I am a faculty member of the School of Interactive Computing and Machine Learning Center at Georgia Tech. My research lies at the intersections of machine learning, natural language processing, and social media. I direct the NLP X Lab which currently focuses on (1) large language models, such as cultural bias, multilingual capability, temporal shifts, and personalization; (2) text generation, such as constrained decoding and learnable evaluation metric; and (3) interdisciplinary NLP applications that can make impact in education, security, accessibility, etc. I received the NSF CAREER Award, Faculty Research Awards from Google, Sony, and Criteo, CrowdFlower AI for Everyone Award, Best Paper Awards at COLING'18 and ACL'24, as well as research funds from DARPA and IARPA. I am a member of NAACL executive board. I was a postdoctoral researcher at the University of Pennsylvania. I received my PhD in Computer Science from New York University, BSMS from Tsinghua University.
I'm recruiting 1-2 PhD students every year (apply to Machine Learning or CS PhD program and list me as a potential advisor; if you have EE background, consider also apply to ML ECE program). I recruit MS students (apply to MSCS program and email me) and undergraduates who have sufficient time and motivation for research theses.
Mar 2024, talk at USC and UCLA on "Amazing Multilingual Capabilities and Concerning Cultural Biases in LLMs"
Dec 2023, invited talk on "Amplifying Multilingual LLM’s Cross-lingual Ability'' at the BrainLink event
Oct 2023, demo of Thresh 🌾 has been accepted to EMNLP 2023 -- a customizable tool for fine-grained human evaluation of LLM generated texts (e.g., MT, summarization, text revision, + more)
Aug 2023, I was quoted in Business Insider about AI-generated content online.
Aug 2023, Mounica Maddela defended her PhD thesis and will join Bloomberg AI's LLM group
While LLMs have demonstrated impressive performance, their success is largely concentrated in English and other high-resource languages. In contrast, many non-English languages remain underrepresented and underserved. Moreover, these models often reflect Western cultural biases and struggle to capture the nuances of non-Western cultural contexts (Naous et al., ACL 2024;Naous et al., NAACL 2025). We work on identifying and closing these gaps in performance and cultural adaptation. Addressing these challenges calls for a deeper analysis of pre-training data to identify and mitigate representational gaps, as well as alignment (Guo et al., arXiv 2025) and inference-time algorithms (Le at al., ICLR 2024) that can dynamically adapt model behavior to diverse linguistic and cultural contexts.
Robustness and Reasoning of LLMs
Artificial General Intelligence (AGI) benchmarks seek to assess an AI system’s capacity to perform tasks that require human-level intelligence, including reasoning, learning, and adapting to novel situations (Zheng et al., ACL 2024;Mendes et al., EMNLP 2024). While current systems fall short of true AGI, there is growing interest in moving beyond static benchmarks toward more realistic, dynamic evaluations. Our research focuses on designing real-world tasks that better reflect practical challenges faced by LLMs, and on developing innovative methods (Zheng et al., arXiv 2025) to enhance their robustness and performance in these complex settings.
Interdisciplinary NLP+X Research
We actively collaborate with researchers to explore impactful real-world applications of large language models in Human-Computer Interaction, Security and Privacy, Healthcare, and Law (Jiang et al., EMNLP 2024;Dou et al., ACL 2024). As LLMs continue to advance, they offer exciting new capabilities across specialized domains. There are a lot of opportunities, as LLMs often exhibit promising but inconsistent performance in domain-specific tasks, where precision, context sensitivity, and domain knowledge are critical.
Chao Jiang (PhD 2025 → now at Apple AI/ML research) Yang Chen (PhD 2024, co-advisor: Alan Ritter → now at NVIDIA) Mounica Maddela (PhD 2023 → now at Bloomberg AI) Wuwei Lan (PhD 2021 → now at Amazon) Marcus Ma (MS 2024 → now PhD student at USC) Anton Lavrouk (MS 2024 → now at Lockheed Martin) David Heineman (BS 2024, CoC Outstanding Undergrad Research Award → Predoctoral young investigator at AI2) Jonathan Zheng (BS 2023 → now PhD student at Georgia Tech) Michael Ryan (BS 2023 → now PhD student at Stanford)
I am or was an executive board member of NAACL (2023-2024), a best paper award committee member for EMNLP 2022 and 2024, a senior area chair for EMNLP 2024 (resource and evaluation), 2022 (generation); NAACL 2025 (generation), 2022 (machine learning for NLP), 2021 (generation), and ACL 2020 (generation), and an area chair for COLM 2024, ACL 2023 (semantics), EMNLP 2021 (computational social science), EMNLP 2020 (generation), AAAI 2020 (NLP), ACL 2019 (semantics), NAACL 2019 (generation), EMNLP 2018 (social media), COLING 2018 (semantics), EMNLP 2016 (generation), a workshop chair for ACL 2017, and the publicity chair for EMNLP 2019, NAACL 2018 and 2016.
Miscellaneous
When I have spare time, I enjoy visiting art museums, hiking, biking, and snowboarding.
photos together with Alan Ritter's group 
