- talks - lists.cs.princeton.edu

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

06 Apr '23

Speaker: Mae Milano, University of California, Berkeley Date: Thursday, April 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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Yuting Yang will present her FPO "Exploiting Program Representation with Shader Applications" on Wednesday, May 3, 2023 at 3:00 PM in CS 402.
by CS Grad Department 05 Apr '23

05 Apr '23

Yuting Yang will present her FPO "Exploiting Program Representation with Shader Applications" on Wednesday, May 3, 2023 at 3:00 PM in CS 402. Location: CS 402 The members of Yuting’s committee are as follows: Examiners: Adam Finkelstein (Adviser), Szymon Rusinkiewicz, Jia Deng Readers: Felix Heide, Connelly Barnes (Adobe Research) A copy of her thesis will be available upon request, two weeks before the FPO. Please email [ mailto:gradinfo@cs.princeton.edu | gradinfo(a)cs.princeton.edu ] if you would like a copy of the thesis. Everyone is invited to attend her talk. Abstract follows below: Programs are widely used in content creation. For example, artists design shader programs to procedurally render scenes and textures, while musicians construct “synth” programs to generate electronic sound. While the generated content is typically the focus of attention, the programs themselves offer hidden potential for transformations that can support untapped applications. In this thesis, we will discuss four projects that exploit the program structure to automatically apply machine learning or math transformations as if they were manually designed by domain experts. First, we propose a compiler framework to automatically approximate the convolution of an arbitrary program with a Gaussian kernel in order to smooth the program for visual antialiasing. Second, we explore the benefit of program representation in deep-learning tasks by proposing to learn from program traces of procedural fragment shaders – programs that generate images. We next describe a compiler-based framework with novel math rules to extend reverse mode automatic differentiation so as to provide accurate gradients for arbitrary discontinuous programs. Finally, we extend the differentiation framework to audio “synth” programs so as to match the acoustic properties of a provided sound clip. In each of these settings, we demonstrate the benefit of exploiting the program structure to generalize hand-crafted techniques to arbitrary programs.

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Princeton Robotics Seminar: Scott Kuindersma, Friday April 7
by Emily C. Lawrence 04 Apr '23

04 Apr '23

Speaker: Scott Kuindersma, Senior Director of Robotics Research, Boston Dynamics Date: Friday, April 7, 2023 Time: 11am ET Location: Computer Science 105 Event page: [ https://robo.princeton.edu/seminar | https://robo.princeton.edu/seminar ] Title: Taskable Agility: Making Useful Dynamic Behavior Easier to Create Abstract: In this talk, I will provide some insights and observations from our recent work on Atlas, the world's most dynamic humanoid robot. I'll cover the core technical ideas that have made an impact for us over the past few years and share my thoughts about the future for robots like Atlas. Bio: Scott Kuindersma is the Senior Director of Robotics Research at Boston Dynamics where he leads behavior research on Atlas. Prior to joining Boston Dynamics, he was an Assistant Professor of Engineering and Computer Science at Harvard. Scott’s research explores intersections of machine learning and model-based control to improve the capabilities of humanoids and other dynamic mobile manipulators.

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ECE Seminar: Kuan Fang TODAY at 4:30pm
by Emily C. Lawrence 04 Apr '23

04 Apr '23

ECE SEMINAR Speaker: Kuan Fang, University of California, Berkeley Title: Scalable Robot Intelligence: Self-Supervised Learning through Generation Day: Tuesday, April 4, 2023 Time: 4:30 PM Location: B205 Engineering Quadrangle Host: Chi Jin Abstract: Rapid advances in deep learning have resulted in promising techniques for robots to boost their capabilities to perceive, reason, and act by leveraging large models and massive datasets. However, the scalability of existing robot learning methods is severely limited by the manual labor and domain knowledge that humans can provide. To acquire general-purpose skills for solving a broad range of tasks, intelligent robots need scalable methods to collect and learn from rich data without extensive human supervision. In this talk, I will present my research on scaling up robot learning through the autonomous generation of environments, goals, and tasks. I will start by describing how to leverage procedural content generation for learning robust skills that can handle the variety and uncertainty of the real world. Then I will present algorithms that train robots to effectively reuse skills learned from prior experiences for novel sequential tasks by learning to generate reachable subgoals. Finally, I will demonstrate how to enable robots to discover a repertoire of novel skills by adaptively generating tasks during training. The acquired skills can be used for solving a variety of complex tasks such as tool use and sequential manipulation based on raw sensory inputs. Bio: Kuan Fang is a postdoctoral researcher in the Department of Electrical Engineering and Computer Sciences at UC Berkeley, working with Sergey Levine. He received his Ph.D. degree in Electrical Engineering from Stanford University, advised by Fei-Fei Li and Silvio Savarese. His research interests lie at the intersection of robotics, computer vision, and machine learning, with a focus on developing data-driven methods to enable intelligent robots to operate in unstructured environments. He is a recipient of the Stanford Graduate Fellowship and the Computing Innovation Fellowship.

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Maxine Perroni-Scharf will present her MSE thesis "Constructing Printable Surfaces with View-dependent Appearance" on Wednesday, April 12, 2023 at 10am in CS 402.
by Nicki Mahler 03 Apr '23

03 Apr '23

Maxine Perroni-Scharf will present her MSE thesis "Constructing Printable Surfaces with View-dependent Appearance" on Wednesday, April 12, 2023 at 10am in CS 402. The members of her committee are as follows: Szymon Rusinkiewicz (Adviser) and Olga Russakovsky (Reader). The title and abstract are as follows: "Constructing Printable Surfaces with View-dependent Appearance" We present a method for the digital fabrication of surfaces whose appearance varies based on viewing direction. The surfaces are constructed from a mesh of bars arranged in a self-occluding colored heightfield which creates the desired view-dependent effects. At the heart of our method is a novel and simple differentiable rendering algorithm specifically designed to render colored 3D heightfields and enable efficient calculation of the gradient of appearance with respect to heights and colors. This algorithm forms the basis of a coarse-to-fine ML-based optimization process that adjusts the heights and colors of the strips to minimize the loss between the desired and real surface appearance from each viewpoint, deriving meshes that can then be fabricated using a 3D printer. Using our method, we demonstrate both synthetic and real-world fabricated results with view-dependent appearance.

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ECE Seminar: Daniel Seita TODAY at 4:30pm
by Emily C. Lawrence 03 Apr '23

03 Apr '23

ECE SEMINAR Speaker: Daniel Seita, Carnegie Mellon University Title: Representations in Robot Manipulation: Learning to Manipulate Cables, Fabrics, Bags, Liquids, and Plants Day: Monday, April 3, 2023 Time: 4:30 PM Location: B205 Engineering Quadrangle Host: Jaime Fernández Fisac Abstract: The robotics community has seen significant progress in applying machine learning for robot manipulation. However, much manipulation research focuses on rigid objects instead of highly deformable objects such as cables, fabrics, bags, liquids, and plants, which pose challenges due to their complex configuration spaces, dynamics, and self-occlusions. To achieve greater progress in robot manipulation of such diverse deformable objects, I advocate for an increased focus on learning and developing appropriate representations for robot manipulation. In this talk, I show how novel action-centric representations can lead to better imitation learning for manipulation of diverse deformable objects. I will show how such representations can be learned from color images, depth images, or point cloud observational data. My research demonstrates how novel representations can lead to an exciting new era for robot manipulation of complex objects. Bio: Daniel Seita is a postdoctoral researcher at Carnegie Mellon University's Robotics Institute, advised by David Held. His research interests are in computer vision and machine learning for robot manipulation, with a focus on using and developing novel observation and action representations to improve manipulation of challenging deformable objects. Daniel holds a PhD in computer science from the University of California, Berkeley, advised by John Canny and Ken Goldberg. He received undergraduate degrees in math and computer science from Williams College. Daniel's research has been supported by a six-year Graduate Fellowship for STEM Diversity and by a two-year Berkeley Fellowship. He has the Honorable Mention for Best Paper award at UAI 2017, was an RSS 2022 Pioneer, and has presented his work at premier robotics conferences such as ICRA, IROS, RSS, and CoRL.

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CS Colloquium Speaker Venkat Arun TODAY at 12:30pm
by Emily C. Lawrence 03 Apr '23

03 Apr '23

Speaker: Venkat Arun, Massachusetts Institute of Technology Date: Monday, April 3 Time: 12:30pm EST Location: CS 105 Host: Ravi Netravali Event page: https://www.cs.princeton.edu/events/26346 Title: Designing Provably Performant Networked Systems Abstract: As networked systems become critical infrastructure, their design must reflect their new societal role. Today, we build systems with hundreds of heuristics but often do not understand their inherent and emergent behaviors. I will present a set of tools and techniques to prove performance properties of heuristics running in real-world conditions. Rigorous proofs can not only inspire confidence in our designs, but also give counter-intuitive insights about their performance. A key theme in our approach is to model uncertainty in systems using non-random, non-deterministic objects that cover a wide range of possible behaviors under a single abstraction. Such models allow us to analyze complex system behaviors using automated reasoning techniques. I will present automated tools to analyze congestion control and process scheduling algorithms. These tools prove performance properties and find counter-examples where widely deployed heuristics fail. I will also show that current end-to-end congestion control algorithms that bound delay cannot avoid starvation and present a method to beamform wireless signals using thousands of antennas. Bio: Venkat Arun is a PhD candidate at MIT working with Hari Balakrishnan and Mohammad Alizadeh. His work spans internet congestion control, video streaming, privacy-preserving computation, wireless networks, and mobile systems. Across these areas, a unifying theme of his work is to bridge between heuristics that systems use in practice and proofs of how well they work. He believes that rigorous proof combined with automated reasoning will enable us to make networked systems more robust and performant. He has won two ACM SIGCOMM best paper awards and the president of India gold medal.

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

31 Mar '23

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

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

29 Mar '23

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

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

28 Mar '23

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

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