MLab
ACMLab is Stanford's premier machine learning club. Its goal is to teach anyone with basic CS experience machine learning. After an intensive ramp-up workshop in the fall, members work on publishing papers at top ML conferences and workshops. We have published 6 workshop papers so far at top conferences and workshops such as ACL and ICLR. Alumni have gone on to Google AI, Stanford ML Group, Stanford NLP Group, and VMWare.
Board
Ryan Rong
2028
Director
Sabrina Yen-Ko
2028
Director
Teaching Assistants
Arpandeep Khatua
Arpandeep Khatua is an MSCS student at Stanford interested in large language model reasoning, alignment, and evaluation, particularly in mid- and post-training analysis, adversarial behavior, and inconsistency detection.
Baani Kaur
Kaitlyn Wang
Kaitlyn is a sophomore studying Math/CS and Art History. She's worked on ML/astrophysics, open-source projects at SAIL, and is currently at Stanford NLP studying pretraining-time interventions to shape RL reasoning behavior in LLMs.
Mark Athiri
Mark Athiri is a senior at Stanford studying Computer Science (AI, Systems). He researches security AI under Profs. Boneh, Liang, and Ho, building RL training infrastructure for LLM agents. His work spans ML systems deployment, security research, and infrastructure engineering across multiple companies.
Stephan Rany
Rany is an M.S. student in Mathematical and Computational Engineering @ Stanford. He works on convex optimization-driven portfolio models, HJB models for fixed-income microstructure, and machine learning models for computational pathology. He is excited to help turn ideas into well-scoped, clear and reproducible ML projects.
Rydham Goyal
Rydham is a junior studying computer science and statistics with a focus on AI and data science. He is passionate about applying machine learning to solve real-world problems in healthcare, finance, and automation.
Vidur Gupta
Vidur is a second year masters student studying electrical engineering and computer science at Stanford, working on the intersection of AI/ML, natural language, and embedded systems. He is interested in working on real-world problems to use natural language to control real world physical devices in a secure and private way.
Meeting Information
Meeting Time: 7:30 - 9:00 p.m. Thursdays at CoDA B90.
First meeting this Thursday, Oct 9th!
Contact: Ryan Rong (ryanrong@stanford.edu), Sabrina Yen-Ko (syenko@stanford.edu)
Winter 2025 SemEval
We submitted to Tasks 2, 3, 8, and 11 of the Workshop on Semantic Evaluation.
Recent Projects
Fall 2024 Onboarding Project
This year 30 teams with 94 participants participated in the Fall Onboarding Project, developing custom models for the task of bird classification.
SemEVAL
We submitted to the Workshop on Semantic Evaluation's Task 1 (lexical complexity modelling) and Task 8 (automatically extracting measurements from scientific text). Our teams performed competitively on both tasks, including second place in one of the Task 8 subcategories. Our task description papers appeared at SemEval at ACL 2021.
Google BIG-Bench
Members proposed 4 tasks to be used in Google's BIG-Bench challenge. The purpose of this challenge was to create a collaborative benchmark for enourmous language models like GPT-3. MLab submitted tasks about temporal sequences, logic puzzles, sarcasm, and IPA translation.