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Himel Mallick

Senior Scientist, Biostatistics

Merck Research Laboratories

Biography

I am a computational biologist researching at Merck Research Laboratories (MRL). I use computational methods to generate and validate testable hypotheses that accelerate data-driven discovery. Prior to MRL, I was a postdoctoral fellow of Computational Biology and Bioinformatics at Harvard and a PhD candidate of Biostatistics at UAB.

Curriculum Vitae: CV, Resume | Researcher Profile: ResearchGate, Publons

Interests

  • Machine Learning
  • AI for Healthcare
  • Human Microbiome
  • Data Science
  • Computational Biology

Education

  • Postdoctoral Fellowship in Computational Biology and Bioinformatics, 2019

    Harvard University and Broad Institute

  • PhD in Biostatistics, 2015

    University of Alabama at Birmingham

  • MSc in Statistics, 2009

    Indian Institute of Technology Kanpur

  • BSc in Statistics, 2007

    University of Calcutta

Skills

R

Statistical computing for reproducible research

Python

Exploratory data analysis and visualization

Git

Version control for scientific workflows

Machine Learning

Development and deployment of ML models

Bayesian Inference

Scalable Bayes and uncertainty quantification

Applied Data Science

Analysis and interpretation of biological data

Experience

 
 
 
 
 

Senior Scientist, Biostatistics

Merck Research Laboratories

Mar 2019 – Present Rahway, NJ

Led multiple data science projects in the preclinical Biostatistics space as part of the Early Development Statistics within the global Biostatistics and Research Decision Sciences (BARDS) organization.

Employed machine learning classification to the discovery of clinically actionable biomarkers from integrated multi-omics profiles to enable better disease outcome prediction and patient stratification.

Published research across several disease areas (infectious diseases, oncology, and microbiology) that utilized innovative statistical methodology and systems biology techniques spanning a wide range of translational applications.

 
 
 
 
 

Postdoctoral Associate, Computational Biology and Bioinformatics

Harvard University and Broad Institute

Oct 2015 – Mar 2019 Cambridge, MA

Developed a computational method MelonnPan to predict metabolite profiles from metagenomic data using concepts from machine learning and ecology, implemented in R/Bioconductor.

Contributed to the development of MaAsLin2, an R/Bioconductor package for associating microbial multi-omics data with arbitrarily complex clinical metadata using linear models.

Contributed to grant writing, manuscript preparation, interdisciplinary collaborations, and teaching and mentoring of graduate and undergraduate students and trainees.

 
 
 
 
 

Intern, Biostatistics

Mayo Clinic

May 2015 – Aug 2015 Rochester, MN
Developed Bayesian adaptive trial designs for clinical trials utilizing surrogate endpoints in the presence of biomarkers.
 
 
 
 
 

Intern, Biostatistics

Novartis Pharmaceuticals Corporation

May 2014 – Aug 2014 East Hanover, NJ
Developed novel Bayesian methods to conduct heterogeneity of treatment effect (HTE) analyses in phase III clinical trials.
 
 
 
 
 

Intern, Biostatistics

University of Arkansas for Medical Sciences

Jun 2013 – Aug 2013 Little Rock, AR
Developed methods for detecting maternal-fetal gene-gene interactions associated with obstructive heart defects in newborns from mother-offspring paired genetic data.
 
 
 
 
 

Research Assistant, Biostatistics

University of Alabama at Birmingham

Aug 2010 – Apr 2015 Birmingham, AL

Developed Bayesian machine learning methods for high-dimensional feature selection in personalized medicine applications.

Developed and validated risk prediction models for assessing short-term mortality in obese adults.

Conducted high-dimensional predictive modeling of zero-inflated count phenotypes to identify genetic susceptibility markers in Rheumatoid Arthritis patients.

 
 
 
 
 

Intern, Biostatistics

Indian Statistical Institute

May 2008 – Jul 2008 Kolkata, India
Performed research on non-linear statistical modeling of cross-sectional growth curve data by the Preece-Baines growth model.

Recent Posts

Automatically update citation metrics in your CV with a bare minimum script - Part II

Following my previous post (Part I), I received a few requests from my fellow mathematical and physical scientist colleagues who …

Automatically update citation metrics in your CV with a bare minimum script - Part I

The inspiration behind this post comes from my non-computational scientist colleagues who simply wanted to import Google Scholar …

Contact

  • 126 East Lincoln Avenue, Rahway, NJ 07065