The NYU Center for Data Science at NeurIPS 2023

NYU Center for Data Science
8 min readNov 15, 2023

We’re excited to announce that many CDS faculty, researchers, and students will present at the upcoming thirty-seventh 2023 NeurIPS (Neural Information Processing Systems) Conference, taking place Sunday, December 10 through Saturday, December 16. The conference began in 1987 and focuses on the advancement of research in AI and machine learning. The conference will take place in-person at the New Orleans Ernest N. Morial Convention Center.

Below is a comprehensive list of projects and their corresponding CDS participants (indicated in bold with *asterisk in front of it):

Accepted Papers

“3D molecule generation by denoising voxel grids”:

Pedro O. Pinheiro, Joshua Rackers, Joseph Kleinhenz, Michael Maser, *Omar Mahmood (PhD alumnus), Andrew Watkins, Stephen Ra, Vishnu Sresht, Saeed Saremi

“A Logic for Expressing Log-Precision Transformers”:

*William Merrill (PhD student), Ashish Sabharwal

“A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks”:

Vignesh Kothapalli, Tom Tirer, *Joan Bruna (Associate Professor of Computer Science and Data Science)

“A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning”:

Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, *Andrew Wilson (Associate Professor of Computer Science and Data Science), Tom Goldstein, *Micah Goldblum (Postdoc Researcher)

“A polar prediction model for learning to represent visual transformations”:

Pierre-Étienne Fiquet, *Eero Simoncelli (Professor of Neural Science, Mathematics, Data Science, and Psychology)

“A Spectral Theory of Neural Prediction and Alignment”:

Abdulkadir Canatar, Jenelle Feather, Albert Wakhloo, *SueYeon Chung (affiliated professor)

“AbDiffuser: full-atom generation of in-vitro functioning antibodies”:

Karolis Martinkus, Jan Ludwiczak, Wei-Ching Liang, Julien Lafrance-Vanasse, Isidro Hotzel, Arvind Rajpal, Yan Wu, *Kyunghyun Cho (Professor of Computer Science and Data Science), Richard Bonneau, Vladimir Gligorijevic, Andreas Loukas

“Adaptive whitening with fast gain modulation and slow synaptic plasticity”:

Lyndon Duong, *Eero Simoncelli (Professor of Neural Science, Mathematics, Data Science, and Psychology), Dmitri Chklovskii, David Lipshutz

“Aligning Language Models with Human Preferences via a Bayesian Approach”:

Jiashuo Wang, Haozhao Wang, Shichao Sun, *Wenjie Li (Assistant Research Scientist)

“An Information Theory Perspective on Variance-Invariance-Covariance Regularization”:

*Ravid Shwartz-Ziv (Faculty Fellow), Randall Balestriero, Kenji Kawaguchi, *Tim G. J. Rudner (Instructor), *Yann LeCun (Professor of Computer Science, Neural Science, Data Science, and Electrical and Computer Engineering)

“Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks”:

*Micah Goldblum (Postdoc Researcher), Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, *Andrew Wilson (Associate Professor of Computer Science and Data Science), Tom Goldstein

“Beyond MLE: Convex Learning for Text Generation”:

Chenze Shao, Zhengrui Ma, Min Zhang, *Yang Feng (affiliated professor)

“Causal-structure Driven Augmentations for Text OOD Generalization”:

Amir Feder, *Yoav Wald (Faculty Fellow), Claudia Shi, Suchi Saria, David Blei

“ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation”:

Sungduk Yu, Walter Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius Busecke, Nora Loose, Charles Stern, Tom Beucler, Bryce Harrop, Benjamin Hillman, Andrea Jenney, Savannah L. Ferretti, Nana Liu, Animashree Anandkumar, Noah Brenowitz, Veronika Eyring, Nicholas Geneva, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Akshay Subramaniam, Carl Vondrick, Rose Yu, *Laure Zanna (affiliated professor), Tian Zheng, Ryan Abernathey, Fiaz Ahmed, David Bader, Pierre Baldi, Elizabeth Barnes, Christopher Bretherton, Peter Caldwell, Wayne Chuang, Yilun Han, YU HUANG, Fernando Iglesias-Suarez, Sanket Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David Randall, Sara Shamekh, Mark Taylor, Nathan Urban, Janni Yuval, Guang Zhang, Mike Pritchard

“Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering”:

Yijun Dong, Kevin Miller, *Qi Lei (Assistant Professor of Mathematics and Data Science), Rachel Ward

“CoLA: Exploiting Compositional Structure for Automatic and Efficient Numerical Linear Algebra”:

*Andres Potapczynski (PhD student), Marc Finzi, Geoff Pleiss, *Andrew Wilson (Associate Professor of Computer Science and Data Science)

“Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise”:

Arpit Bansal, Eitan Borgnia, Hong-Min Chu, Jie Li, Hamid Kazemi, Furong Huang, *Micah Goldblum (Postdoc Researcher), Jonas Geiping, Tom Goldstein

“DASpeech: Directed Acyclic Transformer for Fast and High-quality Speech-to-Speech Translation”:

Qingkai Fang, Yan Zhou, *Yang Feng (affiliated professor)

“Don’t blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy”:

Aahlad Manas Puli, *Lily Zhang (PhD student), *Yoav Wald (Faculty Fellow), *Rajesh Ranganath (Assistant Professor of Computer Science and Data Science)

“Efficient Training of Energy-Based Models Using Jarzynski Equality”:

Davide Carbone, Mengjian Hua, Simon Coste, *Eric Vanden-Eijnden (affiliated professor)

“Estimating Noise Correlations Across Continuous Conditions With Wishart Processes”:

Amin Nejatbakhsh, Isabel Garon, *Alex Williams (affiliated professor)

“EvoPrompting: Language Models for Code-Level Neural Architecture Search”:

*Angelica Chen (PhD student), David Dohan, David So

“Fast Model DeBias with Machine Unlearning”:

Ruizhe Chen, Jianfei Yang, Huimin Xiong, Jianhong Bai, Tianxiang Hu, Jin Hao, *Yang Feng (affiliated professor), Joey Tianyi Zhou, Jian Wu, Zuozhu Liu

“Feature learning via mean-field Langevin dynamics: classifying sparse parities and beyond”:

Taiji Suzuki, *Denny Wu (Faculty Fellow), Kazusato Oko, Atsushi Nitanda

“Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer”:

Zikai Xiao, Zihan Chen, Songshang Liu, Hualiang Wang, *Yang Feng (affiliated professor), Jin Hao, Joey Tianyi Zhou, Jian Wu, Howard Yang, Zuozhu Liu

“Formalizing locality for normative synaptic plasticity models”:

Colin Bredenberg, Ezekiel Williams, *Cristina Savin (Assistant Professor of Neural Science and Data Science), Blake Richards, Guillaume Lajoie

“GAUCHE: A Library for Gaussian Processes in Chemistry”:

Ryan-Rhys Griffiths, Leo Klarner, Henry Moss, Aditya Ravuri, Sang Truong, Yuanqi Du, *Samuel Stanton (PhD alumnus), Gary Tom, Bojana Rankovic, Arian Jamasb, Aryan Deshwal, Julius Schwartz, Austin Tripp, Gregory Kell, Simon Frieder, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Johannes Peter Dürholt, Saudamini Chaurasia, Ji Won Park, Felix Strieth-Kalthoff, Alpha Lee, Bingqing Cheng, Alan Aspuru-Guzik, Philippe Schwaller, Jian Tang

“Gradient-Based Feature Learning under Structured Data”:

Alireza Mousavi-Hosseini, *Denny Wu (Faculty Fellow), Taiji Suzuki, Murat Erdogdu

“Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery”:

Yuxin Wen, Neel Jain, John Kirchenbauer, *Micah Goldblum (Postdoc Researcher), Jonas Geiping, Tom Goldstein

“Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning Benchmarks”:

Yun Qu, Boyuan Wang, Jianzhun Shao, Yuhang Jiang, Chen Chen, Zhenbin Ye, Liu Linc, *Yang Feng (affiliated professor), Lin Lai, Hongyang Qin, Minwen Deng, Juchao Zhuo, Deheng Ye, Qiang Fu, Yang Guang, Wei Yang, Lanxiao Huang, Xiangyang Ji

“Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation”:

David Brandfonbrener, Ofir Nachum, *Joan Bruna (Associate Professor of Computer Science and Data Science)

“Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting”:

*Miles Turpin (Junior Research Scientist), *Julian Michael (Research Scientist), Ethan Perez, *Samuel Bowman (Associate Professor of Linguistics and Data Science)

“Large Language Models Are Zero-Shot Time Series Forecasters”:

Nate Gruver, Marc Finzi, Shikai Qiu, *Andrew Wilson (Associate Professor of Computer Science and Data Science)

“Learning and Collusion in Multi-unit Auctions”:

Simina Branzei, Mahsa Derakhshan, Negin Golrezaei, *Yanjun Han (Assistant Professor of Mathematics and Data Science)

“Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representations”:

Thomas Yerxa, Yilun Kuang, *Eero Simoncelli (Professor of Neural Science, Mathematics, Data Science, and Psychology), *SueYeon Chung (affiliated professor)

“Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective”:

Jimmy Ba, Murat Erdogdu, Taiji Suzuki, Zhichao Wang, *Denny Wu (Faculty Fellow)

“Mean-field Langevin dynamics: Time-space discretization, stochastic gradient, and variance reduction”:

Taiji Suzuki, *Denny Wu (Faculty Fellow), Atsushi Nitanda

“NetHack is Hard to Hack”:

Ulyana Piterbarg, *Lerrel Pinto (affiliated professor), *Rob Fergus (associated professor)

“Non-autoregressive Machine Translation with Probabilistic Context-free Grammar”:

Shangtong Gui, Chenze Shao, Zhengrui Ma, Xishan Zhang, Yunji Chen, *Yang Feng (affiliated professor)

“On Single-Index Models beyond Gaussian Data”:

Aaron Zweig, Loucas Pillaud-Vivien, *Joan Bruna (Associate Professor of Computer Science and Data Science)

“Protein Design with Guided Discrete Diffusion”:

Nate Gruver, *Samuel Stanton (PhD alumnus), Nathan Frey, *Tim G. J. Rudner (Instructor), Isidro Hotzel, Julien Lafrance-Vanasse, Arvind Rajpal, *Kyunghyun Cho (Professor of Computer Science and Data Science), *Andrew Wilson (Associate Professor of Computer Science and Data)

“Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks”:

Jules Berman, *Benjamin Peherstorfer (affiliated professor)

“Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition”:

Samuel Dooley, Rhea Sukthanker, John Dickerson, Colin White, Frank Hutter, *Micah Goldblum (Postdoc Researcher)

“Reverse Engineering Self-Supervised Learning”:

Ido Ben-Shaul, *Ravid Shwartz-Ziv (Faculty Fellow), Tomer Galanti, Shai Dekel, *Yann LeCun (Professor of Computer Science, Neural Science, Data Science, and Electrical and Computer Engineering)

“Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms”:

Qian Yu, Yining Wang, Baihe Huang, *Qi Lei (Assistant Professor of Mathematics and Data Science), Jason Lee

“Self-Supervised Learning with Lie Symmetries for Partial Differential Equations”:

Grégoire Mialon, Quentin Garrido, Hannah Lawrence, Danyal Rehman, *Yann LeCun (Professor of Computer Science, Neural Science, Data Science, and Electrical and Computer Engineering), Bobak Kiani

Should We Learn Most Likely Functions or Parameters?”:

Shikai Qiu, *Tim G. J. Rudner (Faculty Fellow), *Sanyam Kapoor (PhD student), *Andrew Wilson (Associate Professor of Computer Science and Data Science)

“Simplifying Neural Network Training Under Class Imbalance”:

*Ravid Shwartz-Ziv (Faculty Fellow), *Micah Goldblum (Postdoc Researcher), Yucen Li, C. Bayan Bruss, *Andrew Wilson (Associate Professor of Computer Science and Data)

“Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples”:

Abulhair Saparov, Richard Yuanzhe Pang, *Vishakh Padmakumar (PhD student), Nitish Joshi, Mehran Kazemi, Najoung Kim, *He He (Assistant Professor of Computer Science & Data Science)

“The Adversarial Consistency of Surrogate Risks for Binary Classification”:

Natalie Frank, *Jonathan Niles-Weed (Assistant Professor of Mathematics and Data Science)

“The Contextual Lasso: Sparse Linear Models via Deep Neural Networks”:

Ryan Thompson, Amir Dezfouli, *Robert Kohn (affiliated professor)

“TMT-VIS: Taxonomy-aware Multi-dataset Joint Training for Video Instance Segmentation”:

Rongkun Zheng, Lu Qi, *Xi Chen (affiliated professor), Yi Wang, Kun Wang, Yu Qiao, Hengshuang Zhao

“Towards Distribution-Agnostic Generalized Category Discovery”:

Jianhong Bai, Zuozhu Liu, Hualiang Wang, Ruizhe Chen, Lianrui Mu, Xiaomeng Li, Joey Tianyi Zhou, *Yang Feng (affiliated professor), Jian Wu, Haoji Hu

“Understanding and Mitigating Copying in Diffusion Models”:

Gowthami Somepalli, Vasu Singla, *Micah Goldblum (Postdoc Researcher), Jonas Geiping, Tom Goldstein

“Understanding the detrimental class-level effects of data augmentation”:

*Polina Kirichenko (PhD student), Mark Ibrahim, Randall Balestriero, Diane Bouchacourt, Shanmukha Ramakrishna Vedantam, Hamed Firooz, *Andrew Wilson (Associate Professor of Computer Science and Data Science)

“Uni3DETR: Unified 3D Detection Transformer”:

Zhenyu Wang, Ya-Li Li, *Xi Chen (affiliated professor), Hengshuang Zhao, Shengjin Wang

“Unified Segment-to-Segment Framework for Simultaneous Sequence Generation”:

Shaolei Zhang, *Yang Feng (affiliated professor)

“Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution”:

Ying Wang, *Tim G. J. Rudner (Instructor), *Andrew Wilson (Associate Professor of Computer Science and Data)

“What Can We Learn from Unlearnable Datasets?”:

Pedro Sandoval-Segura, Vasu Singla, Jonas Geiping, *Micah Goldblum (Postdoc Researcher), Tom Goldstein

“When Do Neural Nets Outperform Boosted Trees on Tabular Data?”:

Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, Vishak Prasad C, Ganesh Ramakrishnan, *Micah Goldblum (Postdoc Researcher), Colin White


Backdoors in Deep Learning: The Good, the Bad, and the Ugly:

Khoa D Doan, Aniruddha Saha, Anh Tran, Yingjie Lao, Kok-Seng Wong, Ang Li, Haripriya Harikumar, Eugene Bagdasaryan, *Micah Goldblum (Postdoc Researcher), Tom Goldstein

Learning-Based Solutions for Inverse Problems:

Shirin Jalali, Chris Metzler, Ajil Jalal, Jon Tamir, Reinhard Heckel , Paul Hand, *Arian Maleki (Visiting Research Professor), Richard Baraniuk

Multi-Agent Security: Security as Key to AI Safety:

*Christian Schroeder de Witt (Adjunct Professor), Hawra Milani, Klaudia Krawiecka, *Swapneel Mehta (PhD alumnus), Carla Cremer, Martin Strohmeier

Muslims in ML:

*Sanae Lotfi (PhD student), Hammaad Adam, Hadeel Al-Negheimish, Sarah Fakhoury, Razan Baltaji, Marzyeh Ghassemi, Shakir Mohamed, Aya Salama, S. M. Ali Eslami, Tasmie Sarker

Optimal Transport and Machine Learning:

Anna Korba, *Aram-Alexandre Pooladian (PhD student), Charlotte Bunne, David Alvarez-Melis, Marco Cuturi, Ziv Goldfeld

Socially Responsible Language Modelling Research (SoLaR):

Usman Anwar, David Krueger, *Samuel Bowman (Associate Professor of Linguistics and Data Science), Jakob Foerster, Su Lin Blodgett, Roberta Raileanu, Alan Chan, Katherine Lee, Laura Ruis, Robert Kirk, Yawen Duan, Xin Chen, Kawin Ethayarajh

The conference’s preliminary presentation schedule can be found on the NeurIPS Schedule Overview page.



NYU Center for Data Science

Official account of the Center for Data Science at NYU, home of the Undergraduate, Master’s, and Ph.D. programs in Data Science.