April 2026 - talks - lists.cs.princeton.edu

Tanvi Namjoshi will present her MSE thesis "Major Decisions: Evaluating LLM Homogenization and Personalization in Educational Advisory Tasks" on Friday, April 24th, 2026 in CS 401 at 2:30 pm.
by CS Grad Department 22 Apr '26

22 Apr '26

Tanvi Namjoshi will present her MSE thesis "Major Decisions: Evaluating LLM Homogenization and Personalization in Educational Advisory Tasks" on Friday, April 24th, 2026 in CS 401 at 2:30 pm. Advisor: Lydia T. Liu Reader: Manoel Horta Ribeiro All are welcome to attend. Abstract: Large language models (LLMs) are increasingly being used for advisory tasks in high-stakes settings such as education. While researchers have evaluated their agreement with human advisors, there has been limited work looking at the challenges that emerge with adoption at scale. Unlike factual question-answering, where homogeneous outputs are desirable, and creative generation, where scale is rarely a concern, advisory tasks require recommendations that are both individually relevant and collectively diverse. In this work, we introduce a formal evaluation framework for homogenization and personalization for this class of problems, and apply it to the task of undergraduate major selection. To study the homogenization of responses we use a Shannon entropy-based approach to study 17 open-source LLMs across three levels of provided student context. We find that global recommendation entropy remains below our real-world enrollment baseline across all context levels, and several majors -- disproportionately humanities disciplines -- are never recommended by most models. While models are semantically responsive to the individual student profiles, they exhibit systematic narrowing; across all models, over 50% of the recommendations are concentrated within the top five majors per model. These findings suggest that naive prompt-based advising is insufficient for institutional deployment and highlight the need for population-level metrics to complement capability assessments in future AI system evaluations.

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Rohan Baskar Prabhakar will present his FPO "Hardware-Aware Software Optimizations for and with Machine Learning" on Monday, May 4, 2026 at 10am in CS 302.
by Gradinfo 22 Apr '26

22 Apr '26

Rohan Baskar Prabhakar will present his FPO "Hardware-Aware Software Optimizations for and with Machine Learning" on Monday, May 4, 2026 at 10am in CS 302. His committee is as follows: Examiners: David Wentzlaff (adviser), Aarti Gupta and Ravi Netravali Readers: Jialin Ding and Prateek Mittal Abstract: In recent years, fundamental limits in semiconductor manufacturing have caused a gradual decline in the steady cadence of hardware performance scaling. With pivotal trends like Moore's law and Dennard scaling ending, there is a growing need to ensure that software workloads execute at peak efficiency. Towards this objective, this dissertation describes three hardware-aware optimizations that either accelerate machine learning inference or use machine learning to optimize single-threaded CPU workloads. First, this dissertation presents Kraken, a variation of the Transformer architecture designed to improve the efficiency of tensor parallelism during inference. The new model architecture incorporates an innate notion of model parallelism that complements the topology of multi-device inference hardware and allows communication to overlap with compute. Experiments demonstrated that while preserving the language modeling performance of standard Transformers, the Kraken architecture improves Time To First Token by a geomean of 35.6% across a range of model configurations. Second, the dissertation investigates the feasibility of integrating a binary classifier to increase the efficiency of the verification phase in speculative decoding. Although speculative decoding is effective in accelerating the decode step of Transformer inference, performance gains are limited to small batch sizes where kernels are memory-bound as opposed to compute-bound. Using n-gram matching as the draft method and intermediate activations from early Transformer layers as input allows binary classifiers to filter 75% of draft tokens that would otherwise be rejected. Doing so decreases the effective batch size of the verification step, expanding the range of scenarios in which speculative decoding is effective. Finally, the dissertation introduces Toggle, a dynamic optimization system that enables single-threaded CPU programs to switch both compilers and optimization choices at runtime. Relying on the premise that the best choice of compiler and optimizations is a function of the current program phase and input, the system uses otherwise idle inference accelerators and statistics from hardware performance counters to perform continuous optimization. When evaluated on the SPEC CPU 2017 benchmark suite, integrating Toggle improved program runtime by a geomean of 4.32%, effectively extracting a year's worth of hardware performance gains.

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Max Gupta will present his MSE talk "Inductive Biases for Relational Reasoning in Neural Networks" on Thursday 4/23/26 at 9:30am in Friend 004.
by Gradinfo 22 Apr '26

22 Apr '26

Max Gupta will present his MSE talk " Inductive Biases for Relational Reasoning in Neural Networks" on Thursday 4/23/26 at 9:30am in Friend 004. Advisor: Tom Griffiths Reader: Brenden Lake Title: Inductive Biases for Relational Reasoning in Neural Networks Abstract: This thesis examines how the inductive biases of neural networks (architecture, training data, training objective, and weight initialization) affect the emergence of visual relational reasoning capabilities. I present both prior published work and ongoing experimental work under review. By examining the effect of each of these four inductive biases, the thesis aims to disentangle the relative influence of each bias on the relational reasoning abilities of vision models. I cover a series of increasingly large vision models across a suite of increasingly abstract relational reasoning tasks. Along the task complexity axis, I start with the most fundamental relation: same-different reasoning, classifying two objects as 'same' or 'different'. Scaling up task complexity along this axis to higher order relational tasks involves composing intermediate same-different judgements in tasks like odd-one-out and relational match to sample, tasks often used in cognitive science and visual IQ tests. I compare with human data to understand certain perceptual biases towards regularity and simplicity in humans versus neural networks. Throughout, I focus on meta-learning (learning to learn) as a particularly effective training method for endowing neural networks with relational reasoning capabilities. In particular, the episodic partitioning of training data into mini train/test sets enables strong, generalizable relational reasoning even in tiny convolutional networks and at a fraction of the scale of data as pre-trained vision models. Throughout, I employ interpretability techniques for understanding what is going on inside models meta-trained from scratch and inside off-the-shelf pre-trained models. One compelling finding is the recurrence of a two-stage perceptual-to-relational reasoning pipeline that vision models across architectures and scales employ to reason relationally. Moreover, we find that the concepts of 'same' and 'different' are more strongly represented as orthogonal dimensions in smaller, meta-trained models than larger off-the-shelf models like CLIP and DINOv2. In these larger models, I also find correlational evidence that similar attention heads are employed for relational reasoning across this suite of tasks. I highlight this evidence as a promising avenue for future work investigating causal evidence for a shared subspace encoding first and second order relations. Initial results show that this is easiest to do in smaller models for simpler relations like same-different, which live in relatively low-dimensional subspaces. I conclude by addressing the possibility for intervention in these subspaces and downstream consequences for model controllability.

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Lauren Wang will present her General Exam "Explainable and Controllable Augmented Reality Interfaces through Embodied Interaction and Situated Visualization" on Monday, April 27, 2026 at 10:30 AM in CS 401.
by CS Grad Department 22 Apr '26

22 Apr '26

Lauren Wang will present her General Exam "Explainable and Controllable Augmented Reality Interfaces through Embodied Interaction and Situated Visualization" on Monday, April 27, 2026 at 10:30 AM in CS 401. Committee Members: Parastoo Abtahi (advisor), Andrés Monroy-Hernández, Tom Griffiths Abstract: As technologies become more sophisticated, users are expected to interact with intelligent systems whose reasoning, capabilities, and limitations are opaque. I identify two shortcomings in such human-computer interfaces: a gulf of execution[1], where users struggle to communicate abstract, multimodal, or spatial intent, and a gulf of evaluation[1], where limited feedback makes it difficult to understand how inputs influence outcomes. Current language-based interfaces lack the bandwidth to convey non-textual information and are ambiguous in spatial contexts. My work investigates augmented reality (AR) as a unifying interaction paradigm to bridge both gaps by enabling embodied multimodal input (voice, gesture, gaze) to better capture user intent, and by providing in-situ, spatially grounded visualizations that help establish shared mental models. I demonstrate this approach across real-world applications. In human-robot interaction, failures arise when users do not understand what robots can and cannot do. I develop Explainable OOHRI[2] to expose robot constraints and action possibilities through AR visualizations, and enable users to issue personalized instructions via direct manipulation of GhostObjects[3] —life-size, world-aligned virtual twins. Extending this paradigm to interaction with generative AI, I explore embodied 3D/4D scene editing for content creation and enhancing human-human communication. I study methods that fuse multimodal inputs to infer user intent across multiple levels of abstraction, while producing immediate, interpretable outputs that support counterfactual exploration and maintain controllability throughout iterative design processes. Taken together, my research aims to advance AR interfaces that make intelligent systems more explainable, controllable, and aligned with human intent, grounded in cognitive theories and designed to drive impacts in real-world settings. Reading List: https://docs.google.com/document/d/1RLaQ7eUAq7MStWIkt8QvtzNj-UkaHyJQJboRh7n… 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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Alexander Wettig will present his FPO "Building Better Language Models with Data Curation" on Tuesday, April 28, 2026 at 2:00 PM in FC 110.
by CS Grad Department 21 Apr '26

21 Apr '26

Alexander Wettig will present his FPO "Building Better Language Models with Data Curation" on Tuesday, April 28, 2026 at 2:00 PM in FC 110. Zoom Link: [ https://princeton.zoom.us/j/98043325388?pwd=IIFV4labMYR0YyBXNC5ocNaaZ2i8aQ.… | https://princeton.zoom.us/j/98043325388?pwd=IIFV4labMYR0YyBXNC5ocNaaZ2i8aQ.… ] The members of Alex’s committee are as follows: Examiners: Danqi Chen (Adviser), Sanjeev Arora, Karthik Narasimhan Readers: Peter Henderson, Ludwig Schmidt (Stanford) 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: Training data is among the most consequential choices when developing a language model, and the key ingredients of successful data curation have become closely guarded secrets of industrial research labs. This thesis works toward making data curation an open and reproducible science, developing tools and methods for shaping the training data distribution of language models and studying how quality filtering, domain mixing, and data composition affect downstream capabilities. Our methodology is grounded in data ablations: controlled experiments that vary the training data while holding all other variables fixed. The major part of this thesis focuses on data curation for pre-training, where we introduce three complementary techniques. (1) QuRating selects documents by quantifying human notions of text qualities, such as writing style, educational value, and required expertise. With the right balance of quality and diversity, we curate training datasets that produce strong language models that perform on par with models trained with 50% more compute. (2) WebOrganizer operates at the corpus level, introducing topic and format taxonomies that classify web pages into meaningful categories to enable explicit optimization of the data mixture. This allows us to achieve well-calibrated performance across tasks, and the best results are achieved when domain mixing is combined with quality selection. (3) Finally, we consider the relationship of quality filtering and deduplication as part of the Olmo-3 project and develop quality-aware upsampling strategies used in curating trillions of tokens of high-quality pre-training data. In the second part, we extend our methodology to the setting of continued pre-training for long-context understanding. Through systematic ablations guided by reliable downstream evaluation, we identify good sources of long-context data and uncover details such as the importance of mixing both long and short documents. Our final recipe produces the ProLong model, which achieves state-of-the-art long-context performance at a fraction of the data budget of comparable efforts. We conclude by discussing how data curation will co-evolve with future progress in AI.

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Alexander Wettig will present his FPO "Building Better Language Models with Data Curation" on Tuesday, April 28, 2026 at 2:00 PM in FC 110.
by CS Grad Department 21 Apr '26

21 Apr '26

Alexander Wettig will present his FPO "Building Better Language Models with Data Curation" on Tuesday, April 28, 2026 at 2:00 PM in FC 110. The members of Alex’s committee are as follows: Examiners: Danqi Chen (Adviser), Sanjeev Arora, Karthik Narasimhan Readers: Peter Henderson, Ludwig Schmidt (Stanford) 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: Training data is among the most consequential choices when developing a language model, and the key ingredients of successful data curation have become closely guarded secrets of industrial research labs. This thesis works toward making data curation an open and reproducible science, developing tools and methods for shaping the training data distribution of language models and studying how quality filtering, domain mixing, and data composition affect downstream capabilities. Our methodology is grounded in data ablations: controlled experiments that vary the training data while holding all other variables fixed. The major part of this thesis focuses on data curation for pre-training, where we introduce three complementary techniques. (1) QuRating selects documents by quantifying human notions of text qualities, such as writing style, educational value, and required expertise. With the right balance of quality and diversity, we curate training datasets that produce strong language models that perform on par with models trained with 50% more compute. (2) WebOrganizer operates at the corpus level, introducing topic and format taxonomies that classify web pages into meaningful categories to enable explicit optimization of the data mixture. This allows us to achieve well-calibrated performance across tasks, and the best results are achieved when domain mixing is combined with quality selection. (3) Finally, we consider the relationship of quality filtering and deduplication as part of the Olmo-3 project and develop quality-aware upsampling strategies used in curating trillions of tokens of high-quality pre-training data. In the second part, we extend our methodology to the setting of continued pre-training for long-context understanding. Through systematic ablations guided by reliable downstream evaluation, we identify good sources of long-context data and uncover details such as the importance of mixing both long and short documents. Our final recipe produces the ProLong model, which achieves state-of-the-art long-context performance at a fraction of the data budget of comparable efforts. We conclude by discussing how data curation will co-evolve with future progress in AI.

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Nimra Nadeem will present her MSE thesis "Interpretive Discretion in AI Systems" on Wednesday, April 22nd, 2026 in CS 402 and Zoom at 2:00pm.
by gradinfo＠cs.princeton.edu 21 Apr '26

21 Apr '26

Nimra Nadeem will present her MSE thesis "Interpretive Discretion in AI Systems" on Wednesday, April 22nd, 2026 in CS 402 and Zoom at 2:00pm. Zoom Link: https://princeton.zoom.us/j/5310001339 Advisor: Peter Henderson Reader: Lydia Liu All are welcome to attend. Abstract: Natural language is inherently ambiguous because it is open to interpretation. Resolving this ambiguity requires the exercise of interpretive discretion: the freedom to choose between multiple plausible interpretations of a given statement. We study this problem in the context of AI alignment. AI systems are increasingly governed by natural language principles, such as OpenAI's ModelSpec or Anthropic's Constitution for Claude. As in legal systems, ambiguity arises both from how principles are written and how they are applied. But while legal systems use institutional safeguards to manage such ambiguity, rule-based AI alignment pipelines lack comparable mechanisms. Drawing on US legal theory, we identify key gaps in current rule-based alignment pipelines by examining how legal systems constrain ambiguity at both the rule creation and rule application steps. We propose a computational framework that formalizes interpretive ambiguity as constrained entropy minimization and introduces two law-inspired mechanisms: (1) a rule refinement pipeline that minimizes interpretive disagreement by revising ambiguous rules, and (2) prompt-based interpretive constraints that reduce disagreement during rule application. We evaluate our framework on a 5,000-scenario subset of the WildChat dataset and show that both interventions improve judgment consistency across a panel of reasonable interpreters. We then extend our analysis to the multi-rule setting and identify some important open challenges. We conclude by discussing the normative implications of interpretive discretion in AI systems, including its impact on fairness in decision-making settings. Our work draws attention to the overlooked challenge of interpretive discretion in AI systems and offers an initial step toward systematically managing it.

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AI Lab Distinguished Lecture-Elif Ertekin: Friday April 24
by Emily C. Lawrence 21 Apr '26

21 Apr '26

AI Lab Distinguished Lecture Series: Elif Ertekin, Associate Professor, University of Illinois at Urbana-Champaign Apr 24, 2026 2:00 pm – 3:00 pm Location: Robertson 002 “Physical Mechanisms or Learned Patterns? Embedding Symmetries and Equations of Motion as Inductive Biases for Materials Simulations” [ https://invent.ai.princeton.edu/events/2026/physical-mechanisms-or-learned-… | For more information and to RSVP, please visit our website. ]

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Metropolis Student & Postdoc Research Symposium: Friday, April 24
by Emily C. Lawrence 21 Apr '26

21 Apr '26

Register Today to Attend the 2026 Metropolis Student and Postdoc Research Symposium | Friday, April 24, 2026 Dear CS Community, The Metropolis Initiative invites you to the 2026 Metropolis Student and Postdoc Research Symposium, co-sponsored by the Princeton Initiative. This half-day event is designed to foster interdisciplinary exchange, collaboration, and engagement across the research community. The symposium provides a platform for students and postdoctoral researchers to share their work and engage with faculty and peers in a constructive and supportive environment. It aims to deepen connections across disciplines and highlight innovative research. This year, we are excited to feature a diverse group of graduate students, undergraduate students, and postdoctoral researchers from several departments across Princeton presenting their work. Date: Friday, April 24, 2026 Time: 12:00 PM to 3:30 PM Location: Friends Center Convocation Room (Friend 113) Lunch: Provided [ https://docs.google.com/forms/d/e/1FAIpQLSeyqjIrt3IGIXtc7f9eIEJ8qJ_EStV0nkV… | Register here ] or use the QR Code Below We hope to see many of you there!

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Computational Math IDeAS seminar
by Emily C. Lawrence 21 Apr '26

21 Apr '26

COMPUTATIONAL MATH – IDeAS SEMINAR Recurring weekly series · Thursdays · 224 Fine Hall ** NOTE: Earlier time than previous weeks ** This week's talk: Speaker: Tom Trogdon (University of Washington) Title: Towards PCA without the SVD: Lanczos-based spike detection Date: Thursday, April 23, 2026 Time: 1:30 PM – 2:30 PM (note earlier time) Room: 224 Fine Hall Abstract: This talk will give an overview of the study of algorithms on random data, and in particular, algorithms from numerical linear algebra (NLA) on random matrices. The combination of ideas from numerical linear algebra and random matrices goes back, at least, to the seminal work of Goldstine and von Neumann. The works of Trotter, Silverstein, Edelman, Dumitriu & Edelman, Pfrang, Deift & Menon, and many others, developed these ideas further. A core subset of NLA algorithms, the Krylov subspace methods, play particularly well with existing random matrix theory. Through the study of random orthogonal polynomials, as perturbations of deterministic orthogonal polynomials, the concentration phenomenon in these methods can be explained using local laws from random matrix theory. We will use these ideas, and methods, to efficiently and robustly perform spike detection in the spiked sample covariance model and move towards PCA without the SVD. About the speaker: Thomas Trogdon is a Professor and Chair of Applied Mathematics, an adjunct professor in the Department of Mathematics and an affiliate of the Algorithmic Foundations of Data Science Institute, all at the University of Washington. Trogdon works on a variety of computational, applied and theoretical problems from the fields of mathematical physics, random matrix theory and approximation theory. He was an NSF Postdoctoral Fellow at the Courant Institute at NYU from 2013-2016, and he won the SIAM Richard C. DiPrima Prize in 2013. Then he joined the Department of Mathematics at UC Irvine and won the 2017 SIAM Gábor Szegő Prize before he joined the University of Washington in 2019. Up next: Thursday, April 30 – Boris Landa (Yale University). There are a few slots to meet with the speakers, please let us know if you would like one (gilles(a)princeton.edu). - Liza Rebrova, Marc Aurèle Gilles, and Jorge Garza Vargas.

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