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Viola Chen will present her General Exam "Inferring Ancestral Cell States Using Transcriptome Geometry and Lineage Trees" on Thursday, May 8, 2025 at 10:30 AM in CS 302.
by CS Grad Department 05 May '25

05 May '25

Viola Chen will present her General Exam " Inferring Ancestral Cell States Using Transcriptome Geometry and Lineage Trees " on Thursday, May 8, 2025 at 10:30 AM in CS 302 . Committee Members: Benjamin Raphael (advisor), Mona Singh , Yuri Pritykin Abstract: Understanding how transcriptomic states evolve along a developmental lineage is a fundamental question in single-cell biology. While CRISPR-based lineage tracing technologies now enable high-resolution reconstruction of cell lineage trees, the transcriptomic states of internal (ancestral) nodes remain unobserved due to the destructive nature of sequencing. Existing methods for inferring these states either rely on strong parametric assumptions (e.g., Gaussian random walks in treeVAE) or discard true lineage information altogether (e.g., pseudotime methods). We present a geometry-aware parsimony framework for inferring ancestral transcriptomic states in cell lineage trees. Our approach leverages the principle of minimal transcriptomic change along a known tree structure, using either discrete or continuous representations of cell states. In the continuous case, we extend squared-change parsimony to non-Euclidean manifolds by computing Fréchet means under biologically meaningful distance metrics, such as graph geodesics derived from k-nearest neighbor (kNN) transcriptomic similarity graphs. We develop an efficient iterative optimization scheme, applicable to Euclidean, manifold, or graph-structured spaces, and benchmark our method on synthetic manifolds and real lineage-resolved datasets (e.g., C. elegans). Our results show improved recovery of internal cell states and differentiation trajectories compared to baseline methods. This framework provides a general and interpretable tool for reconstructing unobserved developmental states from lineage-traced single-cell data. Reading List: https://docs.google.com/document/d/1CtluDXuGFs2Z6IOfPlQg8gYGnNjemOPkjpYyuZZ… 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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Kart Kandula will present his General Exam "Competition by Design: Experimental Evidence on Search Engine Use and Choice Architecture Interventions" on May 6, 2025 at 2:30pm in 306 Sherrerd Hall.
by Gradinfo 02 May '25

02 May '25

Kart Kandula will present his General Exam "Competition by Design: Experimental Evidence on Search Engine Use and Choice Architecture Interventions" on May 6, 2025 at 2:30pm in 306 Sherrerd Hall. Committee: Jonathan Mayer (adviser), Peter Henderson, Andrés Monroy-Hernández All are welcome to attend the talk. Abstract: On both sides of the Atlantic, regulators have intensified scrutiny of Google’s dominance in the market for general search services, concerned that the company has engaged in exclusionary practices to maintain its position as the default gateway to the internet. In the United States, this culminated in a landmark ruling on August 5, 2024, in which a federal court found Google in violation of § 2 of the Sherman Act for unlawfully monopolizing the online search market. Now in the remedies phase, the U.S. Department of Justice has proposed a suite of interventions, including the use of “choice screens” to prompt users to select a default search engine. Yet critics argue that such measures, introduced too late and with limited enforcement mechanisms, fall short of meaningfully disrupting Google's entrenchment—an argument bolstered by the European Union’s tepid results following a similar Android-based remedy in 2019. To assess the real-world effectiveness of such interventions, this study aims to examine how default search engine settings shape user choices and how a variety of design interventions—modeled on plausible remedial frameworks available to regulators and courts —influence search behavior. We employ a two-phase experimental design targeting both desktop and mobile platforms, with participants recruited via Prolific. The first phase establishes a behavioral baseline while administering the intervention across the experimental conditions. The second phase, conducted after a delay, assesses the durability of observed behavioral shifts, enabling us to evaluate the long-term efficacy of choice architecture interventions. For mobile participants, we target iPhone users and implement choice architecture interventions using iOS Shortcuts, collecting behavioral data through Safari history exports. These methods, made possible by recent iOS updates, enable a novel and scalable approach to studying user behavior in naturalistic settings and open new possibilities for field experiments in digital platform research. Our findings will provide empirical insight into the real-world potency of behavioral remedies in digital platform markets—an area where policymaking has outpaced empirical evidence and measurement. Reading List 1. Dark Patterns and Privacy Choice 1. Jamie Luguri and Lior Jacob Strahilevitz. 2021. Shining a light on dark patterns. Journal of Legal Analysis 13, 1 (2021), 43–109. 2. Arunesh Mathur, Mihir Kshirsagar, and Jonathan Mayer. 2021. What makes a dark pattern . . . dark? Design attributes, normative considerations, and measurement methods. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems . ACM, New York, NY, USA, 1–18. 3. Christine Utz, Martin Degeling, Sascha Fahl, Florian Schaub, and Thorsten Holz. 2019. (Un)informed consent: Studying GDPR consent notices in the field. In Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS’19) . ACM, 973–990. 4. Midas Nouwens, Ilaria Liccardi, Michael Veale, David Karger, and Lalana Kagal. 2020. Dark patterns after the GDPR: Scraping consent pop-ups and demonstrating their influence. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems . 1–13. 2. Field Experiments Involving Online Activity 1. Hunt Allcott, Juan Camilo Castillo, Matthew Gentzkow, Leon Musolff, and Tobias Salz. 2024. Sources of Market Power in Web Search: Evidence from a Field Experiment. Working Paper (2024). 2. Andrew M Guess, Neil Malhotra, Jennifer Pan, Pablo Barberá, Hunt Allcott, Taylor Brown, Adriana Crespo-Tenorio, Drew Dimmery, Deen Freelon, Matthew Gentzkow, et al. 2023. How do social media feed algorithms affect attitudes and behavior in an election campaign? Science 381, 6656 (2023), 398–404. 3. Erik Brynjolfsson, Avinash Collis, and Felix Eggers. 2019. Using massive online choice experiments to measure changes in well-being. Proceedings of the National Academy of Sciences 116, 15 (2019), 7250–7255. 3. Human-Computer Interaction 1. Alma Whitten and J. Doug Tygar. 1999. Why Johnny can’t encrypt: A usability evaluation of PGP 5.0. In Proceedings of the USENIX Security Symposium . USENIX Association, Berkeley, CA, 1–16. Alan Dix, Janet Finlay, Gregory D. Abowd, and Russel Beale. 2004. Human-Computer Interaction (3rd ed.). Pearson Education Limited.

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Kaya Alpturer will present his General Exam "Optimal RANDAO Manipulation" on May 12, 2025 at 12:30pm in CS 301.
by Gradinfo 02 May '25

02 May '25

Kaya Alpturer will present his General Exam "Optimal RANDAO Manipulation" on May 12, 2025 at 12:30pm in CS 301. Committee: Matt Weinberg (adviser), Mark Braverman, Pramod Viswanath All are welcome to attend the talk. Abstract: It is well-known that RANDAO manipulation is possible in Ethereum if an adversary controls the proposers assigned to the last slots in an epoch. We provide a methodology to compute, for any fraction α of stake owned by an adversary, the maximum fraction f(α) of rounds that a strategic adversary can propose. We further implement our methodology and compute f(⋅) for all α. For example, we conclude that an optimal strategic participant with 5% of the stake can propose a 5.048% fraction of rounds, 10% of the stake can propose a 10.19% fraction of rounds, and 20% of the stake can propose a 20.68% fraction of rounds. * Roughgarden, Tim. Twenty lectures on algorithmic game theory . Cambridge University Press, 2016. * Dwork, Cynthia, Nancy Lynch, and Larry Stockmeyer. "Consensus in the presence of partial synchrony." Journal of the ACM (JACM) 35.2 (1988): 288-323. * Fischer, Michael J., Nancy A. Lynch, and Michael S. Paterson. "Impossibility of distributed consensus with one faulty process." Journal of the ACM (JACM) 32.2 (1985): 374-382. * Yin, Maofan, et al. "HotStuff: BFT consensus with linearity and responsiveness." Proceedings of the 2019 ACM symposium on principles of distributed computing . 2019. * Cleve, Richard. "Limits on the security of coin flips when half the processors are faulty." Proceedings of the eighteenth annual ACM symposium on Theory of computing . 1986. * Eyal, Ittay, and Emin Gün Sirer. "Majority Is Not Enough: Bitcoin Mining Is Vulnerable." International Conference on Financial Cryptography and Data Security . Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. * Sapirshtein, Ayelet, Yonatan Sompolinsky, and Aviv Zohar. "Optimal selfish mining strategies in bitcoin." Financial Cryptography and Data Security: 20th International Conference, FC 2016, Christ Church, Barbados, February 22–26, 2016, Revised Selected Papers 20 . Springer Berlin Heidelberg, 2017. * Cai, Yang, Nikhil R. Devanur, and S. Matthew Weinberg. "A duality based unified approach to bayesian mechanism design." Proceedings of the forty-eighth annual ACM symposium on Theory of Computing . 2016. * Beyhaghi, Hedyeh, and S. Matthew Weinberg. "Optimal (and benchmark-optimal) competition complexity for additive buyers over independent items." Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing . 2019. * Roughgarden, Tim. "Transaction Fee Mechanism Design." Journal of the ACM 71.4 (2024): 1-25. * Heilman, Ethan, et al. "Eclipse attacks on {Bitcoin’s}{peer-to-peer} network." 24th USENIX security symposium (USENIX security 15) . 2015. * Matheus V. X. Ferreira, S. Matthew Weinberg. “Proof-of-Stake Mining Games with Perfect Randomness” EC 2021.

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Murali Srirangam Ramanujam will present his FPO "Leveraging Structural Stability for Efficient Compute-Memory Tradeoffs in Edge Systems" on 5/16/25 at 1:30pm in Friend 125.
by Gradinfo 02 May '25

02 May '25

Murali Srirangam Ramanujam will present his FPO "Leveraging Structural Stability for Efficient Compute-Memory Tradeoffs in Edge Systems" on 5/16/25 at 1:30pm in Friend 125. Examiners: Ravi Netravali (adviser), Maria Apostolaki, and Kai Li Readers: Wyatt Lloyd and Amit Levy Abstract: Edge systems - such as mobile devices, IoT nodes, and embedded sensors - increasingly operate in dynamic environments while facing severe resource constraints. Among these, compute is often the primary bottleneck, limited not only by hardware capacity but also by thermal and energy constraints inherent to untethered operation. A common strategy in systems design is to trade memory for compute, e.g., via caching or memoization. But on the edge, memory is also scarce, making indiscriminate reuse infeasible. This thesis is built on a central insight: despite the dynamic inputs and behaviors observed at runtime, most deployed applications - from system software to ML pipelines - rely on underlying structural stability. This stability arises from how these systems are developed: through modular frameworks, predictable control paths, and abstractions that preserve behavior across updates. In this dissertation, we show how to dissect and extract these stable computational elements and selectively reuse them to reduce compute overhead, even when memory is tightly limited. This allows us to affirmatively answer a fundamental question: can edge systems, without the luxury of abundant resources, still benefit from the kind of reuse strategies common in richer environments? First, we present Floo, a system that memoizes function-level computations in mobile applications by leveraging persistent interaction flows and function signatures, enabling effective reuse with minimal overheads. Then, we introduce Remembrall, which reuses parts of neural network pipelines in video analytics workloads by identifying redundant structure in learned embedding spaces - enabling lightweight adaptation and compute reduction under memory constraints. Together, these systems demonstrate that judicious use of limited memory, grounded in structural stability, can enable efficient compute reuse on the edge. All are welcome to attend.

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Sunnie Kim will present herFPO "Advancing Responsible AI with Human-Centered Evaluation" on Wednesday, May 7, 2025 at 1:30 PM in CS 302 and Zoom.
by gradinfo＠cs.princeton.edu 02 May '25

02 May '25

Sunnie Kim will present herFPO "Advancing Responsible AI with Human-Centered Evaluation" on Wednesday, May 7, 2025 at 1:30 PM in CS 302 and Zoom. Location: Zoom link: https://princeton.zoom.us/my/sunniesuhyoung The members of Sunnies committee are as follows: Examiners: Olga Russakovsky (Adviser), Andrés Monroy-Hernández, Q. Vera Liao (Microsoft) Readers: Parastoo Abtahi, Jennifer Wortman Vaughan (Microsoft) A copy of her 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 her talk. Abstract follows below: As Artificial Intelligence (AI) technologies are increasingly transforming how we live, work, and communicate, AI evaluation must take a human-centered approach to realistically reflect real-world performance and impact. In this thesis, I present research that advances human-centered evaluation, and subsequently responsible development and deployment of AI, by integrating knowledge and methods from AI and HumanComputer Interaction (HCI). First, using explainable AI as an example, I discuss the challenges and necessity of human (as opposed to automatic) evaluation and present human evaluation studies that identify new issues and opportunities in explainable AI research. Next, I bring context into the picture and present contextualized studies with real users that provide a holistic and nuanced understanding of users explainability needs and trust in AI. Then, I illustrate the importance of evaluating human-AI interaction through studies that examine how users trust, reliance, and other perceptions and behaviors are shaped by different features of AI technologies. In closing, I discuss the implications of these work, provide recommendations for human-centered evaluation, and describe future directions for responsible AI.

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Dexin Zhang will present his General Exam "Modular Verification and Repair for Networks" on Monday, May 5, 2025 at 10am in CS 402.
by Gradinfo 02 May '25

02 May '25

Dexin Zhang will present his General Exam "Modular Verification and Repair for Networks" on Monday, May 5, 2025 at 10am in CS 402. Committee: Aarti Gupta (adviser), David Walker, Mae Milano Abstract: Large industrial networks connect millions of servers with hundreds of thousands of network devices. To improve these systems’ reliability, researchers have turned to modular verification techniques to break these large networks into smaller pieces to analyze independently. Unfortunately, in doing so, researchers face tradeoffs. More expressive frameworks can verify a wider range of properties, but often place a greater burden on the user to describe network behavior at the interfaces between components. We present CB-graphs, a new framework for modular network control plane verification that navigates these tradeoffs using graph-based abstractions, allowing it to verify a rich set of properties while simplifying the interfaces a user must supply. Users construct CB-graphs that describe which network devices converge before others. Interfaces between nodes in these graphs may contain more or less information, depending on what is convenient and needed to prove the desired properties. We develop a theory of CB-graph-based verification and prove it sound via a formalization in the Lean theorem prover. We exploit our framework to develop a new, scalable method for local repair of network configurations. We implement our verification and repair algorithms in a tool called CB-Ver, and evaluate its performance on a range of benchmarks.

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Meenal Parakh will present her General Exam "AnyBody: A Benchmark Suite for Cross-Embodiment Manipulation" on Tuesday, May 6, 2025 at 4:30 PM in FC 202.
by CS Grad Department 02 May '25

02 May '25

Meenal Parakh will present her General Exam "AnyBody: A Benchmark Suite for Cross-Embodiment Manipulation" on Tuesday, May 6, 2025 at 4:30 PM in FC 202. Committee Members: Jia Deng (advisor), Szymon Rusinkiewicz, Adam Finkelstein Abstract: Generalizing control policies to novel embodiments remains a fundamental challenge in enabling scalable and transferable learning in robotics. While prior works have explored this in locomotion, a systematic study in the context of manipulation tasks remains limited, partly due to the lack of standardized benchmarks. In this paper, we introduce a benchmark for learning cross-embodiment manipulation, focusing on two foundational tasks, reach and push, across a diverse range of morphologies. The benchmark is designed to test generalization along three axes: interpolation (testing performance within a robot category, sharing the same link structure), extrapolation (testing on a robot with a different link structure), and composition (testing on combinations of link structure). We primarily test RL agents trained under multitask objectives and evaluated for their multi-task performance and zero-shot transfer. Our study aims to answer whether morphology-randomized training can outperform single-embodiment baselines, whether zero-shot generalization to unseen morphologies is feasible, and how consistently these patterns hold across different generalization regimes. The results highlight the current limitations of multi-embodiment learning and provide insights into how architectural and training design choices influence policy generalization. Reading List: https://docs.google.com/document/d/1Ey-U4ydkMTrTO6Qdr9FmycVom5daALctNbeyRjw… 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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Xingyu Zhu will present his General Exam "On the Power of Context-Enhanced Learning in LLMs" on Monday, May 5, 2025 at 1:00 PM in FC 106 and via zoom.
by CS Grad Department 01 May '25

01 May '25

Xingyu Zhu will present his General Exam "On the Power of Context-Enhanced Learning in LLMs" on Monday, May 5, 2025 at 1:00 PM in FC 106 and via zoom. Zoom link: https://princeton.zoom.us/my/jupiter Committee Members: Sanjeev Arora (advisor), Danqi Chen, Chi Jin Abstract: We formalize a new concept for LLMs, context-enhanced learning. It involves standard gradient-based learning on text except that the context is enhanced with additional data on which no auto-regressive gradients are computed. This setting is a gradient-based analog of usual in-context learning (ICL) and appears in some recent works. Using a multi-step reasoning task, we prove in a simplified setting that context-enhanced learning can be exponentially more sample-efficient than standard learning when the model is capable of ICL. At a mechanistic level, we find that the benefit of context-enhancement arises from a more accurate gradient learning signal. We also experimentally demonstrate that it appears hard to detect or recover learning materials that were used in the context during training. This may have implications for data security as well as copyright. Reading List: https://docs.google.com/document/d/1_SYs1g8A9cSF__u9dRffQ30ZYAArjijfiyW-IwV… 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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Amrit Daswaney will present the General Exam "Fair and Efficient Allocation of Indivisible Goods via Pseudomarkets with Price Floors" on May 2nd, 10 am - 12 pm. Friend Center 005.
by Gradinfo 01 May '25

01 May '25

Amrit Daswaney will present the General Exam "Fair and Efficient Allocation of Indivisible Goods via Pseudomarkets with Price Floors" on May 2nd, 10 am - 12 pm. Friend Center 005. Committee: Mark Braverman (adviser), Matt Weinberg, Fedor Sandomirskiy. Abstract: The combinatorial assignment problem involves allocating m indivisible goods to n agents without any monetary transfers such that the resulting allocation is Pareto efficient and each agent is (approximately) envy-free. This problem models many practical settings of interest: from assigning courses to students to allocating truckloads of food to food banks. A classical route, pioneered by Varian (1974) and extended by Hylland and Zeckhauser (1979), is to search for a Competitive Equilibrium from Equal Incomes (CEEI): endow every agent with one unit of artificial currency and find prices so that when each agent buys their favourite affordable bundle, the market clears (i.e., supply equals demand for all goods). With indivisible goods, however, a CEEI need not exist: in a two-agent, one-good instance any price makes either both agents or neither demand the good. Budish (2011) restores existence by introducing an arbitrarily small budget inequality between agents. The rationale behind introducing this budget inequality is to use these small differences to break ties between otherwise identical agents. However, our first result shows they can legitimize highly unequal outcomes, allowing agents with negligibly higher budgets to potentially leverage this advantage across multiple goods. To mitigate this, we introduce price floors and define a CEEI-with-price-floors mechanism. Our second result proves that this concept is equivalent to a discrete analogue of Varian (1974)'s coalition fairness: in the replica economy, no coalition can seize the bundle of another coalition with the same total endowment and make all of its members better off. The equivalence echoes the Debreu–Scarf core-equivalence theorem (1963). Crucially, whenever a CEEI-with-floors can be proved to exist for a structured domain (e.g., additive values, bounded numbers of agents), an EFX allocation exists for that very domain, providing a concrete pathway toward resolving this long standing open problem in fair division. Our third result establishes the existence of CEEI-with-floors when (i) n = 2 agents or (ii) n agents have identical valuations. If time permits, we will also discuss alternative combinatorial and randomized techniques that have been applied to the problem.

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Adithya Bhaskar will present his General Exam "Effective mechanistic interpretability via pruning" on Monday, May 5, 2025 at 3:00 PM in FC 008 and via zoom.
by CS Grad Department 30 Apr '25

30 Apr '25

Adithya Bhaskar will present his General Exam "Effective mechanistic interpretability via pruning" on Monday, May 5, 2025 at 3:00 PM in FC 008 and via zoom. Zoom link: https://princeton.zoom.us/j/98937358810 Committee Members: Danqi Chen (advisor), Sanjeev Arora, Benjamin Eysenbach Abstract: Mechanistic interpretability strives to explain language models bottom-up by understanding their components. In this talk, I will describe how pruning—removing less important parameters or connections in a model—can enable a new approach to interpretability. I will introduce a curious phenomenon as a case study: fine-tuning a language model with different random seeds can lead to similar in-domain performance but exhibit distinct patterns of generalization. Pruning the model reveals that even a single model contains many subnetworks that match its in-domain performance, but generalize differently. By connecting this result to a “grokking”-like phenomenon observed for out-of-domain generalization, I will develop a theory of why these subnetworks exist in the model, and what role they fulfill. Zooming out of this example, we will apply pruning to develop an efficient, accurate, and scalable tool to automatically discover transformer circuits—basic units of mechanistic interpretability. We develop Edge Pruning, a method that focuses on pruning edges rather than the parameters of a language model. Edge Pruning discovers circuits substantially more faithful to the model as measured by KL divergence while being more efficient and scaling to 100x larger models than prior approaches. We will see via an example how this tool enables future work to understand phenomena that only emerge in large (1B+ parameters) models, such as in-context learning. Our results imply that pruning holds great promise for interpretability—both as a paradigm and as a standalone method. Reading List: https://docs.google.com/document/d/1VJ5busQNlb-er_yt7-ae34x2UC0KXZh2irvR0vi… 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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