I am on the job market for full-time positions and open to opportunities in the industry. I would be grateful for any opportunities you might consider sharing via email (xushenbo857 {@ | at} gmail.com).

I completed my PhD at MIT. My research centers on the next generation of foundational models for data-driven dynamic decision-making for real-world problems. Specifically, I focus on devising methods that explain the consequences of actions:

• Reasoning on knowledge graphs, involving pre-training, fine-tuning, reinforcement learning, and evaluation
• Causal inference on multivariate stochastic processes
• Reinforcement learning with uncertainty quantification (e.g., conformal prediction or Bayesian active learning)
• Applications in finance, crypto, medicine, manufacturing, etc

Below is my industry experience

Machine Learning Research experience:
I worked at Scale AI, Inc and MIT-IBM Watson AI Lab as a researcher.

Quantitative Research experience:
I worked at Point72 Asset Management as a Quantitative Researcher and Liberty Mutual Investments and the MIT Quest for Intelligence) as a Research Assistant.

Selected Publications

• Do Larger Language Models Generalize Better? A Scaling Law for Implicit Reasoning at Pretraining Time by X. Wang, S. Tan, S. Xu, M. Jin, W. Wang, R. Panda, Y. Shen. Under review at the International Conference on Learning Representations 2026. arXiv preprint arXiv:2305.02373.

• Double/Debiased Machine Learning for Time-to-Event Outcomes Under Poor Overlap by S. Xu, S. Finkelstein, R. Welsch, K. Ng, I. Tzoulaki, and Z. Shahn. Under review at the International Conference on Learning Representations 2026. arXiv preprint arXiv:2305.02373.

• Foundational model-aided automated high-throughput drug screening using self-controlled cohort study by S. Xu, R. Cobzaru, S. Finkelstein, R. Welsch, and K. Ng. AI for New Drug Modalities (AIDrugX) at 38th NeurIPS, Vancouver, Canada, Dec 10-15, 2024

• Estimating heterogeneous treatment effects on survival outcomes using counterfactual censoring unbiased transformations by S. Xu, R. Cobzaru, S. Finkelstein, R. Welsch, K. Ng, and Z. Shahn. Under review at the Journal of Machine Learning Research. arXiv preprint arXiv:2401.11263.

• Can metformin prevent cancer relative to sulfonylureas? A target trial emulation accounting for competing risks and poor overlap via double/debiased machine learning estimators by S. Xu, B. Zheng, B. Su, S. Finkelstein, R. Welsch, K. Ng, and Z. Shahn. American Journal of Epidemiology, 2025, 194(2), 512-523

• Systematically exploring repurposing effects of anti-hypertensives by Z. Shahn, P. Spear, H. Lu, S. Jiang, S. Zhang, N. Deshmukh, S. Xu, K. Ng, R. Welsch, and S. Finkelstein. Pharmacoepidemiology and Drug Safety, 2022, 31(9), 944-952