- talks - lists.cs.princeton.edu

Special Quantum Seminar: Eli Goldin (NYU): "One-Way Puzzles: A Central Primitive for Quantum Cryptography" 2 pm, Mon. Nov. 10 (Comp Sci, 105)
by Emily C. Lawrence 05 Nov '25

05 Nov '25

Quantum Seminar Speaker: Eli Goldin, NYU Date: Monday, Nov. 10 Time: 2:00 pm Location: Computer Science Building, CS105 Host: Prof. Alex Lombardi, CS Title: One-Way Puzzles: A Central Primitive for Quantum Cryptography" Abstract: One-way functions are generally seen as a fundamental (or "central") primitive for classical cryptography. The existence of nearly any cryptography implies the existence of one-way functions, and one-way functions satisfy a number of useful properties which make them easy to work with. In the quantum setting, we give evidence that a recently introduced primitive, the one-way puzzle, is a natural analogue of one-way functions in the quantum regime. In particular, they are a useful tool for understanding the fundamental hardness behind quantum cryptography. Towards this end, we show that one-way puzzles satisfy nearly all the "nice properties" of one-way functions. We present a one-way puzzle combiner, a universal construction, a security amplification theorem, and show that one-way puzzles are equivalent to many natural variants. As a result of these nice properties, we demonstrate that one-way puzzles can be constructed from a large class of quantum cryptographic primitives (in particular, those broken by a #P oracle). Furthermore, we show a number of interesting connections between one-way puzzles and the fields of meta-complexity and (efficiently verifiable) quantum advantage, and discuss black-box separations between one-way puzzles and other primitives.

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Dan Friedman will present his FPO "Algorithmic Interpretability for Language Models" on Tuesday, November 11, 2025 at 1:00 PM in AI Lab Room 274 and Zoom.
by CS Grad Department 04 Nov '25

04 Nov '25

Dan Friedman will present his FPO " Algorithmic Interpretability for Language Models " on Tuesday, November 11, 2025 at 1:00 PM in AI Lab Room 274 and Zoom. Location: Zoom link: https://www.google.com/url?q=https%3A%2F%2Fprinceton.zoom.us%2Fj%2F86367410… The members of Dan ’s committee are as follows: Examiners: Danqi Chen (Adviser), Tom Griffiths , Adji Bousso Dieng Readers: Sanjeev Arora , Alexander Rush (Cornell University) 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: Modern natural language processing systems are black boxes. As a result, these models are fundamentally difficult to audit, debug, or trust. Standard post-hoc interpretability methods offer partial insight into model behavior but do not provide complete or faithful descriptions of the underlying computation. The aim of my research is to achieve an “algorithm-level” understanding of language processing systems, by aligning neural models with human-readable, rule-based programs. This thesis is organized into two parts. The first part involves developing intrinsically interpretable methods—machine learning models that are designed from the outset to be easily converted into symbolic form. I will present a method for finding interpretable features in text data sets using probabilistic grammars, which can recover “shortcuts” in NLP classification datasets—shallow patterns in the training data that are correlated with the target labels but do not generalize reliably. Next, I will introduce my work on developing transformers that are mechanistically interpretable by design, which can be trained using gradient-based optimization and then automatically converted into discrete, human-readable programs. These Transformer Programs are capable of learning effective solutions for a variety of tasks, and can be analyzed using standard code-analysis tools. The second part of the thesis involves aligning existing black-box models with symbolic rules, either by theoretical analysis or by extracting rule-based descriptions of their behavior. This includes a theoretical and empirical analysis of how transformers can represent classic, rule-based chatbot algorithms like ELIZA, and a method for extracting rule-based explanations of attention features using Sparse Autoencoders. I will conclude by outlining a roadmap for future research towards the goal of algorithm-level understanding of neural language models.

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PPH seminar: Digital Phenotyping for Precision Mental Health - Friday, Nov 7th at 12pm
by Emily C. Lawrence 03 Nov '25

03 Nov '25

Please join the Princeton Precision Health (PPH) Initiative for the next talk of our Fall 2025 semester series on Friday, November 7th, at 12:00 pm, at 252 Nassau Street. Dr. Nelson Freimer is Director of UCLA’s Depression Grand Challenge, a campus-wide initiative that aims to cut the burden of depression in half by 2050. He will present a seminar on, Digital Phenotyping for Precision Mental Health. Widespread application of digital phenotyping – assessments using data passively obtained from smartphones and wearables – could transform mental health research and clinical care by providing nearly continuous, quantitative, and objective information across broad physiological, behavioral, and emotional domains. The UCLA-Apple Digital Mental Health Study (DMHS) is the largest and longest digital mental health study conducted to date, having collected up to12 months of sensor data from phones and watches (resulting in >900 phenotypic features) in a sample of >4,000 participants, diverse by age, sex at birth, ethnicity, and depression symptom severity. Analyses of DMHS data identify highly specific associations between depression and anxiety symptoms and sensor domains. Additionally, the results suggest that longitudinal digital phenotyping will enable dissection of heterogenous diagnostic categories, a key step towards precision mental health. Lunch will be provided. Getting to the seminar space currently requires that you climb a set of stairs. If an accommodation is needed, please contact PPH in advance at: [ mailto:PrincetonPPH@princeton.edu | PrincetonPPH(a)princeton.edu ]

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Blockchain and Belief: Histories and Futures of Money, Power, and Faith
by Emily C. Lawrence 29 Oct '25

29 Oct '25

Please join the Princeton University DeCenter and the Princeton Project in Philosophy & Religion for a public panel discussion exploring the intersections of blockchain technology, religion, and power. This conversation will examine how historical and religious frameworks can shed light on emerging blockchain phenomena and offer ethical guidance in times of shifting authority and trust. The event will take place on November 5th at 4:30 p.m. in the Friend Center Convocation Room (113) . Panelists include Devin Singh (Dartmouth College), David Miller (Princeton University), Gordon Gant (Chi-Squared), and Meltem Demirors (Crucible Capital). The discussion will be moderated by Andrew Chignell (Princeton University). This event is free and open to the public. If you are interested in attending the full-day workshop, please email [ mailto:decenter@princeton.edu | decenter(a)princeton.edu ] . 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

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CS Colloquium speaker: Alexandra Ion TODAY at 12:10pm
by Emily C. Lawrence 27 Oct '25

27 Oct '25

CS Colloquium Speaker Speaker: Alexandra Ion, Carnegie Mellon University Date: Monday, October 27 Time: 12:10pm EST Location: CS 105 Host: Parastoo Abtahi Event page: https://www.cs.princeton.edu/events/human-centered-physical-ai Register for live-stream online here: https://princeton.zoom.us/webinar/register/WN_q7rNj3QZQ7q3Uf5aSYB4Dw Title: Human-centered Physical AI Abstract: The world around us is inherently physical, yet adaptive interfaces focus mostly on digital content and representations. Physical AI is moving in the right direction by creating models that can understand instructions and perform physical tasks in the real world, typically with humanoid or quadrupedal robots as physical AI agents. In this talk, I envision a future where such physical AI agents move into the background—instead of interacting with large general-purpose robots, we should interact with physical objects that are familiar to us. I call this *Unobtrusive Physical AI*. Bottles, desks, chairs, walls; any object surrounding us should dynamically adapt to our needs, by not only adjusting their user interfaces to fit the context but also by transforming their physical features, material properties, and affordances to instantly become exactly what users need in that moment. I will discuss how creating such intelligent agents requires two main components: (1) adaptive physical architectures to facilitate physical change, e.g., through metamaterials, shape-changing interfaces, soft robotics, etc., and (2) sensing and prediction systems to understand when and what functionality users require. With new approaches in predictive user modeling and innovations in manufacturing and material science, the time might just be ripe to make this challenging vision a reality. Bio: I am an Assistant Professor at the Human-Computer Interaction Institute at Carnegie Mellon University's School of Computer Science. I direct the Interactive Structures Lab, where we investigate and develop physical interfaces that adapt to users' needs. My lab’s work is published at and awarded by top-tier HCI (ACM CHI & UIST) and graphics venues (ACM SIGGRAPH) and has been recognized with the prestigious NSF CAREER award. It was showcased in multiple exhibitions, including a permanent exhibition at the Ars Electronica Center in Austria. Our work also captured the interest of media such as Wired, Dezeen, Fast Company, Gizmodo, etc., and presented at TEDx. Before joining CMU, I was a postdoctoral researcher at ETH Zurich in Computer Graphics and completed my PhD in HCI at the Hasso Plattner Institute, a small, highly selective, top-tier institute for computer science in Germany.

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Fri Oct 24 lunch seminar: Ileana Cristea, cellular communication in virus microenvironments
by Emily C. Lawrence 20 Oct '25

20 Oct '25

Please join Princeton Precision Health for a lunchtime seminar on Friday, October 24 , at 12 pm at 252 Nassau Street . Professor Ileana Cristea, from Princeton University, will present, “ [ https://pph.princeton.edu/events/2025/intra-and-inter-cellular-communicatio… | Intra- and inter-cellular communication within virus microenvironments. ] ” Abstract. Viral infections spread within complex and dynamic cellular microenvironments that shape the outcome of infection. As such, intra-cellular communication cascades, as well as communication between infected cells and cells in the surrounding tissue create a virus microenvironment. Here, we will describe some of our efforts to characterize communication at the intra- and inter-cellular levels using integrative microscopy, proteomics, and computational approaches. At the intra-cellular level, we define organelle remodeling events that drive changes in organelle-organelle contacts and organelle networks during infections with several DNA and RNA viruses. We discover infection-induced organelle contact structures that suppress immune signaling, increase mitochondrial bioenergetics, and promote virus production. At the inter-cellular level, we establish an approach to characterize a virus microenvironment, distinguishing and separating cell populations based on their proximity to an infection site. We show that an infection can prime uninfected, proximal cells for infections with several viruses. Mechanistic investigations point to the contribution of alterated cell cycle, organelle-organelle contacts, and mitochondrial functions in the increased susceptibility of uninfected, proximal cells to viral infections. We further show that the metabolic rewiring of an infected cell can drive changes in the host cell posttranslational modification landscape, including the lysine lactylation of host immune factors that suppreses their functions and facilitates virus spread. Overall, these findings demonstrate how infection reshapes the surrounding microenvironment through intra- and inter-cellular signaling to facilitate viral spread and how spatial proximity to an infection guides cell fate. Lunch will be provided. Hope to see you there! [ https://pph.princeton.edu/events | View all upcoming PPH events. ] Please note that getting to the seminar space at 252 Nassau Street currently requires that you climb a set of stairs. If an accommodation is needed, please contact PPH in advance at: [ mailto:princetonPPH@princeton.edu | princetonPPH(a)princeton.edu ]

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Fwd: Tomorrow's ORFE Optimization Seminar: Thursday, October 9, 2025 at 4:30 PM: Antoine Deza, McMaster University
by Emily C. Lawrence 08 Oct '25

08 Oct '25

Tomorrow’s ORFE Optimization Seminar Date & Time: Thursday, October 9, 2025 at 4:30 PM Location: 101 - Sherrerd Hall Speaker: [ https://orfe.princeton.edu/events/2025/antoine-deza-mcmaster-university | Antoine Deza, McMaster University ] Title: Integrality properties of kissing polytopes distances Abstract: A lattice (d,k)-polytope is the convex hull of a set of points in dimension d whose coordinates are integers ranging from 0 to k. We investigate the smallest possible distance between two disjoint lattice (d,k)-polytopes. A pair of polytopes achieving this minimal distance are called kissing polytopes. This question arises in various contexts where the minimal distance between such polytopes appears in complexity bounds for optimization algorithms. We provide nearly matching lower and upper bounds for this distance and propose an algebraic model. Our formulation yields explicit formulas in dimensions 2 and 3, and allows for the computation of previously intractable values. We also discuss related results, such as the Alon–Vu bounds for flat simplices — that is, the minimum distance between a vertex of a lattice (d,1)-simplex and the affine space spanned by the remaining vertices. Finally, we observe that all the known squared distances between kissing polytopes are inverses of integers, and we ask whether this observation holds in general. Based on joint-work with Shmuel Onn (Technion), Sebastian Pokutta (Zuse Institute Berlin and TU Berlin), and Lionel Pournin (Université Paris 13).

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Howard Yen will present his General Exam "Long-Context Language Models for Long-Horizon Agents" on Friday, October 10, 2025 at 2:00 PM in CS 401.
by CS Grad Department 07 Oct '25

07 Oct '25

Howard Yen will present his General Exam " Long-Context Language Models for Long-Horizon Agents " on Friday, October 10, 2025 at 2:00 PM in CS 401 . Committee Members: Danqi Chen (advisor), Tri Dao , Karthik Narasimhan Abstract: Agentic systems that autonomously interact with their environments enable powerful large language models (LLMs) across numerous downstream applications, such as deep research and software engineering. Due to the complex, long-horizon nature of these tasks, agentic systems need to effectively scale to extremely long trajectories with many steps. Thus, the building block of these systems—the underlying LLMs—must be both capable of processing long sequences and interacting with environments through tool use. In this talk, I will first establish the foundation for these systems by developing new evaluations for long-context language models (LCLMs) and retrieval systems, a key tool in many agentic systems. I introduce HELMET, a holistic and robust benchmark for LCLMs, and show that diverse evaluations provides more reliable signals for developing LCLMs and how they perform in real settings. Then, I propose reasoning-intensive retrieval, which extends the information retrieval task beyond lexical and semantic matching. Reasoning-intensive retrieval requires in-depth reasoning to identify relevant documents and enables agentic systems to tackle more complex problems. I will conclude with context management for long-horizon agentic search, where agents must exhaustively search and reason over many sources to solve complex tasks. I first develop an automated analysis pipeline and error taxonomym to gain a better understanding of long-horizon agentic systems and how they fail, where one of the major failure modes is context mismanagement. Then, I design a simple framework, SLIM, that allows these systems to scale effectively to long trajectories with effective context management. Reading List: https://docs.google.com/document/d/1XH4QblNAg9rEaeFWuCDBTcgl6iGbRJ6jgLRaT4R… 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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Siyang Wu will present his General Exam "How to evaluate monocular depth estimation?" on Wednesday, October 8, 2025 at 3:00 PM in CS 105
by CS Grad Department 03 Oct '25

03 Oct '25

Siyang Wu will present his General Exam "How to evaluate monocular depth estimation?" on Wednesday, October 8, 2025 at 3:00 PM in CS 105 Committee Members: Jia Deng (advisor), Adam Finkelstein, Zhuang Liu Abstract: Monocular depth estimation is an important task with rapid progress, but how to evaluate it remains an open question, as evidenced by a lack of standardization in existing literature and an unhelpfully large selection of evaluation metrics whose trade-offs and behaviors are not well understood. This paper contributes a novel, quantitative analysis of existing metrics in terms of their sensitivity to various types of perturbations of ground truth, emphasizing comparison to human judgment. Our analysis reveals that existing metrics are severely under-sensitive to curvature perturbation such as making flat surfaces wavy. To remedy this, we introduce a new metric based on relative surface normals, along with new depth visualization tools and a principled method to create composite metrics with better human alignment. Reading List: https://docs.google.com/document/d/1bX49rxxy3z-RrqDJHKuIKXfQAySIYS7e7mZ5Pgj… 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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QCB Seminar with Su-In Lee: Monday Oct 6 at 3pm
by Emily C. Lawrence 03 Oct '25

03 Oct '25

[ https://lsi.princeton.edu/events/2025/qcb-seminar-su-lee-university-washing… | QCB Seminar with Su-In Lee, University of Washington ] Monday, October 6, 2025 3:00 – 4:00 p.m. Carl Icahn Lab, 101 “ Explainable AI for health: where we are and how to move forward. ” The first part of my talk explores various research initiatives undertaken by my lab, emphasizing the principles and techniques of explainable AI and their application across a range of biomedical fields. I will demonstrate how explainable AI can elucidate novel scientific inquiries, with a primary emphasis on understanding neurodegenerative diseases and biological age. In the second part, we will explore the evolving landscape of explainable AI, uncovering its potential to chart new scientific directions in biomedicine, exemplified by our recent work in dermatology, emergency medicine and precision cancer medicine. This discussion aims to shed light on the necessary enhancements for explainable AI to effectively tackle a wide array of real-world challenges in biomedicine. BQ_BEGIN Professor Su-In Lee is the Boeing Endowed Professor of Computer Science at the University of Washington (UW). She earned her Ph.D. from Stanford University in 2009 under Professor Daphne Koller and joined UW in 2010 after serving as a Visiting Assistant Professor at Carnegie Mellon University. She is renowned for her groundbreaking research at the intersection of AI, biology, and medicine, and widely recognized as a pioneer in explainable AI (XAI). Among her seminal contributions is the SHAP framework (Lundberg and Lee, NeurIPS’17 oral; cited over 40,000 times), which has transformed the interpretation of machine learning models across disciplines. She has been honored with major awards, including the Samsung Ho-Am Prize in Engineering (the “Korean Nobel Prize,” as its first woman recipient in 34 years), the ISCB Innovator Award, and the NSF CAREER Award. She is an American Cancer Society Research Scholar, an AIMBE Fellow, and an ISCB Distinguished Fellow. Her recent work advances fundamental principles of XAI and applies them to biomedicine—from uncovering molecular drivers of disease to auditing clinical AI systems—fundamentally reshaping how AI is integrated into biomedical research and healthcare. This integration has enabled novel discoveries and produced [ https://aims.cs.washington.edu/su-in-lee | numerous awards ] and [ https://aims.cs.washington.edu/publications | highly cited publications ] spanning AI, molecular biology, and clinical medicine. Hosted by Yuri Pritykin and Mona Singh BQ_END BQ_BEGIN BQ_END

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