- talks - lists.cs.princeton.edu

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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CS Colloquium Speaker: Mackenzie Leake TODAY at 12:30pm
by Emily C. Lawrence 23 Mar '23

23 Mar '23

CS Colloquium Speaker Speaker: Mackenzie Leake, Massachusetts Institute of Technology Date: Wednesday, March 8 Time: 12:30pm EST Location: CS 105 Host: Andrés Monroy-Hernández Event page: https://www.cs.princeton.edu/events/26347 Title: Integrating expertise into computational tools for design and media authoring Abstract: Finding a good computational representation for a problem allows us to map high level objectives to low level details and select the appropriate set of algorithmic tools. Selecting this representation requires not only computational knowledge but also a deep understanding of the application domain. In this talk I will discuss my work on building design and media authoring tools by combining domain expertise with a wide range of algorithmic techniques. I will describe how this approach helps us to offload tedious steps to computation and guide users’ attention toward the more creative, open-ended decisions. As two different examples of this approach, I will discuss my work on video editing and quilt design tools. I will also discuss future opportunities to combine domain expertise and algorithmic insights to build novel computational tools. Bio: Mackenzie Leake is a METEOR postdoctoral fellow at MIT CSAIL. She received her PhD and MS in computer science from Stanford University and a BA in computational science and studio art from Scripps College. Her research in human-computer interaction and computer graphics focuses on designing computational tools for various creative domains, including textiles and video. Her research has been supported by Adobe Research, Brown Institute for Media Innovation, and Stanford Enhancing Diversity in Graduate Education (EDGE) fellowships. In 2022 she was named a Rising Star in EECS and a WiGraph Rising Star in Computer Graphics.

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TODAY 3/22 - ECE Seminar Riccardo Paccagnella, UIUC
by Emily C. Lawrence 22 Mar '23

22 Mar '23

ECE SEMINAR Speaker: Riccardo Paccagnella, UIUC Title: Software Security Challenges in the Era of Modern Hardware Day: Wednesday, March 22, 2023 Time: 4:30 PM Location: B205 Engineering Quadrangle Host: David Wentzlaff Abstract: Today's hardware cannot keep secrets. Indeed, the past two decades have seen the discovery of a slew of attacks where an adversary exploits hardware features to leak software's sensitive data. These attacks have shaken the foundations of computer security and caused a major disruption in the software industry. Fortunately, there has been a saving grace, namely the widespread adoption of models that have enabled developers to build secure software while comprehensively preventing hardware vulnerabilities. In this talk, I will present two new classes of vulnerabilities that fundamentally undermine these prevailing models for building secure software. In the first part, I will demonstrate that the current constant-time programming model is insufficient to guarantee constant-time execution. In the second part, I will demonstrate that the current resource partitioning model is insufficient to guarantee software isolation. Finally, I will provide an overview of my future research plans for enabling the design of more secure software and hardware systems. Bio: Riccardo Paccagnella is a PhD candidate in Computer Science at the University of Illinois Urbana-Champaign. His research is in system and hardware security. Riccardo is a recipient of a Distinguished Reviewer Award at the IEEE S&P 2021 Shadow PC, a Siebel Scholars Award, and a Chirag Foundation Graduate Fellowship. His work has been covered by national and international press - including Ars Technica, New Scientist, and Wired - and recognized with prestigious awards, including a MICRO Top Picks 2023 distinction, the Pwnie 2022 Award for Best Cryptographic Attack, the CSAW 2022 Applied Research Competition Best Paper Runner-up Award, a Pwnie 2021 Nomination for Most Innovative Research, and a CSLSC 2022 Best Presentation Award. In light of his research, the cryptographic community and several companies (including Cloudflare, Microsoft, Intel, AMD, Ampere, ARM) have taken action that includes patching cryptographic libraries, issuing security advisories, and creating new guidance for writing secure cryptographic code.

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ECE Seminar - Marijn Heule: TODAY at 4:30pm
by Emily C. Lawrence 21 Mar '23

21 Mar '23

Speaker: Marijn Heule, Carnegie Mellon University Title: Computer-aided Mathematics: Successes, Advances, and Trust Day: Tuesday, March 21, 2023 Time: 4:30 PM Location: B205 Engineering Quadrangle Host: Sharad Malik Abstract: Progress in satisfiability (SAT) solving has made it possible to determine the correctness of complex systems and answer long-standing open questions in mathematics. The SAT-solving approach is completely automatic and can produce clever though potentially gigantic proofs. We can have confidence in the correctness of the answers because highly trustworthy systems can validate the underlying proofs regardless of their size. We demonstrate the effectiveness of the SAT approach by presenting some recent successes, including the solution of the Boolean Pythagorean Triples problem, computing the fifth Schur number, and resolving the remaining case of Keller’s conjecture. Moreover, we constructed and validated proofs for each of these results. The second part of the talk focuses on notorious math challenges for which automated reasoning may well be suitable. In particular, we discuss advances in applying SAT-solving techniques to the Hadwiger-Nelson problem (chromatic number of the plane), optimal schemes for matrix multiplication, and the Collatz conjecture. Bio: I am an Associate Professor in the Computer Science Department at Carnegie Mellon University and received my PhD at Delft University of Technology in the Netherlands. My research focuses on solving hard-combinatorial problems in areas such as formal verification, number theory, and extremal combinatorics. Most of my contributions are related to theory and practice of satisfiability (SAT) solving. I have developed award-winning SAT solvers, and my preprocessing techniques are used in state-of-the-art SAT solvers.

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DeCenter Seminar - Lei Yang, Tuesday, March 21 at 11:30am
by Emily C. Lawrence 21 Mar '23

21 Mar '23

Speaker: Lei Yang, MIT Date: Tuesday, March 21 Time: 11:30am EST Location: 103 Maeder Hall Host: Andrés Monroy-Hernández Event page: https://www.cs.princeton.edu/events/26366 Title: Designing Blockchain Systems for Real-World Networks Abstract: The past decade saw renewed interest in decentralized systems, fueled by their promises to harden vital services in our society against intentional and unintentional corruption, and to equalize access to these services. A key enabler of modern decentralized applications are blockchains, which are fault-tolerant distributed systems operated by trust-minimized entities across the world. Blockchains are inherently network systems, but existing designs critically overlook the dynamics of real-world networks such as variability and congestion. In this talk, I will explain how this mismatch leads to performance bottlenecks, security vulnerabilities, and operational challenges that are hindering the goal of decentralization for blockchains. I will then discuss two techniques to close this gap: (1) DispersedLedger, a network-aware decomposition of Byzantine-fault-tolerant state machine replication (BFT) protocols, which enables blockchains to robustly achieve high throughput and low latency in variable networks like the Internet; (2) a unified framework to analyze and secure longest-chain BFT protocols under bandwidth constraints. These techniques use tools from distributed systems, security, and computer networks, and have wide-ranging implications for the entire blockchain system stack Bio: Lei Yang is a PhD candidate at MIT advised by Prof. Mohammad Alizadeh. His research focuses on the intersection of networks and blockchains, using tools from distributed systems, computer networks, and security. A theme of his works is to examine the explicit and implicit assumptions that existing blockchain systems make of the network, and design novel techniques to bridge the gap between the assumptions and the reality. His works are backed by high-performance system implementations and end-to-end evaluations in real-world scenarios.

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TODAY 3/13/2023 - ECE Seminar Sabrina Neuman, Harvard University
by Emily C. Lawrence 20 Mar '23

20 Mar '23

ECE SEMINAR Speaker: Sabrina Neuman, Harvard University Title: Designing Computing Systems for Robotics and Physically Embodied Deployments Day: Monday, March 13, 2023 Time: 4:30 PM Location: B205 Engineering Quadrangle Host: David Wentzlaff Abstract: Emerging applications that interact heavily with the physical world (e.g., robotics, medical devices, the internet of things, augmented and virtual reality, and machine learning on edge devices) present critical challenges for modern computer architecture, including hard real-time constraints, strict power budgets, diverse deployment scenarios, and a critical need for safety, security, and reliability. Hardware acceleration can provide high-performance and energy-efficient computation, but design requirements are shaped by the physical characteristics of the target electrical, biological, or mechanical deployment; external operating conditions; application performance demands; and the constraints of the size, weight, area, and power allocated to onboard computing-- leading to a combinatorial explosion of the computing system design space. To address this challenge, I identify common computational patterns shaped by the physical characteristics of the deployment scenario (e.g., geometric constraints, timescales, physics, biometrics), and distill this real-world information into systematic design flows that span the software-hardware system stack, from applications down to circuits. An example of this approach is robomorphic computing: a systematic design methodology that transforms robot morphology into customized accelerator hardware morphology by leveraging physical robot features such as limb topology and joint type to determine parallelism and matrix sparsity patterns in streamlined linear algebra functional units in the accelerator. Using robomorphic computing, we designed an accelerator for a critical bottleneck in robot motion planning and implemented the design on an FPGA for a manipulator arm, demonstrating significant speedups over state-of-the-art CPU and GPU solutions. Taking a broader view, in order to design generalized computing systems for robotics and other physically embodied applications, the traditional computing system stack must be expanded to enable co-design with physical real-world information, and new methodologies are needed to implement designs with minimal user intervention. In this talk, I will discuss my recent work in designing computing systems for robotics, and outline a future of systematic co-design of computing systems with the real world. Bio: Sabrina M. Neuman is a postdoctoral NSF Computing Innovation Fellow at Harvard University. Her research interests are in computer architecture design informed by explicit application-level and domain-specific insights. She is particularly focused on robotics applications because of their heavy computational demands and potential to improve the well-being of individuals in society. She received her S.B., M.Eng., and Ph.D. from MIT. She is a 2021 EECS Rising Star, and her work on robotics acceleration has received Honorable Mention in IEEE Micro Top Picks 2022 and IEEE Micro Top Picks 2023.

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