Plain Language Summarization Professors and Supervisors

Byron Wallaceمشاهده پروفایل

دانشیار

Northeastern UniversityUnited States

Byron Wallace is the Sy and Laurie Sternberg Interdisciplinary Associate Professor at Northeastern University's Khoury College of Computer Sciences, where he also serves as Associate Dean for Research and Director of the BS in Data Science Program. His research focuses on Natural Language Processing and Machine Learning applications in healthcare. Education Details of formal education are not explicitly provided in the available text, though he holds a PhD from Tufts University (mentioned in thesis award context). Research Interests His work centers on developing NLP and ML models for health applications, with particular emphasis on: Biomedical evidence synthesis automation Electronic Health Record processing Model interpretability and trustworthiness Human-in-the-loop systems Learning with limited supervision Research Trends Recent publications demonstrate strong focus on large language model applications in healthcare, including factuality evaluation for medical summarization, evidence extraction from clinical trials, and interpretable risk prediction models. Notable contributions include work on GPT-3 applications in medical evidence synthesis and neural methods for EHR analysis. Scientific Awards ACL Outstanding Paper Award (2022) ICLR Spotlight (top 5% acceptance) (2024) Best Student-led Paper Award at AMIA 2021 NSF CAREER Award (2018-2023) Advising and Grants Currently advises 4 PhD students and has mentored numerous others. Major funding includes: NSF CAREER Award ($500K+) NIH R01 grant for EHR summarization NSF Medium grant for healthcare summarization Support from Army Research Office, Amazon, and Seton Hospital Labs and Teams Leads the Evidence Inference project team working on automated biomedical evidence synthesis. Collaborates with Brigham and Women's Hospital, Mass General Hospital, and Reboot Rx for clinical translation of research.

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Tal Micah Augustمشاهده پروفایل

استادیار

University of Illinois Urbana-ChampaignUnited States

Tal Micah August is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois. His research focuses on Natural Language Processing, Plain Language Summarization, and Human-Computer Interaction, emphasizing audience-adaptive communication and AI-driven educational tools. Key research interests include developing intelligent writing assistants, evaluating AI-generated summaries across audiences, and leveraging large language models for legal and mathematical education. He collaborates on projects involving user-centered design and curriculum-grounded AI assessment. His work has been presented at major conferences like CHI and EMNLP, addressing topics such as interactive writing systems, plain-language metrics, and educational storytelling. No specific grants or student advisees are listed in the provided materials. Research highlights include the MathFish framework for math reasoning evaluation and studies on audience-specific plain language generation.

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Lucy Wangمشاهده پروفایل

استادیار

University of WashingtonUnited States

Lucy Lu Wang is an Assistant Professor at the University of Washington Information School and holds adjunct appointments in Biomedical Informatics & Medical Education , Human Centered Design & Engineering , and Computer Science & Engineering . She leads the LARCH lab focused on language accessibility research. Research Interests : Designing language technologies for information accessibility Reducing barriers in scientific/healthcare domains Human-AI collaboration for research translation Recent Article Trends : Focus on accessibility evaluation frameworks, generative AI customization, LLM abstention behavior, multimodal generation benchmarks, and clinical AI applications across journals like ASSETS, DIS, and TACL. Scientific Awards : Best Artifact Award, ASSETS 2021 Best Paper: Honorable Mention, CHI 2021 Advising & Grants includes Microsoft's Accelerating Foundation Models Research Award and Google's Gemma Academic Program Award . She contributes to open-access tools like FigurA11y and PaperPlain .

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Karlheinz Friedbergerمشاهده پروفایل

پژوهشگر

Ludwig Maximilian University of MunichGermany

Karlheinz Friedberger is a researcher at Ludwig Maximilian University of Munich's Department of Computer Science. His work focuses on formal methods, software verification, and program analysis. He has contributed to tools like CPAchecker, JavaSMT, BenchExec, and VerifierCloud, and has participated in software verification competitions. Education : PhD in Computer Science (Ludwig Maximilian University of Munich, 2021). Research Interests : Formal verification techniques, program analysis, BDD libraries, SMT solvers, and multi-threaded program validation. He has won a gold medal in Reachability Plain and a silver medal in Reachability Arithmetic at the RERS Challenge 2016. His recent publications emphasize domain-independent interprocedural analysis and tool development for formal verification.

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Byron C. Wallaceمشاهده پروفایل

پژوهشگر

Beuth University of Applied Sciences BerlinGermany

Byron C. Wallace is a prominent researcher in Natural Language Processing with a focus on biomedical and clinical applications . His work spans model distillation, factuality evaluation, and medical text simplification. Key contributions include developing methods for tracing teacher models in distillation and creating benchmarks like FactPICO and RedHOT. Research interests include LLM interpretability , medical evidence synthesis , and cross-modal alignment in clinical NLP His recent publications analyze syntactic template repetition in LLMs, token erasure effects , and definition-augmented biomedical NER Wallace's work has advanced zero-shot summarization techniques, pharmacovigilance automation , and patient data privacy analysis in clinical models. Current projects explore chain-of-thought distillation , multilingual medical simplification , and interpretable feature extraction from EHR data. Key collaborators include researchers from institutions like Allen Institute for AI, MIT, and various biomedical NLP teams. His methodological innovations in instance attribution , data augmentation , and counterfactual reasoning have influenced modern NLP research paradigms.

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Tal Augustمشاهده پروفایل

استادیار

University of Illinois Urbana-ChampaignUnited States

Tal August is an Assistant Professor at the University of Illinois at Urbana-Champaign's Siebel School of Computing and Data Science. His research focuses on adapting language to improve communication between humans and technology, emphasizing personalized language systems for science, health, and legal domains. He holds a PhD from the University of Washington (Allen School of Computer Science and Engineering), advised by Katharina Reinecke and Noah Smith. Research Interests: Human-Computer Interaction (HCI) : Designing tools that bridge communication gaps through language adaptation. Natural Language Processing (NLP) : Developing AI systems for automatic language personalization. Science Communication : Studying how language affects knowledge sharing in technical domains. Interdisciplinary Collaboration : Breaking down barriers through tailored communication strategies. Awards : Honored on the List of Teachers Ranked as Excellent by Their Students (2025). Recognized for contributions to personalized language tools via grants from the Allen Institute for AI. Advising & Grants : Recruiting 1-2 PhD students for Fall 2025. Recent grant supported work on personalizing research literature searches for scholars. Courses Taught : CS 598 TAL - Language, Interfaces, and Communication (focuses on HCI/NLP intersections). Key Projects : Innovations include TermSight (simplifying legal contracts), Qlarify (expandable scientific summaries), and Semantic Reader (AI-augmented reading interfaces).

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Lucy Lu Wangمشاهده پروفایل

استادیار

University of WashingtonUnited States

Lucy Lu Wang is an Assistant Professor at the University of Washington Information School, where she leads the Language Accessibility Research (LARCH) Lab. She holds adjunct appointments in the Paul G. Allen School of Computer Science & Engineering, Biomedical Informatics & Medical Education, and Human Centered Design & Engineering. Additionally, she serves as a Research Scientist at the Allen Institute for AI (Ai2). Dr. Wang is an NLP and health informatics researcher focusing on designing and building language technologies to improve access to and understanding of information, particularly in high-expertise domains like science and healthcare. Her work has produced techniques and tools that enhance access to scholarly content, synthesize scientific evidence, and support better health decision-making. She specializes in creating AI systems that make complex information more accessible, with particular attention to accessibility for visually impaired users and improved health communication. Her recent publications demonstrate strong trends across natural language processing for scientific literature, accessibility research, and health informatics. The work shows consistent focus on developing practical tools that bridge the gap between advanced AI capabilities and real-world information needs, particularly in scholarly and healthcare contexts. Many projects address critical issues of accessibility, reliability, and usability of AI systems for diverse user populations. Dr. Wang actively mentors a diverse group of students across multiple departments, including PhD students in the iSchool, HCDE, and CSE. She has successfully guided numerous students who have gone on to positions at institutions like UIUC, Meta, Microsoft, and Netflix. Her research is generously supported by multiple grants and gifts from Google, the National Institutes of Health, Microsoft, and various University of Washington funding programs, focusing on AI-driven accessibility solutions, healthcare applications, and research translation. At the LARCH Lab, Dr. Wang leads a team working on designing, building, and evaluating language technologies that make information more accessible and understandable for everyone, with special emphasis on high-expertise domains such as healthcare and science communication.

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Yue Guoمشاهده پروفایل

استادیار

University of Illinois Urbana-ChampaignUnited States

Yue Guo is an Assistant Professor in the Department of Information Sciences at the University of Illinois. Her research focuses on natural language processing (NLP), biomedical informatics, and healthcare communication. She explores how large language models (LLMs) can improve lay language summarization of complex scientific content, particularly in healthcare contexts. Her work emphasizes personalized jargon detection, factuality evaluation of summaries, and accessibility of health information for diverse audiences. Guo has also conducted extensive studies on radiation oncology outcomes, including xerostomia (dry mouth) recovery in head and neck cancer patients and the impact of radiation dose patterns on patient-reported symptoms. Her interdisciplinary research bridges computational methods with clinical needs, aiming to enhance patient understanding and care through advanced NLP techniques. Her recent articles (2021–2025) highlight trends in LLM-driven biomedical communication, evaluation of health summaries, and data-driven approaches to cancer treatment optimization. Notable projects include APPLS (a summarization metric framework) and personalized jargon identification systems for interdisciplinary collaboration. Guo teaches IS 504: Sociotechnical Information Systems , integrating technical and human factors in information systems design.

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