University of Michigan · Computer Science

I am a Ph.D. student in Computer Science at the University of Michigan. My research focuses on uncertainty quantification for sequential prediction — particularly the design of reliable predictive sets and intervals for non-stationary time series. My work spans conformal prediction, causal inference, and applications in epidemiology and public health, weather forecasting, and autonomous driving.

Ann Arbor, MI Ph.D. student Uncertainty quantification Conformal prediction Time series forecasting Causal inference
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Publications

Selected papers

Links are attached directly on each paper card for faster access.

AAAI 2025

Neural Conformal Control for Time Series Forecasting

Ruipu Li and Alexander Rodríguez

Introduces a neural conformal prediction framework for time series that adapts to non-stationary environments. Leverages auxiliary multi-view encoders end-to-end, enforces monotonicity constraints for consistent prediction intervals across quantiles, and supports few-shot learning via related-task data. Achieves state-of-the-art coverage and calibration across epidemic, weather, and energy demand benchmarks.

AAAI record arXiv

Submitted to KDD 2026

Optimization-based Online Conformal Prediction for Multi-step Forecasting

Ruipu Li, Diego Menacho, and Alexander Rodríguez

Proposes a unified conformal prediction framework that converts ensemble trajectory samples into calibrated prediction intervals with formal coverage guarantees. Introduces an online update step and an optimization step that explicitly captures inter-step temporal dependencies, yielding sharper and more adaptive uncertainty estimates across autonomous driving, hurricane forecasting, and epidemic modeling.

arXiv Google Scholar

Research projects

Current and recent work

Lab-based research projects and applied forecasting collaborations with external partners.

CausalSim: Generative Causal AI for Strategic Decision Support

Graduate Researcher, University of Michigan

Jan 2026 – Present

  • Build an LLM-driven reasoning pipeline that combines retrieval with structural causal models.
  • Anchor model explanations in domain-specific constraints to support trustworthy counterfactual analysis and systematic simulator validation.

ML to Reduce Uncertainty in Climate Forcing by Aerosols

Research Assistant, Climate and Space Sciences and Engineering, University of Michigan

Aug 2022 – Aug 2024

  • Processed satellite observations using interpolation and PCA for aerosol-cloud modeling inputs.
  • Trained predictive models for aerosol optical depth and cloud droplet number concentration with feature analysis using SHAP.

FluSight Forecasting Challenge (CDC)

Team Lead, UM-DeepOutBreak, University of Michigan

Jul 2023 – Present

  • Built an end-to-end forecasting pipeline from data ingestion through evaluation and submission.
  • Submitted weekly real-time probabilistic forecasts used in CDC ensemble modeling.
  • Developed calibrated multi-step prediction intervals using conformal prediction methods.
  • Mentored undergraduates on reproducible experiments, deployment, and pipeline reliability.

Reliable Uncertainty Cones for Cyclone Trajectory Prediction

Collaboration with Google DeepMind WeatherLab

Oct 2025 – Present

  • Applied an adaptive conformal method (CP-Traj) to ensemble cyclone forecasts for multi-step coverage.
  • Coordinated large-ensemble experiments and explored integration of uncertainty cones into operational trajectory forecasting products.

Experience

Teaching and industry

Graduate Student Instructor, EECS 492

Introduction to Artificial Intelligence · Jan 2026 – Present

Design weekly assignments, lead discussion sections, and support students in understanding core AI concepts and problem-solving strategies.

NIO, Autonomous Driving System

Machine Learning Intern · May 2021 – Aug 2021

Worked on lane-change modeling, motion prediction, and Python tooling for large-scale road-test analysis.

Education

Training

University of Michigan, Ann Arbor

Ph.D. Student, Computer Science

Aug 2024 – Present

University of Michigan, Ann Arbor

M.S., Computer Science

Aug 2022 – Apr 2024

University of Michigan, Ann Arbor

B.S., Computer Science

Aug 2020 – Apr 2022

Shanghai Jiao Tong University

B.S., Electrical and Computer Engineering

Aug 2018 – Aug 2022

Hobbies

Outside research

Badminton

Competitive play, including UofM OPEN Fall 2025 men's doubles champion in the B group.

Table tennis

Regular play for fun and fitness, with an ongoing interest in improving technique.

Skiing

Recreational skiing on groomed runs and occasional off-piste terrain.

Contact

Get in touch

I am happy to connect regarding research collaborations, forecasting systems, uncertainty quantification, causal modeling, and related topics in machine learning and statistics.

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