Find Academic Supervisors

Jinhan KimView profile

Researcher

• Software Engineering
• Mutation Testing
• Fault Localization
• +4 more

Jinhan Kim is a Postdoctoral Researcher at the Università della Svizzera italiana (USI) in the Faculty of Informatics, working in the TAU lab under Prof. Paolo Tonella. He earned his Ph.D. from KAIST under Prof. Shin Yoo, focusing on software engineering research in mutation testing, fault localization, and deep learning system testing. His work bridges traditional software engineering techniques with AI-driven methodologies, emphasizing AI4SE and SE4AI paradigms. Education: Ph.D. in Software Engineering, KAIST, 2023 Research Interests: Mutation Testing Deep Learning System Testing Autonomous Systems Testing Adversarial Attack Detection Empirical Software Engineering Service and Leadership: Organized SBFT 2026 and DeepTest 2026 (co-located with ICSE 2026) Program Committee Member for ASE, ISSTA, Mutation, and DeMeSSAI Board of Distinguished Reviewers for TOSEM (2024–2025) Labs and Teams: Active contributor to the TAU Lab at USI, focusing on advanced software testing and AI integration.

Desmond ElliottView profile

Associate Professor

• Vision-Language Models
• Multimodal Machine Learning
• Natural Language Processing
• +5 more

Desmond Elliott is an Associate Professor and Villum Young Investigator at the Department of Computer Science, University of Copenhagen. His research focuses on vision-language models, multilingual and multimodal processing, with particular emphasis on tokenization-free language modeling approaches. He leads a research group actively working on pixel language models and cross-lingual multimodal understanding. University of Copenhagen, Department of Computer Science Villum Young Investigator Associate Editor for JAIR (2025-2028) Senior Area Chair for ACL 2025 Elliott's research spans vision-language integration, multilingual NLP, and multimodal machine learning. His work explores how language models can operate directly on visual pixels without traditional tokenization, enabling more seamless integration of vision and language processing. He investigates compositional generalization in multimodal systems, retrieval-augmented image captioning, and cross-lingual transfer in vision-language tasks. His group develops methods for low-resource language processing and creates benchmarks for evaluating multimodal systems across diverse cultural contexts. His recent publications demonstrate strong trends in pixel-based language modeling, synthetic dataset generation through retrieval augmentation, and multilingual vision-language processing. The work spans theoretical advances in model architectures and practical applications in areas like medical text analysis, food culture understanding, and social media content moderation. His research often bridges computer vision and natural language processing with a focus on making these technologies accessible across diverse languages and cultures. Best Paper Honorable Mention at CVPR Visual Concepts Workshop 2025 Best Long Paper Award at EMNLP 2021 Area Chair Favourite paper at COLING 2018 Elliott actively supervises student projects in BSc and MSc programs related to his research interests. His research has received substantial funding from Google (2024-2025), Facebook (2022-2024), Villum Foundation (2021-2026), Novo Nordisk Foundation (2019-2024), and European Union (2023-2026). He regularly recruits postdocs for projects including the Danish Foundation Models project and the Responsible AI for the People Project. His group holds regular meetings on Tuesdays from 13:00-14:00 in IF G.03, with an active mailing list for announcements. The research environment appears collaborative, with frequent co-authorship across institutions and regular participation in major NLP and computer vision conferences.

Boris BuffoniView profile

Senior Lecturer

• Calculus of Variations
• Lagrangian and Hamiltonian systems
• Optimal transportation
• +7 more

Boris Buffoni is a Senior Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences, Institute of Mathematics, specifically within the Chair of Partial Differential Equations. He maintains his office at MA C2 605 (MA Building), Station 8, 1015 Lausanne, Switzerland, and can be contacted at boris.buffoni@epfl.ch or +41 21 693 49 87. His academic role spans both teaching responsibilities across multiple mathematics programs and active research in theoretical and applied mathematics. Dr. Buffoni's research program centers on the calculus of variations applied to Lagrangian and Hamiltonian systems, with significant contributions to optimal transportation in Lagrangian dynamics and hydrodynamics. His work explores semi-global minimization methods for quasi-linear elliptic variational problems and the variational approach to capillary-gravity water waves and their energetic stability. Additional research foci include local bifurcation and center-manifold theory for elliptic PDEs, the configurations of infinite elastic cylinders under compression or traction, and the analytic theory of global bifurcation with applications to gravity waves and their secondary bifurcations. The trajectory of his recent publications reveals a deepening focus on three-dimensional water wave phenomena, particularly steady rotational flows, gravity-capillary solitary waves, and advanced mathematical techniques for analyzing these complex systems. His 2025 publications demonstrate continued innovation in applying Kato's approach to locally coercive problems and developing the theoretical foundations of global bifurcation. The consistent application of variational methods and bifurcation theory across his work represents a unifying theme in addressing challenging problems in fluid dynamics and nonlinear partial differential equations. Dr. Buffoni has received research support including an EPSRC grant (GR/L41059) for work on 'Multibump localised solutions for spatially homogeneous partial differential equations,' reflecting the significance of his contributions to the field. His teaching portfolio at EPFL includes foundational courses such as Analysis II, Functional Analysis I, and Partial Differential Equations of Evolution, where he imparts knowledge of differential and integral calculus of real functions of several variables, linear functional analysis, and fundamental techniques for solving evolution equations.

• Software Testing
• Deep Learning Systems
• Mutation Testing
• +4 more

Nargiz Humbatova is a postdoctoral researcher at the Testing Automated (TAU) research group within the Software Institute (SI) at Università della Svizzera italiana (USI). She holds a PhD from USI (2023), an MSc in Advanced Computing from the University of Bristol, and a BSc in Mathematics from Moscow State University. Her research focuses on mutation testing of deep learning systems, fault localization, and program repair, with a particular emphasis on real-world fault analysis and testing methodologies for AI systems. Education: PhD in Informatics, Università della Svizzera italiana (2023) MSc in Advanced Computing, University of Bristol BSc in Mathematics, Moscow State University Research interests include mutation testing techniques, test input prioritization, deep learning fault benchmarks, and the application of large language models (LLMs) to fault localization and repair. She has contributed to the ERC-AdG project PRECRIME, dedicated to testing AI-based systems. Her work emphasizes practical validation through empirical studies and real-world fault injection. Her publications span topics such as mutation testing pipelines (e.g., muPRL), spectral analysis of neural activation values, and the development of tools like DeepCrime for deep learning testing. These articles highlight advancements in evaluating and improving the robustness of AI systems through rigorous testing frameworks. Labs/Teams: Member of the TAU (Testing Automated) research group at USI’s Software Institute, collaborating on interdisciplinary projects at the intersection of software engineering and artificial intelligence.

David SuterView profile

Associate Professor

• Gene Regulation
• Cell Identity
• Transcriptional Memory
• +13 more

David Suter is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Life Sciences (SV) and the Institute of Bioengineering (IBI-SV), leading the UPSUTER research unit focused on Gene Regulation & Cell Identity. He is based in the AI building at Station 19, Lausanne, and holds active teaching and leadership roles across multiple departments, including Life Sciences Engineering. He serves as Director of the Doctoral Program in Molecular Life Sciences (EDMS), and is a member of the Doctoral School Committee and the PhD Program Committee for Computational and Quantitative Biology. His research centers on the quantitative analysis of gene expression dynamics and cell identity, employing cutting-edge techniques such as ultrasensitive bioluminescence imaging, fluorescent protein timers, genome editing, and high-throughput genomics (ChIP-seq, ATAC-seq, CUT&RUN). His lab investigates transcriptional memory, proteostasis, transcription factor search dynamics, and cell fate decisions using embryonic stem cells and cancer cells as model systems. These approaches enable real-time, single-cell resolution studies of transcription factor behavior, mRNA production, and protein turnover across cell divisions. The publication trends in his work emphasize live-cell imaging, quantitative molecular biology, and systems-level understanding of gene regulation. His articles reflect a strong focus on visualizing and quantifying biological processes in living cells, particularly around transcription factors like Sox2 and their role in pluripotency and differentiation. The integration of biophysical tools with molecular biology allows his team to decode the mechanisms underlying cell identity maintenance and fate transitions. He actively supervises PhD students, both current and past, contributing significantly to doctoral education at EPFL. His leadership in the EDMS program underscores his commitment to training the next generation of scientists in molecular and quantitative life sciences. While no specific grants are mentioned in the text, his lab's advanced technological development (e.g., bioluminescence imaging tools) suggests strong funding support. David Suter leads the Suter Lab, a multidisciplinary research group combining molecular and cell biology with computational and biophysical methods. The lab fosters close collaborations and maintains a strong technical core in imaging and genomics, enabling innovative research on the fundamental principles of gene regulation during development and disease.

Christophe MoserView profile

Professor

• Applied Photonics
• Microengineering
• Optical Computing
• +3 more

Christophe Moser is a Full Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL), affiliated with the School of Engineering (STI) and the Institute of Microengineering (IMT). He leads the Laboratory of Applied Photonics Devices (LAPD) and holds additional roles in EPFL's educational programs, including directorship of SMT-GE (General Engineering) and teaching responsibilities in the EDPO, EDAM, and SMT-ENS departments. His work bridges academic research, industrial innovation, and education in photonics and microengineering. His research focuses on applied photonics , laser technology , and microengineering , with applications in telecommunications, sensing, fiber lasers, and micro-machining. Recent publications highlight advancements in volume holographic gratings, tunable lasers, and wavelength-stabilized laser diodes. The articles demonstrate expertise in optical engineering , particularly tunable filters , holographic components , and photonic device integration . Key sub-fields span telecommunications, Raman systems, semiconductor lasers, optical signal processing, and photorefractive materials. As a co-inventor of 24 patents and author of 14 peer-reviewed papers, Moser contributes significantly to industrial photonics . He advises numerous PhD students and teaches courses in additive manufacturing , laser fundamentals , optical computing , and practical venture capital. He is associated with EPFL's Laboratory of Applied Photonics Devices (LAPD), where his team develops advanced photonic solutions. His career combines academic excellence with entrepreneurial success, having co-founded Ondax, Inc., and raised $15 million for holographic component production.

Martin RajmanView profile

Researcher

• Artificial Intelligence
• Computational Linguistics
• Natural Language Processing
• +7 more

Martin Rajman is a Senior Scientist at École Polytechnique Fédérale de Lausanne (EPFL) with multiple affiliations across the institution. He holds positions in the School of Computer and Communication Sciences (SIN - Teaching, SCI IC MR Group, SSC - Teaching) as well as in the Vice Presidency for Strategic Development (VPS Artificial Intelligence) and the Vice Presidency for Academic Affairs (SNAI Administration). He serves as the Executive Director of Nano-tera.ch, a large Swiss Research Program funding collaborative multi-disciplinary projects in Health and the Environment. Rajman's research spans the intersection of artificial intelligence, natural language processing, and information retrieval. His work demonstrates a consistent focus on developing practical applications of computational linguistics and machine learning techniques. Early in his career, he contributed significantly to syntactic parsing, stochastic language models, and vector space representations for text. More recently, his research has expanded into deep learning applications for 3D reconstruction, empathetic conversational agents, and distributed analytics systems. His publications reveal a trajectory from foundational NLP research toward increasingly applied and interdisciplinary work connecting AI with healthcare, environmental monitoring, and human-computer interaction. Analysis of his recent publications (2015-2024) shows a clear evolution toward more applied AI research with strong interdisciplinary connections. While maintaining his core expertise in natural language processing and information retrieval, his work has expanded into computer vision, healthcare applications, and sustainable computing. The publications demonstrate increasing collaboration across disciplines, with applications in medical imaging, mental health support systems, environmental monitoring, and human-centered AI. His leadership role in the Nano-tera.ch program reflects this interdisciplinary approach, connecting computing research with real-world challenges in health and environmental contexts. Rajman has mentored several PhD students including Ailomaa Marita, Eckard Emmanuel, Melichar Miroslav, and Veselý Martin. His research has been supported through the Nano-tera.ch program, which has funded more than 100 research projects with over 95 million CHF in public funding. He has also managed more than 20 European projects during his tenure as Director of the EPFL Global Computing Center. As Executive Director of Nano-tera.ch, Rajman leads a significant research initiative connecting EPFL with national and international partners. His work bridges academic research with industry applications, notably through collaborations with eBay on product ranking technology and with Elsevier on article recommendation systems. His leadership extends to managing large-scale research programs while maintaining an active research agenda and mentoring the next generation of computer scientists.

• Artificial Intelligence in Education
• Digital Transformation in Education
• Educational Technologies
• +3 more

Sabine Seufert is a Full Professor of Management of Educational Innovations at the University of St. Gallen (HSG), where she has held leadership roles including Director of the Institute of Educational Management and Educational Technologies (since 2021) and Director of the Institute of Business Education and Educational Management (2009–2020). She holds a PhD in Information Management (1996) and a Habilitation in Business Education (2006) from the University of St. Gallen, specializing in educational innovation and technology integration. Her research focuses on digital transformation in education, AI applications in learning environments, and competence development in vocational training. She has pioneered initiatives like the Swiss Centre for Innovations in Learning (scil) and contributed to frameworks for hybrid intelligence in education. Her work bridges academic research and practical implementation, emphasizing ethical AI use and teacher preparedness for technological change. Key affiliations include the European Foundation for Management Development (EFMD), the editorial board of the International Journal of Learning Technology , and advisory roles for journals like Empirical Research in Vocational Education and Training . Her recent publications explore AI literacy, social robots in classrooms, and the impact of generative AI on teaching and learning. Her career spans over 25 years, including guest professorships at the University of Southern Queensland and Stanford University, leadership in educational institutions, and policy advisory roles. She is actively involved in shaping future-ready education systems through digital innovation and human-AI collaboration strategies.

• Quantum Theory of Condensed Matter
• Topological Insulators
• Spin Phenomena
• +7 more

Prof. Jelena Klinovaja is a Professor in the Department of Physics at the University of Basel, affiliated with the Philosophisch-Naturwissenschaftliche Fakultät. She holds leadership roles in research groups focusing on quantum theory of condensed matter, topological systems, and spin phenomena. Her career includes a PhD from the University of Basel (2012), a Harvard Fellowship (2013), and tenure as an assistant (2014) and associate professor (2019) before her current rank. She leads research on topological insulators, graphene, and Majorana fermions, with applications to topological quantum computing. Notable awards include the Swiss Physical Society Prize (2013) and an ERC Starting Grant (2017). Her work combines theoretical physics with experimental collaborations, particularly in nanowires and superconducting systems. She mentors students in the Honors Track program and contributes to interdisciplinary initiatives like NCCR SPIN. Education: Bachelor/Master from Moscow Institute of Physics and Technology (2007-2009); PhD in Theoretical Physics from University of Basel (2012). Research Interests: Topological effects in condensed matter, spintronics, quantum transport, Majorana fermions, and cavity quantum electrodynamics. Publications span over 50 peer-reviewed articles since 2012, focusing on topics like Josephson junctions, topological superconductivity, and hybrid systems. Her work bridges theoretical models with experimental realizations, emphasizing practical applications in quantum technologies. Awards: Swiss Physical Society Prize (2013), ERC Starting Grant (2017). Active in the scientific community, she collaborates with institutions worldwide and advises PhD students in quantum physics and nanoscience. Her research group also explores magnonic systems and topological materials engineering.

• Federated Learning
• Optimization for Machine Learning
• Privacy-Preserving Machine Learning
• +12 more

Sai Praneeth Karimireddy is an Assistant Professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), with a courtesy appointment in the Ming Hsieh Department of Electrical and Computer Engineering. He previously held an SNSF postdoctoral fellowship at UC Berkeley under Michael I. Jordan and earned his PhD at EPFL advised by Martin Jaggi. He co-leads the Federated Learning and Data Quality working group at MONAI (NVIDIA) and collaborates with researchers at Apple Research. His research lies at the intersection of optimization, machine learning, statistics, and economics, with a strong focus on federated learning, privacy-preserving machine learning, data valuation, and AI for healthcare. He investigates how data quality, privacy, and incentives shape collaborative ML systems, especially in high-stakes domains like medicine. His work has been deployed at companies such as Meta, Google, OpenAI, and Owkin. His recent publications span top-tier venues including NeurIPS, ICML, ICLR, and JMLR, with influential contributions such as the SCAFFOLD algorithm for federated learning. His research shows a consistent trend toward building robust, private, and incentive-compatible collaborative learning systems, with increasing emphasis on real-world deployment in healthcare and decentralized data markets. 2023 SNSF Mobility Fellowship 2022 Patrick Denantes Memorial Prize for best thesis in computer science 2022 EPFL thesis distinction (top 8%) 2021 Chorafas Foundation Prize for exceptional applied research Capitol One Fellow (2025) He is actively mentoring PhD students and leads a research group focused on foundational and applied challenges in federated and privacy-preserving ML. He teaches graduate courses at USC, including CSCI 599 on Optimization for Machine Learning and CSCI 699 on Privacy-Preserving Machine Learning. He serves as an area chair for ICLR 2025 and co-organizes major workshops on incentives in data sharing and federated learning. His lab collaborates with institutions like NVIDIA, Apple, and Argonne National Laboratory, and he is building a research program centered on sustainable, equitable, and trustworthy AI ecosystems.

Zsolt KulcsárView profile

Professor

• Neurovascular diseases
• Cerebral aneurysms and vascular malformations
• Neuroendovascular interventions
• +2 more

Prof. Dr. med. Zsolt Kulcsár is Director of the Department of Neuroradiology at the University Hospital Zurich (USZ) and a board member of the Clinical Neurocenter. He is an internationally recognized interventional neuroradiologist with expertise in the diagnosis and endovascular treatment of complex neurovascular diseases—including cerebral arteriovenous malformations, dural arteriovenous fistulas, aneurysms, and acute stroke—as well as pediatric cerebrovascular malformations and pulse-synchronous tinnitus. Education & Academic Qualifications 2006 – Specialist in Radiology, Semmelweis University, Budapest 2008 – Specialist in Neuroradiology, Semmelweis University, Budapest 2012 – Dr. phil. (PhD) in Clinical Neuroscience, Semmelweis University, summa cum laude 2013 – Habilitation & Venia Legendi in Diagnostic Radiology, University of Essen-Duisburg, Germany 2015 – Re-certification (Umhabilitation), Faculty of Medicine, University of Geneva 2016 – Promotion to Professor Assistant , University of Geneva Research Focus Prof. Kulcsár’s research concentrates on three pillars: (1) aneurysm biology and hemodynamics , exploring how wall shear stress and flow patterns influence aneurysm evolution; (2) novel neuroendovascular techniques and materials , including intra-saccular biological fillers and liquid embolics for vascular malformations; and (3) revascularization strategies in acute ischemic stroke . Pre-clinical studies are conducted in collaboration with the Musculoskeletal Research Unit (MSRU) of the Vetsuisse Faculty, while multicentre clinical trials validate new devices and treatment paradigms. Professional Service & Leadership President, European Society of Minimally Invasive Neurological Therapy (ESMINT) 2022-2024 Secretary-General, ESMINT 2020-2022 Treasurer, ESMINT 2018-2020 Chair, ESMINT Industrial Liaison Committee 2014-2018 Member, Executive Committee, ESMINT 2012-2018 Medical Director, ESMINT Portal 2012-2017 Board member, Swiss Society of Neuroradiology (SGNR) since 2022 Editorial roles: Associate Editor Journal of Neuroradiology ; Editorial Committees Clinical Neurosciences and Neuroradiology Laboratory & Collaborative Networks Prof. Kulcsár leads an integrated clinical-experimental program that bridges the USZ Department of Neuroradiology with the MSRU pre-clinical imaging platform and multiple industry partners. The team develops next-generation endovascular devices and biologically active embolic materials, translating findings from bench-top flow models to first-in-human trials.

• Coffee Chemistry
• Food Science
• Analytical Chemistry
• +3 more

Samo Smrke is a Researcher at the ZHAW School of Life Sciences and Facility Management, specializing in the Coffee Competence Centre and Analytical Technologies. His work focuses on advancing coffee science through projects like the SCA Coffee Expertise Program and collaborations with industry leaders such as Nespresso and Julius Meinl. He has led multiple research initiatives including studies on coffee freshness, roasting effects, aroma release mechanisms, and packaging technologies. Smrke's research interests span coffee chemistry, analytical techniques, and sensory analysis. He has published extensively on topics such as degassing kinetics of roasted coffee, antioxidant generation during roasting, and aroma modulation in instant coffee products. His work often employs advanced methods like proton transfer reaction mass spectrometry (PTR-MS) and gravimetric analysis. He is a co-author of influential books including The Coffee Freshness Handbook and The SCA Water Quality Handbook , which provide foundational knowledge for industry professionals. Smrke’s projects frequently bridge academic research with practical applications, addressing challenges in coffee production, storage, and quality assurance.

Philippe SchwallerView profile

Assistant Professor

• Artificial Intelligence in Chemistry
• Machine Learning for Molecular Discovery
• Sustainable Chemistry
• +6 more

Philippe Schwaller is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), School of Basic Sciences, within the Institute of Chemical Sciences and Engineering. He leads the Laboratory of Artificial Chemical Intelligence (LIAC), a research group focused on leveraging artificial intelligence to accelerate molecular discovery and sustainable chemistry. He is also a core Principal Investigator of the NCCR Catalysis, a national Swiss research center. His research lies at the intersection of chemistry, materials science, and computer science, with a strong emphasis on developing machine learning models for molecular design and synthesis. LIAC's work is driven by real-world sustainability challenges, aiming to reduce the time and cost of discovering new functional molecules and materials. The recent publications and projects from his lab highlight a strong trend in generative AI for chemistry, including memory-augmented models, hypergraph neural networks, and large language models tailored for scientific discovery. These efforts are complemented by educational initiatives such as the 'AI for Chemistry' course and practical programming resources for chemists. He actively supervises a diverse group of PhD students and contributes to multiple doctoral programs at EPFL, including EDCH and EDPY. His teaching portfolio includes courses on computational chemistry, AI applications in chemistry, and scientific machine learning. Philippe Schwaller is deeply involved in advancing AI-driven scientific discovery through both research and education, positioning his lab at the forefront of artificial chemical intelligence. The lab maintains active open-source contributions on GitHub, fostering collaboration and transparency in scientific AI development.

Dan OlteanuView profile

Professor

• Database Systems
• Database Theory
• Artificial Intelligence
• +7 more

Prof. Dan Olteanu is a full professor at the Department of Informatics, University of Zurich, leading the Data Systems and Theory (DaST) group. He holds visiting professorships at the University of Oxford and is an emeritus fellow of St Cross College. His academic journey includes a PhD from Ludwig Maximilian University (2005), postdoctoral roles at Saarland University and Cornell University, and prior faculty positions at Oxford (2007–2020). He has also worked in industry with companies like LogicBlox and RelationalAI, focusing on database systems and AI. Education: Bachelor’s in Computer Science, Politehnica University of Bucharest (2000) PhD in Computer Science, Ludwig Maximilian University (2005) Professional Roles: Full Professor, University of Zurich (since 2020) Visiting Professor, University of Oxford Emeritus Fellow, St Cross College Editorial Roles: ACM TODS, VLDBJ, SIGMOD Conference Chair: ICDT Council (since 2022) His research focuses on data systems theory, including query optimization, probabilistic databases, factorized databases, and in-database machine learning. He co-authored the seminal book Probabilistic Databases (2011) and has pioneered algorithms for efficient machine learning over relational data and incremental maintenance of analytical workloads. His work emphasizes scalable, theoretically grounded solutions for real-world data challenges. Awards: ICDT 2019 Best Paper Award ACM SIGMOD 2018 Distinguished PC Member Award ERC Consolidator Grant (2016) Oxford Outstanding Teaching Award (2009) Grants & Funding: Supported by Google, Microsoft Azure, Amazon AWS, EPSRC, and the European Commission. His research bridges academia and industry, with contributions to commercial systems like LogicBlox and RelationalAI. Labs & Teams: Heads the DaST group at Zurich, focusing on data systems theory and applications. Collaborates widely in the database and AI communities.

• Natural Language Processing
• Question Answering
• Human-AI Collaboration
• +7 more

Jordan Boyd-Graber is a Professor in the Department of Computer Science at the University of Maryland's College of Computer, Mathematical, and Natural Sciences. He serves as a leading researcher in Natural Language Processing with significant contributions across multiple NLP subfields. His work bridges theoretical advances with practical applications requiring human-AI collaboration. His research interests span Natural Language Processing , Question Answering systems , Human-AI collaboration , Machine Translation , and Topic Modeling . He focuses on developing systems that work effectively with humans rather than replacing them, emphasizing interpretability and user-centered design. His work often involves creating evaluation frameworks that better capture real-world utility rather than just technical metrics. His publication record shows consistent leadership in the field, with numerous papers at top venues including ACL, EMNLP, and NAACL. Recent work (2023-2024) demonstrates strong engagement with LLMs, human evaluation methodologies, and practical applications in health and translation domains. His research often involves student collaborators, indicating active mentorship. ACL Fellow (2021) Program Chair for ACL 2023 Organizer of prompt hacking competition Leader in human-centered NLP evaluation Boyd-Graber has secured substantial funding for his research, particularly in projects involving human-AI collaboration and question answering systems. His work often involves interdisciplinary teams spanning computer science, linguistics, and domain-specific applications. He has mentored numerous graduate students who have gone on to successful careers in academia and industry. He leads research groups focused on developing interpretable NLP systems that work effectively with humans, particularly in high-stakes domains like healthcare and education. His lab frequently develops novel evaluation methodologies that better capture real-world utility rather than just technical metrics.