Flaherty Lab

Foundations of Data Science for Molecular Biology and Genetics


Our lab develops novel statistical methodology for genomic data to understand fundamental biological mechanisms and improve the treatment of complex human diseases.


Our long-term goal is to enhance the utility of genomic data for basic science and clinical decision-making to improve human health.


We adapt and improve methods from statistics, computer science, and genetics.

July 16, 2021
Our paper on methods for analysis of transposon sequencing data is now available on bioRxiv.

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May 13, 2021
Paper on exact hierarchical clustering appears in UAI2021.

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May 13, 2021
Paper on representation learning for bounded data appears in UAI2021.

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More News

Methods for predicting risk of multiple infections in trauma patients.

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Maximum a-posteriori clustering involving constraints, stability, optimality, and computability.

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Methods for analyzing next-generation sequencing data.

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All Publications

Patrick Flaherty

Assistant Professor, Math & Stat UMass Amherst

Tingting Zhao

Postdoctoral Scholar, UMass TRIPODS

Shai He

PhD Student, Math & Stat

Ji Ah Lee

PhD Student, Math & Stat

Vishal Sarsani

PhD Student, Math & Stat

Anjali Nagulpally

PhD Student, Math & Stat

Harsh Dubey

PhD Student, Math & Stat

Alexis Edozie

Undergraduate Student, Math & Stat

Berent Aldikacti

PhD Student, Molecular & Cellular Biology

Members, Alumni, and Collaborators


We gratefully acknowledge financial support from our sponsors: