My name is Shengpu Tang (唐笙普), and I am a PhD candidate in the computer science department at the University of Michigan. I'm a member of the Machine Learning for Data-Driven Decisions (MLD3) research group led by Jenna Wiens.

My current research focuses on developing computational methods that help solve important problems in healthcare, such as risk stratification and dynamic treatment recommendations. More generally, I'm interested in broader applications of AI, machine learning and graph mining, computer game design, self-driving cars, security and hacking, as well as teaching.

PS: I have many genius friends and this LINK will redirect you to one of them.

Interests
  • Artificial Intelligence
  • Machine Learning
  • Healthcare
Education

  • MS in Computer Science & Engineering, 2020

    University of Michigan, Ann Arbor

  • BSE in Computer Science, 2018

    University of Michigan, Ann Arbor

Media Coverage

Featured Publications

August 2021 MLHC 2021

Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings

We present an in-depth analysis of popular OPE methods when used for model selection in offline RL, highlighting the additional hyperparameters and computational requirements, and propose a simple two-stage approach to accelerate model selection while balancing accuracy with computational costs.

code
Shengpu Tang, Parmida Davarmanesh, Yanmeng Song, Michael W. Sjoding, Danai Koutra, Jenna Wiens
October 2020 JAMIA

Democratizing EHR Analyses with FIDDLE - A Flexible Data-Driven Preprocessing Pipeline for Structured Clinical Data

We developed FIDDLE (Flexible Data-Driven Pipeline), an open-source framework that streamlines the preprocessing of data extracted from the EHR.

Cite code JAMIA article MiCHAMP talk MLHC abstract MLHC poster

Clinician-in-the-loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies

We proposed a model-free algorithm based on temporal difference learning and a near-greedy heuristic for action selection – with the goal of learning set-valued policies to capture near-equivalent actions that lead to similar cumulative rewards.

Cite code proceeding arXiv website video

Recent Publications

Predicting Postoperative Opioid Use with Machine Learning and Insurance Claims in Opioid-Naïve Patients

Jaewon Hur*, Shengpu Tang*, Vidhya Gunaseelan, Joceline Vu, Chad M. Brummett, Michael Englesbe, Jennifer Waljee†, Jenna Wiens†

Evaluating a Widely Implemented Proprietary Deterioration Index Model Among Hospitalized COVID-19 Patients

Karandeep Singh, Thomas S. Valley, Shengpu Tang, Benjamin Y. Li, Fahad Kamran, Michael W. Sjoding, Jenna Wiens, Erkin Otles, John P. Donnelly, Melissa Y. Wei, Jonathon P. McBride, Jie Cao, Carleen Penoza, John Z. Ayanian, Brahmajee K. Nallamothu

Predicting Acute Graft-versus-Host Disease Using Machine Learning and Longitudinal Vital Sign Data from Electronic Health Records

Shengpu Tang, Grant T. Chappell, Amanda Mazzoli, Muneesh Tewari, Sung Won Choi*, Jenna Wiens*

Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series

mixture-based LSTM model to handle time-varying relationships

A BioHackathon on the challenges of aging: Rapid need-finding and solution modeling in a student-led event

an 8-hour “BioHackathon” of brainstorming to tackle current challenges with “Aging”

Visitors

Activities

short films, games, etc.

Set Sail

Set Sail

Campus Movie Fest 2019

Set Sail (part 2)

Set Sail (part 2)

Campus Movie Fest 2019

FreshWalk

FreshWalk

EECS 598 - Advanced Data Mining

pandas

pandas

Contributed bug fixes

Legend of Zelda NES remastered

Legend of Zelda NES remastered

EECS 494 Game

Zelda Windy_World

Zelda Windy_World

EECS 494 Game

FlappyBird coop

FlappyBird coop

EECS 494 Game

Flash-mob Oct.1 @ UM diag

Flash-mob Oct.1 @ UM diag

Celebrating National Day of China, 2015