Experience

MIT Institute for Medical Engineering and Science (IMES)

• United States
• Higher Education
• 1 - 100 Employee

• Graduate Student Researcher

  • Jan 2023 - 1 year

  Cambridge, Massachusetts, United States Collins Lab



Harvard Medical School

• United States
• Higher Education
• 700 & Above Employee

• Graduate Student Researcher

  • Sep 2022 - Dec 2022

  Boston, Massachusetts, United States



Eye BeamIt

• Consultant / Advisor

  • Sep 2021 - Aug 2022

  San Francisco Bay Area

• Technical Lead

  • Apr 2021 - Sep 2021

  San Francisco Bay Area

• Software Development Engineer

  • May 2019 - Apr 2021

  San Francisco Bay Area • Designed and implemented a mobile application, using Swift and React Native, that interacts with hardware technology to facilitate impulse buying for a pre-seed stealth fashion startup seeking to transform impulse fashion consumption. • Experienced startup environment as one of first non-founding members and learned about venture capital funding and other aspects of startups.



Caltech

• United States
• Research Services
• 700 & Above Employee

• Research Assistant

  • May 2020 - Aug 2022

  Pasadena, California, United States • Center for Molecular and Cellular Neuroscience – Gradinaru Lab • Advisors: Prof. Viviana Gradinaru, Dr. Min Jee Jang, Dr. Anat Kahan, Dr. David Brown • Worked on development of a single-cell sequencing and machine learning pipeline to characterize adeno-associated viral (AAV) tropisms at high resolution. • Trained imbalanced classification models to pioneer a "virtual knockout" methodology for identification of genes whose expression facilitates or inhibits AAV transduction. •… Show more • Center for Molecular and Cellular Neuroscience – Gradinaru Lab • Advisors: Prof. Viviana Gradinaru, Dr. Min Jee Jang, Dr. Anat Kahan, Dr. David Brown • Worked on development of a single-cell sequencing and machine learning pipeline to characterize adeno-associated viral (AAV) tropisms at high resolution. • Trained imbalanced classification models to pioneer a "virtual knockout" methodology for identification of genes whose expression facilitates or inhibits AAV transduction. • Worked on development of an experimental and computational pipeline leveraging fluorescence in situ hybridization and spatial transcriptomics to characterize AAV tropisms. • Developed machine learning models for classification of VIP neurons in the suprachiasmatic nucleus and prediction of neuron type based on calcium imaging. • Published as co-author in Nature Biotechnology and Frontiers of Immunology.

• Research Assistant

  • Feb 2020 - Aug 2022

  Pasadena, California, United States • Division of Biology and Biological Engineering – Lester Research Group • Advisors: Prof. Henry Lester, Anand Muthusamy • Worked on development of Inside-Out, a suite of web apps for simulation of drug concentrations and receptor activity during ingestion and elimination of commonly abused drugs. • Analyzed effects of nicotine dose and cytochrome P450 2A6 polymorphisms on activation and chaperoning pathways of nicotine addiction. • Engineered genetically encodable opioid biosensors… Show more • Division of Biology and Biological Engineering – Lester Research Group • Advisors: Prof. Henry Lester, Anand Muthusamy • Worked on development of Inside-Out, a suite of web apps for simulation of drug concentrations and receptor activity during ingestion and elimination of commonly abused drugs. • Analyzed effects of nicotine dose and cytochrome P450 2A6 polymorphisms on activation and chaperoning pathways of nicotine addiction. • Engineered genetically encodable opioid biosensors via directed evolution. • Pioneered computational methods for automating detection of ethologically relevant behavioral responses to opioids in mice from markerless pose estimation readout.

• Head Teaching Assistant

  • Apr 2022 - Jun 2022

  Pasadena, California, United States • Held weekly office hours for an applied mathematics course, IDS/ACM/CS 157: Statistical Inference. • Held review sessions for the midterm and final exams. • Assigned problems each week to team of teaching assistants for grading. • Compiled all students' midterm and final grades to record course progress.

• Peer Tutor

  • Jan 2020 - Jun 2022

  Pasadena, California, United States • Tutored classmates in various Computer Science (CS), Applied and Computational Mathematics (ACM), Bioengineering (BE), Computation and Neural Systems (CNS), Economics (Ec), Mathematics (Ma), Physics (Ph), and Biology (Bi) courses.

• Teaching Assistant

  • Jan 2022 - Mar 2022

  Pasadena, California, United States • Held weekly office hours and graded problem sets for a graduate-level applied mathematics course, ACM/IDS 216: Markov Chains, Discrete Stochastic Processes and Applications. • Compiled all students' final grades to record course progress.



Savor

• United States
• Food & Beverages

• Co-Founder

  • Mar 2019 - May 2022

  San Francisco Bay Area • Founded a startup aiming to use analytics to provide personalized restaurant recommendations. • Designed and implemented a mobile application, using Swift, that learns user dining preferences and presents recommendations. • Awarded the Mayleben Venture Shaping grant by the Zell Lurie Institute of the University of Michigan’s Ross School of Business.



MathWorks

• United States
• Software Development
• 700 & Above Employee

• Software Engineering Intern

  • Mar 2021 - Jun 2021

  Natick, Massachusetts, United States (remote) • Designed and implemented register allocation algorithm to optimize translation of deep learning models from open-source deep learning frameworks into MATLAB. • Constructed framework to adapt computer vision models for application in highway lane identification and following.



Metagenomi

• United States
• Biotechnology Research
• 100 - 200 Employee

• Data Science Intern

  • Sep 2020 - Dec 2020

  Emeryville, California, United States (remote) • Trained machine learning models to predict editing efficiency of CRISPR systems based on secondary structures of guide sequences.



McGill University

• Canada
• Higher Education
• 700 & Above Employee

• Research Assistant

  • Aug 2020 - Dec 2020

  Montreal, Quebec, Canada • Department of Psychiatry – Lifshitz Group • Advisor: Prof. Michael Lifshitz • Studied the structural and functional correlates of imaginative suggestibility via analysis of behavioral and fMRI data. • Conducted network analysis of psychological traits in neurophenomenology survey data.



Cubismi, Inc

• United States
• Hospitals and Health Care

• Biostatistics Intern

  • Aug 2020 - Sep 2020

  Madison, Wisconsin, United States (remote) • Trained deep learning classifiers on chest CT scans for COVID-19 detection based on various radiological features, including ground glass opacity and crazy paving patterns.



Cannon Global Investment Management

• Financial Services
• 1 - 100 Employee

• Quantitative Research Intern

  • Jul 2020 - Sep 2020

  Fort Lauderdale, Florida, United States (remote) • Performed statistical analysis on order imbalance and price data of 500 individual tickers and built trading models and devised quantitative trading strategies. • Implemented simulations to test and evaluate trading strategies.



Riot Ventures

• United States
• Venture Capital and Private Equity Principals
• 1 - 100 Employee

• Investor

  • Jun 2020 - Aug 2020

  Los Angeles, California, United States (remote) • Conducted industry research in the computational drug design/discovery and neuropharmacology space and sourced early-stage deals and startups. • Sought out early-stage emerging technology with significant industry-affecting potential. • Riot Ventures is an LA-based, globally focused fund investing in early stage deep tech companies.



Stanford University

• United States
• Higher Education
• 700 & Above Employee

• Research Assistant

  • Jun 2018 - Aug 2020

  Stanford, CA • Department of Anthropology – Luhrmann Research Group • Advisors: Prof. Tanya Luhrmann, Prof. Michael Lifshitz • Worked on design and development of a neuroimaging paradigm to investigate brain mechanisms involved in auditory verbal hallucinations in hallucination-prone individuals. • Built psychological tasks integrated into an fMRI protocol to simultaneously evaluate behavioral and brain activity data. • Constructed pipeline to process and analyze fMRI and behavioral data.



Western Asset Management

• United States
• Investment Management
• 700 & Above Employee

• Software Engineering Intern

  • Jun 2020 - Jul 2020

  Pasadena, California, United States (remote) • Worked as a member of the Information Technology Team. • Designed and implemented software to construct and visualize network effects and interactions among attributes of securities (specifically bonds). • Trained clustering models on fixed income investment data to classify securities based on interactive effects and improve portfolio management strategies. • Trained anomaly detection models to identify faulty entries in time series portfolio data.



Caltech

• United States
• Research Services
• 700 & Above Employee

• Research Assistant

  • Oct 2019 - Jun 2020

  Pasadena, CA • Decision, Optimization and Learning at Caltech (DOLCIT) – Yue Group • Advisors: Prof. Yisong Yue, Dr. Jialin Song, Dr. Yury Tokpanov • Integrated deep kernel learning into multi-fidelity Bayesian Optimization algorithms to improve model performance. • Applied resulting optimization methods to analyze astronomy and nanophotonics datasets.



NASA Jet Propulsion Laboratory

• United States
• Defense and Space Manufacturing
• 700 & Above Employee

• Computer Vision Engineer

  • Mar 2020 - May 2020

  Pasadena, California, United States • Trained supervised deep learning models for classification of images taken by the HiRISE instrument of the Mars Reconnaissance Orbiter. • Analyzed classification performance of various model architectures.



Zenlayer

• United States
• IT Services and IT Consulting
• 1 - 100 Employee

• Software Engineering Intern

  • Jun 2019 - Sep 2019

  Diamond Bar, California, United States • Worked as a member of the Network Engineering and Platform Team. • Analyzed network flow time series data to optimize allocation of network traffic across available routes. • Trained stateful Long Short-Term Memory (LSTM) Recurrent Neural Networks to forecast network traffic and flow, using TensorFlow and Keras. • Applied model to forecast network activity of a major company client.



Stanford University School of Medicine

• United States
• Higher Education
• 700 & Above Employee

• Research Assistant

  • Jun 2017 - Jun 2019

  Stanford, CA • Center for Genomics and Personalized Medicine – Urban Lab • Advisors: Prof. Alexander Urban, Dr. Xiaowei Zhu • Worked on training, implementation, and packaging of RetroSom, a tool for classification of transposons via a transfer learning model. • Trained an imbalanced classifier of true and false mobile element insertions (MEIs) in Python that achieved 0.96 Area Under Precision-Recall Curve (AUPRC). • Acknowledged in Nature Neuroscience paper.

• Research Assistant

  • Jun 2016 - May 2017

  Stanford, CA • Chan Zuckerberg Biohub – Elias Lab • Advisors: Prof. Joshua Elias, Dr. Lichao Zhang • Analyzed and modeled the effects of noise and interference on the detection of biomarkers in tandem mass tags mass spectrometry data. • Trained neural networks, in Java and Mathematica, to model and filter out noise in mass spectrometry data.