CV

EDUCATION

Icahn School of Medicine at Mount Sinai
Medical Scientist Training Program (MD/PhD)
New York, NY | 2017 - Present
- Graduate School of Biomedical Sciences – Neuroscience Core
- Center for Computational Psychiatry, Dept. of Neuroscience

Rutgers University, New Brunswick
Bachelor of Arts (BA) - Cell Biology and Neuroscience
New Brunswick, NJ | 2011 - 2015
- Minor in Computer Science, Minor in Philosophy
- Magna Cum Laude - GPA 3.76

RESEARCH EXPERIENCE

Icahn School of Medicine at Mount Sinai
Graduate Student at Center for Computational Psychiatry
New York, NY | 2017 - Present
Advisors
- Xiaosi Gu, Ph.D. - Director of Center for Computational Psychiatry
- Daniela Schiller, Ph.D. - Professor of Psychiatry and Neuroscience
Experience
- Conceived and implemented novel computational models to investigate decision-making and craving in populations with addiction
- Revised lab-standard maximum likelihood models of reinforcement learning to utilize Bayesian parameter estimation
- Utilized linear and non-linear machine learning to classify chronic users of cannabis, and graph theoretical methods to investigate neural patterns underlying chronic cannabis use
- Implemented dynamical systems models to probe latent states underlying verbal free recall of traumatic memories in veterans with post-traumatic stress disorder
- Collected behavioral data using online platforms such as Prolific, as well as neural data such as functional MRI and intracranial EEG

Rutgers University - Newark
Lab Manager/Research Assistant - Center for Molecular and Behavioral Neuroscience
Newark, NJ | 2015 - 2017
Advisor
- Michael W. Cole, Ph.D. - Associate Professor of Neuroscience
Experience
- Developed tasks designed to determine of mechanisms underlying rapid instructed task learning (RITL)
- Led a project to identify and decode prototypical mental states, their dynamics, and characteristic graph theoretical measures during resting state using community detection, clustering algorithms, and other machine learning methods
- Collected functional MRI/EEG/neurophysiological data for three protocols from cohorts of healthy college-aged young adults and aging populations

Rutgers University - New Brunswick
Research Assistant - Center for Advanced Biotechnology and Medicine
New Brunswick, NJ | 2011 - 2015
Advisor
- Gaetano Montelione, Ph.D. - Professor of Chemistry and Chemical Biology
Experience
- Engineered a software platform for the identification of novel targets for NMR structure determination using BLAST/HMMER similarity scores in conjunction with dimensionality reduction algorithms to embed protein sets into 2D/3D visualization space
- Developed a novel method for sequence-based structure determination and surface feature characterization based on the Rosetta platform
- Collected protein NMR data through an end-to-end protein imaging pipeline, including protein design, production, purification, and NMR data acquisition

CLINICAL EXPERIENCE

Icahn School of Medicine at Mount Sinai
New York, NY | 2017 - 2019
Art of Science and Medicine - MD Program
- Attended a weekly clinical training course through the medical school involving medical interviewing, interpersonal communication, and physical examination skills while rounding on inpatient wards and outpatient sites
- Developed clinical reasoning and critical thinking skills by formulating differential diagnoses and action plans for clinical cases

Princeton First Aid and Rescue Squad
Princeton, NJ | 2015 - 2017
Emergency Medical Technician

COMPUTER SKILLS

Programming
- Proficient in Python, Matlab, R, Javascript, shell scripting
- Relevant data analysis libraries: scikit-learn, pymc3, brainiak, tidyverse

Applications
- Relevant data analysis methods: reinforcement learning, general linear modeling, multivariate pattern analysis, network analysis, hyperalignment, custom interpretable machine learning algorithms
- Proficient in use of neuroimaging software: nilearn, SPM, fmriprep, AFNI, freesurfer

Platforms
- Basic experience with full-stack web development
- Deployed a small personal application using Flask backend, React frontend, and AWS for hosting

Relevant graduate coursework
- Machine Learning for Biomedical Data Science – 2020
- Biomedical Software Engineering – 2020
- Statistical Rethinking, A Bayesian Course – 2021
- Probability and Inference – 2021

PUBLICATIONS

Kulkarni, K.R., Schafer, M., Berner, L., Fiore, V.G., Heflin, M., Hutchison, K., Calhoun, V., Filbey, F., Pandey, G., Schiller, D., Gu, X., 2022. “An interpretable and predictive connectivity-based neural signature for chronic cannabis use” Accepted for publication at Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.

Shuster, A., O’Brien, M., Luo, Y., Berner, L.A., Perl, O., Heflin, M., Kulkarni, K., Chung, D., Na, S., Fiore, V.G. and Gu, X., 2021. Emotional adaptation during a crisis: decline in anxiety and depression after the initial weeks of COVID-19 in the United States. Translational psychiatry, 11(1), pp.1-7.

Fiore, V.G., DeFelice, N., Glicksberg, B.S., Perl, O., Shuster, A., Kulkarni, K., O’Brien, M., Pisauro, M.A., Chung, D. and Gu, X., 2021. Containment of COVID-19: Simulating the impact of different policies and testing capacities for contact tracing, testing, and isolation. PloS one, 16(3), p.e0247614.

Spronk, M., Keane, B.P., Ito, T., Kulkarni, K., Ji, J.L., Anticevic, A. and Cole, M.W., 2021. A whole-brain and cross-diagnostic perspective on functional brain network dysfunction. Cerebral Cortex, 31(1), pp.547-561.

Luo, Y., Shuster, A., Chung, D., O’Brien, M., Heflin, M., Fiore, V., Kulkarni, K., Na, S. and Gu, X., 2020, July. Dissociable social perception and altruistic choices during the first wave of COVID-19 in the United States. University of the Bundeswehr.

Ji, J.L., Spronk, M., Kulkarni, K., Repovš, G., Anticevic, A. and Cole, M.W., 2019. Mapping the human brain’s cortical-subcortical functional network organization. Neuroimage, 185, pp.35-57.

Chen, R.H., Ito, T., Kulkarni, K.R. and Cole, M.W., 2018. The human brain traverses a common activation-pattern state space across task and rest. Brain Connectivity, 8(7), pp.429-443.

Ito, T., Kulkarni, K.R., Schultz, D.H., Mill, R.D., Chen, R.H., Solomyak, L.I. and Cole, M.W., 2017. Cognitive task information is transferred between brain regions via resting-state network topology. Nature communications, 8(1), pp.1-14.

PRESENTATIONS

Data Blitz. “Computational Mechanisms Underlying Multi-Domain Decision Making and Momentary Craving.” Icahn School of Medicine at Mount Sinai, Neuroscience Department Retreat.

Poster Presentation. “Computational Mechanisms Underlying Drug-Based Decision Making and Momentary Craving in Chronic Cannabis Users.” Society of Biological Psychiatry, 2022.

Poster Presentation. “Revealing Recurrent Latent Brain State Dynamics that Support Cue-Elicited Craving of Marijuana.” Society of Biological Psychiatry, 2021.

Poster Presentation. “Backbone and Sidechain Resonance Assignments for Carboxy-Terminal Domain of Guanylyltransferase using Triple Resonance NMR Data.” Rutgers Aresty Research Colloquium.

WORKSHOPS

• Neuromatch Academy - Computational Neuroscience, 2021.
• Computational Psychiatry Course, Bayesian Learning and Reinforcement Learning Workshop, New York, NY, 2019.
• Brainiak Inter-subject Correlation (ISC) and Shared Response Modeling (SRM) Workshop, Hasson Lab, Princeton, NJ, 2019.

HONORS AND AWARDS

Departmental Highest Honors, Cell Biology and Neuroscience, Rutgers University - 2015
Bachelor of Arts Magna Cum Laude, Rutgers University - 2015
Phi Beta Kappa Honors Society, Rutgers University - 2014 - 2015
CABM Undergraduate Program Scholar, Rutgers University - 2012 - 2013
Presidential Scholarship (Full Tuition), Rutgers University - 2011 - 2012
National Merit Scholarship, National Merit Scholarship Corporation - 2011 - 2012

LANGUAGES

English: Native Language
Marathi: Advanced Listener, Intermediate Speaker, Novice Reading and Writing
Hindi: Intermediate Listener, Novice Speaker, Novice Reading and Writing

OTHER

US Citizen