Faculty Directory
Mehdi Hasan, Ph.D.

Mehdi
Hasan

Adjunct Professor
mehdi.hasan@yu.edu
Ph.D. Information Science Kyushu University, Japan, 2019
M.Sc. Information Systems Kobe Institute of Computing, Japan, 2016
Mehdi Hasan PhD.

Dr. Mehdi is an AI for development (AI4D) professional. His research interests include Human-AI interaction, eHealth and AI for Social Good. He is aspired to make technology as a tool to determine the deficiency in expected results and actual outputs to establish social equity and good governance in emerging economies. He is a founder and Executive Director of Infotix. He is also a reviewer of the International Journal of Advanced Computer Science and Applications, Saga University, Japan.

 

Academia and Industry Experience

Essen Healthcare (New York), One Brooklyn Health (New York), IBM (New York), Toyota R&D (Japan), and BRAC (Dhaka). Dr. Mehdi has taught a wide range of computer science and AI courses at Yeshiva University (New York), Long Island University (New York), City University of New York (New York) and Waseda University (Tokyo).

  • Research grant: G7 (2nd Round), Waseda University, Japan (2023-24)
  • Research grant: G7 (1St Round), Waseda University, Japan (2022-23)
  • The Japan Society of the Promotional of Science, Japan # 18K11529 (2019-21)
  • Best Presentation Award, Kyushu University-TED Conference, Japan (2019)
  • Challenge & Creation (Excellence) Award for Best Project, “Accessible and Assemble Healthcare Service”, QREC, Kyushu University, Japan (2018-19)
  • Grant for research from Institute of Decision Science, Kyushu University (2016-19)
  • Best project award, “dotplus”, Tokyo Institute of Technology, Japan (2018)
  • Poster award, 2nd International Conf on Healthcare, & Technology, Japan (2018) § The Future Earth Research Fund, Japan. # 18-161009264 (2018-19)
  • Monbukagakusho (MEXT) scholarship, Japan (2014-16)
  • Project grant-Microsoft-World Bank Youth Innovation Challenge (2011-2013

Sampa, M. B., Abdul Aziz, N. H., Rahman, Md. S., Hasan, M., & Ab. Aziz, N. A. (2025). Factors influencing the adoption and acceptance of eHealth in Malaysia: a systematic review. Critical Public Health, 35(1). https://doi.org/10.1080/09581596.2025.2519780 

 

Chowdhury, S. Dey, S. Hossain, M. Hasan and S. Chowdhury, "Predicting Heart Failure Survival: A Machine Learning Approach with Explainable AI," 2024 IEEE International Conference on Computing, Applications and Systems (COMPAS), Cox's Bazar, Bangladesh, 2024, pp. 1-6, doi: 10.1109/COMPAS60761.2024.10796275. 

 

Hasan, M., Yokota, F., Islam, R., Hisazumi, K., Fukuda, A., & Ahmed, A. (2020). A predictive model for height tracking in an adult male population in Bangladesh to reduce input error. International journal of environmental research and public health, 17(5), 1806.

 

Hasan, M., Nishikitani, M., Yokota, F., Fukuda, A., Islam, R., & Ahmed, A. (2019). Growth characteristics of age-based anthropometric data from human assisted remote healthcare systems. International Journal of Advanced Computer Science and Applications, 10(3), 615-619. https://doi.org/10.14569/IJACSA.2019.0100379. 

 

M. Hasan, A. Fukuda, R. I. Maruf, F. Yokota and A. Ahmed, "Errors in remote healthcare system: Where, how and by whom?," TENCON 2017 - 2017 IEEE Region 10 Conference, Penang, Malaysia, 2017, pp. 170-175, doi: 10.1109/TENCON.2017.8227856.

mehdi.hasan@yu.edu
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