- talks - lists.cs.princeton.edu

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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CS Department Colloquium Series: week of Mar 6-10
by Emily C. Lawrence 17 Mar '23

17 Mar '23

Here are next week's CS Department Colloquium Series talks. Just a reminder, you can find the full calendar of events here: https://www.cs.princeton.edu/general/newsevents/events Speaker: Mae Milano, University of California, Berkeley Date: Monday, March 6 Time: 12:30pm EST Location: CS 105 Host: Andrew Appel Event page: [ https://www.cs.princeton.edu/events/26344 | https://www.cs.princeton.edu/events/26344 ] Title: Programming Distributed Systems Abstract: Our interconnected world is increasingly reliant on distributed systems of unprecedented scale, serving applications which must share state across the globe. And, despite decades of research, we're still not sure how to program them! In this talk, I'll show how to use ideas from programming languages to make programming at scale easier, without sacrificing performance, correctness, or expressive power in the process. We'll see how slight tweaks to modern imperative programming languages can provably eliminate common errors due to replica consistency or concurrency---with little to no programmer effort. We'll see how new language designs can unlock new systems designs, yielding both more comprehensible protocols and better performance. And we'll conclude by imagining together the role that a new cloud-centric programming language could play in the next generation of distributed programs. Bio: Mae Milano is a postdoctoral scholar at UC Berkeley working at the intersection of Programming Languages, Distributed Systems, and Databases. Her work has appeared at top-tier venues including PLDI, OOPSLA, POPL, VLDB, and TOCS, and has attracted the attention of the Swift language team. She is a recipient of the NDSEG Fellowship, has won several awards for her writing and service, and is a founding member of the Computing Connections Fellowship's selection committee (https://computingconnections.org/). 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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Friday March 10 (Virtual) - Gregory Chirikjian - Princeton Robotics Seminar
by Emily C. Lawrence 07 Mar '23

07 Mar '23

Speaker: Gregory Chirikjian, Professor of Mechanical Engineering, National University of Singapore Date: Friday, March 10, 2023 Time: 11am EST Location: Virtual, zoom link: [ https://princeton.zoom.us/my/robotics | https://princeton.zoom.us/my/robotics ] Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Robot Imagination: Affordance-Based Reasoning about Unknown Objects Abstract: Today’s robots are very brittle in their intelligence. This follows from a legacy of industrial robotics where robots pick and place known parts repetitively. For humanoid robots to function as servants in the home and in hospitals they will need to demonstrate higher intelligence, and must be able to function in ways that go beyond the stiff prescribed programming of their industrial counterparts. A new approach to service robotics is discussed here. The affordances of common objects such as chairs, cups, etc., are defined in advance. When a new object is encountered, it is scanned and a virtual version is put into a simulation wherein the robot ``imagines’’ how the object can be used. In this way, robots can reason about objects that they have not encountered before, and for which they have no training using. After affordances are assessed, the robot then takes action in the real world, resulting in real2sim2real. Videos of physical demonstrations will illustrate this paradigm, which the presenter has developed with his students Hongtao Wu, Meng Xin, Sipu Ruan, Jikai Ye, and others. Bio: Gregory S. Chirikjian received undergraduate degrees from Johns Hopkins University in 1988, and a Ph.D. degree from the California Institute of Technology, Pasadena, in 1992. From 1992 until 2021, he served on the faculty of the Department of Mechanical Engineering at Johns Hopkins University, attaining the rank of full professor in 2001. Additionally, from 2004-2007, he served as department chair. Starting in January 2019, he moved the National University of Singapore, where he is serving as Head of the Mechanical Engineering Department, where he has hired 14 new professors so far. Chirikjian’s research interests include robotics, applications of group theory in a variety of engineering disciplines, applied mathematics, and the mechanics of biological macromolecules. He is a 1993 National Science Foundation Young Investigator, a 1994 Presidential Faculty Fellow, and a 1996 recipient of the ASME Pi Tau Sigma Gold Medal. In 2008, Chirikjian became a fellow of the ASME, and in 2010, he became a fellow of the IEEE. From 2014-15, he served as a program director for the US National Robotics Initiative, which included responsibilities in the Robust Intelligence cluster in the Information and Intelligent Systems Division of CISE at NSF. Chirikjian is the author of more than 250 journal and conference papers and the primary author of three books, including Engineering Applications of Noncommutative Harmonic Analysis (2001) and Stochastic Models, Information Theory, and Lie Groups, Vols. 1+2. (2009, 2011). In 2016, an expanded edition of his 2001 book was published as a Dover book under a new title, Harmonic Analysis for Engineers and Applied Scientists.

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CS Colloquium Speaker: Mae Milano TODAY at 12:30pm
by Emily C. Lawrence 06 Mar '23

06 Mar '23

CS Colloquium Speaker Speaker: Mae Milano, University of California, Berkeley Date: Monday, March 6 Time: 12:30pm EST Location: CS 105 Host: Andrew Appel Event page: https://www.cs.princeton.edu/events/26344 Title: Programming Distributed Systems Abstract: Our interconnected world is increasingly reliant on distributed systems of unprecedented scale, serving applications which must share state across the globe. And, despite decades of research, we're still not sure how to program them! In this talk, I'll show how to use ideas from programming languages to make programming at scale easier, without sacrificing performance, correctness, or expressive power in the process. We'll see how slight tweaks to modern imperative programming languages can provably eliminate common errors due to replica consistency or concurrency---with little to no programmer effort. We'll see how new language designs can unlock new systems designs, yielding both more comprehensible protocols and better performance. And we'll conclude by imagining together the role that a new cloud-centric programming language could play in the next generation of distributed programs. Bio: Mae Milano is a postdoctoral scholar at UC Berkeley working at the intersection of Programming Languages, Distributed Systems, and Databases. Her work has appeared at top-tier venues including PLDI, OOPSLA, POPL, VLDB, and TOCS, and has attracted the attention of the Swift language team. She is a recipient of the NDSEG Fellowship, has won several awards for her writing and service, and is a founding member of the Computing Connections Fellowship's selection committee (https://computingconnections.org/).

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