Mohammad Soltanieh-ha

Clinical Assistant Professor, Information Systems
  • Phone 617-358-5872
  • Office 633A

  • BOSTON UNIVERSITY
    Questrom School of Business
    Rafik B. Hariri Building
    595 Commonwealth Avenue
    Boston, MA 02215

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Mohammad Soltanieh-ha is a Clinical Assistant Professor in the Information Systems department at Boston University’s Questrom School of Business. Mohammad obtained his Ph.D. in computational physics in 2015 from Northeastern University where he studied strongly correlated electronic systems in low dimensions. His current research interest revolves around computer vision applications in automating cancer diagnosis, macroeconomics time-series forecasting, and large-scale high-performance computing. His teaching experience includes data science programming, big data analytics, and applications of data science in business. Mohammad has leadership roles at Google and the American Physical Society (APS). In 2019, he founded the data science unit within APS and served as the founding chair. Additionally, he is a Faculty Expert at Google Cloud, where he advocates for cloud computing education and helps other faculty members with best practices.

    Education
  • PhD, Northeastern University, 2015
  • M.Sc., University of Wyoming, 2012
    Publications
  • Soltanieh Ha, M., Luigi, L. (In Press). "A verifable estimation and parametric inference of nonlinear equations using neural networks",
  • Noorbakhsh, J., Farahmand, S., Foroughi pour, A., Namburi, S., Caruana, D., Rimm, D., Soltanieh-ha, M., Zarringhalam, K., Chuang, J. (2021). "Abstract PO-003: Deep learning identifies conserved pan-cancer tumor features", Clinical Cancer Research, 27 (5_Supplement)
  • Noorbakhsh, J., Farahmand, S., Foroughi Pour, A., Namburi, S., Caruana, D., Rimm, D., Soltanieh-Ha, M., Zarringhalam, K., Chuang, J. (2020). "Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images.", Nat Commun, 11 (1), 6367-
  • Noorbakhsh, J., Farahmand, S., Soltanieh Ha, M., Zarringhalam, K., Chuang, J. (2020). "Pan-cancer classifications of tumor histological images using deep learning", bioRxiv
  • Noorbakhsh, J., Farahmand, S., Ha, M., Namburi, S., Zarringhalam, K., Chuang, J. (2019). "Abstract 1632: Deep learning functional associations using histopathology images", Cancer Research, 79 (13_Supplement), 1632-1632
  • Noorbakhsh, J., Farahmand, S., Ha, M., Namburi, S., Zarringhalam, K., Chuang, J. (2019). "Abstract 1632: Deep learning functional associations using histopathology images", Bioinformatics, Convergence Science, and Systems Biology
  • Nocera, A., Soltanieh-ha, M., Perroni, C., Cataudella, V., Feiguin, A. (2014). "Interplay of charge, spin, and lattice degrees of freedom in the spectral properties of the one-dimensional Hubbard-Holstein model", Physical Review B, 90 (19)
  • Soltanieh-ha, M., Feiguin, A. (2014). "Spectral function of theU?8one-dimensional Hubbard model at finite temperature and the crossover to the spin-incoherent regime", Physical Review B, 90 (16)
  • Soltanieh-ha, M., Feiguin, A. (2012). "Class of variational Ansätze for the spin-incoherent ground state of a Luttinger liquid coupled to a spin bath", Physical Review B, 86 (20)
    Research Presentations
  • Soltanieh Ha, M. , Seidmann, A. , Xu, L. Pick the Right Tactics When Online Sales Go Live: An Empirical Analysis of Livestreaming for Amazon Sellers, 56th Hawaii International Conference on System Sciences, Maui, HI, 2023