---
title: "Parth Asawa"
category: "people"
role: "CS PhD student"
company: "UC Berkeley"
linkedin: "https://www.linkedin.com/in/pgasawa/"
twitter: "https://x.com/pgasawa"
website: "https://pgasawa.github.io/"
sourceLabels: ["Official speaker roster", "Official conference schedule"]
---
# Parth Asawa

## Profile
CS PhD student at [[uc-berkeley|UC Berkeley]].

- [LinkedIn](https://www.linkedin.com/in/pgasawa/)
- [X / Twitter](https://x.com/pgasawa)
- [Website](https://pgasawa.github.io/)

## Biography
Parth Asawa is a PhD student at [[uc-berkeley|UC Berkeley]] advised by Professor Matei Zaharia and Professor Joey Gonzalez. Parth's research is on continual learning, studying how to enable models to stably learn from streams of experiences over time. His work focuses on sample-efficient learning and spans the stack of data, learning algorithms, architectures, and evaluation.

## Conference Sessions
- [[2026-06-30-parth-asawa-beyond-static-intelligence-evaluating-continual-learning]] — Beyond Static Intelligence: Evaluating Continual Learning (2026-06-30, 10:45am-11:05am)

## Evidence Graph
This evidence graph summarizes how this person appears across the conference source graph: scheduled sessions, linked videos, transcripts, and slide-derived evidence.

### Linked Sessions
- [[2026-06-30-parth-asawa-beyond-static-intelligence-evaluating-continual-learning|Beyond Static Intelligence: Evaluating Continual Learning]]

### Media Signals
No linked video, transcript, or slide source has been attached yet.