Jerome Niyirora, Ph.D.
Director · Associate Professor · SUNY Poly · Health Informatics Program
Research Areas: Systems science, data science, and artificial intelligence.
Health and Bioinformatics Lab Data. Intelligence. Discovery.
HB Lab develops computational and AI-driven methods for understanding complex biomedical problems. Our work combines data science, artificial intelligence, health informatics, multi-omics, digital twins, and scientific computing to transform complex data into actionable knowledge.
01
Interdisciplinary
Biomedical science, health, computing, and engineering
02
AI-Enabled
Machine learning, generative AI, and intelligent agents
03
Data-Driven
Multi-omics, clinical, sensor, and scientific data
04
Translational
Methods designed to move from analysis to impact
Research Areas
Research at the intersection of data, AI, and domain science.
HB Lab works across disciplines to develop computational approaches for complex problems spanning biomedicine, data science, and engineering.
Our work integrates biological, clinical, and scientific data with computational methods to understand complex systems and support new approaches to discovery.
01 | BIOMEDICAL DATA SCIENCE
Biomedical Data Science & Multi-Omics
We develop computational approaches for extracting biological insight from complex multi-omics data, with applications in disease biology, biomarker discovery, RNA regulation, and precision health.
02 | HEALTH INTELLIGENCE
Health AI & Clinical Informatics
We build intelligent systems that connect health data to better analysis and decision-making, including clinical AI, interoperable health records, decision support, foundation models, and health AI agents.
03 | GENERATIVE MODELING
AI, Digital Twins & Synthetic Data
We investigate generative and physics-informed models that learn realistic representations of complex systems and generate high-fidelity synthetic data as complements or alternatives to costly data collection and simulation.
04 | RESEARCH INFRASTRUCTURE
Scientific Data Engineering & Automation
We build and automate computational workflows that collect, integrate, model, and analyze health, biomedical, and scientific data—helping researchers ask better questions and move faster from data to discovery.
Our Approach
From data to discovery.
01 | Collect
Complex Data
Multi-omics, clinical, sensor, imaging, spatial, and scientific datasets.
02 | Integrate
Connected Information
Harmonization, interoperability, multimodal integration, and knowledge representation.
03 | Model
Intelligent Methods
Statistics, machine learning, generative AI, simulation, and scientific computing.
04 | Translate
Actionable Knowledge
Methods and evidence that support scientific, engineering, and clinical discovery.
Selected Directions
Selected directions at HB Lab.
Computational Genomics
Understanding RNA Regulation Through Computational Transcriptomics
Integrating gene expression, alternative splicing, transcript structure, and isoform-level analysis to investigate how RNA regulation alters cellular function.
Scientific Machine Learning
Physics-Informed Synthetic Data for Complex Environments
Developing generative models that reproduce complex physical observations and can reduce dependence on computationally expensive simulation.
Clinical Informatics
Private and Intelligent Clinical AI
Exploring local AI systems that connect clinical data, interoperable health records, domain models, and intelligent agents while supporting privacy and human oversight.
Interdisciplinary by Design
The most interesting problems rarely belong to one discipline.
HB Lab brings together domain knowledge, quantitative methods, and artificial intelligence. This allows the same computational foundations to support biomedical discovery, healthcare applications, and engineered systems.
Biomedical Sciences
Scientific expertise provides the foundation for addressing complex biological and health questions.
Data & Computing
Scientific computing and data engineering organize, integrate, and analyze complex datasets.
Artificial Intelligence
AI and machine learning provide new tools for modeling, prediction, discovery, and decision-making.
People
A collaborative research team.
HB Lab brings together faculty, collaborators, postdoctoral researchers, and graduate students with complementary expertise across health informatics, bioinformatics, data science, biomedicine, and artificial intelligence.
Lab Leadership
Amir Manzourolajdad, Ph.D.
Co-Director · Assistant Professor · SUNY Poly · Computer Science Program
Research Areas: Bioinformatics, data science, and artificial intelligence.
Faculty & Collaborators
William Thistleton, Ph.D.
Associate Professor · SUNY Poly · Mathematics Program
Research Areas: Mathematics, data science, and artificial intelligence.
Nicolau DePaula, Ph.D.
Assistant Professor · SUNY Poly · Health Informatics Program
Research Areas: Information management, health data, and AI policy.
Winnie Mkandawire, Ph.D.
Postdoctoral Associate · SUNY Poly · College of Health Sciences
Research Areas: Biomedicine, computational biology, and artificial intelligence.
Joanne Joseph, Ph.D.
Professor · SUNY Poly · Community Behavioral Health Program
Research Areas: Trauma, resilience, and humanitarian engineering.
Students & Alumni
Tammy So, MS, RN
Graduate Student · SUNY Poly · Health Informatics Program
John Nunez, MS
Graduate Student · SUNY Poly · Health Informatics Program
Carlie Lubin, MS, MPH
Graduate Student · SUNY Poly · Health Informatics Program
Lisha Hassan Vasudev, MS
Graduate Student · SUNY Poly · Data Science and Analytics Program
