Hong Pan, a dedicated professional in data science and statistics, was born to a family of educators. He attended Shanghai Jiao Tong University, where he studied Biomedical Engineering, and then joined Purdue University in the U.S. for his PhD program in Electrical and Computer Engineering.
After obtaining his PhD, Hong first joined Cornell University Medical College as a faculty member, where he conducted and oversaw technical, analytic, and engineering aspects of human in vivo functional and molecular neuroimaging research and trained multidisciplinary students, research fellows, and clinician scientists; and then moved to Harvard Medical School as a faculty member where he further his invention to innovation technology transfer journey in data science applications for medical imaging.
For over 20 years, Hong has been a leader in data science efforts, serving as the subject matter expert on over 20 federal and institutional projects. His influence and impact in the field, particularly his expertise in AI/ML algorithms and advanced statistics, have been instrumental in developing statistical, data-driven diagnostic tools for guiding the treatment of brain disorders. He has created best practice approaches for optimized data acquisition, data science solutions for biomarker discovery, and automated analytics and informatics pipelines based on functional neuroimaging methodology. His work has resulted in 4 patents, a successful spin-out startup, and earned him the Mass General Brigham Excellence in Innovation Award twice and Brigham and Women’s Hospital’s Pillar Award in Research & Innovation, with over 60 journal publications, solidifying his professional standing in the field.
In 2023, Hong joined Simmons University as a faculty member and started focusing on full-time teaching in data science and statistics.
Education
- PhD, School of Electrical and Computer Engineering, from Purdue University (West Lafayette, Indiana)
- MS, BS, School of Biomedical Engineering, Shanghai Jiao Tong University (Shanghai, China)
Area of Expertise
- Computational Biomedicine
- Data Science
- Machine Learning
- Medical Imaging
Courses
- STAT118 Intro Stats
- STAT228 Introduction to Data Science
- STAT239 Regression and Design of Experiments
- MATH/STAT338 Probability Theory
- MATH/STAT339 Statistical Theory
Professional Affiliations & Memberships
- Association for Computing Machinery
- American Statistical Association
- Institute of Electrical and Electronics Engineers
Publications/Presentations
Awasthi S, Pan H, LeDoux JE, Cloitre M, Altemus M, McEwen B, Silbersweig D, Stern E. The bed nucleus of the stria terminalis and functionally linked neurocircuitry modulate emotion processing and HPA axis dysfunction in posttraumatic stress disorder. Neuroimage Clin. (2020 Sep 24) 28: 102442.
Coiner B, Pan H, Bennett ML, Bodien YG, Iyer S, O’Neil-Pirozzi TM, Leung L, Giacino JT, Stern E. Functional neuroanatomy of the human eye movement network: a review and atlas. Brain Struct Funct. (2019 Nov) 224(8): 2603-2617.
Pergolizzi D, Root JC, Pan H, Silbersweg DA, Stern E, Passik SD, Ahles TA. Episodic Memory for Visual Scenes Suggests Compensatory Brain Activity in Breast Cancer Patients: A Prospective Longitudinal fMRI Study. Brain Imaging and Behavior (2019 Jan 24) 13: 1674–1688.
Perez DL, Pan H, Weisholtz DS, Root JC, Tuescher O, Fischer DB, Butler T, Vago DR, Isenberg N, Epstein J, Landai Y, Smith TE, Savitz AJ, Silbersweig DA, Stern E. Altered threat and safety neural processing linked to persecutory delusions in schizophrenia: A two task fMRI study. Psychiatry Research: Neuroimaging (2015 Aug 20) 233(3): 352-366.
Pan H, Epstein J, Stern E, Silbersweig D. New and Emerging Imaging Techniques for Mapping Brain Circuitry. Brain Research Review (2011 Jun 24) 67(1-2): 226-251.
Beutel ME, Stark R, Pan H, Silbersweig D, Dietrich S. Changes of brain activation pre- post short-term psychodynamic inpatient psychotherapy: an fMRI study of panic disorder patients. Psychiatry Res. (2010 Nov 30) 184 (2): 96-104.
Ma Y, Huang C, Dyke JP, Pan H, Alsop D, Feigin A, Eidelberg D. Parkinson’s disease spatial covariance pattern: noninvasive quantification with perfusion MRI. J Cereb Blood Flow Metab. (2010 Mar) 30 (3): 505-509.
Duan S, Wan L, Fu WJ, Pan H, Ding Q, Chen C, Han P, Zhu X, Du L, Liu H, Chen Y, Liu X, Yan X, Deng M, Qian M. Nonlinear cooperation of p53-ING1-induced bax expression and protein S-nitrosylation in GSNO-induced thymocyte apoptosis: a quantitative approach with cross-platform validation. Apoptosis. (2009 Feb) 14 (2): 236-45.
Protopopescu X, Butler T, Pan H, Root J, Altemus M, Polanecsky M, McEwen B, Silbersweig D, Stern E. Hippocampal structural changes across the menstrual cycle. Hippocampus. (2008) 18 (10): 985-988.
Epstein J, Pan H, Kocsis JH, Yang Y, Butler T, Chusid J, Hochberg H, Murrough J, Strohmayer E, Stern E, Silbersweig DA. Lack of ventral striatal response to positive stimuli in depressed versus normal subjects. Am J Psychiatry. (2006 Oct) 163 (10): 1784-1790.
Butler T, Imperato-McGinley J, Pan H, Voyer D, Cordero J, Zhu YS, Stern E, Silbersweig D. Sex differences in mental rotation: top-down versus bottom-up processing. Neuroimage. (2006 Aug 1) 32 (1): 445-456.
Protopopescu X, Pan H, Altemus M, Tuescher O, Polanecsky M, McEwen B, Silbersweig D, Stern E. Orbitofrontal cortex activity related to emotional processing changes across the menstrual cycle. Proc Natl Acad Sci U S A. (2005 Nov 1) 102 (44): 16060-16065.
Sigman M, Pan H, Yang Y, Stern E, Silbersweig D, Gilbert CD. Top-down reorganization of activity in the visual pathway after learning a shape identification task. Neuron. (2005 Jun 2) 46 (5): 823-835.
Boscolo R, Pan H, Roychowdhury VP. Independent component analysis based on nonparametric density estimation. IEEE Trans Neural Networks (2004 Jan) 15 (1): 55-65.
Pan H, Chen Q, Stern E, Silbersweig DA. Multilevel nonlinear mixed-effects models for nested factors in functional brain imaging. Proceedings of 2003 Joint Statistical Meeting, San Francisco, CA, American Statistical Association, pp. 3162-3167, August 2003.
Feng J, Pan H, Roychowdhury V. On neurodynamics with limiter function and Linsker’s developmental model. Neural Computation. (1996) 8(5): 1003-1019.
Feng J, Pan H, Roychowdhury V. A rigorous analysis of Linsker-type Hebbian learning. In Tesauro G, et al. (eds.) Advances in Neural Information Processing Systems 7. MIT Press, Cambridge, MA, 1995, pp. 319-326.