I'm a 4th year computer science and engineering PhD candidate at the
University of Michigan, advised by professor Jenna Wiens
My current research focuses on model interpretability. However, I'm
interested in a wide range of topics including time series analysis,
non-convex optimization, reinforcement learning, and sports analytics. My
work is usually motivated by application in healthcare.
I spent most of my pre-college years in Beijing. For the past 7 years,
I've been living in Ann Arbor. I completed my undergrad at the Unisersity
of Michigan as a computer science major and math minor in 2017, and
started the PhD program in Fall 2017. In undergrad, I worked with
professor Jia Deng
augment CNN with rotation invariant filters (2015-2016). I also worked as
a machine learning intern at Bloomberg L.P. (2016) on parsing and ranking
natural language query for financial charts, under the supervision of
Dr. Konstantine Arkoudas
and Dr. Srivas
. Last summer (2020), I worked as a research intern in
Systems and Interaction Group
at Microsoft Research, mentored
by Scott Lundberg
, working on
unifying Shapley value based model interpretation methods with a causal
* denotes equal contribution
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions
TL;DR: Don't choose between true to the model or true to the data: do both and have a system level view.
Jenna Wiens, Scott Lundberg
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
AdaSGD: Bridging the gap between SGD and Adam
TL;DR: Speed of Adam and performance of SGD may be achieved by adapting a single learning rate.
, Jenna Wiens
arxiv preprint, 2020
Relaxed Weight Sharing: Effectively Modeling Time-Varying Relationships
in Clinical Time-Series
TL;DR: Reducing temporal conditional shift using multi task learning by treating each time step as a separate task.
Jeeheh Oh*, Jiaxuan Wang
*, Shengpu Tang, Michael Sjoding, Jenna Wiens
Machine Learning for Healthcare, 2019
Learning Credible Models
TL;DR: Learning models that are accurate and comply with human intuition so that they don't use proxy variables.
, Jeeheh Oh, Haozhu Wang, Jenna Wiens
ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2018
The Advantage of Doubling: A Deep Reinforcement Learning Approach to
Studying the Double Team in the NBA
TL;DR: It is a bad idea to double team star playes like LeBron who can pass and score.
*, Ian Fox*, Jonathan Skaza, Nick Linck, Satinder Singh, Jenna Wiens
MIT Sloan Sports Analytics Conference, 2018
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks
TL;DR: A learnable data preprocessing module for time series data in healthcare.
Jeeheh Oh, Jiaxuan Wang
, and Jenna Wiens
Machine Learning for Healthcare, 2018
HICO: A Benchmark for Recognizing Human-Object Interactions in Images
TL;DR: A new image dataset focusing on who did what.
Yu-Wei Chao, Zhan Wang, Yugeng He, Jiaxuan Wang
, Jia Deng
International Conference on Computer Vision (ICCV) 2015