Dr. Rashid Saadman Karim

Dr. Rashid Saadman Karim is a PhD scientist specializing in computational biology, bioinformatics, and machine learning, with nearly 10 years of experience applying artificial intelligence and advanced computational methods to biological and biomedical research. His work focuses on applying machine learning to genomics, transcriptomics, drug discovery, and disease research, using computational approaches to analyze complex biological datasets, identify biomarkers, and understand disease mechanisms.

A major emphasis of Rashid's research is applying machine learning and deep learning to computational biology. His research has involved developing graph neural networks to predict drug mechanisms of action using gene-expression data, alongside applying predictive modeling, neural networks, and other machine learning techniques to genomic and transcriptomic datasets. He has extensive experience working with single-cell RNA sequencing, proteomics, multi-omics analysis, and computational genomics.

Rashid has conducted research at leading pharmaceutical and biotechnology organizations, including Novartis Biomedical Research, Rancho Biosciences, and Gilead Sciences. His work has included developing machine learning models for Alzheimer's and Parkinson's disease classification, identifying genes associated with drug toxicity, analyzing cancer-related molecular pathways, and developing AI-driven approaches for mRNA vaccine optimization. He has also contributed to published research in Nature Communications and Communications Biology.

As a research mentor, Rashid can guide students through developing original computational biology and machine learning research projects, from identifying meaningful biological questions and selecting publicly available datasets to designing computational workflows, building predictive models, analyzing results, and preparing research papers. His expertise in Python, R, PyTorch, TensorFlow, and bioinformatics tools makes him particularly well suited to students pursuing dry-lab research combining machine learning with biological and medical applications.

Rashid is especially well suited to students interested in machine learning applications in biology, computational biology, bioinformatics, genomics, cancer research, neuroscience, drug discovery, and AI-driven biomedical research. His combination of machine learning expertise and extensive biological research experience makes him a strong mentor for students seeking to apply AI and computational methods to meaningful biological and medical problems.