Rupam Bhattacharyya

Research Profile

Tue, 04 Aug 2026 00:00:00 +0000

With my core training in Statistics and Biostatistics, my research focuses on the development of robust statistical methodology, with applications spanning precision oncology, high-dimensional multi-omic integration, and public health modeling.

For a complete bibliography, please refer to my Google Scholar profile, ResearchGate profile, or my CV.

💡 Note: Publications highlighted in teal represent first-authored works.

Academic Publications

Filter by research topic (Methods selected by default). Publications are numbered chronologically, matching my CV.

Software & Dashboards

Tue, 04 Aug 2026 00:00:00 +0000

I design and build interactive web applications and dashboards to make complex statistical models, research datasets, and scientific findings accessible to researchers, policy-makers, and the general public.

TransPRECISE

Interactive platform for modeling pan-cancer proteomic pathway networks and mapping drug sensitivity across multiple cell line databases.

Launch Dashboard →

fiBAG

Interactive dashboard for functional integrative Bayesian analysis of proteogenomic data in cancer studies.

Launch Dashboard →

BaySyn

Bayesian evidence synthesis portal designed for multi-system multiomic integration and drug response modeling in oncology.

My First Post

Mon, 03 Aug 2026 23:02:33 +0000

This is my very first post on my new Hugo site!

I migrated this from RMarkdown. Now I can edit it from anywhere!

About Me

Mon, 03 Aug 2026 00:00:00 +0000

Hi! I am Rupam Bhattacharyya, a Data Scientist III at Google (YouTube Ads).

I am a methodology-first quantitative researcher specializing in bridging advanced statistical theory with high-scale computational engineering. Over the past eight years, I have developed and deployed robust probabilistic models, predictive machine learning pipelines, and simulation frameworks across diverse fields - from cancer genomics to platform-scale ads economics.

🛠️ Core Expertise

Applied Statistics: Bayesian Hierarchical Modeling, Causal Inference, Spatial Network Analysis, High-Dimensional Data Fusion.
Machine Learning: Robust Outlier Management, Multi-Agent Simulation, Predictive Modeling.
Data Engineering: Petabyte-scale database querying, distributed schema design, automated pipeline monitoring.

💼 Professional Journey

Data Scientist III (Research) | Google (YouTube Ads) | 2024 - Present
- Focusing on ad auction theory, causal revenue estimation, and optimization systems (such as tCPM bidding recommendations and conversion forecasting) utilizing Google-scale data systems.
Post-doctoral Biostatistician | Michigan Center for Translational Pathology | 2023 - 2024
- Led by Prof. Arul Chinnaiyan, focusing on multi-platform genomics integration and cancer therapeutics.
PhD in Biostatistics | Department of Biostatistics, University of Michigan | 2018 - 2023
- Supervised jointly by Prof. Veerabhadran Baladandayuthapani and Prof. Nicholas Henderson.

🎓 Education & Roots

M.Stat. & B.Stat. in Statistics | Indian Statistical Institute, Kolkata
Roots: Born in Krishnanagar, West Bengal, India. Raised in Shantipur, where I did my schooling from Shishu Kakali Primary School and Santipur Municipal High School.

To know more about my work, check out the other sections of this website!

Academic Pursuits

Mon, 03 Aug 2026 00:00:00 +0000

Here is an overview of my academic career and teaching activities. For more details, please take a look at my CV.

Education

PhD in Biostatistics, University of Michigan, 2018 - 2023.
- Dissertation: Bayesian Models for Multi-omic Multi-system Integration for Precision Oncology.
Master of Statistics, Indian Statistical Institute, Kolkata, 2016 - 2018.
- Specialization: Applied Statistics, Biostatistics track.
Bachelor of Statistics (Hons.), Indian Statistical Institute, Kolkata, 2013 - 2016.

Teaching

Lecturer at the Big Data Summer Institute.
- Introduction to Linear Algebra, 2023.
- Introduction to Linear Algebra, 2022.
- Introduction to Probability, 2021.
Grader and project reviewer in departmental courses.
- Biostat 651: Theory and Application of Generalized Linear Models, Winter 2023.
- Biostat 651: Theory and Application of Generalized Linear Models, Winter 2021.
- Biostat 802: Advanced Inference II, Winter 2020.

Beyond the Numbers

Mon, 03 Aug 2026 00:00:00 +0000

Outside of my main research and data science work, I am passionate about popular science communication, media engagement, and creative pursuits like theatre and music.

Science & Media

I strive to make scientific concepts and data-driven insights accessible to the general public through tutorials, popular science writing, and media features.

📺 YouTube Tutorials

Available on the BayesRx YouTube channel.

📰 Media Interactions

Newstalk (Ireland): Discussion on lockdown dynamics (Listen here).
The World: Feature on the impact of lockdown on India's poorest populations (Read here).
The Guardian: Coverage of the early national lockdowns (Read here).
NDTV India: Television broadcast feature (Watch video).
The Wire (India): Feature on state-level data trends (Read here).

Creative & Hobbies

I believe in maintaining a balanced lifestyle and exploring creative outlets that engage different parts of my brain. Click below to expand each activity.