- talks - lists.cs.princeton.edu

DeCenter Seminar: Ling Ren, Nov 15 at 4:30pm
by Emily C. Lawrence 06 Nov '23

06 Nov '23

DeCenter Seminar Speaker: Ling Ren, University of Illinois at Urbana-Champaign Talk Title: What insights can we learn from the Bitcoin Algorithm? November 15, 2023 4:30pm talk followed by a reception Friend 113 Convocation Room [ https://decenter.princeton.edu/events/what-insights-can-we-learn-from-the-b… | https://decenter.princeton.edu/events/what-insights-can-we-learn-from-the-b… ] Abstract: Bitcoin's longest-chain algorithm, also known as Nakamoto consensus, is a novel solution to the classic Byzantine fault-tolerant (BFT) consensus problem. What is fundamentally novel about it, and what algorithmic insights can we learn from it? In this talk, I will discuss some insights and inspirations we gained from Bitcoin that led to new advances in BFT consensus. First, Nakamoto consensus supports dynamic participation. The Bitcoin system has operated for over a decade, during which it has witnessed a rapid increase and sudden drops in the participation level. But to support this phenomenal level of robustness, longest-chain protocols suffer from long latency as a fundamental trade-off. Classic BFT protocols, on the other hand, can achieve constant latency but cannot make progress under dynamic participation. I will present a protocol that simultaneously supports dynamic participation and achieves constant latency. The core technique is to extend the classic BFT approach from static quorum size to dynamic quorum size according to the current participation level. Second, Bitcoin uses a sparse peer-to-peer network to reduce communication. This ingredient loosely inspired us to design a Byzantine agreement protocol tolerating minority faults with quadratic communication. This meets the well-known Dolev-Reischuk lower bound and closes a 40-year open problem on the optimal communication complexity of Byzantine agreement. Third, Bitcoin allows individual clients to make their own assumptions about the network and opt for different security-efficiency trade-offs. We propose the notion of flexible Byzantine fault tolerance to capture and generalize this property. If time permits, I will also briefly discuss other core features of Nakamoto consensus that may inspire important future research. Bio : Ling Ren is an assistant professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Prior to joining the University of Illinois, he obtained his Ph.D. from MIT and worked at VMware Research. He has won several awards, including NSF CAREER Award, Google Research Scholar Award, VMware Early Career Faculty Grant, Bitcoin Research Prize, Top Picks in Hardware and Embedded Security, and Best Paper Runner-Up and Best Student Paper at CCS.

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ECE Korhammer Seminar: Fei Miao, UConn, Tues Nov. 14
by Emily C. Lawrence 06 Nov '23

06 Nov '23

ECE KORHAMMER SEMINAR SERIES Speaker: Fei Miao, University of Connecticut Title: Learning and Control for Safety, Efficiency, and Resiliency of Embodied AI Day: November 14, 2023 Time: 4:30 PM Location: B205 Engineering Quadrangle Hosts: Jaime Fernández Fisac and Chi Jin Abstract: With rapid evolution of sensing, communication, and computation, integrating learning and control presents significant Embodied AI opportunities. However, current decision-making frameworks lack comprehensive understanding of the tridirectional relationship among communication, learning and control, posing challenges for multi-agent systems in complex environments. In the first part of the talk, we focus on learning and control with communication capabilities. We design an uncertainty quantification method for collaborative perception in connected autonomous vehicles (CAVs). Our findings demonstrate that communication among multiple agents can enhance object detection accuracy and reduce uncertainty. Building upon this, we develop a safe and scalable deep multi-agent reinforcement learning (MARL) framework that leverages shared information among agents to improve system safety and efficiency. We validate the benefits of communication in MARL, particularly in the context of CAVs in challenging mixed traffic scenarios. We incentivize agents to communicate and coordinate with a novel reward reallocation scheme based on Shapley value for MARL. Additionally, we present our theoretical analysis of robust MARL methods under state uncertainties, such as uncertainty quantification in the perception modules or worst-case adversarial state perturbations. In the second part of the talk, we briefly outline our research contributions on robust MARL and data-driven robust optimization for sustainable mobility. We also highlight our research results concerning CPS security. Through our findings, we aim to advance Embodied AI and CPS for safety, efficiency, and resiliency in dynamic environments. Bio: Fei Miao is Pratt & Whitney Associate Professor of the Department of Computer Science and Engineering, a Courtesy Faculty of the Department of Electrical & Computer Engineering, University of Connecticut, where she joined in 2017. She is affiliated to the Institute of Advanced Systems Engineering and Eversource Energy Center. She was a postdoc researcher at the GRASP Lab and the PRECISE Lab of UPenn from 2016 to 2017. She received Ph.D. degree and the Best Doctoral Dissertation Award in Electrical and Systems Engineering, with a dual M.S. degree in Statistics from the University of Pennsylvania in 2016. She received the B.S. degree in Automation from Shanghai Jiao Tong University in 2010. Her research focuses on multi-agent reinforcement learning, robust optimization, uncertainty quantification, and game theory, to address safety, efficiency, robustness, and security challenges of Embodied AI and CPS, for systems such as connected autonomous vehicles, sustainable and intelligent transportation systems, and smart cities. Dr. Miao is a receipt of the NSF CAREER award and a couple of other awards from NSF. She received the Best Paper Award and Best Paper Award Finalist at the 12th and 6th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) in 2021 and 2015, Best paper Award at the 2023 AAAI DACC workshop, respectively. This seminar is supported with funds from the Korhammer Lecture Series.

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Bhargav Reddy Godala will present his Pre FPO "Criticality Aware Front-End" on November 6th, 2023 in Friend Center 125 at 10:30am.
by Gradinfo 01 Nov '23

01 Nov '23

Bhargav Reddy Godala will present his Pre FPO "Criticality Aware Front-End" on November 6th, 2023 in Friend Center 125 at 10:30am. The members of his committee are as follows: Examiners: Prof. David August (adviser) Prof. Margaret Martonosi Prof. David Wentzlaff Readers: Gilles Pokam (Intel) Svilen Kanev (Google) (approved by Grad School) Prof. David August (adviser) Talk title and abstract follow below. Title: Criticality Aware Front-End Abstract: Code footprints continue to grow faster than instruction caches, putting additional pressure on existing front-end structures like iCaches, BTBs, and iTLBs. Even with aggressive decoupled front-ends and accurate fetch-directed instruction prefetching (FDIP), modern processors experience significant front-end stalls. To address this, this thesis proposes Priority Directed Instruction Prefetching (PDIP), a novel instruction prefetching technique that complements FDIP by issuing prefetches for targets along the resteer path where FDIP stalls occur. PDIP identifies these targets and associates them with a trigger for future prefetch. When paired with EMISSARY, a recently proposed cost-aware cache management policy, PDIP achieves a geomean IPC speedup of 3.7% across a set of industry-relevant benchmark programs using a budget of only 43.5KB. PDIP achieves 62% of the ideal prefetching performance.

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MAE Seminar Speaker Monroe Kennedy III, Friday, Nov 3 at 12:30
by Emily C. Lawrence 01 Nov '23

01 Nov '23

Collaborative Robotics: From Dexterity to Teammate Prediction Friday, November 3rd at 12:30 PM Bowen Hall Room 222 MAE Seminar Series [ https://mae.princeton.edu/about-mae/events/collaborative-robotics-dexterity… | https://mae.princeton.edu/about-mae/events/collaborative-robotics-dexterity… ] Prof. Monroe Kennedy III, Stanford University Abstract: There have been amazing advances in the field of robotic autonomy over the past few decades, we’ve seen robots move from factory floors to working examples of autonomous vehicles on public roads. However, there is still a notable barrier to having robots in everyday environments performing functional tasks. These barriers are usually due to current robots having limited dexterity when it comes to manipulating objects that humans can handle with minimal difficulty, to barriers that exist when a robot is supposed to work alongside a human to complete a task or enable a human to perform a task better. In this talk, we will address the catalyst problem of robotic autonomy in human environments by addressing robotic dexterity and the necessity of improving the sense of robotic touch. We will then look at how robots can become effective teammates by modeling the behavior of their collaborators in the context of the task or environment, which enables them to predict future behavior and take collaborative actions. Bio: Monroe Kennedy is an assistant professor in Mechanical Engineering and by courtesy, Computer Science at Stanford University. Monroe is the recipient of the NSF Faculty Early Career Award. He directs the Assistive Robotics and Manipulation Laboratory (ARMLab), where the focus is on developing collaborative, autonomous robots capable of performing dexterous, complex tasks with human and robotic teammates. Monroe received his Ph.D. in Mechanical Engineering and Applied Mechanics and master’s in Robotics from the University of Pennsylvania.

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Princeton Robotics Seminar: Russ Tedrake, Friday, Nov 3 at 11am
by Emily C. Lawrence 31 Oct '23

31 Oct '23

Speaker: [ https://groups.csail.mit.edu/locomotion/russt.html | Russ Tedrake ] , MIT Date: Friday, 11/03/23 Time: 11:00 AM ET Location: CS 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Dexterous Manipulation with Diffusion Policies Abstract: At the Toyota Research Institute (TRI), we've been working on behavior cloning for dexterous manipulation. Building on the Diffusion Policy framework that we've recently developed in collaboration with Shuran Song, we now have a very solid pipeline for taking ~50-100 bimanual haptic teleop demonstrations and turning that into a [ https://medium.com/toyotaresearch/tris-robots-learn-new-skills-in-an-aftern… | surprisingly effective visuomotor (+tactile) policy ] . Because there is no explicit state representation required, these skills work equally well manipulating deformable, liquid, or other difficult to model tasks as they do for more traditional rigid-object manipulation. We're actively scaling this up into the multi-task setting and now see a plausible path towards "Large Behavior Models." Bio: Russ Tedrake is the Toyota Professor at the Massachusetts Institute of Technology (MIT) in the Department of Electrical Engineering and Computer Science, Mechanical Engineering, and Aero/Astro, and he is a member of MIT’s Computer Science and Artificial Intelligence Lab (CSAIL). He is also the Vice President of Robotics Research at Toyota Research Institute (TRI). He received a B.S.E. in Computer Engineering from the University of Michigan in 1999, and a Ph.D. in Electrical Engineering and Computer Science from MIT in 2004. Dr. Tedrake is the Director of the MIT CSAIL Center for Robotics and was the leader of MIT’s entry in the DARPA Robotics Challenge. He is a recipient of the NSF CAREER Award, the MIT Jerome Saltzer Award for undergraduate teaching, the DARPA Young Faculty Award in Mathematics, the 2012 Ruth and Joel Spira Teaching Award, and was named a Microsoft Research New Faculty Fellow. His research has been recognized with numerous conference best paper awards, including ICRA, Robotics: Science and Systems, Humanoids, Hybrid Systems: Computation and Control, as well as the inaugural best paper award from the IEEE RAS Technical Committee on Whole-Body Control.

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TOMORROW: LLM Forum with Meredith Whittaker (please RSVP)
by Emily C. Lawrence 24 Oct '23

24 Oct '23

Please join us for the first event in the Princeton LLM Forum Series: A Conversation with Meredith Whittaker (President of Signal) on the implications of LLM technology on society. This session's Princeton respondent will be Arvind Narayanan (Professor of Computer Science and Director of the Center for Information and Technology Policy). Wednesday, Oct 25, 4:30 – 6:00 pm Friend Center 101 RSVPs encouraged ( [ https://docs.google.com/forms/d/e/1FAIpQLSdSH04YBq2W-W1nRVxh5DmveM6qY7Khtis… | RSVP link ] ) R eception to follow [ https://princeton.us16.list-manage.com/track/click?u=a77a39541c9fc9c8b625cb… | Add to Calendar ] | [ https://princeton.us16.list-manage.com/track/click?u=a77a39541c9fc9c8b625cb… | More info ] Meredith Whittaker is the President of Signal. She is the current Chief Advisor, and the former Faculty Director and Co-Founder of the AI Now Institute. Her research and advocacy focus on the social implications of artificial intelligence and the tech industry responsible for it, with a particular emphasis on power and the political economy driving the commercialization of computational technology. Prior to founding AI Now, she worked at Google for over a decade, where she led product and engineering teams, founded Google’s Open Research Group, and co-founded M-Lab, a globally distributed network measurement platform that now provides the world’s largest source of open data on internet performance. She has advised the White House, the FCC, FTC, the City of New York, the European Parliament, and many other governments and civil society organizations on artificial intelligence, internet policy, measurement, privacy, and security. The Princeton LLM Forum Recent breakthroughs in Artificial Intelligence (AI) have produced a new class of neural networks called Large Language Models (LLMs) that demonstrate a remarkable capability to generate fluent, plausible responses to prompts posed in natural language. While LLMs have already revolutionized certain industry applications, the debut of ChatGPT has generated new anxiety and curiosity about machine intelligence, especially in the way we teach, research, tell stories, and report facts. Co-organized with the Department of Computer Science and supported by the Humanities Council, the Princeton LLM Forum brings together leading scholars and researchers to discuss the implications that LLMs have on our understanding of language, society, culture, and theory of mind. [ https://princeton.us16.list-manage.com/track/click?u=a77a39541c9fc9c8b625cb… | Read more about the forum and upcoming events in the series. ]

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Princeton Robotics Seminar: Henny Admoni, Friday Oct 20
by Emily C. Lawrence 17 Oct '23

17 Oct '23

Princeton Robotics Seminar Speaker: [ https://hennyadmoni.com/ | Henny Admoni ] , CMU Date: Friday, 10/20/23 Time: 11:00 AM ET Location: CS 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Robots that Learn From and Collaborate with People Abstract: Robots hold the promise of serving human needs, like helping older adults live independently at home or assisting drivers in preventing crashes. For these robots to integrate seamlessly into people's lives, they must provide proactive assistance that is responsive to their human partners' needs. Often, these needs are a result of underlying mental states like intent or awareness. Conversely, it is also useful for people to have an accurate mental model of their robot assistant's policy and knowledge. Mental states may be revealed implicitly through actions the agents take, such as gazing at a certain object or moving in a certain way. This talk describes research on developing collaborative robots that infer people's needs through interaction, adapt to people's individual preferences, and communicate their own models to make the interaction more explainable. These robots are evaluated in a range of human-robot interaction domains, such as manipulation and driving. Bio: Dr. Henny Admoni is an Associate Professor in the Robotics Institute at Carnegie Mellon University, where she leads the Human And Robot Partners (HARP) Lab. Dr. Admoni’s research interests include human-robot interaction, assistive robotics, and nonverbal communication. Dr. Admoni holds a PhD in Computer Science from Yale University, and a BA/MA joint degree in Computer Science from Wesleyan University.

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ECE KORHAMMER SEMINAR SERIES: Wenzhen Yuan, UIUC (Mon., Oct. 23, 2023 @ 4:30 PM, E-Quad B205)
by Emily C. Lawrence 16 Oct '23

16 Oct '23

Speaker: Wenzhen Yuan, UIUC Date: Monday, October 23, 2023 Time: 4:30 PM Location: B205 Engineering Quadrangle Host: Pramod Viswanath and Jaime Fernández Fisac Event page: [ https://ece.princeton.edu/events/making-sense-physical-world-high-resolutio… | https://ece.princeton.edu/events/making-sense-physical-world-high-resolutio… ] Title: Making Sense of the Physical World with High-Resolution Tactile Sensing Abstract: In this talk, I will introduce the development of a high-resolution robotic tactile sensor GelSight, and how it can help robots understand and interact with the physical world. GelSight is a vision-based tactile sensor that measures the geometry of the contact surface with a spatial resolution of around 25 micrometers, and it also measures the shear forces and torques at the contact surface. With the help of high-resolution information, a robot could easily detect the precise shape and texture of the object surfaces and therefore recognize them. But it can help robots get more information from contact, such as understanding different physical properties of the objects and assisting with manipulation tasks. The talk will cover our work on using GelSight to detect slip during grasping and perceiving object properties such as hardness and viscosity of the liquid. I will also present our work in simulating the tactile sensor and using the simulated sensor input to boost the robot’s capability to perform perception and grasping tasks in reality. These simulation tools can also help us rethink the sensor design challenge and how tactile sensors can be used for various types of robots. Bio: Wenzhen Yuan is an assistant professor at the Computer Science Department at University of Illinois Urbana-Champaign and the director of the CMU RoboTouch Lab. She is a pioneer in high-resolution tactile sensing for robots, and she also works in multi-modal robot perception, soft robots, robot manipulation, and haptics. Yuan received her Master of Science and PhD degrees from MIT and her Bachelor of Engineering degree from Tsinghua University. She also worked as a Postdoctoral researcher at Stanford University and an assistant professor at Carnegie Mellon University. This seminar is supported with funds from the Korhammer Lecture Series.

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Devon Loehr will present his FPO "Lucid: A High-Level, Easy-To-Use Dataplane Programming Language" on Friday, December 1, 2023 in CS 302 at 1pm and Zoom
by Nicki Mahler 11 Oct '23

11 Oct '23

Devon Loehr will present his FPO "Lucid: A High-Level, Easy-To-Use Dataplane Programming Language" on Friday, December 1, 2023 in CS 302 at 1pm and Zoom Zoom link: [ https://princeton.zoom.us/j/97653187562?pwd=TmFNVjl1TWJDMkk5bW5Eaitxb05qUT09 | https://princeton.zoom.us/j/97653187562?pwd=TmFNVjl1TWJDMkk5bW5Eaitxb05qUT09 ] The members of his committee are as follows: Readers: Aarti Gupta and Andrew Appel Examiners: David Walker (Adviser), Jennifer Rexford, and Amit Levy Abstract : The introduction of programmable switches and the P4 language for programming them has made it possible for network operators to write ever-more sophisticated network applications, and execute them at high speed. However, possible is not the same as easy. The de facto standard dataplane programming language, P4, is akin to an “assembly language” for programmable switches: it provides a powerful but low-level interface to switch hardware. While this gives the programmer fine-grained control, it makes it diﬀicult to write and reason about programs with complicated high-level behavior. Furthermore, modern hardware is heavily specialized for packet processing. While this specialization allows blazing fast speeds, it also heavily restricts the sorts of programs a programmable switch can run. Unfortunately, such restrictions are poorly-represented in modern dataplane languages. Should a program violate one or more of these restrictions, it will fail to compile, often with an arcane or unhelpful error message. This dissertation presents Lucid, a high-level, event-based language for programmable switches. Lucid raises the level of abstraction for data-plane program- ming in several ways. Control flow is represented by events, each of which has an associated handler that is executed when the event is generated. Different threads of control may be interleaved easily by writing them as handlers for separate events. Lucid provides high-level representations of switch hardware restrictions, which are enforced by syntactic checks, including a novel type system for detecting ordering violations. Lucid is compiled to P4 for the Intel Tofino, and we find that Lucid programs typically contain around 10 times fewer lines of code than equivalent P4 programs. Furthermore, the Lucid interpreter may be used to quickly evaluate different configurations of a Lucid program, in order to automatically optimize a single program for different networking environments.

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Yebin Chon will present her General Exam "Augmenting Alias Analysis With An Enhanced KillFlow Algorithm" on Wednesday, October 11th, 2023 at 3pm in Friend Center 202
by Gradinfo 06 Oct '23

06 Oct '23

Yebin Chon will present her General Exam "Augmenting Alias Analysis With An Enhanced KillFlow Algorithm" on Wednesday, October 11th, 2023 at 3pm in Friend Center 202. The members of her committee are as follows: David August (adviser), Zachary Kincaid, and Amit Levy Title "A Unified API for Memory Dependence Analysis Tools" Abstract: Stronger memory dependence analysis creates additional and more efficient options for automatic parallelization. CAF, the collaborative analysis framework, developed at Princeton, is to the best of our knowledge the strongest open source memory analysis engine for automatic parallelization of general purpose programs. Using memory profiling, we identify areas of weakness in the CAF memory dependence analysis engine. Despite CAF’s precision, our investigation reveals that its KillFlow module, while effective in many scenarios, faces limitations when dealing with certain loop patterns found in many applications. We propose a stronger KillFlow algorithm that is able to handle dependences involving inner loops, and show how it disproves many dependences not already disproven by CAF. Links to Abstract and Reading List: https://docs.google.com/document/d/1Sd5IqojKpFB24PLRgmyqdrxPRrmodts-0TF_w2K… https://docs.google.com/document/d/1m0vGIihX2A60Z1xRnkTnH7XxSWf7Wn669k9A5tb…

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