Kan Chen (陳侃)

New York, NY

Weill Cornell Medicine, Cornell University

knc7005@med.cornell.edu

Kan Chen is an incoming tenure-track Assistant Professor of Biostatistics in the Division of Biostatistics, Department of Population Health Sciences at Weill Cornell Medicine, Cornell University.

He is currently a postdoctoral research fellow jointly mentored by Prof. Xihong Lin at the Biostatistics Department of Harvard T.H. Chan School of Public Health and Prof. Zhonghua Liu at the Biostatistics Department of Columbia University since 2024.

Prior to this, Kan received his PhD in Applied Mathematics and Computational Science from the University of Pennsylvania in 2023 and dual MA degrees in Statistics and Data Science from the Wharton School in 2021, jointly mentored by Prof. Dylan Small and Prof. Qi Long.

His research spans causal inference, causal machine learning and AI, high-dimensional statistics, and large-scale biomedical data analysis, with applications in clinical trials, oncology studies, genomics, public health, and policy evaluation.

news

Mar 01, 2026	Kan will join Weill Cornell Medicine, Cornell University as a tenure-track Assistant Professor of Biostatistics in the Department of Population Health Sciences.
Feb 01, 2026	Received 2025 Statistics in Biosciences Best Paper Award
Jul 01, 2025	Paper published in Biometrics: Sensitivity Analysis for Attributable Effects in Case-2 Studies.
Jun 01, 2025	Paper published in Journal of the Royal Statistical Society Series A: Using Case Description Information to Reduce Sensitivity to Bias for the Attributable Fraction Among the Exposed.
May 15, 2025	Presented at ACIC 2025. Talk titles: “The Blessing of Multiple Mediators: Taming Unmeasured Confounding Bias via Factor Analysis” and “Sensitivity Analysis for Attributable Effects in Case² Studies.”
May 01, 2025	Presented at the NJIT Statistics Seminar. Title: “The Blessing of Multiple Mediators: Taming Unmeasured Confounding Bias via Factor Analysis.”
Jan 01, 2025	Paper published in Nature Medicine: Evaluating Generalizability of Oncology Trial Results to Real-World Patients Using Machine Learning-Based Trial Emulations.
Dec 01, 2024	Paper published in Journal of the American Statistical Association (JASA): Combining Broad and Narrow Case Definitions in Matched Case-Control Studies: Firearms in the Home and Suicide Risk.
Aug 01, 2024	Presented at JSM 2024 in the Statistics and Health Policy Section. Title: “A Differential Effect Approach to Partial Identification of Treatment Effects.”
Apr 15, 2024	Spotlight paper accepted at ICML 2024 for DISCRET: Synthesizing Faithful Explanations for Treatment Effect Estimation.
Apr 01, 2024	Received the IMS New Researcher Travel Award.

selected publications

PhysRev

Can Quantum-Mechanical Description of Physical Reality Be Considered Complete?

A. Einstein^*†, B. Podolsky^*, and N. Rosen^*

Phys. Rev., New Jersey. More Information can be found here , May 1935

Abs DOI HTML PDF Video

In a complete theory there is an element corresponding to each element of reality. A sufficient condition for the reality of a physical quantity is the possibility of predicting it with certainty, without disturbing the system. In quantum mechanics in the case of two physical quantities described by non-commuting operators, the knowledge of one precludes the knowledge of the other. Then either (1) the description of reality given by the wave function in quantum mechanics is not complete or (2) these two quantities cannot have simultaneous reality. Consideration of the problem of making predictions concerning a system on the basis of measurements made on another system that had previously interacted with it leads to the result that if (1) is false then (2) is also false. One is thus led to conclude that the description of reality as given by a wave function is not complete.