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Rohit Tripathy

Postdoctoral Researcher

Cold Spring Harbor Laboratory

About Me

I am currently a postdoctoral researcher in the Koo Lab at Cold Spring Harbor Laboratory, focussing on interpretable machine learning for genomics. Previously, I was a PhD candidate at the Predictive Science Lab at Purdue. My academic and research interests include scientific machine learning, uncertainty quantification, dynamical systems, Bayesian inference and more. In my spare time, I like to read, go for a run, watch football and listen to metal.

Interests

  • Scientific Machine Learning
  • Uncertainty Quantification
  • Probabilistic Numerics
  • Computational Science

Education

  • PhD in Mechanical Engineering, 2020

    Purdue University

  • MS in Mechanical Engineering, 2015

    Purdue University

  • B.Tech. in Mechanical Engineering, 2014

    VIT University

Research

Under Construction

Conference Talks

Gradient-free active subspace recovery using deep neural networks

A problem of considerable importance within the field of uncertainty quantification (UQ) is the development of efficient methods for …

Deep neural networks for multifidelity uncertainty quantification

A common scenario in computational science is the availability of a suite of simulators solving the same physical problem, such that …

Learning nonlinear correlations between multifidelity models using deep neural networks

A typical scenario in computational science is the availability of a suite of simulators the solve the same physical problem at varying …

Solving multiscale stochastic partial differential equations using deep neural networks

The applicability of traditional methods for stochastic partial differential equations (SPDEs) including Monte Carlo (MC) methods (and …

Probablistic active subspaces

Gaussian process regression (GPR) is an immensely popular choice for computational scientists as a surrogate model for various …

Work Experience

 
 
 
 
 

Postdoctoral Researcher

Cold Spring Harbor Laboratory

Jun 2020 – Present Cold Spring Harbor, NY
Deep learning for genomics.
 
 
 
 
 

Quantitative Research - Machine Learning Summer Associate

JPMorgan Chase & Co.

May 2019 – Aug 2019 New York City, NY
Summer intern in the Spread Electronic Market Making Quantitative Research team working on a machine learning model for credit spread of investment grade corporate bonds.
 
 
 
 
 

Quantitative Research - Machine Learning Summer Associate

JPMorgan Chase & Co.

May 2018 – Aug 2018 New York City, NY
Summer intern in the Commodities Quantitative Research team working on a deep neural network approach to pricing bivariate lognormal spread options.
 
 
 
 
 

Givens Associate

Argonne National Laboratory

May 2017 – Aug 2017 Lemont, IL
Summer intern in the Mathematics & Computer Science division working on wind speed forecasting with recurrent neural networks.

Recent Posts

Contact

  • 765 476 6988
  • 585 Purdue Mall, West Lafayette, IN 47906
  • Room 3139 in the Mechanical Engineering Building
  • DM Me