Suchir Salhan

Suchir Salhan is a PhD Candidate in the Department of Computer Science & Technology at the University of Cambridge (Gonville & Caius College) researching Small Language Models and Cognitively-Inspired AI. He previously completed a BA and MEng in Computer Science & Linguistics at Gonville & Caius College, obtaining a “starred First” (Class I with Distinction) and a Distinction respectively.

 My interdisciplinary background in Computer Science, Cognitive Science, and Linguistics drives my interest in leveraging insights from human cognition to develop AI systems that are interpretable, fair, and equitable.

I’ve had a long fascination with the intersection of language and computation—how humans have developed the capability to acquire natural language to communicate, learn, and reason, despite the diversity of linguistic systems; and how we might build machines that can do the same. I arrived in Cambridge in 2020 to pursue a BA and MEng in Computer Science & Linguistics at Gonville & Caius College, Cambridge, where I earned a “starred First” and a Distinction. 

 During my time as an undergraduate, I explored code-switching with Dr Li Nguyen, worked on multimodal vision-language models with Prof Nigel Collier and Fangyu Liu (now at Google DeepMind), and participated in a funded internship at the ALTA Institute. I probed models like CLIP to understand their semantic representations, experimented with Nearest Neighbour Algorithms for Offline Imitation Learning, and investigated Explainable AI, Argumentation Mining, and Shortcut Learning in NLP. At the same time, my linguistic interests – mainly in typology and theoretical linguistics (syntactic theory, morphology, and phonology)—taught me the deep diversity and structure of human language, and inspired me to think about how AI might better reflect this complexity.

These experiences have shaped my current PhD work, where I aim to build AI systems that are both powerful and cognitively inspired, bridging insights from human language and computation. My Masters Thesis focused on the BabyLM Shared Task to train Small Language Models using acquisition-inspired strategies on “cognitively-plausible” corpora (e.g., child-directed speech) for several languages.

My PhD work now connects the BabyLM paradigm with the fast-moving Language Modelling ecosystem. While Large Language Models (LLMs) are increasingly used in high-stakes applications—such as assessing human performance—they often lack steerability, alignment, and interpretability. I work to address this by developing Cognitively-Inspired Small Language Models (SLMs). These SLMs can guide and calibrate LLM behavior in multi-agent environments, aligning AI outputs with user preferences and domain-specific tasks. By explicitly modeling underrepresented populations of speakers and learners, these models help make AI systems more equitable, robust, and human-aligned.

Organiser and Host of the Natural Language & Information Processing Seminars, 2024 -. Natural Language & Information Processing Group (CST).  Organising 30+ departmental seminars with leading academics and industry researchers on Language Models, Computational Linguistics and Natural Language Processing. List of Organised Seminars.

Language Sciences Annual Symposium 2025: Ambitions for language science in 2050. Poster Session Organiser for 2025 Cambridge Language Sciences Symposium with Sammy Weiss (MRC Cognition and Brain Sciences Unit) and Shanshan Hu (TAL). CLS 2025 Website. 

23rd Old-World Conference in Phonology (OCP23). Member of Organising Committee. Gonville & Caius College (January 2026). OCP23 Website (Phonetic Laboratory, Department of Theoretical & Applied Linguistics). 

Reviewer for BabyLM 2024.

ACL 2025 Emergency Reviewer.

Reviewer for The First Workshop on Large Language Model Memorization. L2M2 Proceedings @ ACL 2025.