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

Alkin Kaz will present his General Exam "Ab Initio Reconstruction Ensembles for Resolving Heterogeneity in Cryo-EM" on Friday, May 8, 2026 at 11:00 AM in Friend 009.
by CS Grad Department 05 May '26

05 May '26

Alkin Kaz will present his General Exam "Ab Initio Reconstruction Ensembles for Resolving Heterogeneity in Cryo-EM" on Friday, May 8, 2026 at 11:00 AM in Friend 009. Committee Members: Ellen Zhong (advisor), Ben Raphael, Jia Deng Abstract: Biomolecular structure elucidation through cryo-electron microscopy relies heavily on computational pose-free (ab initio) scene reconstruction methods. Increasing capabilities of these methods enable successful capture of structures in increasingly relaxed experimental settings, where multiple different molecules present in the samples can be identified and reconstructed individually in data processing time. For the datasets which lie outside of the present capability frontiers, researchers resort to ad hoc and subjective exploration strategies such as iterative filtering and consistency across replicate runs. In this work, we expand upon such strategies under a framework of “reconstruction ensembles” that can systematically reconstruct heterogeneous datasets by considering the ab initio methods as weak classifiers to be aggregated. Through the construction of a weighted class assignment similarity score between particles across the job ensemble, we demonstrate the utility of this approach by exceeding the state-of-the-art reconstruction capabilities on the heterogeneous benchmark datasets. Reading List: https://docs.google.com/document/d/1zJHxkur2R9xy02JhwpgtS_nDv5Pe4jG7YO7L7aJ… 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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Sreemanti Dey will present her General Exam "Handling Hidden Holes in View-Based 3D Scene Generation" on Friday, May 8, 2026 at 9:00 AM in Friend 202 and via zoom.
by CS Grad Department 05 May '26

05 May '26

Sreemanti Dey will present her General Exam "Handling Hidden Holes in View-Based 3D Scene Generation" on Friday, May 8, 2026 at 9:00 AM in Friend 202 and via zoom. Zoom link: https://princeton.zoom.us/j/2473759731 Committee Members: Jia Deng (advisor), Ellen Zhong, Felix Heide Abstract: High-quality 3D scene generation from monocular image inputs is a challenging tasks with many promising downstream applications in AR/VR, gaming, embodied AI, and robotics training. One strategy for this problem is view-based iterative inpainting, which consists of repeatedly adding to a 3D scene by rendering novel views, inpainting them with a pretrained 2D model, and adding the new points to the existing scene. An implicit assumption in these methods is that all missing geometry can be completed by inpainting empty rendered pixels. However, this assumption does not always hold: a pixel existing in a render does not necessarily imply that nothing should be inpainted over that pixel. In other words, an object, such as a background wall, could be rendered in an area where an object should be completed. We refer to such cases as “hidden holes,” since they are uncaught via rendering alone. To detect these regions, we introduce Quadmask, a geometry-based system for labeling rendered pixels as potential hidden holes. QuadMask can help downstream view-based methods by indicating areas that have pixel information but may nevertheless be missing visually important geometry. Reading List: https://docs.google.com/document/d/1q2kTx-uQSK0UUKtT3dn0Psfk3uF4sTgKCmcDRdB… 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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CS Colloquium Speaker: Hamsa Bastani, Tues May 5 at 12:10pm
by Emily C. Lawrence 05 May '26

05 May '26

CS Colloquium Speaker: Hamsa Bastani, University of Pennsylvania Date & Time: Tuesday, May 5 - 12:10pm Location: CS 105 Event page: https://www.cs.princeton.edu/events/unpacking-unintended-consequences-ai-ed… Webinar registration: https://princeton.zoom.us/webinar/register/WN_lKB_0wIFQnWF3YfnIgL7tw Title: Unpacking the Unintended Consequences of AI in Education Abstract: The rapid integration of AI into educational settings presents challenges and opportunities—this talk will discuss findings from three large-scale field studies investigating the impact of AI on student learning. First, we found that unfettered access to ChatGPT negatively impacted short-term student learning outcomes in high school mathematics. Second, to understand longer-term effects, we examined learning in chess academies. Contrary to the popular strategy of promoting student agency, our findings show that self-regulated learning (where students decide when to request AI help) can substantially harm learning by undermining productive struggle and motivation. Third, we found that carefully designed AI can be a potent way to boost productive struggle—we leverage student-chatbot interactions (which encode rich information about the student’s knowledge state) to personalize practice problems, significantly improving learning over a 5-month Python certification course. Taken together, these studies suggest that while providing students with unguided AI tools can be detrimental, targeted AI tools that are designed to critically engage students can be beneficial. Bio: Hamsa Bastani is an Associate Professor of Operations, Information, and Decisions at the Wharton School, University of Pennsylvania. Her research focuses on developing novel machine learning algorithms for social impact, including in healthcare and education. She has worked with national governments to deploy algorithms at the country scale for targeted border COVID-19 screening and essential medicine access, and co-led one of the first large field studies of generative AI tutors in high schools. Her research has been published in leading outlets including Nature, Management Science, Operations Research, and PNAS, and has garnered numerous recognitions, including the Wagner Prize for Excellence in Operations Research, the INFORMS Pierskalla Award for best healthcare paper, and the George Nicholson Prize.

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William Howard-Snyder will present his General Exam "Inferring cell population dynamics from lineage histories" on Thursday, May 7, 2026 at 1:00 PM in CS 302.
by CS Grad Department 04 May '26

04 May '26

William Howard-Snyder will present his General Exam "Inferring cell population dynamics from lineage histories" on Thursday, May 7, 2026 at 1:00 PM in CS 302. Committee Members: Ben Raphael (advisor), Yuri Pritykin, Ellen Zhong Abstract: Understanding how cell populations change over time during development is a key challenge in developmental biology. Often population-level changes are summarized by a differentiation map, which describes the possible transitions between cell types during development. Recent single-cell lineage tracing technologies generate lineage trees that describe the history of cell divisions during a developmental process but do not directly measure differentiation events between cell types. Current approaches to infer cell differentiation maps from cell lineage trees make unrealistic assumptions about the developmental process, do not allow for unobserved progenitor cell types, or do not infer cell-type specific rates of growth and transitions. To address these issues, we introduce TROUPE, a likelihood-based framework that infers differentiation and growth dynamics directly from leaf-labeled cell lineage trees while allowing for unobserved progenitor types via biologically motivated potency constraints. We provide an efficient algorithm for computing the maximum likelihood transition and growth rates, as well as a simple model-selection scheme to choose the number of unobserved types. On simulations, TROUPE recovers transition and growth rates better than previous approaches even when there are unobserved types. We apply TROUPE to a model of mammalian development, Trunk-Like Structures (TLS), and show that TROUPE computes reasonable rates of self-renewal and differentiation in both standard and perturbed conditions. Reading List: https://docs.google.com/document/d/1DKRX6J4UCAT7mdRp1YQ8zyJXpOxXbQZV97u4tEU… 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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Nicholas Richardson will present his FPO "Gradient-based Optimization of Geometry with Application to Optical Computing" on Monday, May 4, 2026 at 11:00 AM in CS 401.
by CS Grad Department 03 May '26

03 May '26

Nicholas Richardson will present his FPO "Gradient-based Optimization of Geometry with Application to Optical Computing" on Monday, May 4, 2026 at 11:00 AM in CS 401. The members of Nicholas’s committee are as follows: Examiners: Ryan Adams (Adviser) Margaret Martonosi, Benjamin Eysenbach, Readers: Szymon Rusinkiewicz, Elif Ertekin (UIUC) 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 the talk. Abstract follows below: The automated design of engineered systems — reducing human expertise to a specification and delegating search to computation — is one of the central ambitions of computational science and optimization. In this thesis, I present work at the intersection of optimization techniques and their application to inverse design of computational hardware. First, I present a novel method, Fiber Monte Carlo, which enables gradient-based optimization in optimization problems containing parametric discontinuities; this is a class of problems that arise in a wide variety of applications, including graphics, topology optimization, computational geometry, and physical simulation. Next, I turn to the computational design of electromagnetic systems. I first detail the development of a custom differentiable electromagnetic field solver, JaxEM, with native support for inverse design applications. The construction of this simulator enables straightforward gradient-based design of structured hardware. Finally, I apply Fiber Monte Carlo and JaxEM to the inverse design of specialized hardware for an optical classifier. This chapter contains a standalone contribution by way of a novel method to achieve nonlinear computation using only linear optics. The hardware is designed in silico using Fiber Monte Carlo based topology optimization, and we use gradient methods by differentiating through simulations performed with JaxEM. The resulting design was fabricated as a silicon photonic chip, providing an end-to-end demonstration of gradient-based automated hardware design in physical silicon.

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Keerthana Nallamotu will present her General Exam "Reliable Reasoning in Genomics" on Wednesday, May 6, 2026 at 11:15 AM in CS 302 and via zoom.
by CS Grad Department 03 May '26

03 May '26

Keerthana Nallamotu will present her General Exam "Reliable Reasoning in Genomics" on Wednesday, May 6, 2026 at 11:15 AM in CS 302 and via zoom. Zoom link: https://princeton.zoom.us/j/3674686562 Committee Members: Olga Troyanskaya (advisor), Ellen Zhong, Yuri Pritykin Abstract: Understanding the fundamental drivers of disease requires bridging the gap between molecular mechanisms and clinical phenotypes across different biological scales. However, the fragmentation of genomic data across isolated repositories and the tendency of Large Language Models (LLMs) to hallucinate domain-specific facts make it difficult to design a system for reliable discovery. To address these challenges, we first introduce MiSTIC-KG, a multi-scale, cell type-specific knowledge graph that serves as a structured foundation for biological inquiry. The schema integrates diverse entities, including cell types, tissues, genes, diseases, phenotypes, and drugs, and embeds tissue-specificity directly into gene-gene functional interaction edges to model more complex biological processes. By capturing these relationships, MiSTIC-KG enables advanced downstream applications, including synthetic lethality prediction, drug repurposing, and drug toxicity assessment. We then introduce Alvessa, an agentic framework designed to orchestrate complex reasoning over static knowledge bases and dynamic foundation models. We evaluate this approach by constructing GenomeArena, a comprehensive benchmark suite that demonstrates how Alvessa’s evidence-grounded strategy significantly reduces hallucination and outperforms general-purpose LLMs in genomic reasoning tasks. By advancing both high-resolution knowledge representation and verifiable agentic reasoning, this work takes a step towards utilizing fragmented genomic data for reliable biological discovery. Reading List: https://docs.google.com/document/d/1qsh3eItEWunmqCqGrzp-4nDLMOu__CSfGu9L_iA… 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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Jipeng Sun will present his General Exam "Scaling Nano-Optics for the Future of Machine Perception" on Wednesday, May 6, 2026 at 10:00 AM in Friend 108.
by CS Grad Department 01 May '26

01 May '26

Jipeng Sun will present his General Exam "Scaling Nano-Optics for the Future of Machine Perception" on Wednesday, May 6, 2026 at 10:00 AM in Friend 108. Committee Members: Felix Heide (advisor), Parastoo Abtahi, Kyle Jamieson Abstract: While billions of vision sensors power modern machine perception, the physics of traditional refractive optics limits the form factor, efficiency, and multi-modality of these edge devices. End-to-end optimized nano-optics offer a powerful, compact alternative, but conventional design methods severely oversimplify diffractive modeling due to computational constraints. This failure to capture complex light interactions across varying depths, wavelengths, and angles creates a massive sim-to-real gap, traditionally requiring heavy, power-hungry digital reconstruction to correct. To resolve this gap at the physical layer, this talk introduces a distributed nano-optics optimization framework capable of differentiably simulating real-world optical conditions. This scales nano-optics optimization across unprecedented parameters, including lens counts, polarization states, spectral bands, and macroscopic physical apertures. I will present the new capabilities unlocked by this large-area, fabrication-aware approach—including energy-efficient nano-optical eye tracking for smart glasses, broadband imaging that beats traditional spectral limits, and thin collaborative on-sensor array cameras. Ultimately, this work shifts the computational burden to the speed-of-light physics of the optical front-end, providing a scalable foundation for the next generation of machine eyes. Reading List: https://docs.google.com/document/d/15dZ8wys3EevOj1ZZAZb_aKgiRglExNqO5tsPYHi… 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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