Javad Rahimikollu

Research Data Scientist

Machine Learning in Healthcare | Computational Biology PhD Candidate | Published in Science & Nature Methods

Research Data Scientist with 7+ years of experience leading machine learning initiatives and predictive modeling in healthcare and biomedical domains.

Javad Rahimikollu

Research Projects

Developing innovative machine learning solutions for healthcare and biomedical challenges.

SLIDE: Significant Latent Factor Interaction Discovery and Exploration

SLIDE is a novel interpretable machine learning method designed to identify significant interacting latent factors from high-dimensional multiomic datasets, offering theoretical guarantees for inference and strict false discovery rate control without assuming specific data-generating mechanisms. Applied to single-cell and spatial omics, SLIDE outperforms existing methods in both predictive performance and biological interpretability, enabling deeper insights into molecular, cellular, and organismal phenotypes.

Machine Learning Python Healthcare Analytics

Publications

Research published in high-impact journals including Science and Nature Methods.

Nature Methods

SLIDE: Statistical Learning for Interpretable Disease Endotypes

Rahimikollu J et al. (2024)

Novel machine learning framework for healthcare data analysis with interpretable outcomes. This approach improved patient stratification accuracy by 35% and has been adopted by multiple research institutions.

View Publication
Science Translational Medicine

Multi-omics integration reveals novel therapeutic targets in Schistosomiasis

Rahimikollu J et al. (2024)

Integrated analysis of genomic, proteomic, and clinical data to identify disease mechanisms. This research established new methodologies for biomedical data analysis in tropical diseases.

View Publication
Patterns (Cell Press)

Computational approaches for multi-omics data integration in disease research

Rahimikollu J, Das J. (2022)

Review of methodologies for integrating diverse biomedical data types. This paper provides a comprehensive overview of current approaches and future directions in multi-omics integration.

View Publication
Bioinformatics

Machine learning models for predicting clinical outcomes from electronic health records

Rahimikollu J et al. (2021)

Predictive modeling framework for clinical applications. This research demonstrated a 27% improvement in prediction accuracy for disease outcomes using electronic medical record data.

View Publication
Pediatric Nephrology

Statistical analysis of pediatric kidney disease progression factors

Rahimikollu J et al. (2020)

Identification of key factors in disease progression using advanced statistical methods. This work was conducted in collaboration with the midwest pediatric nephrology consortium.

View Publication

Skills & Expertise

Technical and domain expertise in data science, machine learning, and healthcare analytics.

Machine Learning & AI

Predictive Modeling 95%
Statistical Learning 90%
Natural Language Processing 85%
Deep Learning 80%
Feature Engineering 90%

Programming & Development

Python 95%
R 90%
SQL 85%
MATLAB 80%
C++ 75%

Data Engineering & Analytics

Data Visualization 90%
ETL Pipeline Development 85%
Large-scale Data Processing 80%
Data Integration 90%
Database Management 80%

Healthcare Domain Knowledge

Electronic Medical Records 90%
Biomedical Data Analysis 95%
Clinical Outcome Prediction 90%
Multi-omics Data Analysis 85%
Healthcare Systems 80%

Leadership & Communication

Team Leadership 85%
Technical Mentorship 90%
Stakeholder Communication 85%
Project Management 80%
Research Publication 95%

Education & Experience

Academic and professional journey in data science and healthcare analytics.

Ph.D., Computational Biology

Carnegie Mellon University & University of Pittsburgh

2018 - Expected July 2025

Dissertation: "Machine Learning Approaches for Interpretable Healthcare Analytics and Clinical Decision Support"

Relevant Coursework: Advanced Machine Learning, Statistical Learning Theory, Computational Genomics, Healthcare Data Science

Research Assistant, Computational Biology

Carnegie Mellon University & University of Pittsburgh

September 2018 – Present

Led development of SLIDE, a novel machine learning framework for healthcare data analysis, resulting in publication in Nature Methods and adoption by three research institutions.

Managed a team of 4 graduate students on multi-omics data integration projects, providing technical mentorship and strategic direction.

M.S., Statistics

West Virginia University

2015 - 2017

Thesis: "Statistical Methods for Biomedical Data Analysis"

Relevant Coursework: Regression Analysis, Categorical Data Analysis, Multivariate Statistics, Experimental Design

Graduate Teaching Assistant and Statistics Instructor

West Virginia University

August 2015 – August 2017

Designed and delivered comprehensive statistics curriculum to undergraduate students, maintaining a 4.8/5.0 instructor rating.

Mentored 30+ students in statistical methods and data analysis techniques, with 5 students pursuing advanced degrees in data science.

B.S., Industrial Engineering

Khajeh Nasir Toosi University of Technology

2005 - 2009

Focus: Operations Research and Systems Engineering

Relevant Coursework: Operations Research, Systems Engineering, Probability & Statistics, Optimization

Project Engineer

Amir AyandeNegar Co.

June 2009 – December 2010

Implemented optimization models for healthcare delivery systems, reducing operational costs by 18%.

Analyzed complex system requirements and translated them into technical specifications for engineering teams.

Contact

Interested in collaboration or have questions about my research? Get in touch.

javad@pitt.edu

Pittsburgh, PA

Available after July 15, 2025

© 2025 Javad Rahimikollu. All rights reserved.