Min Zheng

PhD Candidate, Health and AI Lab
Computer science department , Stevens Institute of Technology
mzheng3 at stevens dot edu

I'm a PhD candidate in the department of computer science at Stevens Institute of Technology, advised by Prof. Samantha Kleinberg , and working in the Health and AI Lab (HAIL) at Stevens. My main research interests are in causal inference and decision making in human health area. I also work on automatic eating detection using machine learning methods.


Causal Inference and Causal Explanation

In this project, I focused on using causal inference and causal explanation methods to help understand why things happen. I focus on causal inference and causal explanation on time series data. Key application I'm working on is to explain why clinical events happen in medical dataset. (E.g. Why a seizure happens and why a patient suffered from hypo/hyperglycemia).

Papers related:
A Method for Automating Token Causal Explanation and Discovery. M. Zheng and S. Kleinberg.
FLAIRS, 2017. [pdf-link]
[Under Review ]

Causality and Decision Making

In this project, I focus on improving decision making using personalized causal models. We aim to develop methods that make the output of machine learning, specifically causal inference, useful for decision-making. In particular, we focus on challenging decisions where causal models may have the most benefit: when costs and rewards are at different timescales, sequential decisions that are linked over time, and cases where actions have complex and potentially uncertain effects.

Papers related:
[Preparing for submission]

Automatic Eating Detection

In this project, we focus on using machine learning to perform eating detection using body worn sensors including two Android watches for wrist motion, one in-ear microphone for audio data, and one Google Glass for head motion. We developed multi-modality eating detection methods to detect when, what, and how much people are eating in both lab and free-living environment.

Papers related:
Recognizing Eating from Body-Worn Sensors: Combining Free-living and Laboratory Data M. Mirtchouk, D. Lustig, A. Smith, I. Ching, M. Zheng, and S. Kleinberg.
IMWUT 1 (3) (previously UbiComp), 2017 [pdf-link]
Multimodality Sensing for Eating Recognition, C. Merck, C. Maher, M. Mirtchouk, M. Zheng, Y. Huang, and S. Kleinberg.
Pervasive Health, 2016 [pdf-link]
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