# 10 People Advancing the Frontiers of Natural Language Processing to Follow in 2025 - Canonical: https://33rdsquare.com/people-to-follow-in-the-field-of-natural-language-processing-nlp/ - Published: 2024-09-03 - Author: Jordan Brown - Categories: [Artificial Intelligence & Machine Learning & ChatGPT](https://33rdsquare.com/category/tech/ai/) --- ## Introduction Natural language processing, or NLP for short, is a dynamic field at the intersection of linguistics, computer science, and artificial intelligence focused on enabling computers to understand, generate, and reason about human language. From virtual assistants to machine translation to analyzing unstructured text data at massive scale, NLP powers many of the intelligent technologies transforming our world. Driving this progress is a global community of researchers and practitioners pushing the boundaries of what‘s possible with language AI. In this post, we‘ll highlight ten individuals making pivotal contributions to NLP—from foundational models and algorithms to open source tools to science communication and community building. These are the people to follow to stay up to date on the latest developments in this fast-moving field. While not an exhaustive list, it covers a diverse set of leaders across academia, industry, and the open source community. For each, we‘ll dive into their background and key work shaping NLP‘s present and future. ## 1. Yann LeCun We‘ll start with Yann LeCun, VP and Chief AI Scientist at Meta and Silver Professor at NYU. LeCun is best known for his pioneering work on convolutional neural networks and deep learning, which have become foundational to modern NLP and AI as a whole. In 1989, LeCun developed the convolutional neural network (CNN) architecture, originally applied to computer vision tasks like character recognition. But in the early 2010s, CNNs were shown to also excel at NLP tasks, kicking off the deep learning revolution in language AI. Today, CNN-based models power state-of-the-art systems for machine translation, language understanding, text generation, and more. Besides this seminal technical work, LeCun is a prominent voice arguing for the importance of AI safety, ethics, and beneficial impact on humanity. You can follow his latest insights on [Twitter](https://twitter.com/ylecun), [LinkedIn](https://www.linkedin.com/in/yann-lecun-0b999/), and [Google Scholar](https://scholar.google.com/citations?user=WLN3QrAAAAAJ). ## 2. Fei-Fei Li Fei-Fei Li is a Professor of Computer Science at Stanford and Co-Director of the Stanford Institute for Human-Centered AI (HAI). She served as Director of Stanford‘s AI Lab from 2013 to 2018 and VP at Google Cloud AI from 2017 to 2018. Li is renowned for her work at the intersection of computer vision, NLP, and "human-centered AI"—AI systems that augment and empower rather than replace people. A prominent advocate for diversity in AI and tech, she co-founded the nonprofit AI4ALL to increase inclusion in the field. Recently, Li has focused on the challenge of commonsense reasoning in NLP—imbuing models with the vast background knowledge humans take for granted. To this end, she co-developed the ATOMIC knowledge graph of everyday concepts. You can learn about her latest work on [Twitter](https://twitter.com/drfeifei), [her Stanford page](https://hai.stanford.edu/people/fei-fei-li), and [Google Scholar](https://scholar.google.com/citations?user=rDfyQnIAAAAJ). ## 3. Sebastian Ruder Sebastian Ruder is a Research Scientist at DeepMind working on general-purpose language models, transfer learning, and multilingual NLP. He created and maintains NLP-Progress, a community-driven resource tracking the latest datasets and SOTA results across NLP tasks. In 2019, Ruder released a highly influential paper on "Transfer Learning in NLP," synthesizing best practices for pretraining models on large text corpora and fine-tuning them for downstream applications—now a dominant paradigm. He also publishes the NLP News newsletter, a fantastic resource to keep up with the field‘s rapid developments. Follow Ruder on [Twitter](https://twitter.com/seb_ruder), [his blog](https://ruder.io/), [GitHub](https://github.com/sebastianruder), and [Google Scholar](https://scholar.google.com/citations?user=8ONXPV8AAAAJ) for the latest updates. His newsletter consistently provides valuable round-ups and analysis. ## 4. Ani Kembhavi Ani Kembhavi is a Research Scientist at the Allen Institute for AI (AI2) working at the intersection of computer vision, NLP, and reasoning. He currently leads AI2‘s Vision & Language Group. Kembhavi has pushed the boundaries of multimodal AI with systems that can perceive and reason about both images and text. Prominent examples include VQA (visual question answering), VCR (visual commonsense reasoning), and GQA (generated question answering). These tasks require models to understand and integrate both visual and linguistic information, a key frontier. Recently, Kembhavi contributed to AI2‘s Unified-IO, a framework to train versatile agents that can see, talk, reason, and act—moving toward more general intelligence. You can follow his work on [Twitter](https://twitter.com/anikembhavi), [his website](https://anikembhavi.github.io/), and [Google Scholar](https://scholar.google.com/citations?user=50ZkVaYAAAAJ). ## 5. Mona Diab Mona Diab is a Research Scientist at Meta AI, where she leads the Situated AI & Language team, and a Professor of CS at The George Washington University (on leave). Her research focuses on cross-lingual and low-resource NLP, semantic processing, and AI for social good. A pioneer in Arabic NLP, Diab has developed foundational resources like PATB (Penn Arabic Treebank), tools for morphological analysis & disambiguation, and cross-lingual models that facilitate processing of lower-resourced languages. She co-founded GW‘s NLP lab to advance this work. Diab is also a champion for "AI for All"—making AI beneficial and accessible across cultures and demographics. To this end, she co-founded the Workshop on NLP for Positive Impact and founded the lab for NLP & Social Good. Follow her on [Twitter](https://twitter.com/DrMonaDiab) and [Google Scholar](https://scholar.google.com/citations?user=Palm2EQAAAAJ). ## 6. Thomas Wolf Thomas Wolf is Co-Founder & Chief Science Officer at Hugging Face, an AI startup whose open source libraries like Transformers have become go-to tools for NLP researchers and practitioners. He previously worked at Google and Facebook AI Research. Wolf has been instrumental in democratizing access to state-of-the-art NLP models and techniques. Hugging Face‘s tools make it easy to leverage Transformer-based language models for a range of applications. And initiatives like BigScience exemplify the power of community-driven AI development. In his own research, Wolf has advanced transfer learning, language modeling, text generation, and NLP robustness & fairness. Follow him on [Twitter](https://twitter.com/Thom_Wolf), [LinkedIn](https://www.linkedin.com/in/thomas-wolf-a056857/), and [Google Scholar](https://scholar.google.com/citations?user=o2jmYToAAAAJ) for insights on both the technical and societal dimensions of NLP. ## 7. Emily Bender Emily M. Bender is a Professor of Linguistics at the University of Washington. Her research focuses on multilingual grammar engineering, technology for endangered language documentation, and the societal impacts of NLP. Bender has been a prominent voice advocating for a more critical, human-centered approach to NLP that foregrounds issues of ethics, fairness, transparency and accountability. She co-authored the highly influential paper "On the Dangers of Stochastic Parrots," which ignited mainstream discourse on the limitations and risks of large language models. In the open source space, Bender has contributed to projects like the Grammar Matrix and AGGREGATION to support development of grammars and NLP tools for a wide range of the world‘s languages. Follow her on [Twitter](https://twitter.com/emilymbender), [her website](https://faculty.washington.edu/ebender/), and [Google Scholar](https://scholar.google.com/citations?user=9r_f1w4AAAAJ). ## 8. Rada Mihalcea Rada Mihalcea is a Professor of Computer Science & Engineering at the University of Michigan and Director of the Michigan AI Lab. Her research spans many areas of NLP, with a focus on multimodal & multidisciplinary applications. One of Mihalcea‘s key contributions has been at the intersection of NLP and the social sciences & humanities. She has pioneered "computational sociolinguistics" techniques to analyze language variation and change, as well as tools to support multilingual NLP and digital humanities scholarship. Mihalcea has also advanced research on sentiment analysis, figurative language processing, and multimodal perception & reasoning. A proponent of interdisciplinary collaboration, she co-authored a textbook on "Text Mining" geared toward those new to NLP. Follow her on [her website](https://web.eecs.umich.edu/~mihalcea/) and [Google Scholar](https://scholar.google.com/citations?user=UetM7FgAAAAJ). ## 9. Percy Liang Percy Liang is an Associate Professor of CS at Stanford, where he directs the Stanford NLP Group, and a key contributor to the Stanford AI Lab (SAIL). Liang has made significant advances in areas like question answering, semantic parsing, language grounding, and robustness & generalization in NLP models. On the latter front, he proposed the influential concept of "test-time distributional shift" to characterize models‘ fragility in real-world conditions. A major focus of Liang‘s work is developing NLP techniques that are more reliable, interpretable, and controllable—for example, his recent research on instruction tuning and constrained generation to imbue language models with desired behaviors and values. You can follow him on [Twitter](https://twitter.com/percyliang), [his website](https://cs.stanford.edu/~pliang/), and [Google Scholar](https://scholar.google.com/citations?user=faywFHwAAAAJ). ## 10. Diyi Yang Last but not least is Diyi Yang, an Assistant Professor in the School of Interactive Computing at Georgia Tech. Yang works at the intersection of NLP, social computing, and health—advancing language technologies to better understand and support human values. One key thrust of Yang‘s work is NLP for social good, as showcased by the Social Language Processing course she developed at Georgia Tech. Projects in this space include analyzing counseling conversations to support mental health interventions and identifying online misinformation & toxicity. Yang has also made core NLP contributions in areas like dialogue systems, commonsense reasoning, and model robustness & interpretability. A champion for human-centered AI, she co-founded the ACL Workshop on NLP for Social Good. Follow her on [Twitter](https://twitter.com/diyiy), [her website](https://www.cc.gatech.edu/~dyang888/), and [Google Scholar](https://scholar.google.com/citations?user=j3KTi-UAAAAJ). ## Conclusion We‘ve highlighted ten individuals at the forefront of NLP, but this only scratches the surface of the dynamic global community advancing language AI. The field has made remarkable strides in recent years, but major challenges and opportunities remain in making NLP systems more robust, interpretable, multimodal, and beneficial to society. Following the researchers and practitioners covered here—from pioneers to rising stars, academia to industry to open source—is a great way to stay informed about the latest developments. But even more importantly, we hope their work inspires you to get involved, whether through learning, research, engineering, or multidisciplinary collaboration. NLP is an incredibly exciting field because language is at the heart of human knowledge and interaction. As such, it will take a diverse community to realize language AI‘s potential in an ethical, inclusive, and impactful way. Whatever your background, you have a role to play in shaping that future. --- Source: [10 People Advancing the Frontiers of Natural Language Processing to Follow in 2025](https://33rdsquare.com/people-to-follow-in-the-field-of-natural-language-processing-nlp/)