An interdisciplinary group spanning machine learning, statistics, engineering, and biology.
Md Tauhidul Islam develops novel AI techniques for biomedical applications, with a focus on translating biomedical data-science research into benefits for clinical practice and biological investigation. His work applies large foundation models and neural networks to high-dimensional medical data — genomics, radiomics, proteomics, and images across modalities — to advance patient care. He earned his PhD at Texas A&M University and trained as a postdoctoral scholar at Stanford with Dr. Lei Xing.
Machine learning, deep learning, and integrative omics. Builds AI-driven tools for multi-omics integration with applications in cancer diagnosis and precision medicine. Previously a postdoctoral associate at the National Research Council of Canada; PhD, University of Saskatchewan.
Develops robust, interpretable statistical machine-learning methods for complex biomedical data — with applications in early cancer diagnostics through cfDNA liquid biopsy, spatial transcriptomics, and imaging-based biomarker discovery. PhD in Biostatistics, UC Berkeley. Co-mentored with Dr. Mohammad Esfahani.
Deep learning, multimodal learning, and foundation models for healthcare, with an emphasis on real-world data challenges and broad impact across industry and academia. Published at ICML, AAAI, KDD, Nature Communications, and Medical Image Analysis; interned at Google leading foundation-model and benchmark development, and selected for the Meta PhD Forum. PhD in Computer Science, University of Kentucky; BSc, Rajshahi University of Engineering & Technology.
AI and biomedical signal processing — cardiac monitoring, disease detection, respiratory analysis, and silent speech recognition. Focuses on multimodal biomedical reasoning and interpretable representations that connect physiological data, biological networks, and clinical knowledge, toward transparent, dependable AI for clinical research. PhD in Computer Science, Texas Tech University; BSc, Bangladesh University of Engineering and Technology.
PhD student in Electrical Engineering, advised by Prof. Ehsan Adeli and Prof. Md Tauhidul Islam. Works on large-scale, clinically transformative brain models to advance our understanding of the human brain through AI. BS, Bilkent University; Fulbright Scholar.
Applies graph neural networks that integrate biological-pathway structure with multi-omics cancer data to better understand the interactions driving tumor behavior. BS/MS in Bioengineering, UCLA, with prior research spanning neurodegeneration, nephrotoxicity modeling, and drug-screening platforms.