Tim Baldwin is a Melbourne Laureate Professor in the School of Computing and Information Systems at the University of Melbourne in the Faculty of Engineering and Information Technology. He concurrently holds the position of Provost and Professor of Natural Language Processing at Mohamed bin Zayed University of Artificial Intelligence. Baldwin obtained his BSc in Computer Science and Mathematics and BA in Linguistics and Japanese from the University of Melbourne in 1995, an MEng in Computer Science in 1998, and a PhD in Computer Science in 2000 from the Tokyo Institute of Technology. His research focuses on natural language processing and artificial intelligence, encompassing algorithmic fairness, computational social science, social media analytics, text mining, language identification, topic modeling, and sentiment analysis. Author of approximately 500 peer-reviewed publications with over 32,000 citations and an h-index of 73 on Google Scholar, Baldwin has made substantial contributions to the field, including co-developing large language models for Arabic, English, and Indonesian with over one million downloads on Hugging Face.

Baldwin's distinguished career includes leadership as President of the Association for Computational Linguistics in 2022 and service on editorial boards such as Transactions of the Association for Computational Linguistics since 2015. He is a permanent member of the International Committee on Computational Linguistics since 2014 and has chaired numerous conferences and workshops, including EMNLP 2015 and COLING 2018. Notable awards include the ARC Future Fellowship from 2013 to 2016, Outstanding Paper Award at EACL 2023, Best Paper Award at the EMNLP 2021 Workshop on Multilingual Representation Learning, multiple outstanding reviewer awards, IBM Faculty Award in 2016, and several teaching excellence awards from the University of Melbourne such as the 2008 Teaching Excellence Award. Key publications feature 'Automatic evaluation of topic coherence' (2010, 1811 citations), 'Multiword expressions: A pain in the neck for NLP' (2002, 1790 citations), 'An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation' (2016, 1036 citations), and 'Machine reading tea leaves: Automatically evaluating topic coherence and topic model quality' (2014, 924 citations). His research has attracted funding from the Australian Research Council, Google, Microsoft, and others.

Professional Email: tbaldwin@unimelb.edu.au