Paula Buttery (pjb48@cam.ac.uk) is the Deputy Head of the Department of Computer Science & Technology at the University of Cambridge and a Full Professor of Language and Machine Learning. She is co-Director of the Cambridge Language Sciences Interdisciplinary Research Centre and Principal Investigator of the Cambridge Institute for Automated Language Teaching and Assessment (ALTA). Paula, together with her research group, focuses on designing and pretraining performant Small (and Baby) Language Models, developing interpretable and efficient text-based and multimodal language model architectures. Her group has won four outstanding paper awards for their research on BabyLM.

Leshem Choshen (leshem.choshen@weizmann.ac.il) is a Zuckerman Faculty Senior Scientist (eq. Assistant Professor) at the Weizmann Institute of Science. He co-founded the BabyLM challenge and has led various open science and community research efforts, including TextArena (supporting millions of users at its peak) and EveryEvalEver (including a shared task and wide community outreach). His work on future humane AI includes broad evaluation efforts and human-focused research.

Boqing Gong (bgong@bu.edu) is a computer science faculty member at Boston University and a part-time research scientist at Google DeepMind. His research on machine learning and computer vision focuses on visual recognition, video, and AI models' generalization and efficiency. He received an NSF CAREER award for his project on BabyVLM.

Aaron Mueller (amueller@bu.edu) is an Assistant Professor of CS at Boston University. His research centers on language modeling, developmentally inspired evaluation methods, and interpretability. He co-founded and regularly organizes the BabyLM Challenge. He is actively involved in the NLP and ML communities through research, as well as event planning and conference chairing (e.g., publicity chair for EMNLP 2025, program chair for the New England Mechanistic Interpretability Workshop).

Suchir Salhan (sas245@cam.ac.uk) is a PhD Candidate at the University of Cambridge, working on Small Language Models and Cognitively-Inspired AI. He is an organiser of the BabyLM Shared Task and Workshop at EMNLP 2026, and has won an Outstanding Paper Award for his work on interpretable approaches for multimodal language modeling. His research focuses on developmental interpretability, language model learning dynamics, and tokenization.

Shengao Wang (wsashawn@bu.edu) is a Ph.D. student at Boston University co-advised by Prof. Boqing Gong and Prof. Venkatesh Saligrama. His research interests lie among vision language model pretraining, developmental learning, and robotics. He was the team lead of the BabyVLM-v2 project. He also participated in the second BabyLM challenge and contributed to the evaluation codebase.

Wenqi Wang (wqwang@bu.edu) is a second-year Ph.D. student at Boston University, advised by Prof. Boqing Gong. His research focuses on vision language model evaluation and world models. He was a co-author on the BabyVLM-v2 team.

Max Whitton (maxwh@bu.edu) is pursuing his Ph.D. at Boston University, advised by Prof. Boqing Gong. He does research at the intersection of computer vision and developmental learning, and was a co-author on the BabyVLM-v2 team.

Our program committee draws on eight institutions across four countries (the US, the UK, the Netherlands, and Germany), with expertise in multimodal machine learning, language modeling, evaluation, and training dynamics: Canaan Breiss (U Chicago), Bastian Bunzeck (Bielefeld U), Andrew Caines (U Cambridge), Arjun Chandra (Boston U), Bianca-Mihaela Ganescu (U Cambridge), Fuhang Kuang (Boston U), Lingxiao Li (Boston U), Mahir Patel (Boston U), Yulu Qin (Boston U), Yuan Qing (Boston U), Maan Qraitem (Boston U), Arijit Ray (Boston U / Ai2), Naomi Saphra (Harvard, Kempner Institute), Ece Takmaz (Utrecht U), Yuwen Tan (Boston U), Zilu Tang (Boston U), Nazia Tasnim (Boston U), Piotr Teterwak (Boston U), Dheeraj Varghese (U Amsterdam), Manushree Vasu (Boston U), and Michael Wakeham (Boston U).