Precision Medicine




Biomedical Informatics









Machine Learning





Population Genomics



















Nice to meet you!

Vivek Kanpa

AI & Computational Genomics @ Mount Sinai

Current Research - Under Renovation!

  • In the meanwhile, here's my Substack.
  • Predicting protein localization using deep mutational screening data
  • Identifying rare disease variants with AlphaMissense
  • Evaluating clinical and biomedical applications of AI to reduce patient precision oncology expenditures
  • Developing and comparing AI and rules-based clinical notetaking assistants
  • Developing a statistical network approach for missing value imputation in large heterogenous datasets

Experience

Precision -Omics Lab, Mount Sinai

Doctoral Research Student

Goal: To train deep learning algorithms on genomic biobanks to identify rare genetic variants associated with disease.

August 2024 — present

AI_PREMie, UCD Conway Institute of Biomolecular Research

ML Research Assistant

Goal: To develop a best-in-class AI-assisted clinical decision making tool for maternal preeclampsia.

Preeclampsia (PE) is a maternal hypertensive disorder that claims the lives of 50,000 mothers and 500,000 babies annually. Because its symptoms are very generic, early and accurate detection of PE is a difficult task.

Outcomes: I helped develop a best-in-class AI diagnostic aid and maternal risk stratification tool for PE to assist clinicians tailor patient care strategies. I improved model sensitivity and recall (PPV) by >10% for PE status prediction and developed and evaluated a predicted PE severity score, which demonstrated strong correlation with physician-recorded severity status (no/low/high severity). Feature analysis revealed insight into potentially novel, high-specificity biomarkers for PE diagnosis. I will present an excerpt of this at the BMEII Symposium in New York.

October 2023 — present

Revolution Medicines

Data Science Research Intern

Goal: perform hierarchical clustering on AlphaFold-predicted structures to inform cancer target strategic priorities.

In cancer pharmacology, there is great interest in identifying common actionable features between oncogenic structures to reduce wastefulness of lead discovery.

Outcome: I scraped 600+ cancer driver proteins and hierarchically clustered them by structural similarity. I then rendered a polar dendrogram where nodes represented oncoproteins and encoded patient mutation frequency (AACR GENIE) in the node's radius. I also developed a tool to swap GENIE data with any other cancer datasets of interest (cBioPortal, CRISPRko screens, COSMIC). I documented this project's workflow over here.

Bioinformatics & Cancer Technology Co-op

Goals: develop generalizable tools to increase pharmacogenomics throughput and expidite ADME screening.

  • Trained graph convolution network (DeepChem) using molecular featurization of RevMed hit library to improve permeability (Caco-2) predictions by 28% compared to conventional molecular weight cutoff.
  • Automated mass spectrometry (MS) cross-linking data analysis, calculating a normalized percent cross-linking value given raw relative abundance outputs. I enabled high throughput screening for drug-target binding, which shaved dozens of hours off each 384-well MS experiment.
  • Performed in vitro dose-response assays of KRAS-mutant CRC cell lines and quantified c-Myc mRNA (PAGE, qRT-PCR) and protein (BCA assay, western blotting/ELISA) expression to understand downstream effect of KRAS inhibition on Myc activity.

July 2022 — June 2023

Apfeld Lab, Northeastern University

Undergraduate Research Assistant

Goal: determine the mechanism of action for the TGF-B/smad peroxide resistance response in C. elegans nematodes.

The peroxide immune response is key to managing ROS load in C. elegans neurons. The TGF-B pathway mediates this response and is tightly regulated, leaving the peroxide defense mechanism of action unknown.

Outcome: I performed lifespan and behavioral assays on type I and type II TGF-B ligand/receptor RNAi nematode knockouts during peroxide exposure. I observed a signal integration phenotype between type I and type II TGF-B that causes half the double ligand knockouts to live longer than wildtype. I followed this with bulk RNA-seq and GO analysis of C. elegans smad knockouts (TGF-B receptor-binding proteins) to trace differential gene expression from TGF-B silencing. I discovered that both smad-2 and smad-4 knockouts overexpressed lon-1, which is found abundantly in peroxide-sensing neurons and implicated in negative regulation of the TGF-B pathway.

January 2022 — April 2023

Takeda Pharmaceuticals

Bioanalytics & Automation Sciences Co-op

Goal: develop bioanalytic software systems to increase throughput for immunotherapeutic discovery.

  • Designed end-to-end Django web apps for data processing of protein quantification assay (HTP Lunatic) and HPLC chromatography (Agilent 1260 Infinity), increasing analysis throughput, data availability, and data standardization across multi-national team.
  • Built an assay fulfillment translator to operationalize the Lynx liquid handler by interpreting a scientists' planned experimental plate layout (96/384-well). Increased assay fulfillment throughput exponentially with picomolar precision.

July 2021 - December 2021

Selected Publications

* asterisk indicates co-first author.

David D'Onofrio*, Shrey Patel*, Nandini Samanta*, Mikio Tada*, Vivek Kanpa*. Cost-benefit analysis of non-AI and AI models implemented for predicting chief complaints. PRISM, 2024.

Birgitta Taylor-Lillquist, Vivek Kanpa, Maya Crawford, Mehdi El Filali, Julia Oakes, Alex Jonasz, Amanda Disney, Julian Paul Keenan. Preliminary Evidence of the Role of Medial Prefrontal Cortex in Self-Enhancement: A Transcranial Magnetic Stimulation Study. Brain Sciences, 2020.

Education

PhD | Artificial Intelligence in Medicine

Icahn School of Medicine at Mount Sinai

Thesis supervisor: Dr. Kuan-lin Huang, Precision -Omics Lab.
Technical interests: computational genomics, machine learning, network biology, multi-omics, precision medicine
Non-technical interests: healthcare economics, public health, AI policy

  • Teaching: First Gen Scholars Mentor
  • Leadership: Academic IT & Libraries Committee
  • Research: Precision oncology healthcare economics & AI, tele-health for uninsured populations, digital aids for clinical
    document translation, non-AI clinical notetaking assistants

MSc | AI for Medicine & Medical Research

University College Dublin
  • Teaching: Medical Informatics Student Journal Club founder
  • Leadership: Mitchell Scholar (here)
  • Research: Cardiometabolomics ML @ Conway Institute; Network statistics @ Systems Biology Ireland
First class Honours Degree
2023—2024

BS | Data Science & Biology

Northeastern University
,
  • Teaching: CS1800: Discrete Structures — Fall 2020, GE1501: Cornerstone of Engineering — Fall 2020, BIOL2309: Biology Project Lab — Spring 2022
  • Leadership: Undergraduate Husky Ambassador — 2019-2023, Access & Diversity Fellow — 2020-2021, Freshman Resident Assistant — 2021-2023
  • Research: Undergraduate Research Fellow Distinction, 3x PEAK Award Recipient ($10,000)
Cum Laude, Honors
2019—2023

Contact

Non-academic shennanigans: vkanpa184@gmail.com
Academic shennanigans: vivek.kanpa@icahn.mssm.edu
LinkedIn: Vivek Kanpa