Skills

Languages, systems, and tools

Proficient: Python, Tensorflow, High Performance Computing, UNIX
Competent: R, LaTeX
Past work experience: Perl, MATLAB, Java, Android, C#, .NET Framework, C++, Oracle, SQL

Relevant graduate coursework

Machine Learning, Bayesian Statistics, Mathematical Statistics, Deep Learning, Advanced Computational Biology, RNA World, High-throughput Datasets for Biologists, Interpretation of Deep Learning Models, Adversarial and Secure Machine Learning, Computational Linguistics.

Research and Professional Experience

CIS Ph.D. Student

Dr. Yoseph Barash, Biociphers Lab, University of Pennsylvania
September 2014–present

My research projects have involved predicting splicing differences between tissues and regulatory networks between RNA-binding proteins with deep learning and developing EIG, an interpretation method for splicing code and other deep learning models based on genomic data. In parallel, I have contributed to research towards reliable identification and quantification of splicing events from RNA-Seq data and understanding the role of RNA-binding proteins in post-transcriptional regulation.

Master Thesis Student

Dr. Peter Struss, Model-Based Systems and Qualitative Reasoning Group, TU Munich
November 2013–August 2014 (10 months)

Conceptualization and implementation of a generic tool for selection of learning goals for a knowledge-based machine learning system, application of the tool for the fitness training domain.

Graduate Research Assistant

Dr. Florian Röhrbein, Human Brain Project, Neurorobotics, TU Munich
November 2013–March 2014 (5 months)

Surveyed on the availability of robot simulation tools and game engines for the neurorobotics platform of the Human Brain Project.

Graduate Research Assistant

Christian Vögele, fortiss GmbH, Munich
October 2013–April 2014 (7 months)

Project: PARO-Performance analysis for role-based applications. Created a tool for the performance modeling of an application and analysis of potential low performance areas during the design phase.

Graduate Research Assistant

Dr. Tobias Hamp, Rostlab, TU Munich
March 2013–September 2013 (7 months)

Project: Prediction of Interaction sites in Proteins using only sequences. Developed neural network models for prediction of interaction hot spots in proteins using amino acid sequences and evolutionary information.

Systems Engineer

Infosys Limited, India and TomTom Int. BV., Netherlands
June 2010-September 2012 (2 years 3 months)

Responsibilities included development of UI and back end for GPS enabled embedded navigation device with Android for TomTom Int. BV. Trainings Undertaken at Infosys: Android, ASP.NET, ADO.NET, C#, C, UNIX, Oracle, SQL Certifications at Infosys: STAR, Geographical Information Systems, PERL

Selected Publications

In progress

RBP-Pokedex: Prediction of RBP knockdown effect via DNN experiment modeling
Anupama Jha, Matthew R Gazzara, Caleb Radens, Paul Jewell, Yoseph Barash
In preparation, 2020
[talk| website]

MOCCASIN: A method for correcting for known and unknown confounders in RNA splicing analysis
Barry Slaff, Caleb Matthew Radens, Paul Jewell, Anupama Jha, Nicholas Lahens, Gregory R Grant, Andrei Thomas-Tikhonenko, Kristen W Lynch, Yoseph Barash
bioRxiv, 2020
[paper]

Multi-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits
William P. Bone, Katherine M. Siewert, Anupama Jha, Derek Klarin, Scott M. Damrauer, the VA Million Veteran Project, Kyong-Mi Chang, Philip S. Tsao, Themistocles L. Assimes, Marylyn D. Ritchie, Benjamin F. Voight
medRxiv, 2020
[paper]

RNA binding proteins PCBP1 and PCBP2 are critical determinants of murine erythropoiesis
Xinjun Ji, Anupama Jha, Jesse Humenik, Louis R. Ghanem, Andrew Kromer , Christopher Duncan-Lewis, Elizabeth Traxler, Mitchell J. Weiss, Yoseph Barash, Stephen A, Liebhaber
In submission, 2020

Published

Enhanced Integrated Gradients: improving interpretability of deep learning models using splicing codes as a case study
Anupama Jha, Joseph K Aicher, Matthew R Gazzara, Deependra Singh, Yoseph Barash
Genome Biology, 2020
[paper| code]

Ancient antagonism between CELF and RBFOX families tunes mRNA splicing outcomes
Matthew R Gazzara, Michael J Mallory, Renat Roytenberg, John P Lindberg, Anupama Jha, Kristen W Lynch, Yoseph Barash
Genome Research, 2017
[paper]

Integrative deep models for alternative splicing
Anupama Jha, Matthew R Gazzara, Yoseph Barash
Bioinformatics, 2017
[paper| code]

Talks

RBP-Pokedex: Prediction of RBP knockdown effect via DNN experiment modeling
ISMB 2020, Virtual Conference

Improving interpretation of deep learning models: splicing codes as a case study
ISMB/ECCB 2019, Basel, Switzerland
SAGES 2019, Philadelphia, USA

Integrative Deep Models for Alternative Splicing
ISMB/ECCB 2017, Prague, Czech Republic
Penn Research in Machine Learning (PRiML) Group, October 2017, Philadelphia, USA
RNA Discussion Group, April 2017, Philadelphia, USA

GCB 537 Guest Lecture: Support Vector Machine
April 2016-2019, Upenn, Philadelphia, USA

Panel: Is Graduate School for me?
CAPWIC 2019, Harrisonburg, USA

Guest Lecture with Dr. Yoseph Barash: AI and Computational Biology
WICS High School Day for Girls, Spring 2018-2019, Philadelphia, USA

Posters

Assembling the Building Blocks for a Unifed Splicing Code
Anupama Jha, Matthew R. Gazzara, Yoseph Barash
ISMB 2018

Deep Learning Models for Alternative Splicing
Anupama Jha, Matthew R. Gazzara, Yoseph Barash
SAGES 2017
Best Poster Award

Integrative Deep Models for Alternative Splicing
Anupama Jha, Matthew R. Gazzara, Yoseph Barash
RNA Biology & Cancer Symposium 2017
Best Poster Award

Predictive modeling framework for splice factor knockdown experiments.
Anupama Jha, Matthew R. Gazzara, Jorge Vaquero-Garcia, Yoseph Barash
ISMB 2016

Awards

Selected student abstract for oral presentation, SAGES 2019
GHC 18 Scholarship, Grace Hopper Celebration 2018
Travel Fellowship, ISMB/ECCB 2017
Best Poster Award, SAGES 2017
Best Poster Award, RNA Biology & Cancer Symposium 2017

Teaching

GCB 537: Advanced Computational Biology

TA with Dr. Yoseph Barash, UPenn
Spring 2016, 2017, 2018 (3 terms)

Teaching Assistant for the Ph.D. level course with three components: statistical data analysis and machine learning techniques for computational biology, discussion on current topics in genomics and computational biology, and hands on experience in data analysis, coding and evaluation of computational biology tools/algorithms.

Deep Learning Reading Group

Co-organizer with Dr. Yoseph Barash, UPenn
Summer 2016, Spring 2018 (2 terms)

Co-organizer of reading groups to cover deep learning book and interpretation methods for deep learning models.

Service and Outreach

Reviewer

Co-reviewer with Prof. Yoseph Barash for NeurIPS 2015-2020, ISMB/ECCB 2016-2019, ICLR 2018-2020, ICML 2019-2020, PLOS Computational Biology Journal, Bioinformatics Journal, Nucleic Acids Research Journal. Independent reviewer for TEAMC-2018, Nature Scientific Reports

Project Mentor

Mentored undergraduate students at University of Pennsylvania in their final year project in Fall 2017 and Spring 2018.