Chengchun Shi

(i), I am an associate professor of data science at London School of Economics and Political Science (LSE).

(ii) Pior to (i), I was an assistant professor of data science at LSE.

(iii) Prior to (ii), I was a Ph.D. student in Statistics at North Carolina State University (NCSU). I work with Dr. Wenbin Lu and Dr. Rui Song.

(iv) Prior to (iii), I obtained a B.S. in Statistics from Zhejiang University in July 2014.

(v) Prior to (iv), I graduated from Ningbo Xiaoshi High School in July 2010.

I am looking for PhD students interested in reinforcement learning and large language models (LLMs); see

my recent paper and slides on LLMs;
my lecture slides on RL and my survey paper on statistical inference in RL (its Chinese version);
our AAAI tutorial (Youtube, Bilibili) on RL for two-sided marketplaces;
my slides on off-policy evaluation and our review paper.

My research is motivated from the following applications:

LLMs (see our recent papers DRPO, VRPO, slides on fine-tuning);
Ridesharing (simulated environments for Order Dispatch and Spatio-temporal Policy Evaluation; see also our tutorial (Youtube, Bilibili) and my talk);
Video-sharing (see our KDD paper for details about our proposal successfully deployed in a widely used mobile app with millions of daily active users)
Mobile health (some simulated environments for Diabetes and Intern Health);
Neuroscience (see our paper on using RL for modelling human decision making)
Precision medicine (a simulated STARD data example).

Some of my recent talks and slides on statistical inference, RL, causal inference, data integration, time series and experimental design:

StatRL Bilibili, Youtube; Chinese versions: Bilibili, Youtube; slides; the accompanying paper and its Chinese version
CausalRL Youtube, slides; the accompanying paper
ARMAdesign 2-hour slides, 1-hour slides, 30-minute slides; the accompanying paper
Pessimistic Data Integration slides; the accompanying paper
Doubly Inhomogeneous policy learning, policy evaluation.

A summary of my past research: Statistical Methods in Reinforcement Learning (partly supported by EPSRC)