Headshot photo of Mohammad Adibuzzaman, Ph.D.<span class="profile__pronouns"> (he/him)</span>

Mohammad Adibuzzaman, Ph.D. (he/him)


Dr. Adibuzzaman is a computational scientist by training with extensive experience in model, method, and system development for large clinical data analysis and research infrastructure development with high-performance computing system. His motivation to work in healthcare arises from a deep desire to contribute in the human well being, live life happily and abundantly. Dr. Adibuzzaman wants to use technology, extremely large data sources, and new methods to complement the clinical expertise for the future of medicine.

Mohammad Adibuzzaman (Adib) is an Assistant Professor at the Department of Medical Informatics and Clinical Epidemiology and the Co-Director of the Informatics Program at the Oregon Clinical and Translational Research Institute (OCTRI) located at Oregon Health and Science University, Portland, Oregon. He was the Assistant Director of Data and Computing at the Regenstrief Center for Healthcare Engineering (RCHE) at Purdue University, Indiana. Before that, he was also a Research Scientist at RCHE for five years. He did his Ph.D. in Computational Sciences from Marquette University, Milwaukee, Wisconsin under the supervision of Dr. Stephen Merrill and Dr. Sheikh Iqbal Ahamed. His Ph.D. research focused on mathematical model development using eigenvalue-based methods such as eigenface for facial image classification, mixing rate or the second largest eigenvalue-based algorithm for hemorrhage detection from blood pressure, among others. Adib also worked at the US Food and Drug Administration (US FDA) under the supervision of Dr. David Strauss on applications of the mathematical models with clinical data and high performance computing system. Before his Ph.D., he worked as a Junior Research Fellow at the Human Computer Interaction Lab with Professor Shendong Zhao at the National University of Singapore (NUS). He also worked as a Software Engineer at the Structured Data Systems Ltd. (SDSL). Adib have an undergraduate degree in Computer Science and Engineering (CSE) from Bangladesh University of Engineering and Technology (BUET).

Education and training

    • Ph.D., 2015, Marquette University
    • MSc, 2012, Marquette University
    • B.Sc., 2008, Bangladesh University of Engineering and Technology (BUET)
  • Fellowship

    • Oak Ridge Institute of Science and Engineering Fellow (ORISE), US Food and Drug Administration (FDA), 2013-2014

Memberships and associations:

  • American Medical Informatics Association (AMIA)

Areas of interest

  • Causal Inference, Artificial Intelligence, Computational Systems, Informatics System to Support FAIR and CARE principle

Honors and awards

  • Oak Ridge Institute of Science and Engineering (ORISE) Fellow, US FDA, 2013-2014
  • Above and Beyond Award, Purdue University Executive Vice President for Research and Partnerships, 2018
  • Best Paper Award ACM RACS 2013
  • Best Paper Nomination, ACM CHI 2011


Selected publications

  • Adibuzzaman, M., DeLaurentis, P., Hill, J., & Benneyworth, B. D. (2017). Big data in healthcare–the promises, challenges and opportunities from a research perspective: A case study with a model database. In AMIA Annual Symposium Proceedings (Vol. 2017, p. 384). American Medical Informatics Association.
  • Patidar, Kavish R., Mobasshir A. Naved, Ananth Grama, Mohammad Adibuzzaman, Arzina Aziz Ali, James E. Slaven, Archita P. Desai et al. "Acute kidney disease is common and associated with poor outcomes in patients with cirrhosis and acute kidney injury." Journal of Hepatology 77, no. 1 (2022): 108-115.
  • Chen, Yao, Xiao Wang, Yonghan Jung, Vida Abedi, Ramin Zand, Marvi Bikak, and Mohammad Adibuzzaman. "Classification of short single-lead electrocardiograms (ECGs) for atrial fibrillation detection using piecewise linear spline and XGBoost." Physiological measurement 39, no. 10 (2018): 104006.
  • Adib, Riddhiman, Paul Griffin, Sheikh Iqbal Ahamed, and Mohammad Adibuzzaman. "A causally formulated hazard ratio estimation through backdoor adjustment on structural causal model." In Machine Learning for Healthcare Conference, pp. 376-396. PMLR, 2020.
  • Fang, Chih-Hao, Vikram Ravindra, Salma Akhter, Mohammad Adibuzzaman, Paul Griffin, Shankar Subramaniam, and Ananth Grama. "Identifying and analyzing sepsis states: A retrospective study on patients with sepsis in ICUs." PLOS Digital Health 1, no. 11 (2022): e0000130.
  • Gani, Md Osman, Shravan Kethireddy, Riddhiman Adib, Uzma Hasan, Paul Griffin, and Mohammad Adibuzzaman. "Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the icu." Artificial Intelligence in Medicine (2023): 102493.
  • Khan, Ayesha, Vida Abedi, Farhan Ishaq, Alireza Sadighi, Mohammad Adibuzzaman, Martin Matsumura, Neil Holland, and Ramin Zand. "Fast-track long term continuous heart monitoring in a stroke clinic: a feasibility study." Frontiers in Neurology 10 (2020): 1400.