- talks - lists.cs.princeton.edu

Minkyu Jeon will present his General Exam "CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM" on Friday, May 2, 2025 at 12:00 PM in Friend 009.
by CS Grad Department 29 Apr '25

29 Apr '25

Minkyu Jeon will present his General Exam " CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM " on Friday, May 2, 2025 at 12:00 PM in Friend 009 . Committee Members: Ellen D. Zhong (advisor), Felix Heide , Jia Deng Abstract: Cryo-electron microscopy (cryo-EM) is a powerful technique for determining high-resolution 3D biomolecular structures from imaging data. Its unique ability to capture structural variability has spurred the development of heterogeneous recon- struction algorithms that can infer distributions of 3D structures from noisy, unlabeled imaging data. Despite the growing number of advanced methods, progress in the field is hindered by the lack of standardized benchmarks with ground truth information and reliable validation metrics. Here, we introduce CryoBench, a suite of datasets, metrics, and benchmarks for heterogeneous reconstruction in cryo-EM. CryoBench includes five datasets representing different sources of heterogeneity and degrees of difficulty. These include conformational heterogeneity generated from designed motions of antibody complexes or sampled from a molecular dynamics simulation, as well as compositional heterogeneity from mixtures of ribosome assembly states or 100 common complexes present in cells. We then analyze state-of-the-art heterogeneous reconstruction tools, including neural and non-neural methods, assess their sensitivity to noise, and propose new metrics for quantitative evaluation. We hope that CryoBench will be a foundational resource for accelerating algorithmic development and evaluation in the cryo-EM and machine learning communities. Reading List: https://docs.google.com/document/d/1QqGrfGd_nDdNno5_lZNBbGTktW9NFxE_52wzXC4… Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.

1 0

You're Invited! AI Lab Distinguished Lecture Series: May 2
by Emily C. Lawrence 29 Apr '25

29 Apr '25

[ https://pli.princeton.edu/events/2025/ai-lab-distinguished-lecture-series-a… | AI Lab Distinguished Lecture Series ] “AI as a New Paradigm for Scientific Discovery”: Insights from Google DeepMind Pushmeet Kohli, VP of Research, Google DeepMind Friday May 2 at 3:30 p.m. in Computer Science 104 Pushmeet Kohli is a researcher who holds the position of vice president of research for Google DeepMind. Before joining DeepMind, Pushmeet was the director of research at the cognition group at Microsoft. During his 10 years at Microsoft, Pushmeet worked in Microsoft labs in Seattle, Cambridge and Bangalore and took a number of roles and duties, including being technical advisor to Rick Rashid, the Chief Research Officer of Microsoft. Pushmeet’s research revolves around intelligent systems and computational sciences, and he publishes in the fields of machine learning, computer vision, information retrieval, and game theory. His current research interests include 3D reconstruction and rendering, probabilistic programming, interpretable and verifiable knowledge representations from deep models. He is also interested in conversation agents for task completion, machine learning systems for healthcare and 3D rendering and interaction for augmented and virtual reality. Reception to follow

1 0

Ethan Tseng will present his FPO "Neural Proxy Functions for Computational Cameras and Displays" on Friday, May 2, 2025 at 2:30 PM in CS 105 and Zoom.
by CS Grad Department 28 Apr '25

28 Apr '25

Ethan Tseng will present his FPO "Neural Proxy Functions for Computational Cameras and Displays" on Friday, May 2, 2025 at 2:30 PM in CS 105 and Zoom. Location: Zoom link: https://princeton.zoom.us/j/97976329281 The members of Ethan’s committee are as follows: Examiners: Felix Heide (Adviser), Adam Finkelstein, Jia Deng Readers: Szymon Rusinkiewicz, Tian-Ming Fu 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: Cameras and displays serve as portals between the physical world and the digital realm. We use cameras to take pictures of our environment and we use computer vision algorithms to analyze these digital images. These images and their associated analyses can then be shared with others through programmable displays ranging from smartphone screens to mixed reality headsets. However, despite their importance as gateways to the physical world, these hardware devices are still usually developed in isolation from the downstream software. We would like to be able to “learn” new cameras and displays in a manner similar to how modern machine learning models learn new functions by being trained on large volumes of data. Today’s machine learning models are typically trained using some form of gradient descent and the parameters of the model are progressively updated over many iterations. Extending the same design strategy to cameras and displays is challenging because gradients may not be available (e.g., proprietary black-box systems) or the gradients may be too computationally expensive to acquire (e.g., full wave physics models). This thesis introduces neural proxy functions to address and overcome these obstacles. Neural proxy functions act as differentiable approximations for systems that do not readily admit gradients. The gradients provided by neural proxy functions allow for the co-evolution of hardware and software, extending the reach of machine learning beyond the digital world. We demonstrate the utility of neural proxy functions through three research contributions: (1) Hyperparameter optimization of image signal processors that connect high-level visual machine learning (e.g., object detection) to low-level irradiance measurements recorded by a camera sensor. (2) Fast differentiable forward models for optical stacks consisting of both traditional optics and emerging nanophotonic elements. (3) Design of neural algorithms and optics for high-performance holographic displays.

1 0

Seth Karten will present his General Exam "Multi-Agent Frontiers with Foundation Models: Cooperative and Competitive Games" on May 6th, 2025 in Friend 125 at 12:30pm.
by Gradinfo 28 Apr '25

28 Apr '25

Seth Karten will present his General Exam "Multi-Agent Frontiers with Foundation Models: Cooperative and Competitive Games" on May 6th, 2025 in Friend 125 at 12:30pm. Committee: Chi Jin (adviser), Benjamin Eysenbach, Danqi Chen Title: "Multi-Agent Frontiers with Foundation Models: Cooperative and Competitive Games" Abstract: Recent advances in foundation models, particularly large language models (LLMs), have created unprecedented opportunities to enhance multi-agent reinforcement learning (MARL) systems by leveraging their rich world knowledge and reasoning capabilities. This examination explores two complementary approaches to integrating LLMs with MARL across different game-theoretic settings. In zero-sum environments, we introduce PokéChamp, an expert-level minimax language agent for Pokémon battles that replaces three key components of traditional search algorithms with LLM-based modules: player action sampling, opponent modeling, and value function estimation. PokéChamp achieves human-expert performance without additional training, demonstrating the potential of LLMs to effectively constrain search spaces using informative priors in partially observable environments. For general-sum scenarios, we present the LLM Economist, a novel framework that employs LLMs to create realistic economic simulations with varied agent populations. By formulating the problem as a Stackelberg game, we show how LLMs can model complex economic behaviors through in-context optimization of utility functions, generation of synthetic human preferences, and mechanism design for social welfare maximization. These approaches collectively demonstrate a powerful synergy: MARL architectures benefit from the rich knowledge and reasoning capabilities embedded in foundation models, while reinforcement learning planning techniques provide the grounding and optimization mechanisms necessary to overcome the limitations of foundation models in strategic decision-making contexts.

1 0

Nick Sudarsky will present the MSE talk "Dual Exponential Momentum Filtering for First Order Gradient Based Optimization" on Thursday, April 24, 2025 at 3:45 pm in Friend Center 005.
by Gradinfo 24 Apr '25

24 Apr '25

Nick Sudarsky will present the MSE talk "Dual Exponential Momentum Filtering for First Order Gradient Based Optimization" on Thursday, April 24 , 2025 at 3:45 pm in Friend Center 005. Thesis adviser: Szymon Rusinkiewicz and Adam Finkelstein (Reader) Abstract: Recent years have seen machine learning be applied to an increasingly diverse range of optimisation problems, with a number of generally-applicable optimizer algorithms being created to more effectively and efficiently solve these problems. Among the most broadly applicable and widely used of these optimization algorithms are those combining some form of first-order gradient-based optimization with momentum, which generally involves filtering gradient estimates or a function thereof via a single exponentially weighted running mean function. This work proposes a series of novel first order gradient descent optimizers, employing momentum filtering based upon a linear combination of two exponentially-weighted running gradient means. Through a combination of theoretical analysis and practical evaluation, we find that these dual exponential optimizers have the potential to outperform their single exponential counterparts in terms of both the number of iterations taken to reduce training cost and the stability with which training cost decreases.

1 0

Haichen Dong will present his masters' thesis "Optimal Strategy in Longest-Chain Proof-of-Stake Mining with External Randomness" on April 25, 2025 at 10:15am in the CS 402.
by CS Grad Department 23 Apr '25

23 Apr '25

Haichen Dong will present his masters' thesis "Optimal Strategy in Longest-Chain Proof-of-Stake Mining with External Randomness" on April 25, 2025 at 10:15am in the CS 402. The members of his committee are as follows: Matt Weinberg (Adviser) and Mark Braverman(reader). All are welcome to attend. Please see the title and abstract below. Title: Optimal Strategy in Longest-Chain Proof-of-Stake Mining with External Randomness The longest-chain Proof-of-Stake(PoS) mining protocol is an attractive energy-friendly alternative to the popular and successful Proof-of-Work(PoW) diagram. One foundational property of a blockchain protocol is its robustness against strategic manipulation, that all miners are incentivized to follow the honest mining strategy because no miners can defect from using the honest strategy and earn greater revenue. A mining protocol is robust against strategic manipulation when and only when it is a Nash equilibrium for all miners to play the honest mining strategy. Let ⍺^protocol denote the supremum ⍺ such that whenever no miner is selected to create a new block with probability greater than ⍺, it is a Nash equilibrium for all miners to play the honest mining strategy. Sapirshtein et al. (2019) estimates that ⍺^PoW ≈ 0.329. For the Proof-of-Stake framework, it is known that without access to external randomness, the PoS mining protocol will suffer greatly from strategic manipulation, with ⍺^”PoS w/o External Randomness” = 0. Even with external randomness, Ferreira and Weinberg (2021) showed that 0.3080 ≤ ⍺^PoS ≤ 0.3247, which was later improved by Hein (2022) to 0.3189 ≤ ⍺^PoS ≤ 0.3235. In this work, we closed the profit threshold gap by showing that ⍺^PoS ≈ 0.3234. We presented the optimal strategy for the PoS mining game with external randomness assuming a mining power of ⍺^PoS. We also proposed a computer-assisted algorithm that helps bound the possible future profit for each state in the mining game, the result of which matches the optimal profit threshold implied by the optimal strategy.

1 0

Emma Farkash will present her MSE talk "Studying the Role of CPU Overheads in Modern Vision AI" on Thursday, April 24, 2025 at 12:40pm in CS 302.
by Gradinfo 22 Apr '25

22 Apr '25

Emma Farkash will present her MSE talk "Studying the Role of CPU Overheads in Modern Vision AI" on Thursday, April 24, 2025 at 12:40pm in CS 302. Thesis adviser: Ravi Netravali and Kevin Wayne (Reader) Abstract: GPUs are at the center of AI's rise to prominence and have seen massive improvements in the past years. Though less in the spotlight, CPUs remain an important player in AI pipelines; data processing tasks that run on the CPU are a necessary part of every inference call. As AI workloads are shifting towards incorporating more modalities and growing in complexity, heavy vision data processing tasks are increasingly common. This work explores various Vision AI pipelines and their CPU overheads, specifically CPU time and utilization, under diverse configurations, image resolutions, request rates and serving systems. We find that the CPU is increasingly oversubscribed for multimodal and multi-model pipelines, higher resolution images and request rates. Additionally, evaluations on newer hardware point to a widening gap between GPU inference and CPU data processing efficiencies, supporting the conjecture that the CPU may become increasingly congested in the future, impacting service level objectives.

1 0

DeCenter Seminar Series: Wed April 30 at 3:30pm
by Emily C. Lawrence 22 Apr '25

22 Apr '25

Dear all, Next Wednesday, April 30th, at 3:30 PM, the DeCenter Seminar Series continues with Ciamac Moallemi, a professor of Business in the Decision, Risk, and Operations Division at the Graduate School of Business at Columbia University. He will present a seminar titled "The Economics of Automated Market Making and Decentralized Exchanges.” The event will take place in the Computer Science Building Room (105). Abstract: Automated market making (AMM) protocols such as Uniswap have recently emerged as an alternative to the most common market structure for electronic trading, the limit order book. Relative to limit order books, AMMs are both more computationally efficient and do not require the participation of active market making intermediaries such as high frequency traders. As such, AMMs have emerged as the dominant market mechanism for trust-less decentralized exchanges (DEXs) implemented through smart contracts on programmable blockchain platforms such as Ethereum. In cryptocurrency markets, the aggregate trading volume on the Uniswap DEX exceeds that of the much better known Coinbase centralized exchange. We develop a model the underlying economics of AMMs from the perspective of their passive liquidity providers (LPs). Our central contribution is a “Black-Scholes formula for AMMs”. Like the Black-Scholes formula, we consider the return to LPs once market risk has been hedged. We identify the main adverse selection cost incurred by LPs, which we call “loss-versus-rebalancing” (LVR, pronounced “lever”). LVR captures costs incurred by AMM LPs due to stale prices that are picked off by better informed arbitrageurs. In a continuous time Black-Scholes setting, we are able to derive closed-form expressions for this adverse selection cost. Qualitatively, we highlight the main forces that drive AMM LP returns, including asset characteristics (volatility), AMM characteristics (curvature / marginal liquidity, fee structure), and blockchain characteristics (block rate). Quantitatively, we illustrate how our model’s expressions for LP returns match actual LP returns for the Uniswap v2 WETH-USDC trading pair. Our model provides tradable insight into both the ex-ante and ex post assessment of AMM LP investment decisions. LVR can also inform the design of the next generation of DEX market mechanisms and “LVR mitigation” has already emerged as an important challenge among practitioners in the AMM protocol designer community. We present the auction-managed AMM (am-AMM), a novel DEX design utilizing an auction mechanism to mitigate LVR losses to informed orderflow while maximizing fee revenue from uninformed orderflow. This talk includes joint work with Jason Milionis (Columbia CS), Tim Roughgarden (Columbia CS / a16z crypto), Anthony Zhang (Chicago Booth), Austin Adams (Uniswap Labs), Sara Reynolds (Uniswap Labs), and Dan Robinson (Paradigm). It is based on the following three papers: [ https://moallemi.com/ciamac/papers/lvr-2022.pdf | https://moallemi.com/ciamac/papers/lvr-2022.pdf ] [ https://moallemi.com/ciamac/papers/lvr-fee-model-2023.pdf | https://moallemi.com/ciamac/papers/lvr-fee-model-2023.pdf ] [ https://moallemi.com/ciamac/papers/mm-amm-2024.pdf | https://moallemi.com/ciamac/papers/mm-amm-2024.pdf ] Bio: Ciamac C. Moallemi is William von Mueffling Professor of Business in the Decision, Risk, and Operations Division and the director of the Briger Family Digital Finance Lab at the Graduate School of Business at Columbia University, where he has been since 2007. A high school dropout, he received S.B. degrees in Electrical Engineering & Computer Science and in Mathematics from the Massachusetts Institute of Technology (1996). He studied at the University of Cambridge, where he earned a Master of Advanced Study degree in Mathematics (Part III of the Mathematical Tripos), with distinction (1997). He received a Ph.D. in Electrical Engineering from Stanford University (2007). Prior to his doctoral studies, he developed quantitative methods in a number of entrepreneurial ventures: as a partner in a $200 million fixed-income arbitrage hedge fund and as the director of scientific computing at an early-stage drug discovery start-up. He holds editorial positions at the journals Operations Research and Management Science. He is a past recipient of the British Marshall Scholarship (1996), the Benchmark Stanford Graduate Fellowship (2003), first place in the INFORMS Junior Faculty Paper Competition (2011), and the Best Simulation Publication Award of the INFORMS Simulation Society (2014). Aside from his academic work, he has developed high Sharpe, short-term systematic trading strategies as a quantitative portfolio manager, and he regularly consults for fintech companies. He is currently a Research Advisor at Paradigm, a research-driven technology venture capital firm, and at Uniswap Labs, a developer of decentralized exchanges. His research interests are in the development of mathematical and computational tools for optimal decision making under uncertainty, with a focus on applications areas including market microstructure, quantitative and algorithmic trading, and blockchain technology. We hope to see you there! Best, Madelyn LaBuda Administrative and Events Coordinator DeCenter Engineering Quad, C215 Princeton University E: ml2512 [ mailto:email@princeton.edu | @princeton.edu ] M: 570-599-5338

1 0

Josh Cohen will present his FPO "A Foundationally Verified Intermediate Verification Language" on Friday, 5/2/2025 at 2:30 PM in COS 302 and Zoom
by Gradinfo 22 Apr '25

22 Apr '25

Josh Cohen will present his FPO "A Foundationally Verified Intermediate Verification Language" on Friday, 5/2/2025 at 2:30 PM in COS 302 and Zoom. Committee: Examiners: Andrew Appel (Adviser), Zak Kincaid, and David Walker Readers: Aarti Gupta and Philip Johnson-Freyd (Sandia National Laboratories) Zoom link: [ https://princeton.zoom.us/j/93082391321 | https://princeton.zoom.us/j/93082391321 ] Abstract: Annotation-based deductive verifiers have emerged in recent years as practical, capable, and increasingly scalable tools for verifying programs in languages including C, Rust, Go, OCaml, and Dafny. Intermediate verification languages (IVLs) like Why3, Boogie, and Viper have made it significantly easier to build such verifiers by performing part of the translation from high-level, expressive program logics to SMT formulas once and for all. But these verifiers are difficult to fully trust: their toolchains comprise hundreds of thousands of lines of code and bugs in any part can result in unsoundness. An alternate approach to building program verifiers is the foundational one, where the language semantics, program logic, and user-supplied proofs are defined in a proof assistant, giving an end-to-end soundness theorem. These tools, including VST, CakeML, and Bedrock2, provide very strong guarantees but require manual proof and are inaccessible to non-experts. The two approaches have largely remained separate. IVL-based tools are not themselves verified, while interactive tools cannot take advantage of the automation provided by the IVL and SMT-based pipelines. In this thesis, we take steps towards bridging this gap by providing an implementation of the Why3 IVL in the Coq proof assistant, giving the first foundationally verified IVL. First, we give a novel formal semantics for a core subset of the logic implemented by Why3– polymorphic first-order logic with recursive types, functions, predicates, and pattern matching. We prove sound a compiler from this logic to first-order logic, giving the first machine-checked soundness proofs for a sophisticated pattern matching compiler and a first-order axiomatization of algebraic data types. We then develop a new framework to idiomatically implement OCaml APIs in Coq and use this to produce a Coq implementation of the Why3 logic API. Our IVL implementation is fully executable both within Coq and via extraction to OCaml. Using the latter, we demonstrate our tool’s practicality by running existing Why3-based tools and test suites against our implementation, while the former allows our tool to serve as a future back-end for foundational verifiers such as VST.

1 0

Neil Agarwal will present his FPO, "Offline Processing for Machine Learning-Based Video Systems," on Monday, May 5, 2025, at 9 AM in COS 302.
by Gradinfo 21 Apr '25

21 Apr '25

Neil Agarwal will present his FPO, "Offline Processing for Machine Learning-Based Video Systems," on Monday, May 5, 2025, at 9 AM in COS 302. Please see below for details. Committee: Adviser/Examiner: Professor Ravi Netravali Examiner: Professor Wyatt Lloyd Examiner: Professor Kai Li Reader: Professor Maria Apostolaki Reader: Professor Amit Levy Title: Offline Processing for Machine Learning-Based Video Systems Abstract: Video-based applications proliferate our society, from sophisticated video analysis pipelines to large-scale video content and delivery platforms. Machine learning algorithms are instrumental in supporting these applications, enabling fine-grained, high-quality analysis of video data and supporting highly efficient and dynamic control of underlying system processes in video applications. However, integrating machine learning techniques into existing video pipelines brings significant overheads that hinder usability and deployability. This dissertation examines how we can leverage offline processing techniques to mitigate these potential overheads. By carefully shifting computational tasks from application runtime into an offline phase, it is possible to reduce runtime latency, computational cost, and disruptive behaviors experienced by end users. We present two systems that demonstrate the potential benefits of this approach in real-world video applications. Boggart builds a conservative, model-agnostic video index ahead of time to accelerate retrospective video analytics querying pipelines. Mowgli enables practical, offline-trained learning for real-time video rate control by leveraging historical telemetry logs to learn high-quality bitrate adaptation policies without disrupting user experience during deployment.

1 0