Komlan Atitey
Computational Biologist | Bioinformatics, Machine Learning & AI for Precision Medicine
About
Computational biologist with 7+ years of experience building Bayesian statistical models, machine learning pipelines, and large language model (LLM)-based tools to decode single-cell and spatial multi-omics data. Author of 14 peer-reviewed publications and developer of 7 open-source computational tools with 13,000+ combined downloads, applied to cancer driver-gene discovery, immune-response modeling, and drug/immunotherapy development. Proven track record leading multi-omics research programs at NIH and academic medical centers, mentoring graduate researchers, and securing competitive fellowships and scholarships.
Seeking a senior computational biology, bioinformatics, or AI/data science role in biotechnology, pharma, or research institutions.
Experience
Senior Research Associate - Case Western Reserve University School of Medicine, Department of Neurosciences - Cleveland, OH
(2026-03)
- Lead the design, analysis, and interpretation of multi-omics, single-cell, and spatial datasets, delivering statistical expertise and building AI, machine learning, and LLM-based models to accelerate biomedical discovery.
- Direct bioinformatics and AI workflow development and deliver hands-on training in computational biology, machine learning, and LLM applications to students, postdoctoral fellows, and research staff.
Research Fellow - Biostatistics and Computational Biology Branch (BCBB), National Institute of Environmental Health Sciences (NIEHS), NIH - Research Triangle Park, NC
(2020-11 - 2026-02)
- Developed and applied hybrid statistical and Bayesian models including ODEs, agent-based modeling, and network inference to characterize immune dynamics, gene-gene interactions, and cancer driver identification within heterogeneous tumor microenvironments.
- Designed and implemented computational frameworks integrating spatial, temporal, and multimodal single-cell data using Bayesian inference, Markov modeling, and high-dimensional visualization techniques.
- Directed statistical integration and optimization strategies that improved interpretation and discovery from large-scale single-cell and spatial biomedical datasets.
- Built and released 7 open-source computational tools (MIBCOVIS, DEGBOE, MMQE, GIBOOST, magicSubclonal, CancerTrace, PICDGI), collectively downloaded 13,000+ times by the research community.
Postdoctoral Research Associate - Department of Biochemistry, University of Nebraska-Lincoln - Lincoln, NE
(2019-10 - 2020-10)
- Developed a mechanism-based, multiscale, multicellular model of the immune system incorporating ordinary differential equation (ODE) modeling to capture dynamic biological processes.
- Integrated experimental data with computational models, combining ODE and agent-based modeling to simulate immune responses and disease progression.
Doctoral Researcher - College of Engineering, Swansea University - Swansea, United Kingdom
(2016-10 - 2019-10)
- Conducted statistical analysis of gene expression dynamics in biological systems using Bayesian inference methods.
- Modeled mRNA and protein distributions using stochastic processes combined with Bayesian modeling and posterior sampling techniques.
- Quantified uncertainty in biochemical reaction rates and gene regulation via histogram fitting, Bayesian inference, stochastic modeling, and goodness-of-fit tests to infer gamma-distributed molecule counts.
Education
Ph.D.
- Electrical Engineering: Specialization in Computational Biology - Swansea University (2016-10 - 2019-10)
M.Eng.
- Information and Communication Engineering - Harbin Engineering University (2013-09 - 2015-12)
M.Sc.
- Solid State Physics - University of Lomé (2006-10 - 2007-11)
B.Sc.
- Physics - University of Lomé (2003-10 - 2006-11)
