2021 Cadence Women in Technology Scholar. She has a B.Tech. and M.Tech. (Microelectronics) in Electrical Engineering from the Indian Institute of Technology, Bombay where she received the Akshay Dhoke Memorial Award for the most outstanding student in the program.

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CS Colloquium Speaker Aviral Kumar TODAY at 12:30pm
by Emily C. Lawrence 28 Mar '23

28 Mar '23

CS Colloquium Speaker Speaker: Aviral Kumar, University of California, Berkeley Date: Tuesday, March 28 Time: 12:30pm EST Location: CS 105 Host: Jia Deng Event page: https://www.cs.princeton.edu/events/26377 Title: Reinforcement Learning from Static Datasets: Algorithms, Analysis and Applications Abstract: Typically, reinforcement learning (RL) methods rely on trial-and-error interaction with the environment from scratch to discover effective behaviors. While this sort of paradigm has the potential to discover good strategies, this paradigm also inhibits RL methods from collecting enough experience or training data in real-world problems where active interaction is expensive (e.g., in drug design) or dangerous (e.g., for robots operating around humans). My work develops approaches to alleviate this limitation: how can we learn policies to effectively make decisions entirely from previously-collected, static datasets in an offline manner? In this talk, I will discuss challenges that appear in this kind of offline reinforcement learning (offline RL) and develop algorithms and techniques to address these challenges. I will then discuss how my approaches for offline RL and decision-making have enabled us to make progress in real-world problems such as hardware accelerator design, robotic manipulation, and computational chemistry. Finally, I will discuss how we can enable offline RL methods to benefit from generalization capabilities offered by large and expressive models, similar to supervised learning. Bio: Aviral Kumar is a final year Ph.D. student at UC Berkeley. His research focuses on developing effective and reliable approaches for (sequential) decision-making. Towards this goal, he focuses on designing reinforcement learning techniques to static datasets and on understanding and applying these methods in practice. Before his Ph.D., Aviral obtained his B.Tech. in Computer Science from IIT Bombay in India. He is a recipient of the C.V. & Daulat Ramamoorthy Distinguished Research Award, given to 1 PhD student in EECS at Berkeley for outstanding contributions to a new area of research in computer science, Facebook Ph.D. Fellowship in Machine Learning and Apple Scholars in AI/ML Ph.D. Fellowship.

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CSML Speaker: Rose Yu, Monday, March 27 at 12:30
by Emily C. Lawrence 27 Mar '23

27 Mar '23

CSML Speaker: Rose Yu, University of Californian, San Diego Day: Monday, March 27, 2023 Time: 12:30 p.m. *Lunch available from 12:00 p.m. RSVP to [ mailto:csml@princeton.edu | csml(a)princeton.edu ] Location: CSML Classroom 103 Host: Ryan Adams and Peter Ramadge Event page: [ https://csml.princeton.edu/events/physics-guided-ai-learning-spatiotemporal… | https://csml.princeton.edu/events/physics-guided-ai-learning-spatiotemporal… ] Title: Physics-Guided AI for Learning Spatiotemporal Dynamics Abstract: Applications in climate science, epidemiology, and transportation often require learning complex dynamics from large-scale spatiotemporal data. While deep learning has shown tremendous success in these domains, it remains a grand challenge to incorporate physical principles in a systematic manner into the design and training of such models. In this talk, I will demonstrate how to principally incorporate physics in AI models to improve sample complexity, prediction accuracy, and physical consistency. I will showcase the applications of these models to challenging problems such as turbulence forecasting, jet tagging in particle physics, and trajectory prediction for autonomous vehicles. Bio: Dr. Rose Yu is an assistant professor at the University of California San Diego, Department of Computer Science and Engineering. She earned her Ph.D. in Computer Sciences at USC in 2017. She was subsequently a Postdoctoral Fellow at Caltech. Her research focuses on advancing machine learning techniques for large-scale spatiotemporal data analysis, with applications to sustainability, health, and physical sciences. A particular emphasis of her research is on physics-guided AI which aims to integrate first principles with data-driven models. Among her awards, she has won NSF CAREER Award, Faculty Research Award from JP Morgan, Facebook, Google, Amazon, and Adobe, Several Best Paper Awards, Best Dissertation Award at USC, and was nominated as one of the ’MIT Rising Stars in EECS’.

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TODAY 3/27 at 4:30pm: ECE Seminar Laura Hallock at 4:30pm
by Emily C. Lawrence 27 Mar '23

27 Mar '23

ECE SEMINAR Speaker: Laura Hallock, University of Pennsylvania Title: (Re)building Human Dexterity: Modeling Neuromusculoskeletal Capability & Intent For Improved Collaborative & Rehabilitative Robots Day: Monday, March 27, 2023 Time: 4:30 PM Location: B205 Engineering Quadrangle Host: Jaime Fernández Fisac Abstract: Robotic systems hold extensive promise as assistive technology, to augment the capabilities of both healthy users and those with motor disabilities. At the same time, assistive robots are hampered by insufficient sensing and modeling of the human systems with which they interact, and often fail to account for users' individual capability and intent. In this talk, I will present my work leveraging sensing and modeling technologies - drawn from both classical robot control and biomechanical science domains - in new ways to perform better system identification of human dynamics, enabling the creation of more capable assistive devices and better, more granular characterization of physical human-robot interactions and the human neuromotor system in general. Bio: Dr. Laura A. Hallock is a Postdoctoral Researcher in the General Robotics, Automation, Sensing, and Perception (GRASP) Lab at the University of Pennsylvania, where she works to develop human-aware collaborative robot manipulators and rehabilitation robots. Specifically, her work leverages multiple sensing modalities (including ultrasound, surface electromyography, and motion capture) to better evaluate human capability and intent, enabling safer, more intuitive, and more personalized physical human-robot interactions. Prior to her postdoctoral work, Laura received her PhD in EECS from UC Berkeley, where her graduate thesis pioneered the use of ultrasound-measured muscle deformation as a measure of output force, enabling novel ultrasound-driven models of human arm dynamics applicable to assistive device design, medical diagnostics, and studies of motor control. Previously, she received her SB in EECS from MIT, where she worked on neuromuscular modeling for lower-limb prostheses in the MIT Media Lab's Biomechatronics Group. For her research, teaching and mentorship efforts, she was selected as a 2022 Rising Star in Engineering in Health, a 2020 Rising Star in Mechanical Engineering, and a 2018 NextProf Nexus cohort member. She was an NSF Graduate Research Fellow and recipient of the UC Berkeley EECS Chair's Graduate Award and an Outstanding Graduate Student Instructor Award from the UC Berkeley Graduate Division.

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CS Colloquium Speaker Adam Fisch TODAY at 12:30pm
by Emily C. Lawrence 27 Mar '23

27 Mar '23

CS Colloquium Speaker Speaker: Adam Fisch, Massachusetts Institute of Technology Date: Monday, March 27 Time: 12:30pm EST Location: CS 105 Host: Karthik Narasimhan Event page: https://www.cs.princeton.edu/events/26363 Title: Towards Efficient and Reliable Machine Learning for Natural Language Processing (and Beyond) Abstract: In this talk, I will introduce work on fundamental techniques for building and deploying effective natural language processing (NLP) systems that are also efficient and reliable. Specifically, I will address three interconnected challenges for modern machine learning in NLP: how to quickly adapt foundation models to new tasks with limited data, how to dynamically reconfigure large architectures for more efficient computation, and how to develop powerful theoretical tools for rigorous, yet practical, uncertainty quantification. To conclude, I will highlight a number of my future research directions, as well as extensions to interesting applications beyond natural language. Bio: Adam Fisch is a PhD candidate at MIT working with Regina Barzilay and Tommi Jaakkola, and a recipient of an NSF Graduate Research Fellowship. His research centers around principled methods for efficient and reliable machine learning systems that work effectively in realistic scenarios, and has appeared in top-tier venues such as *ACL, ICLR, ICML, and NeurIPS. Adam also served as a co-instructor for the tutorial on Uncertainty Estimation for NLP at COLING 2022, and as a co-organizer of the Machine Reading for Question Answering workshops at EMNLP 2019 and 2021. Prior to MIT, Adam was a research engineer at Meta (Facebook) AI Research for two years, and studied mechanical engineering as an undergraduate at Princeton University.

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Princeton Robotics Seminar: Dorsa Sadigh, TODAY at 11am
by Emily C. Lawrence 24 Mar '23

24 Mar '23

Speaker: Dorsa Sadigh, Assistant Professor in Computer Science, Stanford University Date: Friday, March 24, 2023 Time: 11:00am ET Location: Computer Science 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Learning Representations for Interactive Robotics Abstract: There have been significant advances in the field of robot learning in the past decade. However, many challenges still remain when considering how robot learning can advance interactive agents such as robots that collaborate with humans. In this talk, I will be discussing the role of learning representations for robots that interact with humans and robots that interactively learn from humans through a few different vignettes. I will first discuss how bounded rationality of humans guided us towards developing learned latent action spaces for shared autonomy. It turns out this “bounded rationality” is not a bug and a feature — i.e. we can develop extremely efficient coordination algorithms by learning latent representations of partner strategies and operating in this low dimensional space. I will then discuss how we can go about actively learning such representations capturing human preferences including our recent work on how large language models can help design human preference reward functions. Finally, I will end the talk with a discussion of the type of representations useful for learning a robotics foundation model and some preliminary results on a new model that leverages language supervision to shape representations. Bio: Dorsa Sadigh is an assistant professor in Computer Science and Electrical Engineering at Stanford University. Her research interests lie in the intersection of robotics, learning, and control theory. Specifically, she is interested in developing algorithms for safe and adaptive human-robot and human-AI interaction. Dorsa received her doctoral degree in Electrical Engineering and Computer Sciences (EECS) from UC Berkeley in 2017, and received her bachelor’s degree in EECS from UC Berkeley in 2012. She is awarded the Sloan Fellowship, NSF CAREER, ONR Young Investigator Award, AFOSR Young Investigator Award, DARPA Young Faculty Award, Okawa Foundation Fellowship, MIT TR35, and the IEEE RAS Early Academic Career Award.

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Sinong Geng will present his FPO "Model‐Regularized Machine Learning for Decision‐Making" on Thursday, April 13, 2023 at 2:30 PM in COS 402 and Zoom.
by CS Grad Department 23 Mar '23

23 Mar '23

Sinong Geng will present his FPO "Model‐Regularized Machine Learning for Decision‐Making" on Thursday, April 13, 2023 at 2:30 PM in COS 402 and Zoom. Location: Zoom link: https://princeton.zoom.us/j/95544518239 The members of Sinong’s committee are as follows: Examiners: Ronnie Sircar (Adviser), Ryan Adams, Karthik Narasimhan Readers: Sanjeev Kulkarni, Tom Griffiths A copy of his thesis is available upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: Thanks to the availability of more and more high‐dimensional data, recent developments in machine learning (ML) have redefined decision‐making in numerous domains. However, the battle against the unreliability of ML in decision‐making caused by the lack of high‐quality data has not ended and is an important obstacle in almost every application. Some questions arise like (i) Why does an ML method fail to replicate the decision‐making behaviors in a new environment? (ii) Why does ML give unreasonable interpretations for existing expert decisions? (iii) How to make decisions under a noisy and high‐dimensional environment? Many of these issues can be attributed to the lack of an effective and sample‐efficient model underlying ML methods. This thesis presents our research efforts dedicated to developing model‐regularized ML for decision‐making to address the above issues in areas of inverse reinforcement learning and reinforcement learning, with applications to customer/company behavior analysis and portfolio optimization. Specifically, by applying regularizations derived from suitable models, we propose methods for two different goals: (i) to better understand and replicate existing decision‐making of human experts and businesses; (ii) to conduct better sequential decision‐making, while overcoming the need for large amounts of high‐quality data in situations where there might not be enough.

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