MATTHEW WICKERSHAM, PHD
MD-PhD Student
Weill Cornell Medicine
ABOUT ME
My name is Matthew Wickersham. I'm a medical student in the Tri-Institutional MD-PhD program at Weill Cornell Medicine/Rockefeller/Sloan-Kettering in New York, NY. In August 2022, I completed my PhD in Dr. Olivier Elemento's lab using machine learning techniques to predict various health outcomes using large electronic health records. Currently, I am finishing medical school where I will graduate in May 2024. I'm also the Co-Founder and former Co-Executive Director of the Weill Cornell Medicine Wellness Qlinic, a student-run LGBTQ mental health clinic in NYC.
EDUCATION
07/2017 - Present
WEILL CORNELL MEDICINE
MD Candidate
Clinical Interests: Psychiatry, LGBTQ+ Health
09/2020Â - 08/2022
WEILL CORNELL MEDICINE
PhD (Olivier Elemento, PhD)
Dept. of Physiology, Biophysics and Structural Biology
08/2012 - 05/2016
COLUMBIA UNIVERSITY
B.A. Biology and Mathematics
WORK EXPERIENCE
Flatiron Health
May 2019Â - August 2020
LEAD CLINICAL
DATA ANALYST
Improved processes on collecting real-world data for life science and healthcare companies
Leveraged approaches on how to collect real-world data for regulatory submissions
Undertook independent projects using the real-world data from the company’s records
Columbia University
Lab of Dr. Alice Prince
February 2014 - June 2017
RESEARCH ASSISTANT
Researched host response to infectious diseases in the skin and lungs
Solved critical problems through hypothesis-driven approaches
Wrote manuscripts and grants for publications and conferences
VOLUNTEERÂ EXPERIENCE
Weil Cornell Medicine Wellness Qlinic
June 2017 - Present
CO-EXECUTIVE DIRECTOR
CO-FOUNDER
Founded clinic from inception to beginning clinical operations over a span of 18 months
Managed operational logistics overseeing outreach, communications, finance and events
Raised over $30,000 through fundraising and grant writing to cover operational and clinical costs
New York Ramblers
November 2017Â - Present
PRESIDENT (CURRENT)
DIRECTOR OF LEAGUES
DIRECTOR OF EVENTS
DIRECTOR OF FUNDRAISING
Developed more inclusive LGBTQ+ soccer club in New York City
Ran club operations managing 8 other individuals as president
Helped raise over $50,000 during tenure of leadership
PUBLICATIONS
Gabryszewski, S. J., Wong Fok Lung, T., Annavajhala, M. K., Tomlinson, K. L., Riquelme, S. A., Khan, I. N., Noguera, L. P., Wickersham, M., Zhao, A., Mulenos, A. M., Peaper, D., Koff, J. L., Uhlemann, A. C., & Prince, A. (2019). Metabolic Adaptation in MRSA Pneumonia. American journal of respiratory cell and molecular biology, 61(2), 185–197.
Wickersham, M., Ahn, D., Peñaloza, H., Bueno, S., Prince, A. (2019). ST258 K. pneumoniae KP35 Exploit IFN-λ Signaling to Invade Across the Airway Epithelial Barrier. American Journal of Respiratory Cellular and Molecular Biology, 60(2), 158-166.
Wickersham, M., Wachtel, S., Lung, T. W. F., Soong, G., Jacquet, R., Richardson, A., Parker, D., Prince, A. (2017). Metabolic Stress Drives Keratinocyte Defenses against Staphylococcus aureus Infection. Cell Reports, 18(11), 2742-2751.
Ahn, D., Peñaloza, H., Wang, Z., Wickersham, M., Parker, D., Patel, P., Koller, A., Chen, E., Bueno, S., Uhelmann, A.C., Prince, A. (2016). Development of Resistance to Myeloid Clearance Mechanisms Promotes Klebsiella pneumoniae ST258 Pulmonary Infection. JCI Insight. 1(17).
Kitur, K., Wachtel, S., Brown, A., Wickersham, M., Paulino, F., Peñaloza, H., Soong, G., Bueno, S., Parker, D., Prince, A. (2016) Necroptosis Promotes Staphylococcus aureus Clearance by Inhibiting Excessive Inflammatory Signaling. Cell Reports, 16(8), 2219-2230.
Soong, G., Paulino, F., Wachtel, S., Parker, D., Wickersham, M., Zhang, D., & Prince, A. (2015). Methicillin Resistant Staphylococcus aureus Adaptation to Human Keratinocytes. mBio, 6(2), e00289-15.
PROJECTS
OLDER ADULT ALCOHOL MISUSE
Older adult life expectancy has steadily increased over the past years, with those over 65 expected to represent 20% of the total population in the upcoming decade. Given this growth, more focus should be put on understanding the health of older adults, particularly mental health. Of mental health concerns, substance use is common with 50% of older adults admitting to consuming alcohol. Yet, of those assessed, not everyone accurately diagnosed or recorded as having alcohol misuse. Given that artificial intelligence has been proven to effectively predict various health outcomes, machine learning methods can be applied to identify older adults at high risk of alcohol misuse that may otherwise go unnoticed and untreated. The long-term goal of my project is to accurately predict whether an older adult misuses alcohol. In this project, I plan to use data from the United Kingdom Biobank (UKB) to develop a machine learning algorithm that can be applied to a large electronic health system to predict older adult alcohol misuse.