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Natural language processing
research labs.
Explore labs working on natural language processing, computational linguistics, and language models. Compare their listed interests and selected publications, then consult official pages for current projects.
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Check the languages, tasks, and evaluation methods in recent work. Ask whether the project focuses on models, datasets, linguistic analysis, or a particular application, and what preparation is expected.
Profiles appear here because their listed research keywords match this topic. Inclusion does not establish an open position. Check the official sources and any published application instructions before reaching out.
Prepare a research introduction ↗8 research-led lab profiles
Browse labs by their listed college affiliation
Independently curated · UnclaimedArman Cohan Research Group
Yale University
Arman Cohan · Assistant Professor of Computer Science
How can language models and representation learning be adapted to long-context, multi-document, and specialized-domain tasks while improving reliability and evaluation? The Yale NLP Lab studies language modeling, representation learning, retrieval, and domain-specific applications across mechanistic interpretability and evaluation. Researchers investigate capabilities and reasoning of large language models, retrieval-augmented methods, and knowledge-intensive environments using experiments and modeling. The group develops pretraining and summarization techniques for long and multi-document inputs and evaluates few-shot and faithfulness metrics for real-world NLP tasks. Lab members publish in major NLP and ML venues and maintain an active research and student training program.
Bing Liu Research Group
University of Illinois Chicago
Bing Liu · Distinguished Professor
How can AI systems continue learning after deployment as the world and their conversations change? The group investigates lifelong and continual learning, autonomous AI and open-world learning. Research also spans natural-language processing, chatbots and data mining, with a longstanding focus on extracting opinions and sentiments from text. Documented work includes detecting deceptive opinions, learning from positive and unlabeled examples, and extracting information from the web. The current program examines self-evolving agents and continually learning dialogue systems, connecting language understanding with adaptation over time.
Independently curated · UnclaimedChenhao Tan Research Group
The University of ChicagoChenhao Tan · Associate Professor of Computer Science
How can AI explanations and interactions be designed so humans make better decisions with machine assistance? The Chicago Human + AI (CHAI) Lab builds algorithms to align AI explanations with human interpretation to improve human-AI decision making. The group analyzes large textual datasets and natural experiments to understand how language shapes human decisions, such as persuasion and bargaining. Researchers develop decision-focused summarization and delegation methods so AI highlights the most decision-relevant information for people. The lab explores few-shot learning from human explanations to enable people to efficiently improve large language models.
Chitta Baral Research Group
Arizona State University (Tempe)Chitta Baral · Professor
How can knowledge and reasoning be combined with statistical models to improve language and vision understanding? The laboratory of Cognition and Intelligence develops neuro-symbolic and knowledge-based approaches to natural language understanding, vision-language question answering, and multi-modal document understanding. Projects investigate prompting and programming large language models, bias origins in NLP, efficient low-data methods, and knowledge assimilation for inference. Researchers apply methods spanning symbolic knowledge representation, reasoning frameworks, and machine learning to develop robust, generalizable models for tasks in cybersecurity, robotics, and biological and health sciences. The group maintains publications and a public lab website documenting tools and project outputs.
Humanitarian Informatics Lab
George Mason UniversityHemant Purohit · Associate Professor of Information Sciences and Technology
During a crisis, useful information can be buried in a flood of online messages. Hemant Purohit develops human-centered AI systems for analyzing behavior and managing unstructured, multi-source data in real time. His Humanitarian Informatics Lab combines natural-language processing, semantic computing, and machine learning with social and cognitive theories. Applications include emergency preparedness and response, public safety, and cybersecurity. The research emphasizes human control and collaboration with intelligent systems, offering an intersection of data science, public services, and crisis informatics.
Mark Dredze Research Group
Johns Hopkins University
Mark Dredze · John C. Malone Professor
How can natural language processing of social media and clinical text detect and monitor public health outcomes in real time? The group develops artificial intelligence systems that apply statistical models of language to social media analysis for public health surveillance. Researchers design and evaluate statistical methods for information extraction addressing syntax, semantics, sentiment, and spoken language processing tasks. They build new clinical natural language processing methods to analyze medical records for applications in clinical informatics and medicine. Applications studied include tobacco control, vaccinations, infectious disease surveillance, mental health, drug use, and gun violence prevention using AI-based language models.
Pranay Joshi Lab
The University of Alabama
PRanay Joshi · Assistant Professor
An illustrative lab studying how people interpret AI uncertainty and how reliable systems support better decisions.
Independently curated · UnclaimedThorsten Joachims Research Group
Cornell University
Thorsten Joachims · Jacob Gould Schurman Professor of Computer Science and Information Science
Clicks, choices, and text edits contain learning signals—but they also carry bias. Thorsten Joachims and his students develop machine-learning methods for using that feedback in search, recommendation, education, and language models. Their work connects counterfactual evaluation, policy learning, ranking, and personalization. POTEC tackles learning from logged decisions when the action space is large; coactive learning studies how a user’s edits can help personalize an LLM without requiring a perfect reference answer. This faculty-led research group offers a route into both the theory of interactive learning and its application to human-centered systems.
Showing 1–8 of 8 labs