
Hi, I’m Vignav Ramesh.
I'm a sophomore studying computer science at Harvard. Broadly, my academic interests lie at the intersections between natural language processing, medicine, and human-computer interaction. I'm currently a researcher at Harvard Medical School, where I am co-advised by Professors Pranav Rajpurkar (HMS DBMI) and Andrew Ng (Stanford CS). I have 5 years of software experience, 10 hackathon wins, and have published several papers in AI/ML conferences and top-tier journals. In my free time, I enjoy playing tennis and practicing the double bass.
Reach out at vignavramesh [at] college [dot] harvard [dot] edu to chat!
Press: Geekwire (x2), Mercury News, CA Times, East Bay Times, CBS News, Business.org, AP News, Central Charts, Markets Insider, Morningstar, GlobeNewswire, KFM BFM, Euroinvestor, OneNewsPage, MarketsAsk, CompuServe
Work & Research Experience

Researcher, Medical AI Bootcamp
In affiliation with Stanford and HMS, I'm currently exploring the use of co-training for unsupervised domain adaptation to develop foundation models for medical image segmentation. Previously, I developed a GPT-3 few-shot approach & BERT-based named entity recognition model to improve chest X-ray (CXR) radiology report generation models by removing hallucinated references to non-existent priors.

Research Intern, Stanford University Laboratory of Quantitative Imaging and Artificial Intelligence
I leveraged self-/semi-supervised learning techniques to address the labeled data bottleneck in medical AI. I worked on two main projects: (1) training a Mask R-CNN on chest X-rays computed from CT scans to automatically segment COVID-19 lung lesions on real patient X-rays; and (2) developing a contrastive learning pipeline for ophthalmic imaging utilizing neural style transfer for out-of-domain data augmentation.

Research and Development Intern, SingularityNET Foundation
I co-led the unsupervised language learning (ULL) and artificial general intelligence (AGI) movements. I built explainable & unsupervised natural language generation, text segmentation, and question answering systems to enhance the general conversational intelligence of proto-AGI pipelines; all models matched or outperformed SOTA benchmarks with zero supervised training.

Cofounder and CEO, Zigantic LLC
Incorporated in Delaware as of March 18, 2018, Zigantic is a platform that provides independent game developers with alpha/beta playtesting insights. Over the years, my team has built a hybrid mobile app (available on the iOS App Store and Google Play Store); acquired 200+ playtesters; serviced 30+ customers; and partnered with global brands in the US and UK.