Bay Area Frontier Research Club | Stanford University (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 open5:30pm – 6:30pm: Networking + light dinner6:30pm – 8:00pm: Research presentations + discussion8:00pm – 8:30pm: Networking Presenters & topics Talk #1 Proxy Evaluation for Self-Improving Auto-Research Agents Presented by Vignesh Baskaran, co-founder and CTO of Hexo Labs. Vignesh is ranked in the top ~1% on Kaggle and author of the recent breakthrough paper LongCOT. Vignesh will explore one of the core bottlenecks in Self Improving Autonomous research agents: evaluation. Every meaningful decision an agent makes must be tested against its hypothesis — a process that is slow, expensive, and dependent on human judgment. This talk presents how proxy evaluation systems can be designed to close that loop, enabling research agents to assess their own progress, learn from their decisions, and continue improving autonomously. READ PAPER 1 | READ PAPER 2 Talk #2 Efficient Vision-Language Models — From Words to Masks Presented by Ethan Reid, Founding Research Scientist at Moondream (M87 Labs), where he builds some of the most efficient open-source vision-language models in production. Moondream's compact VLMs — including the world's smallest at 0.5B parameters — compete with models many times their size on vision understanding tasks, and are backed by Felicis, M12 (Microsoft), and Ascend Venture Capital. Ethan will present his recent work on Moondream Segmentation, a referring image segmentation system that takes a natural language expression and an image, then autoregressively decodes a vector path into a precise mask. The approach introduces a reinforcement learning stage that directly optimizes mask quality, achieving 80.2% cIoU on RefCOCO — showing that small, efficient models can match frontier performance when training is done right. READ PAPER HERE Talk #3 Schema-Based Evaluation and Routing for LLM Gateways Presented by Zecheng Zhang, CEO of Strukto.AI and co-founder of TraceRoot.AI (YC S25). Zecheng holds a Stanford CS MS (3.99 GPA) and spent three years as a Founding Engineer at Kumo.AI — Jure Leskovec's graph neural network company — where he co-created PyTorch Frame and contributed to PyTorch Geometric. Zecheng will present SEAR, a schema-based evaluation and routing framework for LLM gateways that converts production sessions into structured, SQL-queryable signals across quality, context, issue attribution, latency, and cost. SEAR enables interpretable, data-driven routing decisions across models and providers — in experiments on 3,000 production sessions, it identifies routing policies that cut input cost by 90% and output cost by 92% while maintaining comparable quality. READ PAPER HERE Talk #4 Mechanistic Cell Simulation for Generating Deep Learning Training Data in Biology Presented by Cyrus Knudsen, a PhD researcher in the Covert Lab at Stanford Bioengineering, where he works on computational whole-cell models — the same lab that produced the first-ever complete computational model of a living organism, a result recognized by Cell as one of its most important publications in 40 years. Cyrus is an NIH Biotechnology Training Program fellow and Stanford Bio-X awardee. He will present his work on using mechanistic, first-principles simulations of cellular processes to generate high-quality training data for deep learning models in biology — bridging the gap between biophysical modeling and modern machine learning to accelerate scientific discovery in domains where real experimental data is scarce or expensive to produce. READ PAPER HERE Want to present your work? ​If you have a research paper you’d like to discuss at one of our next sessions, please submit it for consideration. ​ SUBMIT YOUR PAPER HERE. Who should attend Experimental researchers Computational scientists across domains (bio/chem/materials/climate/neuro/physics)Research engineers + lab automation peopleFolks 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 Frontier Syndicate is a private venture circle connecting frontier tech researchers, builders, and investors through curated convenings and early-stage capital. Across the Bay Area, we host a recurring series of research forums, builder nights, and intimate investor dinners — and back exceptional companies emerging from the labs, communities, and technical networks we convene.Hexo Labs is building an AI-native platform for scientific discovery. Through Emily, its AI scientist system, Hexo helps researchers generate hypotheses, design experiments, and accelerate research workflows across ambitious scientific domains, with the goal of helping more breakthrough ideas move toward real-world impact.BASES (Business Association of Stanford Entrepreneurial Students) is one of the world's largest and most established student-run entrepreneurship organizations. Founded in 1996, it serves as the hub for student entrepreneurship at Stanford University, bridging the gap between academia, innovation, and industry. The Computing and Data Science (CoDa) building at Stanford UniversityRoom B80389 Jane Stanford Way, Stanford, CA 94305