A Conversation with Dr. Jason G. Mezey

We are pleased to introduce our newest EIPM Member, Dr. Jason G. Mezey.

Dr. Jason Mezey is a Principal Investigator in the Cornell University Department of Biological Statistics and Computational Biology, and a new Member of the Englander Institute for Precision Medicine.

We hope you enjoy learning more about his research interests and background.


Question: Please provide a brief bio and overview of your work.

A: I received a BA from the University of Pennsylvania and a Ph.D. from Yale University.  I’m currently a (tenured, full) Professor at Cornell University in the Department of Computational Biology and concurrently a Professor in the Department of Genetic Medicine and a Professor in the Institute for Computational Biomedicine at Weill Cornell Medicine.  I’m a statistical geneticist by training and I’ve spent the last two decades working with genomic data.  The core of my research program is the development of computational statistics and machine learning methodology for the study, diagnosis, and prediction of complex diseases.


Q:        What makes your research unique? Can you share with us some of your findings?  

A:        My research group focuses on developing computational analytics methodology that can produce precise inferences. Our current areas of work include analysis of genomic, image, and clinical data for recovering human pedigrees, association study mapping and identification of genetic variants impacting disease and other phenotypes, identification of cancer subtypes and cancer outlier profiles, and construction of polygenic risk scores and diagnostic biomarkers.


Q:        What excites you about your work?  

A:        The main objective of my research group: discovering strategies for optimally applying computational statistics and machine learning methods to produce actionable insights and high-value predictions from big biological data.


Q:        When thinking about your research, what are some recent breakthroughs that are propelling the field forward? How will they impact healthcare and patient care in the future?  

A:        Single cell sequencing technologies – these are providing resolution that we have been lacking for a number of genomic inference challenges.


Q:        What are the short-term challenges that your scientific field is facing?  

A:        What I hope will be a short-term challenge: the inability to collect dynamic molecular data at genomic scales.


Q:        How has the Covid-19 pandemic affected your work? 

A:        While definitely disruptive, as we are an entirely computational research group, we have been able to work remotely.


Q:        What do you like to do when you’re not working? 

A:         Spending time with my wife and kids (ages 9 and 6).


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