I am a final-year PhD student at Columbia University, studying computer science. I am co-advised by Professors Vishal Misra and Rachel Cummings.I am currently on the job market for the 2025/26 academic year. My CV and research statement are available here.

My goal is to build tools that provide personalized causal insights. To this end, I study causal inference with connections to econometrics, machine learning, statistics, theory, and design. My research aims to formalize the causal foundations of design methodology to guide the personalized design of complex, evolving systems. Click here to read more.

Education

Columbia University
(Expected) Ph.D. in Computer Science

Massachusetts Institute of Technology
S.M. in Electrical Engineering and Computer Science
S.M. in Technology and Policy

Korea Advanced Institute of Science and Technology
M.S. in Industrial Design
B.S. in Industrial Design

Publications

[Preprint/Poster]

S. Rho, C. Illick, S. Narasipura, A. Abadie, D. Hsu, and V. Misra, “Time-Aware Synthetic Control.” 2025
–> NeurIPS workshop poster

S. Rho, J. Zhang, and E. Bareinboim, “Partial Identification Approach to Counterfactual Fairness Assessment.” 2025
–> Arxiv preprint

[Conference & Journal Papers]

S. Rho, A. Tang, N. Bergam, R. Cummings, and V. Misra, “ClusterSC: Advancing Synthetic Control with Donor Selection,” in International Conference on Artificial Intelligence and Statistics, PMLR, 2025
–> arXiv version

S. Tang, S. Aydore, M. Kearns, S. Rho, A. Roth, Y. Wang, Y.X. Wang, and Z. S. Wu, Improved Differentially Private Regression via Gradient Boosting. (SaTML 2024)
–> arXiv version

S. Rho, R. Cummings, and V. Misra, Differentially Private Synthetic Control. In International Conference on Artificial Intelligence and Statistics (pp. 1457-1491). PMLR., 2023
–> Journal version (JPC 2024); arXiv version

S. Rho, I. Lee, H. Kim, J. Jung, H. Kim, B. G. Jun, and Y. K. Lim, Futureself: what happens when we forecast self-trackers? Future health statuses?. In Proceedings of the 2017 Conference on Designing Interactive Systems (pp. 637-648). Honorable Mention Award.

D. J. Kim, Y. Lee, S. Rho, and Y. K. Lim, Design opportunities in three stages of relationship development between users and self-tracking devices. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 699-703).

Y. Lee, M. G. Kim, S. Rho, D. J. Kim, and Y. K. Lim, Friends in activity trackers: design opportunities and mediator issues in health products and services. Proc. IASDR, 4, pp.1206-1219. Best Paper Award.

J. Seok, S. Rho, E. Kim, B. Min, and H. J. Suk, Color Quantization to Visualize Perceptually Dominant Colors of an Image. Journal of Korea Society of Color Studies, 29(2), pp.95-102.

[Workshop Papers]

S. Rho, C. Illick, S. Narasipura, A. Abadie, D. Hsu, and V. Misra, “Time-Aware Synthetic Control.” Learning from Time Series for Health, NeurIPS ’25 –> NeurIPS workshop poster

L. Panavas, S. Rho, H. Bhimaraju, W. Pintado, R. N. Wright, and R. Cummings, A Visualization Tool to Help Technical Practitioners of Differential Privacy. Theory and Practice of Differential Privacy (TPDP) 2024.

S. Rho, C. Archambeau, S. Aydore, B. Ermis, M. Kearns, A. Roth, S. Tang, Y.X. Wang, and S. Wu, “Differentially private gradient boosting on linear learners for tabular data analysis.” Trustworthy and Socially Responsible Machine Learning (TSRML), NeurIPS ‘22.

S. Rho, R. Cummings, and V. Misra. “Differentially Private Synthetic Control.” Theory and Practice of Differential Privacy, ICML ’22.

M. Charpignon, M. Mironova, S. Rho, M. Majumder, and L. Celi, 2019. Using News Articles to Model Hepatitis A Outbreaks: A Case Study in California and Kentucky. Joint Workshop on AI for Social Good, NeurIPS ‘19.

[Thesis]

S. Rho, 2020. Estimating Lower Bounds for Time Series Prediction Error. MIT.

S. Rho, 2016. Using Consequence Information in Quantified-Self Service Design to Support User Health Motivation. KAIST.

Invited Talks

INFORMS Session on AI and Operations, 2025
Advancing Synthetic Control Methods for Individual Level Causal Inference

Invited Talk at Joint Statistical Meetings (JSM), 2025
Advancing Synthetic Control Methods for Individual Level Causal Inference

Invited Talk at University of Copenhagen, 2025
Advancing Synthetic Control Methods for Individual Level Causal Inference

Symposium on AI & Sports, 2024
Causal Inference and Sports Data Analytics

International Symposium on Artificial Intelligence and Mathematics (ISAIM), 2024
Algorithmic Fairness from Causal Point of View

INFORMS Session on Privacy and Fairness in Optimization and Learning, 2023
Algorithmic Fairness from Causal Point of View

INFORMS Session on Differentially Private Optimization and Learning, 2022
Differentially Private Synthetic Control

Design for Impact, Rhode Island School of Design, 2019
Designer’s creativity in the era of AI