Lagunita Theme

Hello!

Welcome to my site - a collection of my Stanford experiences and other personally significant things.

Technical Experiences

• Quantitative Research Intern - IMC Trading
  06/23 - 09/23
  

• SDE Intern - Meta [Advertiser Products – Optimization Incubation]
  06/22 - 09/22
  

  • Developed multitask learning models for ad targeting and optimization for org-wide impact in Caffe2 and DPER3 framework.
  • Tested new architecture through extensive experimentation to prove efficacy both in isolation and as a sub-module of a larger model.
  • Updated existing pipelines to expand the model's use cases across two datasets.

• Undergraduate Researcher - Stanford Institute of Economic and Policy Research
  03/22 - present
  

  • Collected data concerning the FCC 904 RDOF auction, which subsidized providing ~5M homes and businesses with high-speed internet.
  • Formulated research plan to analyze bidder behavior and FCC cost models as a function of intrinsic bidding lot factors such as geospatial distribution and demographic factors.
  • Processed data to analyze the effect of neighboring census block groups on auction results.
  • Developed methodology to measure bid to footprint distance across all bids.

  Thesis

  Presentation

• AI/ML Intern - You.com
  10/21 - 06/22
  

  • Modeled query data with synthetic language. Developed API interfacing with CosmosDB versioned dataset for dataset maintenance.
  • Experimented on multiheaded named entity extraction NLP model through methods such as cross dataset transfer learning.

• SDE Intern - Amazon [Music]
  6/21 - 9/21
  

  • Developed an automated pipeline for delivering music publisher streaming reports incorporating additional required catalog data.
  • Leveraged Spark Scala for optimized distributed computation due to expanded data throughout.
  • Performed thorough testing using Airflow to ensure success for a variety of business-critical configurations.

• Undergraduate Researcher - Stanford AI Lab [ML Group]
  6/20 - 6/21
  

  • Led project to develop a novel semi-supervised contrastive learning methodology leveraging sample metadata for pair synthesis.
  • Applied algorithm to automatic lung and heart sound diagnosis from electronic stethoscope audio data.
  • Synthesized experimental regimes, developed scripts and code, and compiled experimental results for publication.

• Undergraduate Researcher - Stanford AI Lab [Dror Lab]
  6/20 - 5/23
  

  • Researched the use of self-attention in graph neural networks with applications in protein-ligand pose prediction.
  • Developed PyTorch package to open-source reproducible code for minimally equivariant graph neural networks.
  • Developed Syntactically Aware Emeddings using EGNNs to embed syntactic information in foundation model embeddings.

  Thesis

• Back End Developer
  5/20 - 12/21
  

  • Built a startup dedicated to building communities for parents of children with mental illnesses.
  • Constructed content delivery back-end architecture and Rest API utilizing Django framework.
  • Co-led engineering team-wide reviews for technical product alignment between front-end and back-end development.

• Research Trainee - Psychiatry Neuroimaging Lab, Harvard Medical School
  6/19 - 8/19
  

  • Completed an AI research project under the direction of Dr. Suheyla Cetin-Karayumak as part of the Research Science Institute (RSI), hosted at the Massachusetts Institute of Technology (MIT) by the Center for Excellence in Education (CEE).
  • Built a deep learning model, Flexible Medical Image Super-Resolution (FMISR), to enhance MRI quality using Python/Tensorflow and trained/tested the model with real-world conditions to measure clinical viability.
  • FMISR uses skip-connect layers and a residual block architecture to improve model performance.

  Abstract

• Research Intern - Henry Ford Health System Bone & Joint Center
  6/18 - 8/18
  

  • Algorithmically measured bone properties from Digital Tomosynthesis (DTS) scans of in-vitro and in-vivo vertebras using Digital Volume Correlation analysis under the direction of Dr. Yener Yeni.
  • The method is promising as a way to monitor bone quality for osteoporosis diagnosis and treatment.
  • Co-author of two abstracts accepted to the Orthopedic Research Society.

• Independent Research
  6/17 - 3/18
  

  • Developed a novel context-aware convolutional neural network model (C-SRCNN) to enhance the quality of CT scans using Python/Tensorflow. Explored multiple methods of incorporating context for an expanded effective field, boosting performance compared to prior models.
  • Validated performance on general and medical images.

  Abstract

Courses

Projects

• BallotProof
  Built computer vision methods for BallotProof, a ballot checker that performs all computations client-side for maximum security. BallotProof received a top8 award at HackGT 7. Test files can be found on our GitHub Repo.

  Site

  Repo

• COVID-19 Transmission Simulator
  Created a JavaScript simulator to illustrate the impact of respiratory protection and quarantine policies on transmission initiated by medical professionals. The simulator leverages agent-based modeling to emulate real-world transmission possibilities.

  Site

• Tech2Serve
  Empowering the underserved through technology. Projects include smart devices for visually impaired students, India's first E-Braille Library, laser technology for artificial limb production, educational aids for students with intellectual disabilities, and financial support for unemployed visually impaired individuals. Developing technology clubs in Detroit schools and a wearable ECG-CPR device to monitor and save at-risk hearts.

  Site

Teaching/Other Experiences