March 2022 - talks - lists.cs.princeton.edu

Jens Tuyls will present his General Exam "Multi-Stage Episodic Control for Strategic Exploration in Text Games" on Wednesday, May 4, 2022 at 3 pm in CS 401 and via Zoom.
by Louis W. Riehl 27 Apr '22

27 Apr '22

Jens Tuyls will present his General Exam "Multi-Stage Episodic Control for Strategic Exploration in Text Games" on Wednesday, May 4, 2022 at 3 pm in CS 401 and via Zoom. Zoom Link: https://princeton.zoom.us/j/94658001342 Committee Members: Karthik Narasimhan (advisor), Danqi Chen, Sham Kakade (Harvard University) Abstract: Text adventure games present unique challenges to reinforcement learning methods due to their combinatorially large action spaces and sparse rewards. The interplay of these two factors is particularly demanding because large action spaces require extensive exploration, while sparse rewards provide limited feedback. This work proposes to tackle the explore-vs-exploit dilemma using a multi-stage approach that explicitly disentangles these two strategies within each episode. Our algorithm, called eXploit-Then-eXplore (XTX), begins each episode using an exploitation policy that imitates a set of promising trajectories from the past, and then switches over to an exploration policy aimed at discovering novel actions that lead to unseen state spaces. This policy decomposition allows us to combine global decisions about which parts of the game space to return to with curiosity-based local exploration in that space, motivated by how a human may approach these games. Our method significantly outperforms prior approaches by 27% and 11% average normalized score over 12 games from the Jericho benchmark (Hausknecht et al., 2020) in both deterministic and stochastic settings, respectively. On the game of Zork1, in particular, XTX obtains a score of 103, more than a 2x improvement over prior methods, and pushes past several known bottlenecks in the game that have plagued previous state-of-the-art methods. Reading List: https://docs.google.com/document/d/1y7viEOlx5HiUZWqM1AnHogpBnsWPFUBgLhZo279… Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.

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CS Colloquium speaker Feras Saad: TODAY at 12:30pm
by Emily C. Lawrence 04 Apr '22

04 Apr '22

Speaker: Feras Saad, Massachusetts Institute of Technology Date: Thursday, March 31, 2022 Time: 12:30pm EST Location: CS 105 Host: Ryan Adams Event page: https://www.cs.princeton.edu/events/26183 This talk will be live-streamed at https://mediacentrallive.princeton.edu/ Title: Scalable Structure Learning and Inference via Probabilistic Programming Abstract: Probabilistic programming supports probabilistic modeling, learning, and inference by representing sophisticated probabilistic models as computer programs in new programming languages. This talk presents efficient probabilistic programming-based techniques that address two fundamental challenges in scaling and automating structure learning and inference over complex data. First, I will describe scalable structure learning methods that make it possible to automatically synthesize probabilistic programs in an online setting by performing Bayesian inference over hierarchies of flexibly structured symbolic program representations, for discovering models of time series data, tabular data, and relational data. Second, I will present fast compilers and symbolic analyses that compute exact answers to a broad range of inference queries about these learned programs, which lets us extract interpretable patterns and make accurate predictions in real time. I will demonstrate how these techniques deliver state-of-the-art performance in terms of runtime, accuracy, robustness, and programmability by drawing on several examples from real-world applications, which include adapting to extreme novelty in economic time series, online forecasting of flu rates given sparse multivariate observations, discovering stochastic motion models of zebrafish hunting, and verifying the fairness of machine learning classifiers. Bio: Feras Saad is a PhD candidate in Computer Science at MIT working at the intersection of programming languages, probabilistic machine learning, and computational statistics. His research is accompanied with a collection of popular open-source probabilistic programming systems used by collaborators at Intel, Takeda, Liberty Mutual, IBM, and the Bill & Melinda Gates Foundation for practical applications of structure learning and probabilistic inference. Feras' MEng thesis on probabilistic programming and data science has been recognized with the 1st Place Computer Science Thesis Award at MIT.

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Dora Zhao will present her MSE Talk “Understanding Teenage Perceptions and Configurations of Privacy on Instagram.” on Wednesday, April 6 at 4pm via Zoom.
by Nicki Mahler 30 Mar '22

30 Mar '22

Dora Zhao will present her MSE Talk “Understanding Teenage Perceptions and Configurations of Privacy on Instagram.” on Wednesday, April 6 at 4pm via Zoom. Zoom link: [ https://princeton.zoom.us/j/4036501701 | https://princeton.zoom.us/j/4036501701 ] The members of her committee is as follows: Andrés Monroy-Hernández (adviser), Janet Vertesi As teenage use of social media platform continues to proliferate, so do concerns about teenage privacy and safety online. Prior work has established that privacy on networked publics, such as social media, is complex, requiring users to navigate not only the technical affordances on the platform but also interpersonal relationships and social norms. We investigate how teenagers think about privacy on the popular image-sharing platform, Instagram. We draw on interviews with 12 teenagers (ages 13-18) and an online survey to gain a better understanding how the teenagers configure privacy on the popular image-sharing platform Instagram and why they make these privacy decisions. Finally, based on our findings, we provide design and policy recommendations towards the design of better privacy controls for promoting teenage safety online.

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Robotics Seminar: Michelle Yuen TODAY at 10:45am
by Emily C. Lawrence 30 Mar '22

30 Mar '22

ROBOTICS SEMINAR Speaker: Michelle Yuen Title: Soft robots enabled by functional materials Day: Wednesday, March 30, 2022 Time: 10:45-11:45am Location: Friend Center Convocation Room (FC113) Host: Naveen Verma Abstract: Soft robots have the potential to extend the capabilities currently demonstrated within the field of robotics. By utilizing primarily soft materials in their construction, soft robots are inherently safe to operate around humans, can handle delicate tasks without advanced controls, and are robust to shocks and impacts during deployment. While proof-of-concept devices have been demonstrated successfully, there remains a need for widely applicable, reliable soft robotic components. In this talk, I will present my work on enabling technologies for soft robotic systems and, more broadly, deformable electromechanical systems. Specifically, I will discuss 1) high-deformation strain sensors for state reconstruction and closed-loop control of soft robots, 2) responsive structures enabled by variable-stiffness materials that can switch reversibly from flexible to stiff, and 3) stretchable electronics fabricated using room-temperature liquid metals. These devices rely upon functional materials – materials that have a useful, intrinsic property (e.g., conductivity, thermal-responsiveness) – that can be leveraged for robotic needs. Throughout this talk, I will be highlighting the need for co-developed design, materials selection, and manufacturing processes to produce reliable devices that can be fabricated at scale. The work presented in this talk illustrates a path toward building deformable electromechanical systems that are adaptable and versatile by leveraging functional multi-material systems. Bio: Michelle C. Yuen is a postdoctoral fellow at Harvard University in the School of Engineering and Applied Sciences at the Harvard Microrobotics Lab. She received her Ph.D. in Mechanical Engineering from Purdue University in 2018, following a B.S. in Biological Systems Engineering from University of California, Davis in 2013. During her Ph.D. studies, she worked on the design, materials, and manufacturing methods for soft robotic components and systems, and was awarded an NSF Graduate Research Fellowship. She was then awarded a Research Associateship from the National Academies of Science, Engineering, and Medicine to work on next-generation stretchable electronic devices at the Air Force Research Laboratory. Currently, her work leverages her expertise in soft materials manipulation to discovering advanced adhesion strategies for wet, rough surfaces toward applications in marine mammal tagging.

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CS Colloquium speaker Sai Swaminathan: TODAY at 12:30pm
by Emily C. Lawrence 29 Mar '22

29 Mar '22

Speaker: Sai Swaminathan, Carnegie Mellon University Date: Tuesday, March 29, 2022 Time: 12:30pm EST Location: CS 105 Hosts: Andrés Monroy-Hernández & Adam Finkelstein Event page: https://www.cs.princeton.edu/events/26184 This talk will be live-streamed for the Princeton University community at https://mediacentrallive.princeton.edu/ *Note, this live-stream is only available to Princeton netID holders. Title: Computational Infrastructure Materials for the Networked & Interactive Built Environment Abstract: From roads to roofs, homes to high-rises, my inspiration is the promise of building cyber-physical infrastructure for human interaction and enabling smart city applications. Unfortunately, there are several challenges in achieving this vision due to the end of Moore's law, Dennard scaling, and our limited views on how computing systems are manufactured. To date, device manufacturing has focused primarily on miniaturization—packing the most functionality into the smallest form factor—despite our physical infrastructure being much larger in scale. We need to think creatively, design devices in new form factors (made in structural forms like walls, tables, facades, etc.) and materials of various kinds (those with extreme mechanical strength) that make up our built environments. There remain several challenges at the nexus of device power, form factor, and scale for designing our cyber-physical infrastructure. This talk will introduce "computational infrastructure materials" that enable us to build energy-efficient sensing, actuation, and communication in the networked physical infrastructure (e.g., buildings, sidewalks) forms. Specifically, I will talk about how to enable our infrastructure materials (e.g., concrete, wood, composites) to do computation: (1) as they bear large amounts of forces (~4000 lbs) (2) enable battery-free sensing and activity recognition in long distances (~70km), (3) actuate large-structures in response to user interaction and (4) enable battery-free wireless communication. Taken together, these capabilities in infrastructure materials enable a range of applications in the built environment, such as digital buildings, accessibility, and ultimately towards creating sustainable and resilient cyber-physical infrastructure for human interaction. I will conclude by discussing open problems and challenges for this emerging research area. Bio: Sai Swaminathan is a Ph.D. Candidate at the Human-Computer Interaction Institute in the School of Computer Science of Carnegie Mellon University. He is advised by Scott Hudson in the DevLab. He works at the intersection of Human-Computer Interaction, Ubiquitous Computing, and Computational Materials. He has published award-winning work at top-tier HCI venues, including ACM CHI, IMWUT (UbiComp), UIST, and CSCW. His work has also been featured in news outlets such as the New Scientist, Makezine, and HacksterIO. He has worked at numerous research institutions such as the Manufacturing Science group at Oakridge National Lab (ORNL), Microsoft Research, INRIA, and Xerox Research. You can find out more about him at www.saiganesh.net

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Charlie Murphy will present his Pre FPO "Relational Verification via Weak Simulation" on Thursday April 7th, 2022 at 2pm in CS 402 and Zoom.
by Nicki Mahler 28 Mar '22

28 Mar '22

Charlie Murphy will present his Pre FPO "Relational Verification via Weak Simulation" on Thursday April 7th, 2022 at 2pm in CS 402 and Zoom. Zoom link: https://princeton.zoom.us/j/93257853666 His committee is as follows: Aarti Gupta (Reader) Dave Walker (Examiner) Lennart Beringer (Reader) Wyatt Lloyd (Examiner) Zak Kincaid (Advisor) All are welcome to attend. Title: Relational Verification via Weak Simulation Abstract: Distributed systems are notoriously hard to get right, even when based on existing protocols. The system implementor needs to worry about faulty nodes, asynchronous and delayed or dropped messages, and global correctness properties. Given this complexity, distributed systems are an attractive target for formal verification; however, direct verification of distributed systems is extremely expensive. As such, there is very little work on formally reasoning about distributed systems. Instead, there has been more work on formally verifying distributed protocols, but there is still a gap between the distributed system and a distributed protocol it is based on. In this talk, I propose formally verifying that a distributed system correctly implements a distributed protocol. By formally connecting a distributed system to a protocol, the system inherits the correctness properties of the protocol. This strategy is not new: it’s the method of executable specifications. Executable specifications have long been considered in formally verifying distributed systems; however, existing techniques are unable to handle systems and protocols with infinite state. I propose Contextual Simulation, a relational Hoare logic based on weak simulation for specifying a system implements a protocol. I provide a sound but incomplete set of inference rules for reasoning about contextual simulations. I conclude by presenting simulation synthesis, a semi-algorithm to automatically prove or refute contextual simulations. Simulation synthesis is based on the game semantics of weak simulation (an infinite state infinite duration game). The simulation game is solved by iteratively solving longer finite duration games. When the algorithm terminates, it does so with a winning strategy that (depending on the winning player) either proves or refutes the input contextual simulation.

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Robotics Seminar: Patrick Slade, TODAY at 10:45am
by Emily C. Lawrence 28 Mar '22

28 Mar '22

ROBOTICS SEMINAR Speaker: Patrick Slade Title: Improving Health with Human-Centered Robotics Day: Monday, March 28, 2022 Time: 10:45-11:45am Location: Room E219 EQuad Host: Anirudha Majumdar Abstract: Globally, billions of people face mobility challenges like obesity, age-related diseases, and physical limitations which negatively impact health. Wearable devices that merge robotics, biomechanics, and human-centered AI can enable people to overcome these challenges. We will discuss key ideas for building intelligent assistive devices with several case studies. 1) A simple wearable system worn on one leg that accurately calculates calories expended during exercise, with three-fold lower error than a smartwatch. 2) A portable exoskeleton that personalizes assistance during real-world walking, which reduces walking effort (equivalent to removing an 18-pound backpack) and increases walking speed (by 9%) compared to normal shoes. 3) A robotic cane that improves navigation and increases walking speed by 20% for people with impaired vision. These projects focus on developing technology that is low-cost and easily reproducible to work towards tools for underserved populations. We will also explore how wearable monitoring and assistive devices will impact personal health in the future. Bio: Patrick Slade received his Ph.D. in Mechanical Engineering from Stanford in 2021 and is now a Distinguished Postdoctoral Scholar in Bioengineering as part of the Stanford Human Performance Alliance. His research combines robotics, biomechanics, and human-centered AI to build intelligent health models and assistive devices. He explores how we can safely and effectively integrate human-robot systems so people can live healthier and more independent lives. His work focuses on solutions that are open-source and low-cost to make care accessible for underserved populations. Patrick was an NSF Graduate Research Fellow, named a Forbes 30 under 30 in healthcare, and has published in journals like Science Robotics and Nature Communications.

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CS Colloquium speaker Rowan Zellers: TODAY at 12:30pm
by Emily C. Lawrence 28 Mar '22

28 Mar '22

Speaker: Rowan Zellers, University of Washington Date: Monday, March 28, 2022 Time: 12:30pm EST Location: CS 105 Host: Danqi Chen Event page: https://www.cs.princeton.edu/events/26178 This talk will be live-streamed at https://mediacentrallive.princeton.edu/ Title: Grounding Language by Seeing, Hearing, and Interacting Abstract: As humans, our understanding of language is grounded in a rich mental model about “how the world works” – that we learn through perception and interaction. We use this understanding to reason beyond what we literally observe or read, imagining how situations might unfold in the world. Machines today struggle at this kind of reasoning, which limits how they can communicate with humans. In my talk, I will discuss three lines of work to bridge this gap between machines and humans. I will first discuss how we might measure grounded understanding. I will introduce a suite of approaches for constructing benchmarks, using machines in the loop to filter out spurious biases. Next, I will introduce PIGLeT: a model that learns physical commonsense understanding by interacting with the world through simulation, using this knowledge to ground language. From an English-language description of an event, PIGLeT can anticipate how the world state might change – outperforming text-only models that are orders of magnitude larger. Finally, I will introduce MERLOT, which learns about situations in the world by watching millions of YouTube videos with transcribed speech. Through training objectives inspired by the developmental psychology idea of multimodal reentry, MERLOT learns to jointly reason over language, vision, and sound. Together, these directions suggest a path forward for building machines that learn language rooted in the world. Bio: Rowan Zellers is a final year PhD candidate at the University of Washington in Computer Science & Engineering, advised by Yejin Choi and Ali Farhadi. His research focuses on enabling machines to understand language, vision, sound, and the world beyond these modalities. He has been recognized through an NSF Graduate Fellowship and a NeurIPS 2021 outstanding paper award. His work has appeared in several media outlets, including Wired, the Washington Post, and the New York Times. In the past, he graduated from Harvey Mudd College with a B.S. in Computer Science & Mathematics, and has interned at the Allen Institute for AI.

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CS Colloquium Series: week of March 21-25
by Emily C. Lawrence 25 Mar '22

25 Mar '22

Here is the full list of CS Colloquium talks for next week. All talks will be recorded. ~~~~~ Speaker: Xinyun Chen, University of California, Berkeley Date: Monday, March 21, 2022 Time: 12:30pm EST Location: CS 105 Host: Jia Deng Event page: [ https://www.cs.princeton.edu/events/26175 | https://www.cs.princeton.edu/events/26175 ] This talk will be live-streamed at [ https://mediacentrallive.princeton.edu/ | https://mediacentrallive.princeton.edu/ ] Title: Learning-Based Program Synthesis: Learning for Program Synthesis and Program Synthesis for Learning Abstract: With the advancement of modern technologies, programming becomes ubiquitous not only among professional software developers, but also for general computer users. However, gaining programming expertise is time-consuming and challenging. Therefore, program synthesis has many applications, where the computer automatically synthesizes programs from specifications such as natural language descriptions and input-output examples. In this talk, I will present my work on learning-based program synthesis, where I have developed deep learning techniques for various program synthesis problems. Despite the remarkable success of deep neural networks for many domains, including natural language processing and computer vision, existing deep neural networks are still insufficient for handling challenging symbolic reasoning and generalization problems. My learning-based program synthesis research lies in two folds: (1) learning to synthesize programs from potentially ambiguous and complex specifications; and (2) neural-symbolic learning for language understanding. I will first talk about program synthesis applications, where my work demonstrates the applicability of learning-based program synthesizers for production usage. I will then present my work on neural-symbolic frameworks that integrate symbolic components into neural networks, which achieve better reasoning and generalization capabilities. In closing, I will discuss the challenges and opportunities of further improving the complexity and generalizability of learning-based program synthesis for future work. Bio: Xinyun Chen is a Ph.D. candidate at UC Berkeley, working with Prof. Dawn Song. Her research lies at the intersection of deep learning, programming languages, and security. Her recent research focuses on learning-based program synthesis and adversarial machine learning. She received the Facebook Fellowship in 2020, and Rising Stars in Machine Learning in 2021. Her work SpreadsheetCoder for spreadsheet formula prediction was integrated into Google Sheets, and she was part of the AlphaCode team when she interned at DeepMind. ~~~~~ Speaker: Sherry Tongshuang Wu, University of Washington Date: Tuesday, March 22, 2022 Time: 12:30pm EST Location: CS 105 Hosts: Andrés Monroy-Hernández & Adam Finkelstein Event page: [ https://www.cs.princeton.edu/events/26176 | https://www.cs.princeton.edu/events/26176 ] This talk will be live-streamed at [ https://mediacentrallive.princeton.edu/ | https://mediacentrallive.princeton.edu/ ] Title: Interactive AI Model Debugging and Correction Abstract: Research in Artificial Intelligence (AI) has advanced at an incredible pace, to the point where it is making its way into our everyday lives, explicitly and behind the scenes. However, beneath their impressive progress, many AI models hide deficiencies that amplify social biases or even cause fatal accidents. How do we identify, improve, and cope with imperfect models, while still benefiting from their use? I will discuss my work empowering humans to interact with AI models in order to debug and correct them. I will describe both (1) how I help experts run scalable and testable analyses on models in development, and (2) how I help end users collaborate with deployed AI in a transparent and controllable way. In my final remarks, I will discuss my future research perspectives on building human-centered AI through data-centric approaches. Bio: Sherry Tongshuang Wu is a final year Ph.D. Candidate in Computer Science & Engineering at the University of Washington, advised by Jeffrey Heer and Dan Weld. She received her B.Eng in CSE from the Hong Kong University of Science and Technology. Her research lies at the intersection of Human-Computer Interaction (HCI) and Natural Language Processing (NLP), and aims to empower humans to debug and correct AI models interactively, both when the model is under active development, and after it is deployed for end users. Sherry has authored 19 papers in top-tier NLP, HCI and Visualization conferences and journals such as ACL, CHI, TOCHI, TVCG, etc, including a best paper award (top-1) and an honorable mention (top-3). You can find out more about her at https://homes.cs.washington.edu/~wtshuang/.

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Zoe Ashwood will present her Pre FPO, "Probabilistic models for characterizing animal learning and decision-making" on Friday, April 1, 2022 at 9am via Zoom.
by Nicki Mahler 24 Mar '22

24 Mar '22

Zoe Ashwood will present her Pre FPO, "Probabilistic models for characterizing animal learning and decision-making" on Friday, April 1, 2022 at 9am via Zoom. Zoom link: [ https://princeton.zoom.us/j/98847197070?pwd=U3ZiMFZDbnlYQ1IrYmZkRjQ0VHR6dz09 | https://princeton.zoom.us/j/98847197070?pwd=U3ZiMFZDbnlYQ1IrYmZkRjQ0VHR6dz09 ] The members of Zoe's committee are as follows: Readers: Alex Pouget (University of Geneva), Barbara Engelhardt Non-readers: Jonathan Pillow (advisor), Ryan Adams, Tom Griffiths Abstract: A central goal in neuroscience is to identify the strategies used by animals and humans as they make decisions, and to characterize the learning rules that shape these policies in the first place. In this talk, I will first discuss recent modeling work that revealed that — contrary to common wisdom — mice and humans use multiple strategies over the course of a single session to perform perceptual decision-making tasks. Our modeling framework, based on hidden Markov models with Bernoulli Generalized Linear Model observations, allows us to characterize the nature of these policies and to label the trials associated with each strategy. Next, I will discuss work that sought to uncover the learning rules used by mice and rats as they learned to perform this type of task. Our model tracked trial-to-trial changes in the animals’ choice policies, and separated these changes into components explainable by a reinforcement learning rule, and components that remained unexplained. Whereas the average contribution of the conventional REINFORCE learning rule to the policy update for mice learning a common task was just 30%, we found that adding baseline parameters allowed the learning rule to explain 92% of the animals' policy updates under our model. Finally, I will discuss work in progress that applies inverse reinforcement learning to the trajectories of mice exploring a maze environment. Unlike artificial agents, mice are very good at exploring novel environments, and in this work, we will seek to uncover the extrinsic and intrinsic reward functions that power such exploration.

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