My Work

On a mission to apply ML to impactful domains. Every place I've worked at has challenged me more than the last.

Otter.ai

Software Engineer, LLM — August 2024 - Present

Building low-latency speech and agent systems that power Otter’s next generation of meeting intelligence products.

  • Built a production TTS inference service for flow-matching and autoregressive models using protobuf, gRPC, TRT-LLM, and Terraform-managed AWS GPU clusters, delivering first audio chunks in 150 ms with sub-1.0 real-time factor.
  • Integrated Otter’s meeting stack, WebSocket audio streaming, VAD turn detection, and streaming LLM generation to launch a real-time voice agent that runs live over Zoom.
  • Scaled LLM evaluation pipelines with Celery workers and S3-based artifact storage, piping judge metrics into Snowflake and Grafana dashboards for weekly model reviews.
  • Designed multi-tenant prompt caching, routing, and telemetry infrastructure that reliably serves over one billion tokens per day across internal and external LLM workloads.
  • Own containerized Django and FastAPI backends on AWS ECS/ECR and EC2, layering Redis-backed tooling to keep internal LLM integrations responsive and observable.

Octane Security

Co-founder and Head of ML - 2022-2023

I founded a blockchain security startup that uses ML to find vulnerabilities in smart contracts in 2022. We raised $1.7 million from Alchemy, OrangeDAO, Hyperithm, Duke Capital Partners, Druid Ventures, Symbolic Capital, and Builder Capital, with angels and advisors from Ledger, Apple, Quantstamp, Blowfish, and Meta.

  • Developed a system for traversing and classifying control-flow graphs to determine control-flow vulnerabilities in smart contracts such as reentrancy and arbitrary external calls.
  • Created and tested a Transformer-based fuzzer that filtered transactions based on the likelihood of breaking a predefined invariant or bug oracle. Used this method to augment an existing greybox concolic tester and a symbolic execution engine.
  • Used SFT and RLHF to finetune and steer language models into identifying vulnerable code that evaded all traditional static analyzers.
  • Setup hosting and infra for all ML-based workflows for easy integration into the Octane platform.
  • Left the company after helping build the core tech; the company continues to thrive.

Advising

Machine Learning Advisor - 2023-2024

I am working as an advisor for a few companies looking to integrate AI into their existing business workflow.

  • Sagiliti: Built a PoV AI-OCR system to automate extraction of data from bill copies. Using OpenAI models and clever type-checking, I am able to automate 90% of their manual extraction process.
  • Project Imagine: Helping create AI agents that intelligently extract financial data to help executives make better decisions and get analysis on unfamiliar domains for clients.

Duke Neurotoolbox Lab

Machine Learning Researcher - 2021-2024

I am working in the Neurotoolbox Lab on improving segmentation of neurons in 2-photon calcium imaging videos. I am advised by Dr. Yiyang Gong and Dr. Yijun Bao.

  • Developed an active learning architecture that reduced the number of labeled neurons in videos by 50x to reach SOTA accuracy and 10x for baseline accuracy.
  • Created a technique to use the data from active learning to identify neurons that impact convergence; these neurons happened to be low SNR and low frequency neurons. Isolating these led to faster convergence to the baseline F1.
  • Ran experiments across multiple GPUs efficiently to reduce a process that would've taken days to hours.

Paper coming soon!

AiFi

Computer Vision Team, Summer Intern - 2021

I worked at AiFi, a company that uses a camera-based computer vision system to automate retail stores. They have clients such as Zabka, Microsoft, Verizon, and various NFL, NBA, and Premier League stadiums.

  • Spearheaded an effort to use domain-randomized synthetic data to train product recognition algorithms.
  • Developed a product auto-labeling pipeline based on instance segmentations from simulation data.
  • Generated photorealistic data from unpaired real and simulation images using cycle-consistent GANs.
  • Used synthetic data to pretrain YOLOv5 and a small set of real examples for SFT; this reduced store deployment time from 2 weeks to 2 days and increased new SKU detection accuracy by 80%

Stanford University School of Medicine

Research and Development Intern - 2019-2021

I interned at the Bacchetta Lab in the Stanford School of Medicine Department of Pediatrics. I worked with PhD student Esmond Lee.

  • Designed experiments to increase the NGFR+ percentage (editing rate) for FOXP3 gene editing in hematopoietic stem progenitor cells
  • Used a design of experiments (DoE) approach to optimize CRISPR-Cas9 editing of HSPCs. Analyzed flow cytometry data using FlowJo.
  • Created a cost analysis to show a reduction in cost of reagents for applications in clinical settings.
  • Published a manuscript in the Cytotherapy Journal