Computational Neuroscience
Machine Learning

Mengman Wei

I am currently a postdoctoral researcher in computational neuroscience, genetic analysis, and machine learning at the Scripps Research Institute. My research focuses on developing quantitative models to understand human behavior.

I view the human body as a complex system. Everything we see, hear, and experience acts as input. Our genes provide the basic information we carry, and the brain processes all inputs as a central system. Our behaviors are the output, shaped by both genetic factors and environmental influences.

Genetic factors—including DNA variation, gene-based signals, and gene expression—form the biological foundation. They influence traits such as physical strength, height, weight, and brain function.

On top of this foundation, environmental factors act as important inputs. These include family upbringing, parental monitoring, peer influence, neighborhood conditions, and educational environments etc.

By studying how these inputs are processed in human body over time using quantitative models, it can help better understand why mental health problems develop. This is especially important for long-term patterns, such as substance use disorders and externalizing behaviors.

My goal is to study how these environmental exposures interact with genetic predisposition and human body, to shape long-term trajectories of mental health and behavioral outcomes. In particular, I focus on understanding how these factors contribute to the development of mental health disorders over time.

My current work focuses on developing tools to represent genetic risk, such as polygenic risk scores (PRS), and integrating traditional statistical models—such as Cox survival models and marginal structural models—with modern machine learning approaches, including multi-task learning and causal machine learning etc. Through this framework, I study how time-varying environmental factors influence health outcomes such as substance use disorders and externalizing behaviors, and aim to identify potential intervention strategies.

My research journey began in the lab of Prof. Xinshan Ye at Peking University, where I worked on the development of carbohydrate-based therapeutic strategies. I am deeply grateful for the mentorship and guidance of Prof. Ye, whose rigorous scientific thinking and dedication to translational research greatly shaped my early academic development. Under his supervision, I developed a strong interest in understanding how molecules interact at the microscopic level and how these mechanisms can be quantitatively and rigorously characterized. This experience motivated me to pursue further training in computational and statistic methods.

I then completed my Ph.D. in computational chemistry at University of Cambridge under the supervision of Prof. Jonathan Goodman. I sincerely appreciate Prof. Goodman’s insightful mentorship, intellectual openness, and encouragement of independent thinking, which played a crucial role in shaping my research direction. My doctoral research focused on developing algorithms for molecular conformational searching, which is fundamental to understanding molecular properties and supports downstream computational simulations, including molecular mechanics (MM), quantum mechanics (QM), and machine learning–based approaches. During my Ph.D., I also gained valuable industry experience in machine learning–based drug discovery.

However, I came to realize that developing therapeutic strategies requires not only understanding drug design, but also understanding disease mechanisms. The human body is a highly complex system, spanning multiple levels—from genes to cells to organs—and continuously interacting with the environment.

My long-term goal is to contribute to the development of effective treatment and prevention strategies for complex diseases. To achieve this, I am currently expanding my expertise in bioinformatics and computational modeling.

04/02/2026

Selected Honors & Awards

  • Scripps Research Travel Award, 2025
  • RSA Junior Investigator Award, 2025
  • Outstanding Graduate of Beijing City, 2018
  • Outstanding Graduate of Peking University, 2018
  • National Scholarship of Peking University, 2017
  • First-Class Academic Scholarship of Peking University, 2017