- talks - lists.cs.princeton.edu

Stamatis Alexandropoulos will present his General Exam "Princeton365: A Diverse Dataset with Accurate Camera Pose" on Monday, May 5, 2025 at 2:00 PM in FC 005 and via zoom.
by CS Grad Department 30 Apr '25

30 Apr '25

Stamatis Alexandropoulos will present his General Exam " Princeton365: A Diverse Dataset with Accurate Camera Pose " on Monday, May 5, 2025 at 2:00 PM in FC 005 and via zoom. Zoom link: https://princeton.zoom.us/my/stamalex Committee Members: Jia Deng (advisor), Felix Heide , Adam Finkelstein Abstract: Accurately evaluating SLAM systems often comes at the cost of limited data diversity, while diverse datasets usually lack precise camera pose. In this talk, I will introduce Princeton365, a new dataset designed to bridge this gap. It includes 365 videos captured using a 360° camera and calibration boards to provide accurate ground truth camera poses. The dataset features a wide range of scenarios—including indoor, outdoor, and object scanning scenes—and offers synchronized monocular and stereo RGB video, along with IMU data. I’ll also present a new scale-aware evaluation metric for SLAM based on optical flow induced by pose estimation errors, which enables more consistent comparison across different scenes than traditional metrics like Average Trajectory Error. Finally, I’ll introduce a novel benchmark for view synthesis that tackles challenging cases not handled by current datasets, such as non-Lambertian surfaces and full 360° camera trajectories. Reading List: https://docs.google.com/document/d/1iqBGxihNyFdlsjk5eCaNgA8EV0hOObt1Z_CN9JG… 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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Erich Liang will present his General Exam "ChangeIn: A Benchmark for Self-Calibration of Dynamic Intrinsics of Video Cameras" on Monday, May 5, 2025 at 10:30 AM in FC 202 and via zoom.
by CS Grad Department 30 Apr '25

30 Apr '25

Erich Liang will present his General Exam "ChangeIn: A Benchmark for Self-Calibration of Dynamic Intrinsics of Video Cameras" on Monday, May 5, 2025 at 10:30 AM in FC 202 and via zoom. Zoom link: https://princeton.zoom.us/j/8238970644 Committee Members: Jia Deng (advisor), Szymon Rusinkiewicz , Ellen Zhong Abstract: Accurately tracking camera intrinsics is crucial for achieving 3D understanding from 2D video. However, most 3D algorithms assume that camera intrinsics stay constant throughout a video, which is often not true for many real-world in-the-wild videos. A major obstacle in this field is a lack of dynamic camera intrinsics benchmarks--existing benchmarks typically offer limited diversity in scene content and intrinsics variation, and none provide per-frame intrinsic changes for consecutive video frames. In this paper, we present ChangeIn, a real-world benchmark that provides per-frame ground truth intrinsics annotations for videos with dynamic intrinsics. Compared to prior benchmarks, ChangeIn captures a wider range of intrinsic variations and scene diversity, featuring 143K annotated frames from 386 high-resolution indoor and outdoor videos with dynamic camera intrinsics. To ensure accurate per-frame intrinsics, we build a comprehensive look-up table of calibration experiments and extend the Kalibr toolbox to improve its accuracy and robustness. Using our benchmark, we evaluate existing baseline methods for predicting camera intrinsics and find that most struggle to achieve accurate predictions on videos with dynamic intrinsics. Reading List: https://docs.google.com/document/d/1pu3P9hWcthJRMvBSssPqWLYiq3QZCVfa4p_v50J… 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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Kathryn Wantlin will present her General Exam "Self-Supervised Multi-Task Exploration and RL" on Friday, May 2, 2025 at 3:00 PM in CS 401.
by CS Grad Department 29 Apr '25

29 Apr '25

Kathryn Wantlin will present her General Exam "Self-Supervised Multi-Task Exploration and RL" on Friday, May 2, 2025 at 3:00 PM in CS 401. Committee Members: Ryan Adams (advisor), Ben Eysenbach, Olga Russakovsky Abstract: Learning from demonstrations to imitate a single behavior in a given environment can require hundreds or thousands of expert (human) demonstrations and learning a distribution of tasks in an environment is even more prohibitively inefficient. This work presents a framework for achieving zero-shot generalization capabilities in a given environment without access to expert data. We replace the imitation learning training phase with a self-supervised exploration phase, training a goal-conditioned critic, policy and variational posterior for inferring goals, and at test time, are able to zero-shot infer a goal from a demonstration and then unroll the associated goal-conditioned policy to imitate the demonstration’s behavior with low regret. We make the key assumption that many behaviors are likely goal-reaching, an inductive bias that inferring an agent’s goal provides a good bias-variance tradeoff compared to inferring their reward function. During the self-supervised exploration phase, I learn temporally contrastive features and utilize several methods for automatic goal selection, comparing these results against oracle goal sampling. Our pipeline is compared against related methods for zero-shot imitation, showing competitive performance against goal-conditioned behavioral cloning across our benchmarked environments. Reading List: https://docs.google.com/document/d/1zhhjPl5Ei1f_awCVUBwduVqi6F1AHZ6iMiJdsiT… 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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Han Xu will present his General Exam " Relational Netkat" on May 2, 2025 at 10am in CS 402 and Zoom.
by Gradinfo 29 Apr '25

29 Apr '25

Han Xu will present his General Exam " Relational Netkat" on May 2, 2025 at 10am in CS 402 and Zoom. Committee: David Walker (adviser), Zachary Kincaid, Maria Apostolaki Zoom link: [ https://princeton.zoom.us/j/8011315368 | https://princeton.zoom.us/j/8011315368 ] Abstract: Modern networks are complex, dynamic, and large-scale, making network verification a critical challenge. Relational NetKAT extends traditional NetKAT by providing an expressive and efficient framework for reasoning about network behavior under path changes, forwarding table modifications, and network topology updates. It enables all-path verification, ensuring that network-wide invariants hold across diverse changes such as load balancing and routing updates. Compared to sample-based verification of Rela, which fails to cover all possible paths, Relational NetKAT provides a mathematically sound and scalable approach to checking network correctness. Reading List: Textbook: * Types and programming languages. MIT press, 2002. * Handbook of Model Checking Ch 7.8. Paper: Netkat, Kleene algebra and automata theory * Xieyang Xu, Yifei Yuan, Zachary Kincaid, Arvind Krishnamurthy, Ratul Mahajan, David Walker, and Ennan Zhai. 2024. Relational Network Verification. * Carolyn Jane Anderson, Nate Foster, Arjun Guha, Jean-Baptiste Jeannin, Dexter Kozen, Cole Schlesinger, and David Walker. 2014. NetKAT: semantic foundations for networks. * Nate Foster, Dexter Kozen, Mae Milano, Alexandra Silva, and Laure Thompson. 2015. A Coalgebraic Decision Procedure for NetKAT. * Damien Pous. 2015. Symbolic Algorithms for Language Equivalence and Kleene Algebra with Tests. BDD and Model Checking * Bryant Randy, 1992. Symbolic Boolean manipulation with ordered binary-decision diagrams * Kathi Fisler and Moshe Y. Vardi. 2002. Bisimulation Minimization and Symbolic Model Checking. Network Verification * H. Yang and S. S. Lam, "Real-time verification of network properties using Atomic Predicates," 2013 * Peyman Kazemian, George Varghese, and Nick McKeown. 2012. Header space analysis: static checking for networks. * Ahmed Khurshid, Wenxuan Zhou, Matthew Caesar, and P. Brighten Godfrey. 2012. Veriflow: verifying network-wide invariants in real time.

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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.

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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

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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.

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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.

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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.

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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.

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