Bay Area Frontier Research Club | Google Ventures (dinner + discussion)
The Bay Area Frontier Research Club is a curated forum for rigorous discussion on how AI is reshaping the scientific research process. We convene experimental researchers, computational scientists, and research engineers across domains to examine concrete work—papers, methods, and workflows—covering literature synthesis, hypothesis generation, experimental design, simulation, analysis, and reproducibility.
For each session, we curate 2–3 papers selected for rigor and discussion value. Presentations are intentionally brief so the majority of time is reserved for questions and critique: assumptions, evaluation methodology, failure modes, and what would constitute convincing evidence. Papers and supporting materials are shared in advance to ensure a high-baseline conversation.
Agenda
5:30pm: Doors open
5:30pm – 6:30pm: Networking + snacks
6:30pm – 8:00pm: Research presentations + discussion
8:00pm – 8:30pm: Networking
Presenters & topics
Talk #1: Architectures for Building Self-Improving Agents
READ THE PAPER HERE
A deep dive into the major architectural approaches used to build self-improving agents, covering emerging directions such as Meta's HyperAgents, Stanford's Meta-Harness from Chelsea Finn's lab, and Hexo's Self-Improving AutoResearcher (SIA), with a focus on how these systems iteratively improve their own performance, adapt their strategies, and push toward more autonomous scientific and engineering discovery.
Presenter: Vignesh Baskaran is a machine learning practitioner who has built and trained large neural networks across text, vision, and audio. He previously led ML research at Darts-IP, where his state-of-the-art image recognition system contributed to the company’s acquisition by Clarivate, and he’s since ranked in the top ~1% on Kaggle while building open-source tooling for stabilizing large model training. He is the co-founder of Hexo Labs.
Talk #2: Auditing Self-Evolving Agents Under a Throughput Objective
(paper not yet published)
What does an autonomous agent actually produce when given an iteration budget to optimize an LLM inference engine with nothing but a throughput signal to guide it? This talk audits one such run, including both the optimizations it discovered and the correctness failures that only surfaced under closer review, and reflects on what the result reveals about the current reach and limits of self-evolving agents.
Presenter: Zach Xu is a CS PhD student at the University of Chicago. His research centers on large language models, with a recent focus on self-evolving agentic systems, test-time scaling, and interdisciplinary scientific applications; he also serves as an Assistant Editor for Data-centric Machine Learning Research (DMLR) in the JMLR family.
Talk #3: AI-Native Drug Discovery for High-Mortality Cancers
A look at an AI-native research effort emerging from Stanford that uses frontier models across large biological and medical datasets—including biomarkers, genetics, oncology, AlphaFold / AlphaFold2, and newly identified protein-crevice data—to generate novel therapeutic candidates for the ten deadliest cancers by mortality. Running on a 128x H100 Azure cluster over 96 hours, the project produced more than 56,000 novel molecular compounds, pointing to a new scale of AI-driven discovery in medicine.
Presenter: Edward Alexei is a technology executive with leadership roles across ZeroFox, SOCJedi.AI, Gruve, and AGDI.ai, and a 30-year career spanning startups, cybersecurity, infrastructure, gaming, and enterprise software. His background includes early startup success with HungryMouse.com and strategic technology roles at Supermicro, Ubisoft, Webroot / OpenText, OPSWAT, and Citrix.
Want to present your work?
If you have a research paper you’d like to discuss with a cross-disciplinary room, submit it for consideration.
Who should attend
Experimental researchers
Computational scientists across domains (bio/chem/materials/climate/neuro/physics)
Research engineers + lab automation people
Folks building tools for literature review, experiment planning, robotics, simulation, or scientific data
No ML background required. If you’ve ever wished research moved faster, you belong here.
Capacity is limited.
We will take photos and short video clips for event recap and promotion. By attending, you consent to being photographed and recorded, and to the use of those images and clips by the organizers on social media and other event marketing channels.
Hosted by
Emily (“MLE”) is an AI scientist that generates novel hypotheses, autonomously designs and runs experiments, critiques its own results, and iterates toward real progress on hard, unsolved problems. Built to augment researchers with agentic, closed-loop experimentation, Emily helps scientists reach the next frontier faster.
Frontier Syndicate is a private venture circle for frontier tech builders, researchers, and investors. We convene high-trust rooms and back exceptional companies at the frontier.
Google Ventures (GV) is a venture capital firm investing in category-defining companies, with deep roots across AI, biotech, and the broader innovation ecosystem. GV supports founders and researchers building frontier technologies—and helps connect high-caliber builders, operators, and scientific talent through its network and community.