Prof. Anya Belz is Full Professor of Computer Science at Dublin City University's School of Computing and Science Lead at ADAPT Research Centre. A leading NLP researcher with PhD-level expertise, she specializes in natural language generation, evaluation methodologies, and multimodal systems. Recipient of multiple best paper awards and NAACL Test of Time Award nomination. Research innovations include foundational work on statistical language generation (deployed in weather forecasting systems), comparative evaluation frameworks, vision-language integration, and reproducibility quantification. Current EPSRC-funded ReproHum project coordinates 20 global labs studying evaluation consistency. Achievements : Developed industry-deployed generation systems for accessibility applications Pioneered cross-modal alignment techniques for image description Authored 100+ publications spanning generation, evaluation, and reproducibility
Nils Holzenberger is an Assistant Professor at Télécom Paris, France, since February 2023, affiliated with the Data Intelligence Graphs (DIG) research team within the Information Processing and Communication Laboratory (LTcI). His work bridges artificial intelligence, natural language processing, and legal domains through neuro-symbolic approaches to statutory reasoning, particularly in tax law. Education: PhD in Computer Science, Johns Hopkins University (2017-2022) Master's in Engineering, Mines ParisTech (2013-2017) Preparatory Classes, Lycée Louis-le-Grand (2011-2013) Holzenberger's research centers on legal artificial intelligence with emphasis on statutory reasoning limitations in large language models. He pioneered the SARA dataset for tax law reasoning and LegalBench benchmark, developing hybrid symbolic-neural frameworks that expose LLMs' shortcomings in precise legal interpretation. His work integrates Prolog solvers with NLP techniques to create executable tax code mappings and contract analysis tools, establishing foundational methods for verifiable legal AI systems. Analysis of his 15 most recent publications (2019-2024) reveals three dominant research thrusts: (1) Tax law reasoning benchmarks exposing LLM hallucinations, (2) Neuro-symbolic integration for statutory interpretation, and (3) Low-resource template extraction for legal documents. His work consistently demonstrates that pure neural approaches fail at precise legal reasoning, necessitating symbolic grounding for reliable legal AI applications. The DIG research team at Télécom Paris, where Holzenberger leads legal AI initiatives, is actively hiring faculty for neuro-symbolic projects. While specific grant details aren't public, his collaborations with HEC Paris, Copilex startup, and featured podcast appearances indicate substantial industry-academia engagement in legal tech development. Holzenberger directs the legal AI vertical within LTcI's DIG team, focusing on data intelligence for statutory reasoning. His group develops tools for tax minimization strategy discovery, contract analysis, and legal information extraction, maintaining close ties with legal practitioners through projects like the Prolog-based tax code interpreter. The team's infrastructure supports both academic research and startup partnerships in computational law.
Pengtao Xie is an Associate Professor (with tenure as of June 2025) in the Department of Electrical and Computer Engineering at the University of California San Diego. He also serves as Associate Adjunct Professor in the Division of Biomedical Informatics, Department of Medicine, and holds affiliate appointments with the Halıcıoğlu Data Science Institute, School of Biological Sciences, Shu Chien-Gene Lay Department of Bioengineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, and multiple research institutes including the AI Group, Center for Machine-Intelligence, Computing and Security, Institute of Engineering in Medicine, and Institute for Genomic Medicine. Education: PhD in Machine Learning, School of Computer Science, Carnegie Mellon University Research Interests: His research focuses on machine learning inspired by human learning skills, such as self-explanation, small-group learning, and learning by teaching. He applies these techniques to large language models, foundation models, healthcare, and biomedicine. His work spans generative AI, medical imaging, protein modeling, and drug discovery. Recent Research Trends: His 2024–2025 publications emphasize generative AI for ultra-low-data medical image segmentation, multimodal large language models for biomedical applications, protein function prediction, and novel training strategies like task-adaptive pretraining and bi-level optimization for model adaptation. Scientific Awards: NIH MIRA Award (2025) NSF CAREER Award (2024) Best Graduate Teacher Award – UCSD ECE (2023) ICLR Notable-Top-5% Paper (2023) Global Top-100 Chinese Young Scholars in AI (2022) UCSD Faculty Career Development Award (2022) Tencent Faculty Award (2021) Outstanding Reviewer – ICLR (2021) AMIA Doctoral Dissertation Award Finalist (2020) Amazon AWS Research Award (2020) Tencent AI-Lab Faculty Award (2020) Innovator Award – Pittsburgh Business Times (2018) Siebel Scholarship (2014) Advising and Grants: He currently advises PhD students, postdocs, and master’s students. He has received major grants including the NIH MIRA and NSF CAREER awards, and actively mentors Schmidt AI in Science postdocs and graduate students. Teaching and Labs: He teaches ECE285 Deep Generative Models and ECE175B Probabilistic Reasoning and Graphical Models . His lab focuses on foundational and translational AI research with applications in biomedicine and healthcare.
Tanja Käser is a Tenure Track Assistant Professor at EPFL's School of Computer and Communication Sciences (IC), leading the Machine Learning for Education Laboratory (ML4ED). Her interdisciplinary research bridges machine learning, data mining, and educational technology, focusing on personalized learning systems and human behavior modeling. PhD in Computer Science (ETH Zurich, 2015) - honored with Fritz Kutter Award Former Senior Data Scientist at Swiss Data Science Center (ETH Zurich) Postdoctoral Researcher at Stanford University's Graduate School of Education Research Focus Explainable AI for education Adaptive learning environments Behavioral pattern recognition Generative AI applications in pedagogy User modeling and personalization Learning analytics in unstructured settings Recent Publication Trends Her 2024-2023 work demonstrates: Interpretable clustering of learners Transformer-based language learning prediction GAN applications for creative education Teacher-AI collaboration frameworks Explainability validation methods Modular network architectures Scientific Recognition Fritz Kutter Award for best Swiss computer science thesis (2015) Advising & Collaborations Currently supervises multiple PhD students including: Cock Jade Maï L Glandorf Dominik Güres Fatma-Betül Neshaei Seyed Parsa Radmehr Bahar Shibu Abhinand Shved Ekaterina Research Infrastructure Operates from EPFL's ML4ED laboratory with hybrid on-site and digital educational systems research capabilities.
Prof. Dr. Julia Vogt is an Assistant Professor at the Department of Computer Science at ETH Zürich, leading the Professur für Medizin. Datenwiss. Her research focuses on medical machine learning, data science, and AI applications in healthcare. She specializes in developing interpretable AI systems for clinical decision support, particularly in pediatric diabetes management, medical imaging analysis, and anomaly detection. Her work bridges translational gaps by emphasizing causal approaches and clinical validation. Her academic role includes teaching courses like the Data Science Lab (263-3300-00L/10L) and Topics in Medical Machine Learning (263-5100-00L). Her lab's research spans predictive modeling for nocturnal hypoglycemia, echocardiogram analysis for pulmonary hypertension detection, and multimodal learning in radiology. She also contributes to national pediatric data initiatives like SwissPedHealth. Key technical areas include concept bottleneck models, stochastic AI frameworks, and generative models for medical signal denoising. Her work often emphasizes model interpretability, fairness, and robustness to distribution shifts. She collaborates on projects involving wearable devices for pediatric monitoring and AI-driven rehabilitation tools for post-stroke gait analysis.
Cecilia O. Alm is a Professor in the Department of Psychology within the College of Liberal Arts at Rochester Institute of Technology (RIT), where she serves as the Artificial Intelligence Program Director. She holds multiple leadership roles including Director of the Center for Human-aware AI and Director of the Computational Linguistics and Speech Processing Lab (CLaSP). Her institutional affiliations span the School of Information, Ph.D. Programs in Cognitive Science and Computing and Information Sciences, Department of Computer Science, and MS in Data Science program. Dr. Alm earned her Ph.D. from the University of Illinois at Urbana-Champaign. Her research focuses on human-centered artificial intelligence with particular emphasis on linguistic and multimodal sensing, affective computing, and natural language processing. She investigates how AI systems can better understand and respond to human communication through multimodal dialogue processing, with applications in accessibility, education, and healthcare. Her recent publications demonstrate a strong trend toward developing inclusive AI systems, particularly through projects addressing Deaf community needs (MULTICOLLAB-ASL), subtle emotion recognition (FUSE corpus), and bias mitigation in NLP. The work consistently integrates multimodal data streams (speech, gaze, gesture) to create more responsive human-AI interaction frameworks. Current research directions emphasize diversity in AI education, visual prosody in sign languages, and human-in-the-loop AI development. Dr. Alm leads several significant NSF-funded initiatives including the AWARE-AI program, IRES AI-PROWIL international research experience, and collaborative projects with Gallaudet University focused on Deaf scientist-centered AI research. She has secured over $2.5 million in external funding for her work on human-aware AI systems. She directs the CLaSP lab which provides research opportunities for PhD, MS, and undergraduate students, with graduates employed at major technology companies including Amazon, Apple, Microsoft, and Facebook. The lab focuses on real-world AI applications in accessibility, human-robot interaction, and multimodal communication systems.
Xuezhe Ma is an Assistant Professor in the Department of Computer Science at the University of Southern California's Viterbi School of Engineering. Previously, he was a Ph.D. student at Carnegie Mellon University's Language Technologies Institute, where he worked under the supervision of Professor Eduard Hovy. His academic journey includes a Master's degree from Shanghai Jiao Tong University's Center for Brain-like Computing and Machine Intelligence and a Bachelor's degree in Computer Science from the same institution. Ph.D. in Computer Science, Carnegie Mellon University (completed ~2020) M.S. in Brain-like Computing, Shanghai Jiao Tong University B.S. in Computer Science, Shanghai Jiao Tong University Dr. Ma's research spans multiple areas at the intersection of Natural Language Processing and Machine Learning, with particular focus on structured prediction, syntactic and semantic parsing, machine translation, language generation, and deep generative models. His recent work has expanded into vision-language models, large language model architectures, and applications across computer vision tasks. His research combines theoretical foundations with practical implementations, as evidenced by his development of tools like NeuroNLP2 and MaxParser. His publication record shows a clear trajectory from foundational NLP work during his PhD (including papers on dependency parsing and sequence labeling) to more recent contributions in generative models and large language systems. The 15 most recent publications reveal a strong focus on addressing fundamental challenges in generative modeling, context handling, and multimodal integration, with applications spanning literary translation, medical imaging, and news diffusion analysis. AI2 Outstanding Intern Award (2018) Dr. Ma has secured research funding supporting his work in generative models and language technologies, with projects focusing on improving the efficiency and capabilities of large language models. His research group at USC is actively working on next-generation language understanding and generation systems, with particular emphasis on context-aware modeling and multimodal integration. He has established collaborations with industry partners including the Allen Institute for AI and has contributed to open-source projects like Texar. At USC, Dr. Ma leads research in the Information Sciences Institute, directing projects on efficient large language model architectures and multimodal reasoning systems. His lab focuses on developing novel approaches to context handling, model efficiency, and multimodal integration, with applications across diverse domains including healthcare, literary analysis, and news media.
Jeffrey P. Bigham is an Associate Professor at the Human-Computer Interaction Institute within the School of Computer Science at Carnegie Mellon University . His research spans human-computer interaction , human-AI interaction , accessibility , dialog systems , NLP , and crowdsourcing . Current PhD Students: Hamza El Alaoui, Jessica Yin Huynh, Sara Kingsley, Peya Mowar, Yi-Hao Peng, Atieh Taheri PhD Graduates: Erin Brady, Yu Zhong, Ting-Hao Huang, Anhong Guo, Cole Gleason, Prakhar Gupta, Stephanie Valencia, Kundan Krishna, Jason Wu His work is funded by Apple , Bosch , DARPA , Google , Microsoft , the National Institute of Disability Rehabilitation Research , the National Science Foundation , and Yahoo! He also holds a CMU HCII Career Development Fellowship . Selected Awards: NSF CAREER Award 2019 Best Paper at ASSETS 2021 Best Paper Nomination at CHI 2024 Best Paper Nomination at CHI 2021 Best Paper Nomination at DIS 2021
Vera Liao is a Principal Researcher at Microsoft Research, where she is part of the FATE (Fairness, Accountability, Transparency, and Ethics of AI) group. She will join the University of Michigan Computer Science and Engineering department as an Associate Professor in fall 2025. Her work focuses on human-AI interaction, explainable AI, and responsible computing. Liao has made significant contributions to IBM products such as AI Explainability 360 and Uncertainty Quantification 360 during her time at IBM T.J. Watson Research Center. Dr. Liao received her education from the University of Illinois at Urbana-Champaign and Tsinghua University. Her academic journey has positioned her at the intersection of human-computer interaction and artificial intelligence, with a strong emphasis on creating AI systems that are transparent, accountable, and user-centered. Vera Liao's research primarily centers around human-centered AI explainability and transparency. She investigates how to design AI systems that effectively communicate their capabilities, limitations, and decision-making processes to users. Her work examines the intersection of AI transparency with trust, control, and user experience. Liao has pioneered approaches to bridging the socio-technical gap in AI evaluation and has developed frameworks for contextualized evaluation of explainable AI systems. Her research spans multiple domains including conversational interfaces, data storytelling, and creative work with generative AI. Liao's publications reveal a clear trend toward addressing the challenges of large language models and their impact on human-AI interaction. Her recent work focuses on understanding how uncertainty communication affects user trust, how to design for appropriate reliance on AI systems, and how to create authentic co-creation experiences with generative models. She has been examining the risks in AI-infused information ecosystems and developing methods for human-centered evaluation of language technologies. Her scientific contributions have been recognized with multiple honors: Best Paper Award, Honorable Mention at CHI 2025 (two papers) Best Paper Award at CHI 2024 Best Paper Award, Honorable Mention at FAccT 2023 Best Paper Award, Honorable Mention at HCOMP 2022 Best Paper Award, Honorable Mention at CHI 2021 Best Paper Award, Honorable Mention at CHI 2014 Outstanding Paper Award at IUI 2019 Dr. Liao is an active mentor, having guided numerous research interns from top universities including Cornell, Princeton, CMU, Stanford, and MIT. She serves in editorial roles as Co-Editor-in-Chief of the Springer Human-Computer Interaction Book Series and as an Editor for ACM CSCW. Liao has secured research funding through her work at Microsoft Research and previously at IBM, focusing on projects related to AI explainability, transparency, and responsible AI development. As part of Microsoft Research's FATE group, Liao collaborates with a multidisciplinary team of researchers focused on the ethical implications of AI technologies. Her work bridges the gap between technical AI development and human-centered design principles, ensuring that AI systems are developed with user needs and societal impacts in mind.
Jiawei Han is the Michael Aiken Chair Professor at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Siebel School of Computing and Data Science and the Department of Computer Science. He holds a Ph.D. in Computer Science from the University of Wisconsin-Madison (1985). His research focuses on Data Mining, Text Mining, and Intelligent Systems, with notable contributions to knowledge hypercubes, molecular discovery, and geospatial understanding. Key affiliations include leading the Data Mining Research Group (DMG) and the Data and Information Systems Research Laboratory (DAIS). He is also involved in major initiatives like the NSF AI Institute for Molecular Discovery (Molecule Maker Lab) and the DARPA INCAS project. Recent work emphasizes large language models (LLMs), scientific knowledge integration, and graph-based reasoning. Notable achievements include an ICLR 2024 Outstanding Paper Honorable Mention (co-authored with Suyu Ge) and mentoring Yu Meng, recipient of the ACM SIGKDD 2024 Dissertation Award. Teaching includes courses such as CS 412 (Data Mining), CS 512 (Data Mining Principles), and specialized topics like Text Mining with Large Language Models (Fall 2024). He has authored/co-authored numerous books, including editions of *Data Mining: Concepts and Techniques* and works on taxonomy discovery and text mining. His research spans interdisciplinary areas such as bioinformatics, geospatial analytics, and molecular innovation, with a focus on practical applications and foundational theory.
Monica Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in Stanford University's School of Engineering and holds a courtesy professorship in Electrical Engineering. She leads the Stanford Open Virtual Assistant Laboratory and has pioneered work in virtual assistants, privacy protection, and compiler design. Her research includes the Almond virtual assistant, privacy-preserving IoT systems, and the ThingTalk programming language. She co-authored the seminal 'dragon book' on compilers and co-founded Tensilica (now part of Cadence). Education: Bachelor of Science (Honors), Computer Science, University of British Columbia, 1980 Master of Science, Computer Science, Carnegie Mellon University, 1982 Doctor of Philosophy (PhD), Computer Science, Carnegie Mellon University, 1987 Research Interests: Dr. Lam's work focuses on conversational AI with privacy guarantees, compiler optimization for parallel computing, and open-source virtual assistant ecosystems. She is a leader in decentralized systems, having developed frameworks like SociaLite for large-scale graph analysis and Musubi for mobile social networking without centralized platforms. Article Trends: Recent publications emphasize multimodal interactions, multilingual dialogue systems, and LLM-driven applications in areas like question answering, persuasive chatbots, and adaptive assistants. Her work bridges foundational AI research (e.g., semantic parsing) with real-world deployments (e.g., privacy-compliant IoT). Awards & Honors: Member of the National Academy of Engineering ACM Fellow Popular Science's Best of What's New Award (Security, 2019) Advising & Grants: Lam oversees the Open Virtual Assistant Initiative, a collaborative project to build open-source semantic models. Her NSF CNS grant (CNS Core) focuses on federated privacy systems. She has advised over 50 students in AI and systems research, though specific names are not listed in the provided texts. Labs & Teams: Directs the Stanford Open Virtual Assistant Laboratory, collaborates with the Stanford NLP group, and maintains ties to industry through former startup Tensilica's legacy in embedded processors.
Chantal Pellegrini is a Lecturer and PhD student at the Chair of Computer Aided Medical Procedures (Prof. Navab) at Technical University of Munich (TUM). Her research focuses on Deep Learning applications in medical imaging, including explainable AI for radiology report generation and Vision-Language Models for clinical decision support. She actively contributes to the DHM, NARVIS Lab, and RobUSt research groups. Teaching responsibilities include courses such as 'Computer Aided Medical Procedures', 'Medical Augmented Reality', and 'Surgical Robotics'. She supervises student projects in medical AI and healthcare innovation, with recent projects involving multimodal report generation and graph pretraining for medical applications. Education: BSc/MSc Computer Science (TUM), current PhD student since 2022 Labs: DHM (German Heart Center), NARVIS Lab, RobUSt Robotics & Ultrasound Research Keywords: Medical Image Understanding, Radiology Reports, LLMs in Healthcare Her publications span surgical OR dataset development, reinforcement learning for clinical decisions, and explainable X-ray diagnosis systems. She mentors MA/BA students in medical AI and project management for healthcare applications.
C. S. George Lee is a Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering, located in West Lafayette. His research focuses on Robotics, Transfer Learning, Neuro-fuzzy Systems, Automatic Controls, and Computer Engineering. He holds a BSEE (1973), MSEE (1974) from Washington State University, and a PhD (1978) from Purdue University. His work integrates computational intelligence with AI, robotics, and education technology, emphasizing human-machine co-learning models and bilingual systems. His contributions span domains like quantum computing, generative AI, and knowledge graph applications. He leads the Art Lab at Purdue and has published extensively on topics ranging from humanoid robotics to cross-cultural educational platforms. His research areas include developing intelligent agents for edutainment, robotic assistants for student learning, and advanced machine learning techniques. Notable trends in his publications involve computational intelligence applied to bilingual language models (e.g., Taiwanese/English co-learning), quantum-based AI systems, and human-centric robotics. He has explored applications in healthcare (e.g., blood donor analysis), autonomous navigation, and game AI (e.g., Go). His work often bridges theoretical advancements with real-world implementations, such as Java software tools for motor activity assessment (JKinect) and AI-driven platforms for skill evaluation. Lee's research emphasizes interdisciplinary collaboration, with contributions to IEEE conferences and cross-institutional projects. His lab develops tools for adaptive e-learning, robotic task performance evaluation, and human pose estimation using neural networks. Despite prolific publishing, no specific grants or awards are explicitly mentioned in the provided text. His work continues to explore the intersection of human intelligence and smart machines through platforms like Metaverse integration and BCI (Brain-Computer Interface) applications.
Martin Volk is a Full Professor of Computational Linguistics at the University of Zurich, with a dual affiliation to the Department of Informatics since 2019. He holds a PhD from the University of Koblenz and has held academic positions at institutions including Stockholm University (part-time from 2008-2011), Zurich University of Applied Sciences, and the University of Georgia. His research focuses on grammar engineering, machine translation evaluation, multilingual text analysis, and cross-language information retrieval. Education : Born in Cochem, Germany Studied Computer Science and Computational Linguistics at EWH University, Koblenz Master's in Artificial Intelligence at the University of Georgia (Fulbright Scholar) PhD in Computational Linguistics from the University of Koblenz Research Interests : His work emphasizes data-driven NLP methods, including corpus-based approaches, parsing technologies, and the application of machine learning to historical and multilingual texts. Key focuses include: Machine translation systems and evaluation frameworks Grammar testing environments (e.g., GTU) OCR and digitization of historical documents (e.g., Gothic script) Development of parallel corpora for linguistic research Projects : SMULTRON: Multilingual parallel treebank project Bullinger Digital: Historical document digitization initiative Text+Berg: Digital Humanities project for alpine textual heritage EU-funded MuchMore (cross-language medical IR) Grants & Collaborations : Recipient of grants from the Swiss National Science Foundation, EU projects, and industry partnerships (e.g., Siemens, Xerox). His work integrates academic and industrial perspectives in NLP tool development. Labs & Teams : Leads research teams in the Institute of Computational Linguistics at UZH, focusing on projects like the Zurich Parallel Corpus Collection and MODERN (modeling discourse for MT).
Dr. Meng Fang is a researcher specializing in Artificial Intelligence with a focus on Reinforcement Learning, Large Language Models, and their applications in medical QA, game theory, and causal inference. Their work combines technical innovation with practical problem-solving in safety-critical and domain-specific contexts. Key research areas: Social bias in AI, data augmentation, embodied agents, and model-based reinforcement learning Teaching: Coordinated module COMP532 - Machine Learning and BioInspired Optimisation (2024-25). Recent publications address challenges in offline RL robustness, vision-based safe reinforcement learning, and strategic game generalization.
