Sarangan Ravichandran
Health Data Innovation Advisor | AI/ML | RWD/RWE | PMP-Certified
Sarangan Ravichandran
Health Data Innovation Advisor | AI/ML | RWD/RWE | PMP-Certified
Advisor on data innovation for health and biomedical programs across NIH, FDA, CDC, and ARPA-H
23+ years of experience in data science, AI/ML, and biomedical research with a focus on public health impact
Specialized in applying LLMs and AI/ML to unstructured data (EHRs, publications, free text) for insight generation
PMP-certified project manager with experience leading federally funded, cross-functional data programs
Proven expertise in real-world data (RWD), real-world evidence (RWE), NLP, and health data architecture
Adjunct faculty at Hood College, teaching Bioinformatics and Functional Genomics and mentoring 300+ scientists
Published 50+ peer-reviewed articles and presented at major scientific and federal forums (FDA SCD, CDC Summit)
Recognized for aligning scientific innovation with policy, stakeholder goals, and implementation readiness
This website showcases my experience, areas of expertise, select projects, and how I contribute to innovation in health, science, and technology.
I’m a data science and biomedical innovation advisor with over 23 years of experience working at the intersection of healthcare, public health, and life sciences. My focus is on using AI/ML and large language models (LLMs) to transform complex, unstructured data, such as electronic health records, scientific text, and population datasets, into actionable insights that support research, strategy, and policy.
As an advisor, I’ve contributed to data-driven initiatives across agencies like NIH, FDA, CDC, and ARPA-H. I help organizations evaluate technologies, guide program direction, and align innovation with mission impact—often bridging technical teams, domain experts, and stakeholders.
In addition to my federal and consulting work, I serve as an adjunct faculty member at Hood College, teaching and mentoring students in Bioinformatics and Functional Genomics. I’m passionate about responsible innovation, cross-sector collaboration, and helping teams use data in ways that are both scientifically sound and practically valuable.
You can explore more about my work, projects, and publications across this site.
My expertise encompasses:
Computational Biochemistry/Informatics: I leverage my knowledge in both domains to effectively manage structured/unstructured data. I write codes (e.g., Python and R) to extract valuable insights, build robust models (ideas captured in several of my publications/presentations), and automate data processing tasks.
AI/ML & Large Language Models (LLMs): I utilize these powerful techniques to automate processes, communicate insights effectively (demonstrated through my published work and presentations), and ensure data interoperability and standardization, fostering sustained collaboration across teams.
Beyond my core skills in:
Machine Learning
Artificial Intelligence
Bioinformatics
Data Modeling
Data Mining
Data Visualization
My areas of interest encompass:
Machine Learning for Healthcare: I'm passionate about developing machine learning models to extract valuable insights from healthcare data. These insights can then be used to improve healthcare outcomes and personalize treatment approaches.
Real-World Data/Real-World-Evidence (RWD/RWE) for Healthcare: Building effective models relies on high-quality data. I'm particularly interested in creating high-quality healthcare RWD (as defined by the FDA) using multimodal approaches. This involves integrating data from various sources, including electronic health records, patient surveys, X-ray images, and wearable devices.
Semantic Modeling in Healthcare: Accurately extracting meaning from both structured and unstructured data is crucial for gaining deeper insights. I'm fascinated by the potential of semantic modeling techniques, such as Natural Language Processing (NLP) and Large Language Models (LLMs), to unlock valuable information from text-based and numerical data in the healthcare domain.
Over 100 licenses and online Certifications from Coursera, Udemy, Edx, Stanford OpenCourseware, including:
AWS Cloud Practitioner (since 2022)
Project Management Professional (PMP; since 2018)
Several Udacity Nanodegrees
With proficiency in Python and R programming languages, as evidenced by my GitHub link, I am passionate about utilizing R/RStudio for biocomputing and teaching applications. I also have hands-on experience with cloud computing platforms such as AWS/Google/Azure, where I have used SQL to extract data from databases and applied ETL and ELT (E:Extract, T:Transform, and L:Load) processes to generate and utilize Data Warehouses. Additionally, I have expertise in Anaconda and Google Colab environments for efficient Python/R development and data analysis, with Github integration for version control and collaboration. Over the last two decades, I have gained extensive experience working in the Linux OS environment, specializing in compiling, installing, and testing codes within a Linux compute cluster environment. Moreover, I have skills in semantic modeling and LLM.
Life-long learning (Completed more than 100 online classes); Please check my LinkedIn for details, https://www.linkedin.com/in/sakaravi/details/certifications/
Scientific writing/communication
Machine learning approach to examine the intersectional association of social identities and circumstance with current cigarette smoking among US adults, K. Choi, Wheeler, S. Ravichandran, and D. Buckman ; https://pubmed.ncbi.nlm.nih.gov/40795918/ (2025)
FDA Insights: Simple interactive access to FDA information for public, industry, clinical research Using customized LLMs and RAG, FDA Scientific Computing and Digital Transformation Symposium (2024), Chetan Paul, S. Ravichandran and R. Venkataraman. Poster Link: https://www.fda.gov/media/182811/download (Won the Top-5 Poster Award and was selected to present our work at the upcoming FDA SCB Meeting in December 2024)
Enhancing Public Health Data Quality: CDC Health Data Innovation Summit, CDC, Sep 27-28, 2023, S.Ravichandran and Chetan Paul, Event Event Link: https://www.fbcinc.com/e/cdcga/speakers.aspx
Abstract Link: https://www.fbcinc.com/e/cdcga/speakers.aspx
Scientific Review Alignment and Knowledge Gap Analysis in Data Multiverse, K.Y. Stephen Ho, V. Sam, J. Shah, L. Benson, Ning Yu, S. Ravichandran, Chetan Paul, Sep 12-12 (2023)
YouTube link: https://www.youtube.com/watch?v=_pUwQwjpL0c
Making Sense of Electronic Health Record (EHR) Race and Ethnicity Challenge, Top Performer Webinar and Roundtable discussion, Precision FDA, S. Ravichandran; participated and represented the team (Aug 2023)
Challenge Link: https://precision.fda.gov/challenges/30
Prognostic and Predictive Classification Approaches, NIH Long-Covid Computational Challenge, Bethesda, MD, S. Ravichandran (team lead) 2023.
Simulating in-silico Clinical Research Using Diverse Real-World Data, S. Ravichandran and Chetan Paul, 2022 Scientific Computing Days, FDA, Poster Presentation, 8/19 2022 (selected in the poster contest for live presentation;
Poster Link: https://www.fda.gov/drugs/news-events-human-drugs/2022-scientific-computing-days-poster-gallery )
Simulating in-silico Clinical Research Using Diverse Real-World Data, S. Ravichandran and Chetan Paul, 2022 Scientific Computing Days, FDA, Virtual Presentation, Sep 7-8, 2022 (selected among top-5 in the poster contest for live presentation)
FDA Link: https://www.fda.gov/drugs/news-events-human-drugs/2022-scientific-computing-days-09072022#event-materials )
AVIA 3.0: interactive portal for genomic variant and sample level analysis, Bioinformatics, 2021 Aug 25;37(16):2467-2469;
PubMed Link: https://pubmed.ncbi.nlm.nih.gov/33289511/
S. Ravichandran, PhD, PMP
Email: saka dot ravi at gmail dot com web: https://sites.google.com/site/sakaravi/