Curriculum Vitae

Education

2010–2015: PhD Computational & Mathematical Engineering, Stanford University
Thesis: Regularization in high-dimensional statistics
2006–2010: BA Computational and Applied Mathematics (CAAM), Rice University

Academic appointments

2025–: Researcher, MBZUAI Institute of Foundation Models (IFM)
2025–: MS Data Science Program Director, University of Michigan
2016–: Assistant → Associate Professor of Statistics, University of Michigan
Promoted to Associate Professor (with tenure) in 2023
2017–2018: Visiting Scholar, Stanford University
2015–2016: Neyman Visiting Assistant Professor, UC Berkeley

Other experience

2025–: Academic Collaborator, Meta
2013: Research Intern, Technicolor Research & Innovation
2008–2009: Teaching Fellow, Breakthrough Houston

Honors and awards

2017: ASQ Jack Youden Prize
2015: Stanford ICME Super Hero Award
2010: Stanford School of Engineering Graduate Fellowship
2009: Rice CAAM Chevron Prize
2009: Rice Engineering Louis J Walsh Scholarship
2008: Rice Engineering WM Moody Jr Scholarship

Grants

Sep 2024–Aug 2027: CIF: Small: Learning in Strategic Environments with Applications in Algorithmic Fairness
NSF CCF 2414918. Award amount: $544k.
Jul 2024–Jun 2025: OpenAI Superalignment Fast Grant: A Mathematical Theory of Weak-to-strong Generalization
Award amount: $102k.
Aug 2022– Jul 2024: Confident Learning with Uncertainty Estimates
DARPA HR00112290111. PI: Elizabeth Hou. My/Total award amount: $184k/$1M.
Aug 2021– Jul 2024: A Transfer Learning Approach to Algorithmic Fairness
NSF DMS 2113373. Award amount: $150k.
Sep 2021– Aug 2024: ATD: Algorithmic Threat Detection and Mitigation with Robust Machine Learning
NSF DMS 2027737. Award amount: $330k.
Sep 2019– Aug 2022: Integrative Analysis of Heterogeneous Datasets with High-Dimensional and Non-Standard Models
NSF DMS 1916271. Award amount: $180k.
Aug 2018– Jul 2021: ATD: Collaborative Research: Statistically Principled Real-Time Detection of Anomalies in Temporal Network Data
NSF DMS 1830247. My/Total award amount: $75k/$200k.

Professional activities

2023–: Co-editor (with Samory Kpotufe and Richard Samworth), Statistical Science Special Issue on “Learning Across Distributions”
2023: Organizing committee member, 2023 ICSA Applied Statistics Symposium
2022–: Associate Editor, Statistical Science

Teaching

University of Michigan

Win 2020-23, 25: STATS 606: Computation and Optimization Methods in Statistics
This course combines STATS 608a and STATS 607b.
Fall 2024, 22, 18: STATS 415: Data Mining and Machine Learning
Win 2022: STATS 701: Topics in Algorithmic Fairness
Fall 2021, 17, 16
Win 17: STATS 413: Applied Regression Analysis
Fall 20: STATS 451: Bayesian Data Analysis
Fall 18, 17: STATS 608a: Optimization Methods in Statistics
Sum 18: VE 488: Data Mining and Machine Learning
This course was offered at the UM-SJTU Joint Institute; it is identical to STATS 415.
Win 19, 18: STATS 607b: Numerical Methods in Statistics

UC Berkeley

Spr 2016: STAT 153: Introduction to Time Series
Fall 2015: STAT 201B: Introduction to Statistics at an Advanced Level

PhD students

2024–: Jiwoo Han (co-advised with Mouli Banerjee)
2023–: Andrej Leban
2021–: Seamus Somerstep (co-advised with Ya’acov Ritov)
2021–: Daniele Bracale (co-advised with Mouli Banerjee)
2021–: Felipe Maia Polo (co-advised with Mouli Banerjee)
2020–2025: Pramit Das (co-advised with Mouli Banerjee)
Thesis: Generative Machine Learning, Granger Causality, and Optimal Intervention in Self-exciting Spatiotemporal Processes
Pramit is an operations research analyst at American Airlines.
2018–2024: Songkai Xue
Thesis: Advances in Machine Learning Safety
Songkai is a research scientist at Huawei.
2018–2024: Subha Maity (co-advised with Mouli Banerjee)
Thesis: An Exploration of the Statistical Challenges and Fairness Implications of Transfer Learning
Subha is a faculty member at the University of Waterloo.
2016–2022: Laura Niss (co-advised with Ambuj Tewari)
Thesis: Topics in Sequential Decision Making and Algorithmic Fairness
Laura is a member of the technical staff at MIT Lincoln Laboratory.
2014–2020: Roger Fan (co-advised with Shuheng Zhou)
Thesis: Covariance Estimation with Missing and Dependent Data
Roger is a research scientist at Amazon.
2014–2019: Ruofei Zhao
Thesis: Convergence and Consistency Results in Spectral Clustering and Gaussian Mixture Models
Ruofei is a quantitative researcher at Sunrise Futures LLC.

Papers

See Google Scholar for up-to-date citation metrics.

Conference papers

Bridging Human and LLM Judgments: Understanding and Narrowing the Gap
F Maia Polo, X Wang, M Yurochkin, G Xu, M Banerjee, Y Sun. NeurIPS 2025.

Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families
F Maia Polo, S Somerstep, L Choshen, Y Sun, M Yurochkin. NeurIPS 2025.

Microfoundation Inference for Strategic Prediction
D Bracale, S Maity, F Maia Polo, S Somerstep, M Banerjee, Y Sun. AISTATS 2025.

Learning the Distribution Map in Reverse Causal Performative Prediction
D Bracale, S Maity, S Somerstep, M Banerjee, Y Sun. AISTATS 2025.

LiveXiv — A Multi-Modal Live Benchmark Based on Arxiv Papers Content
N Shabtay, F Maia Polo, S Doveh, W Lin, J Mirza, L Chosen, M Yurochkin, Y Sun, A Arbelle, L Karlinsky, R Giryes. ICLR 2025.

A transfer learning framework for weak-to-strong generalization
S Somerstep, F Maia Polo, M Banerjee, Y Ritov, M Yurochkin, Y Sun. ICLR 2025.

Distributionally robust performative prediction
S Xue, Y Sun. NeurIPS 2024.

Efficient multi-prompt evaluation of LLMs
F Maia Polo, R Xu, L Weber, M Silva, O Bhardwaj, L Choshen, A Oliveira, Y Sun, M Yurochkin. NeurIPS 2024.

Weak Supervision Performance Evaluation via Partial Identification
F Maia Polo, M Yurochkin, M Banerjee, S Maity, Y Sun. NeurIPS 2024.

Aligners: Decoupling LLMs and Alignment
L Ngweta, M Agarwal, S Maity, A Gittens, Y Sun, M Yurochkin. EMNLP Findings 2024.
A short version appeared as a ICLR 2024 TinyPaper.

Prompt Exploration with Prompt Regression
M Feffer, R Xu, Y Sun, M Yurochkin. COLM 2024.

Large Language Model Routing with Benchmark Datasets
T Shnitzer, A Ou, M Silva, K Soule, Y Sun, J Solomon, N Thompson, M Yurochkin. COLM 2024.

tinyBenchmarks: evaluating LLMs with few examples
F Maia Polo, L Weber, L Choshen, Y Sun, G Xu, M Yurochkin. ICML 2024.

Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness
S Somerstep, Y Ritov, Y Sun. FAccT 2024.

Learning in reverse causal strategic environments with ramifications on two-sided markets
S Somerstep, Y Sun, Y Ritov. ICLR 2024.

Fusing Models with Complementary Expertise
H Wang, F Maia Polo, Y Sun, S Kundu, E Xing, M Yurochkin. ICLR 2024.

An Investigation of Representation and Allocation Harms in Contrastive Learning
S Maity, M Agarwal, M Yurochkin, Y Sun. ICLR 2024.

Conditional independence testing under misspecified inductive biases
F Maia Polo, Y Sun, M Banerjee. NeurIPS 2023.

Simple Disentanglement of Style and Content in Visual Representations
L Ngweta, S Maity, A Gittens, Y Sun, M Yurochkin. ICML 2023.

Understanding new tasks through the lens of training data via exponential tilting
S Maity, M Yurochkin, M Banerjee, Y Sun. ICLR 2023.

Predictor-corrector algorithms for stochastic optimization under gradual distribution shift
S Maity, D Mukherjee, M Banerjee, Y Sun. ICLR 2023.

ISAAC Newton: Input-based Approximate Curvature for Newton’s Method
F Petersen, T Sutter, C Borgelt, D Huh, H Kuehne, Y Sun, O Deussen. ICLR 2023.

Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees
S Xue, M Yurochkin, Y Sun. NeurIPS 2022.

Domain Adaptation meets Individual Fairness. And they get along.
D Mukherjee, F Petersen, M Yurochkin, Y Sun. NeurIPS 2022.

Post-processing for Individual Fairness
F Petersen, D Mukherjee, Y Sun, M Yurochkin. NeurIPS 2021.

On sensitivity of meta-learning to support data
M Agarwal, M Yurochkin, Y Sun. NeurIPS 2021.

Does enforcing fairness mitigate biases caused by subpopulation shift?
S Maity, D Mukherjee, M Yurochkin, Y Sun. NeurIPS 2021.

Outlier-Robust Optimal Transport
D Mukherjee, A Guha, J Solomon, Y Sun, M Yurochkin. ICML 2021.

Statistical Inference for Individual Fairness
S Maity, S Xue, M Yurochkin, Y Sun. ICLR 2021.

Individually Fair Rankings
A Bower, H Eftekhari, M Yurochkin, Y Sun. ICLR 2021.

Individually fair gradient boosting
A Vargo, F Zhang, M Yurochkin, Y Sun. ICLR 2021.

SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness
M Yurochkin, Y Sun. ICLR 2021.

Two simple ways to learn individual fairness metrics from data
D Mukherjee, M Yurochkin, M Banerjee, Y Sun. ICML 2020.

Auditing ML models for individual bias and unfairness
S Xue, M Yurochkin, Y Sun. AISTATS 2020.

Federated Learning with Matched Averaging
H Wang, M Yurochkin, Y Sun, D Papailiopoulos, Y Khazaeni. ICLR 2020.

Training individually fair machine learning models with Sensitive Subspace Robustness
M Yurochkin, A Bower, Y Sun. ICLR 2020.

Dirichlet Simplex Nest and Geometric Inference
M Yurochkin, A Guha, Y Sun, XL Nguyen. ICML 2019.

Precision Matrix Estimation with Noisy and Missing Data
R Fan, B Jang, Y Sun, S Zhou. AISTATS 2019.

Debiasing representations by removing unwanted variation due to protected attributes
A Bower, L Niss, Y Sun, A Vargo. FAT/ML 2018.

Feature-distributed sparse regression: a screen-and-clean approach
J Yang, MW Mahoney, M Saunders, Y Sun. NIPS 2016.

Evaluating the statistical significance of biclusters
JD Lee, Y Sun, JE Taylor. NIPS 2015.

Learning Mixtures of Linear Classifiers
Y Sun, S Ioannidis, A Montanari. ICML 2014.

On model selection consistency of regularized M-estimators
JD Lee, Y Sun, JE Taylor. NIPS 2013.
A journal version appeared in the Electronic Journal of Statistics in 2015.

Proximal Newton-type methods for minimizing composite functions
JD Lee, Y Sun, MA Saunders. NIPS 2012.
A journal version appeared in the SIAM Journal on Optimization in 2014.

Journal papers

How does overparametrization affect performance on minority groups?
S Maity, S Roy, S Xue, M Yurochkin, Y Sun. Transactions of Machine Learning Research (2025).

Dynamic Pricing in the Linear Valuation Model using Shape Constraints
D Bracale, M Banerjee, Y Sun, K Stoll, S Turki. Transactions of Machine Learning Research (2025).

A linear adjustment based approach to posterior drift in transfer learning
S Maity, D Dutta, J Terhorst, Y Sun, M Banerjee. Biometrika (2024).

Minimax optimal approaches to the label shift problem
S Maity, Y Sun, M Banerjee. Journal of Machine Learning Research (2022).

Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions
S Maity, Y Sun, M Banerjee. Journal of Machine Learning Research (2022).

Matrix Completion Methods for the Total Electron Content Video Reconstruction
H Sun, Z Hua, J Ren, S Zou, Y Sun, Y Chen. Annals of Applied Statistics (2022+).

Uniform bounds for invariant subspace perturbations
A Damle, Y Sun. SIAM Journal of Matrix Analysis and Applications (2020).

Statistical convergence of the EM algorithm on Gaussian mixture models
R Zhao, Y Li, Y Sun. Electronic Journal of Statistics (2020).

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v. 3.0
L Heirendt et al. Nature Protocols (2019).

A geometric approach to archetypal analysis and nonnegative matrix factorization
A Damle, Y Sun. Technometrics (2017).

Communication-efficient sparse regression
JD Lee, Q Liu, Y Sun, JE Taylor. Journal of Machine Learning Research (2017).

Exact post-selection inference, with application to the lasso
JD Lee, DL Sun, Y Sun, JE Taylor. Annals of Statistics (2016).

Do genome‐scale models need exact solvers or clearer standards?
A Ebrahim et al. Molecular Systems Biology (2015).

Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data
L Yang et al. Proceedings of the National Academy of Sciences (2015).

On model selection consistency of regularized M-estimators
JD Lee, Y Sun, JE Taylor. Electronic Journal of Statistics (2015).
A conference version appeared at NIPS 2013.

Proximal Newton-type methods for minimizing composite functions
JD Lee, Y Sun, MA Saunders. SIAM Journal on Optimization (2014).
A conference version appeared at NIPS 2012.

Humidity effects on anisotropic nanofriction behaviors of aligned carbon nanotube carpets
J Zhang, H Lu, Y Sun, L Ci, PM Ajayan, J Lou. ACS Applied Materials & Interfaces (2013).

Robust flux balance analysis of multiscale biochemical reaction networks
Y Sun, RMT Fleming, I Thiele, MA Saunders. BMC Bioinformatics (2013).

Nanostructure on taro leaves resists fouling by colloids and bacteria under submerged conditions
J Ma, Y Sun, K Gleichauf, J Lou, Q Li. Langmuir (2011).

Regular and reverse nanoscale stick-slip behavior: Modeling and experiments
F Landolsi, Y Sun, H Lu, FH Ghorbel, J Lou. Applied Surface Science (2010).

Nanoscale friction dynamic modeling
F Landolsi, FH Ghorbel, J Lou, H Lu, Y Sun. ASME Journal of Dynamic Systems, Measurement & Control (2009).

Friction and adhesion properties of vertically aligned multi-walled carbon nanotube arrays and fluoro-nanodiamond films
H Lu, J Goldman, F Ding, Y Sun, MX Pulikkathara, VN Khabashesku, BI Yakobson, J Lou. Carbon (2008).

Mesoscale reverse stick-slip nanofriction behavior of vertically aligned multiwalled carbon nanotube superlattices
J Lou, F Ding, H Lu, J Goldman, Y Sun, BI Yakobson. Applied Physics Letters (2008).

Patents

Learning Mahalanobis Distance Metrics from Data
M Yurochkin, D Mukherjee, M Banerjee, Y Sun, S Upadhyay. US 2022/0405529 A1.

Book chapters

Communication Efficient Model Fusion
M Yurochkin, Y Sun.
In Federated Learning: A Comprehensive Overview of Methods and Applications. H Ludwig, N Baracaldo (eds). Springer (2022).

Personalization in Federated Learning
M Agarwal, M Yurochkin, Y Sun.
In Federated Learning: A Comprehensive Overview of Methods and Applications. H Ludwig, N Baracaldo (eds). Springer (2022).