Background

I am passionate about leveraging data analytics and machine learning to tackle complex problems related to human health. I have a BS and PhD in Physics / Computational Biology and completed a postdoc in structural biology. My academic work focused on high-performance computation, simulation, and cryoEM to elucidate the assembly mechanism of bacterial membrane proteins, aiding in the development of novel antibiotics. This research resulted in publications in Nature Communications (2021) and PNAS (2018), where I identified specific protein mechanisms relevant to antimicrobial resistance. In my professional journey, I’ve sought out experiences that draw upon my scientific expertise while embracing modern AI/ML methods to positively impact human health. After completing a Fellowship at the NYC Data Science Academy, I began a role at Calyxt where I developed analytic tools to help researchers identify metabolic pathways in plants and optimize rare-compound production for pharmaceutical manufacturing. At EMD Serono (Merck group), I developed algorithms leveraging ML models and NLP data from over 30 million publications, clinical trials, and patents to improve drug target prioritization for immune disorders. I also built custom web applications for visualizing gene “trendiness” scores to enable efficient drug-target prioritization. Currently, I work as a Scientific Consultant at Metapages (Astera Institute), where I’m helping to develop an interactive computational biology platform that enables the easy creation and sharing of interactive scientific workflows. My technical expertise includes Python (numpy, pandas, scikit-learn, pytorch, tensorflow, biopython, dash, plotly), R (DESeq2, Bioconductor, rshiny, dplyr, tidyr, ggplot2), ML/AI methods, cloud computing, molecular docking, protein modeling, and molecular dynamics simulations. I’m currently looking for my next role. If you have an opportunity that aligns with my experience, let’s chat!

Job experience

• October 2024 - April 2025
  Consultant
  Metapages
  

  Berkeley, CA, USA

  Help develop a software platform that helps scientists share results and workflows. Currently focused on customizing the platform for applications in molecular modeling, protein structure prediction, and protein design.

• May 2023 - November 2023
  Bioinformatics Co-op
  EMD Serono
  

  Rockland, MA, USA

  - Developed a trendiness metric to identify high-interest genes for drug target
  prioritization pipeline using gene annotations present in publications, patents, and
  clinical trials (time-series modeling and tree-based models)
  - Integrated Drugbank, MeSH, DOID, GO, MedDRA, SNOMED, ICD-10 data to optimize
  model toward efficacy in immune disorders
  - Deployed RShiny app for visualization of data and gene trendiness scores

• May 2022 - December 2022
  Data Scientist
  Calyxt
  

  Roseville, MN, USA

  - Developed ML models to predict rearrangement of genetic elements that maximizes production of rare compounds in plants
  - Utilized Jira API along with Azure Functions and Tables to extract employee worklog and project data to create Power BI dashboards for management and finance teams
  - Created a Dash web app to help researchers discover pathways to compounds of interest. Extracted and parsed data from plantcyc.org and deployed app on heroku and Azure App Service

• September 2019 - September 2021
  Postdoctoral Research Scholar
  Purdue University
  

  West Lafayette, IN, USA

  Performed data analysis and designed experiments to reveal insertion intermediates of the beta-barrel assembly machinery (BAM) complex with cryo- EM. Performed and analyzed data from biochemical assays to determine outer- membrane protein assembly model. Wu et al. Nat Comm (2021)

Education

• Georgia Institute of Technology
  PhD, Physics
  

  2012 - 2019

  Carried out statistical modeling and data analysis of time-series molecular dynamics simulations executed on high-performance computing systems to characterize outer-membrane protein assembly. Developed regression models to characterize molecular features. Lundquist et al. PNAS (2018), Lundquist et al. BBA (2020), Botos et al. Structure (2016), Bamert et al. Mol Micro (2017)
  Carried out statistical modeling and data analysis of time-series molecular dynamics simulations executed on high-performance computing systems to characterize outer-membrane protein assembly. Developed regression models to characterize molecular features. Lundquist et al. PNAS (2018), Lundquist et al. BBA (2020), Botos et al. Structure (2016), Bamert et al. Mol Micro (2017)
• University of Michigan
  BS, Physics
  

  2007 - 2012