- talks - lists.cs.princeton.edu

Brendan Wang will present his MSE talk “Computational Modeling of Local Insulation of Chromatin in Drosophila” on Monday, April 21, 2025 at 1:00 pm in Carl Icahn 101.
by CS Grad Department 16 Apr '25

16 Apr '25

Brendan Wang will present his MSE talk “ Computational Modeling of Local Insulation of Chromatin in Drosophila ” on Monday, April 21, 2025 at 1:00 pm in Carl Icahn 101. The members of his committee are as follows: Yuri Pritykin(Adviser) and Mona Singh (reader) All ae welcome to attend. Please see abstract below. In Drosophila melanogaster, chromatin is partitioned into self-interacting regions called topologically-associated domains (TADs). At TAD boundaries, architectural proteins called insulators can provide insulation or the ability to prevent interactions between gene regulatory elements between neighboring domains. While existing methods have analyzed the sequence patterns underlying TAD boundaries, they have not thoroughly characterized how such patterns contribute to the insulatory function of TADs. Characterizing how the binding of insulators to DNA influences insulation in a genome-wide manner is crucial to deciphering the cis-regulatory rules cells use to regulate gene expression through chromatin organization. To this end, we propose Domino, a sequence-based deep learning method that predicts local insulation. Our results demonstrate that Domino can uncover the sequence patterns underlying local insulation in Drosophila and accurately predict in-silico the effects of experimental perturbations.

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Natalia Khotiaintseva will present her MSE talk "GPU Acceleration of Adaptive Mesh Refinement via a Hashtable" on Monday, April 21, 2025 at 3:20pm in Friend Center 004.
by Gradinfo 15 Apr '25

15 Apr '25

Natalia Khotiaintseva will present her MSE talk "GPU Acceleration of Adaptive Mesh Refinement via a Hashtable" on Monday, April 21, 2025 at 3:20pm in Friend Center 004 (basement) Title: GPU Acceleration of Adaptive Mesh Refinement via a Hashtable Abstract: In this work we explore acceleration of computations in AMR for astrophysical simulations. Modern scientific codes widely use Adaptive Mesh Refinement, and they need to leverage GPU supercomputing power. Specifically, this thesis demonstrates a hashtable implementation on GPU to store the grid structure. On a toy problem, we compare the performance of a typical matrix implementation against a hashtable implementation in a worst-case scenario — when all grid cells are uniformly refined. Acceleration of such large-scale astrophysical hydrodynamics simulations is critically important, making optimization techniques such as GPU-based hashtable storage essential. Our result is that there is no disadvantage in the hashtable implementation versus the matrix implementation. This result is crucial for future implementation of a hashtable-based AMR with dynamic refinement and complete hydrodynamics simulation. We also explore the application of the Grace Hopper superchip to AMR code and observe a significant speedup of ~2.3 in gradient calculation, and ~14.7 speedup in hashtable creation. Additionally, we design a linter program to streamline CUDA C++ debugging process, in particular for our AMR implementation. These approaches together aim to speed up scientific code; they combine large-scale scientific computing with cutting-edge tools in computer science.

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Lucy He will present her General Exam "The Hidden Risks of Benign Data: Generative AI’s Safety and Copyright Challenges" on Tuesday, April 15, 2025 at 10:30 AM in CS 302 and via zoom.
by CS Grad Department 14 Apr '25

14 Apr '25

Lucy He will present her General Exam "The Hidden Risks of Benign Data: Generative AI’s Safety and Copyright Challenges" on Tuesday, April 15, 2025 at 10:30 AM in CS 302 and via zoom. Zoom link: https://princeton.zoom.us/my/lucyhe Committee Members: Danqi Chen (co-advisor), Peter Henderson (co-advisor), Tom Griffiths Abstract: Large language models and text-to-image models can be vulnerable to seemingly harmless inputs, resulting in safety degradation or copyright risks. In the first part of the talk, I will discuss critical issues related to preserving the safety of open-weight models. I will describe a general approach to identify toxicity-promoting data. Using this approach, we find that superficially benign data, such as a collection of bullet lists and math, can be even more harmful than explicitly toxic text. In the second part of the talk, I will introduce our work examining copyright concerns in text-to-image models. We introduce an evaluation framework to evaluate both the model’s copyright compliance and consistency with user input. We then apply the framework to study how benign prompts trigger copyrighted character generation and the potential (in)effectiveness of current mitigation strategies. These works critically examine how benign data can compromise model safety and trigger copyright risks, shedding light on a better understanding and improvement of these systems for deployment. Reading List: https://docs.google.com/document/d/1JHuGtMwAplGjamaEB_v8RuBHUUrZRnxGeZwceUT… 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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Jiayi Geng will present her MSE thesis "Understanding the Failure Modes in Large Language Models"on 4/16 at 2pm in Friend 005.
by Gradinfo 14 Apr '25

14 Apr '25

Jiayi Geng will present her MSE thesis "Understanding the Failure Modes in Large Language Models"on 4/16 at 2pm in Friend 005. Date & Time: April 16th, 2:00–2:30 PM Location: Friend Center, Room 005 Thesis Title: Understanding the Failure Modes in Large Language Models Thesis advisors: Danqi Chen, Tom Griffiths Presentation Abstract: Using AI to create autonomous researchers has the potential to accelerate discovery. A prerequisite for this vision to become reality is assessing how well an AI model can identify the underlying structure of a system from its behavior. In this paper, we explore the question of whether a large language model (LLM) can learn from passive observations and actively collect informative data to refine its own hypotheses. To answer this question, we investigate the ability of LLMs to reverse-engineer four types of black-box systems, chosen to represent problems that might appear in different domains of research: list-mapping programs, formal languages, mathematical equations and board games. We use the Bayesian agents as a normative reference to study the gap between LLMs and ideal inference in reverse-engineering black-box systems. Through extensive experiments, we show that while LLMs have difficulty reverse-engineering these systems from observations alone, data generated by LLM-driven interventions can be effective. By testing edge cases, the LLM is able to refine its own hypotheses and avoid failure modes such as overcomplication, where the LLM falsely assumes prior knowledge about the black box, and overlooking, where the LLM fails to incorporate observations. These insights provide practical guidance for helping LLMs more effectively reverse-engineer black-box systems, supporting their use in making new discoveries.

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Yaxin Tu will will present her General Exam "Separating Succinct Non-Interactive Arguments from Cryptographic Assumptions" on April 15, 2025 at 12pm in CS 401.
by Nicki Mahler 10 Apr '25

10 Apr '25

Yaxin Tu will will present her General Exam "Separating Succinct Non-Interactive Arguments from Cryptographic Assumptions" on April 15, 2025 at 12pm in CS 401. Committee: Alex Lombardi (adviser), Ran Raz(adviser), and Pravesh Kothari . Link to abstract and reading list: [ https://docs.google.com/document/d/1FmkljJIThGRgeHYmuChrSgzIas9uP2mUL41lulM… | https://docs.google.com/document/d/1FmkljJIThGRgeHYmuChrSgzIas9uP2mUL41lulM… ] Abstract: An argument system (computationally sound proof) for NP is succinct and non-interactive, if it contains a single prover-to-verifier message whose length is polylogarithmic the instance and witness sizes. In the past decade, the question of what cryptographic assumptions are sufficient and/or necessary for constructing SNARG, has received answers in both directions. On the one hand, the work of Gentry-Wichs’13 exhibits a barrier of using standard assumptions to construct SNARG, showing that efficient black-box reductions cannot prove the security of any SNARG construction based on any falsifiable cryptographic assumption. On the other hand, a recent line of work bypasses the Gentry-Wichs barrier by constructing SNARG based on iO and one-way functions, with a super-polynomial time security reduction. We prove an impossibility result that rules out black-box constructions of SNARG from polynomially-secure iO and one-way functions. Our result extends the Gentry-Wichs barrier, showing that there’s no black-box SNARG construction from even non-falsifiable assumptions like iO, with an efficient security reduction. Our result also shows that the super-polynomial time reduction is inherent for black-box constructions of SNARG from iO.

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Simon Park will present his MSE talk “Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning” on Tuesday, April 15, 2025 at 12:00pm in Room 238, 41 William Street (AI Lab).
by CS Grad Department 10 Apr '25

10 Apr '25

Simon Park will present his MSE talk “Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning” on Tuesday, April 15, 2025 at 12:00pm in Room 238, 41 William Street (AI Lab). The members of his committee are as follows: Sanjeev Arora (Adviser) and Danqi Chen (reader) All ae welcome to attend. Please see abstract below. We introduce INSTRUCT-SKILLMIX, an automated approach for creating diverse, high quality SFT data for instruction-following. The pipeline involves two stages, each leveraging an existing powerful LLM: (1) Skill extraction: uses the LLM to extract core “skills” for instruction-following by directly prompting the model. This is inspired by “LLM metacognition” of (Didolkar et al., 2024); (2) Data generation: uses the powerful LLM to generate (instruction, response) data that exhibit a randomly chosen pair of these skills. Here, the use of random skill combinations promotes diversity and difficulty. The estimated cost of creating the dataset is under $600. Vanilla SFT (i.e., no PPO, DPO, or RL methods) on data generated from INSTRUCT-SKILLMIX leads to strong gains on instruction following benchmarks such as AlpacaEval 2.0, MT-Bench, and WildBench. With just 4K examples, LLaMA-3-8B-Base achieves 42.76% length-controlled win rate on AlpacaEval 2.0, a level similar to frontier models like Claude 3 Opus and LLaMA-3.1-405B-Instruct. Ablation studies also suggest plausible reasons for why creating open instruction-tuning datasets via naive crowd-sourcing has proved difficult. In our dataset, adding 20% low quality answers (“shirkers”) causes a noticeable degradation in performance. The INSTRUCT-SKILLMIX pipeline seems flexible and adaptable to other settings.

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Shuyao Zhou will present her MSE talk “Code It, See It, Touch It: Vision-Powered AR Programming Tool for Children” on Tuesday, April 15, 2025 at 11:00am in CS 402.
by CS Grad Department 10 Apr '25

10 Apr '25

Shuyao Zhou will present her MSE talk “Code It, See It, Touch It: Vision-Powered AR Programming Tool for Children” on Tuesday, April 15, 2025 at 11:00am in CS 402. The members of her committee are as follows: Andrés Monroy-Hernández (Adviser) and Parastoo Abtahi (reader) All ae welcome to attend. Please see abstract below. We present an augmented reality (AR) authoring tool that enables children to create interactive experiences by linking visual code blocks to physical objects recognized by vision-based AI models. Building on the accessibility of block-based programming, our system encourages creativity and exploration by extending traditional visual authoring tools into the physical environment. We evaluated the system through workshops with 20 children (ages 7–16) across the United States and Argentina, where participants collaboratively designed AR experiences. Our findings suggest that the system supports playful, explorative learning, while also highlighting challenges in real-time object tracking accuracy and responsiveness.

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Yanjin Chen will present her MSE talk “Predictive modeling reveals novel sequence features of prime editing efficiency and precision” on Thursday, April 10, 2025 at 1:30pm in LSI 253.
by CS Grad Department 07 Apr '25

07 Apr '25

Yanjin Chen will present her MSE talk “ Predictive modeling reveals novel sequence features of prime editing efficiency and precision ” on Thursday, April 10, 2025 at 1:30pm in LSI 253. The members of hercommittee are as follows: Yuri Pritykin (Adviser) and Britt Adamson (reader) All ae welcome to attend. Please see abstract below. Prime editing (PE) is an exciting genome editing technology that enables precise genetic modifications without requiring donor DNA or inducing double-strand breaks. However, its relatively low efficiency limits broader applications. PE efficiency largely depends on the PE guide RNA (pegRNA) design, which consists of a primer binding site (PBS) and a reverse transcriptase template (RTT) that contains the intended edit. Various computational models have been developed to predict pegRNA efficiencies across different target sites and edit types. Most existing models, however, rely on deep learning, making it difficult to interpret the contributions of individual features and their combinatorial effects on editing efficiency. Besides, existing datasets provide limited coverage in terms of the sequence space for each editing length at a single target DNA locus, offering few insights into how the intended edit sequence influences PE efficiency. Recently, in collaboration with Britt Adamson’s lab at Princeton, we acquired new PE screen datasets including 1-25 bps substitutions, insertions, and deletions at two target loci, in the K562 cell line and a variant of this cell line that is mismatch repair (MMR)-deficient. This thesis aims to dissect the influence of sequence attributes of the desired edits and cellular repair pathways on PE outcomes. We introduce generalized linear models with LASSO feature selection to identify important positional sequence features predictive of editing rates and unintended indel (insertion or deletion) rate at each target locus. Separate models were trained on insertion and substitution datasets. Our results demonstrate that linear models achieve performance comparable to that of deep neural networks, suggesting that sequence features alone are sufficient determinants of editing efficiency and precision. Beyond predictive accuracy, linear models offer greater interpretability. Notably, we observe a new factor in both our dataset and previously published datasets: for insertion edits, incorporating cytosine (C) at the first editing position increases editing efficiency, providing a practical guideline for optimizing pegRNA design. Additionally, our model recaptures the C-C mismatch preference in parental cell lines versus in MMR-deficient cell lines. In summary, this work offers novel insights into the sequence composition and cellular repair mechanisms in PE editing efficiency, which can inform researchers to design PE experiments to achieve higher efficiency.

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Victor Chu will present his MSE talk “Artifact Resilient Holography” on Monday, April 7, 2025 at 2:309m in CS 105.
by CS Grad Department 02 Apr '25

02 Apr '25

Victor Chu will present his MSE talk “Artifact Resilient Holography” on Monday, April 7, 2025 at 2:309m in CS 105. The members of his committee are as follows: Felix Heid (Adviser) and Szymon Rusinkiewicz (reader) All ae welcome to attend. Please see abstract below. Holographic near-eye displays promise unparalleled depth cues, high-resolution imagery, and realistic three-dimensional parallax while possessing a compact form factor, making them promising candidates for emerging augmented and virtual reality systems. However, existing holographic display methods often assume controlled viewing conditions and overlook external factors such as eye floaters and eyelashes—obstructions that can severely degrade perceived image quality. In this work, we investigate holographic displays in the presence of these obstructions and find that these ocular artifacts are not a fundamental barrier but rather a critical design consideration involving the allocation of étendue and selection of phase profiles. Deviating from widely adopted smooth phase holograms in the field, our findings offer new insights into the design of holographic near-eye displays that maintain visual fidelity across a range of practical viewing scenarios.

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Ishita Chaturvedi will present her FPO "In the SHADOW of GhOSTs: Cross-Domain Lessons in Parallelism" on April 9, 2025 at 10am in Friend 007 and Zoom
by Gradinfo 28 Mar '25

28 Mar '25

Ishita Chaturvedi will present her FPO "In the SHADOW of GhOSTs: Cross-Domain Lessons in Parallelism" on April 9, 2025 at 10am in Friend 007 and Zoom. The members of her committee are as follows: Examiners: David August (adviser), Margaret Martonosi, David Wentzlaff Readers: David August, Niraj Jha, Sharad Malik Zoom link: [ https://princeton.zoom.us/my/ishita.c | https://princeton.zoom.us/my/ishita.c ] All are welcome to attend. Title: In the SHADOW of GhOSTs: Cross-Domain Lessons in Parallelism Abstract: Modern processors struggle to balance instruction-level parallelism (ILP) and thread-level parallelism (TLP) efficiently. GPUs rely on massive TLP to hide latency but suffer in workloads with low occupancy or control divergence due to limited ILP. CPUs, optimized for ILP, become bottlenecked in memory-bound workloads where TLP could improve resource utilization. This thesis challenges the rigid separation of ILP and TLP by introducing GhOST and SHADOW, two architectures that extend ILP in GPUs and TLP in CPUs to improve execution efficiency across diverse workloads. GhOST introduces lightweight out-of-order (OoO) execution to GPUs, enabling warp-level instruction reordering without speculative execution or register renaming. Its instruction buffer-based reordering mechanism allows instructions to execute as soon as operands are ready, significantly reducing stalls in low-occupancy scenarios and mitigating control divergence overhead. GhOST achieves a 6.9 \ % geometric mean speedup (up to 36%) with only 0.007% area overhead, demonstrating the feasibility of ILP-aware GPU architectures. SHADOW is the first asymmetric simultaneous multithreading (SMT) core that dynamically balances ILP and TLP by executing OoO and in-order (InO) threads simultaneously on the same core. By leveraging deep ILP in the OoO thread and high TLP in lightweight InO threads, SHADOW maximizes CPU utilization without sacrificing single-thread performance. Unlike conventional SMT, which uniformly shares resources, SHADOW dynamically reallocates execution resources based on workload behavior. It achieves up to 3.16x speedup and 1.38x geomean improvement over an OoO CPU , with just 1% area and power overhead }. demonstrating its ability to accelerate memory-bound workloads efficiently. This thesis demonstrates how cross-domain architectural techniques can improve execution efficiency by integrating ILP into GPUs and TLP into CPUs. GhOST and SHADOW reduce execution inefficiencies, enhancing performance in workloads that challenge conventional architectures.

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