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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.

• 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 CockView profile

Senior Lecturer

• Formal Verification
• Operating Systems
• Hardware-Software Co-Design
• +3 more

Dr. David Cock is a Senior Lecturer and Senior Researcher at ETH Zürich's Department of Computer Science, affiliated with the Systems Group. He holds a PhD from UNSW (2014) and a B.Sc. (hons) from UNSW (2004). His research focuses on formal verification, trustworthy systems, and hardware-software co-design, with notable contributions to projects like Enzian (a CPU/FPGA platform) and seL4 (formally verified kernel). He teaches Advanced Operating Systems and Informal Methods courses. Key achievements include the ACM Software System Award (2022) for seL4 and leadership in projects addressing hardware complexity and security. Research interests include formal methods for hardware modeling (Sockeye project), runtime verification, and mitigating timing channels. His work bridges theoretical foundations with practical systems, emphasizing secure and reliable computing platforms. Projects like Trustworthy BMC aim to enhance baseboard management systems' assurance. Collaborations span academia and industry, with open-source contributions to hardware designs and formal tools. Publications span formal verification, hardware modeling, and secure systems, with recent focus on heterogeneous computing and declarative hardware specifications. Teaching emphasizes practical formal techniques and OS design, leveraging real-world hardware (e.g., Barrelfish). His lab, the Systems Group, explores cutting-edge challenges in systems software and architecture.

• Animation Film Studies
• Comic Art and Avant-Garde
• Film History
• +4 more

Christian Gasser is a Lecturer in Film at the Lucerne School of Design, Film and Art (HSLU D&K). He holds a background in German and Romance Studies from universities in Bern and Paris. Beyond academia, he is a freelance writer, cultural journalist for publications like the NZZ and Swiss-German radio stations, and co-editor of the comic magazine STRAPAZIN. His professional roles include curator for events like the Graphic Novel Days in Hamburg and Avant Comics in Madrid, alongside jury memberships for international comic and animation festivals. Education: Germanistik und Romanistik (Bern/Paris) His research focuses on animation film aesthetics, history of moving images, illustration history, and mentorship in theory/practice. He has curated festivals and contributed to radio programs, earning awards such as the Zürcher Radiopreis (1994) and Prix Europa Berlin (1996). Recent publications include the novel Rakkaus! (2014) and Comic Deluxe (2012) . He has been affiliated with HSLU since 2004, teaching theory in Animation and Illustration programs since 2013. His articles explore diverse themes from Swiss animation history to avant-garde comics, reflecting his interdisciplinary approach. Engagements include moderating panels at international festivals and publishing in academic journals. He co-led the Video Essay research project on audiovisual education methodologies.

• 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.

• 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.

• Gender in Management and Organizations
• Sustainability
• Gender and Technology
• +3 more

Prof. Dr. Julia Nentwich is an Assistant Professor of Organizational Psychology at the University of St. Gallen (HSG), affiliated with the OPSY-HSG department. Her work focuses on gender dynamics in organizations, qualitative research methods, and discursive psychology. She co-leads the non-tenured faculty group and teaches courses on organizational psychology, gender, and diversity. Nentwich's research explores gender practices in management, resistance to organizational change, and inclusive leadership strategies. She has held visiting researcher positions at the Norwich Business School (UK) and serves on editorial boards for journals like Gender: Zeitschrift für Kultur und Gesellschaft and Gender in Management . Her academic contributions emphasize empirical studies of gender socialization in early childhood education, workplace inclusion initiatives, and the intersection of gender with professional roles. Notable projects include Leaders for Equality and Gender in the nursery , which investigate gender norms in childcare and corporate environments. Nentwich has received the Summa Cum Laude award for her doctoral thesis from the University of Tübingen and actively reviews for leading journals in organizational and gender studies. Teaching activities span undergraduate and graduate levels, covering topics like climate change psychology, gender identity, and qualitative research design. She coordinates HSG's Gender Studies program and collaborates with international research networks such as EGOS Standing Working Group Gender and Diversity. Nentwich's recent work addresses male advocacy in gender equality, resistance to diversity policies, and the discursive construction of leadership roles.

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.

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.

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.

Michihiro YasunagaView profile

Assistant Professor

• Large Language Models
• Multimodal Models
• Reasoning Systems
• +6 more

Michihiro Yasunaga is an Assistant Professor in the Department of Computer Science at Stanford University's School of Engineering. He received his PhD in Computer Science from Stanford, advised by Percy Liang, Jure Leskovec, and Chris Manning. Prior to his faculty position, he worked as a researcher at Google DeepMind and Meta. His research focuses on building LLMs and agents that assist humans in diverse tasks, with particular expertise in post-training techniques (RL, reward models, and evaluation), reasoning systems (AnalogicalReasoner), retrieval and tool use for LLMs (LinkBERT, QAGNN, DRAGON, REPLUG, HippoRAG), and multimodality (RA-CM3, Med-Flamingo, Transfusion). His work spans both theoretical foundations and practical applications of large language models. Yasunaga's publication record demonstrates significant contributions to the field of AI, with 15 recent articles (2023-2025) covering diverse aspects of language model development, evaluation, and application. His research shows a clear trajectory toward building more capable, efficient, and reliable multimodal AI systems, with particular emphasis on knowledge integration and robust evaluation frameworks. Among his notable achievements is the Best Paper Award at AAAI 2023 Deep Learning on Graphs Workshop for the DRAGON paper. He has also been deeply involved in major benchmarking efforts including HELM and HEIM, which provide comprehensive evaluation frameworks for language and vision-language models. Yasunaga actively contributes to the research community through service roles including Organizing Committee for the Workshop on Knowledge-Augmented Methods for NLP (ACL 2024), Workshop on Structured and Unstructured Knowledge Integration (NAACL 2022), and the Workshop on Scientific Document Summarization (SIGIR 2017-2020). He has also served on program committees for top conferences including NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, and ICCV from 2020-2025.

Xuming HeView profile

Associate Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +9 more

Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.

Alexander ArtikisView profile

Associate Professor

• Artificial Intelligence
• Distributed Systems
• Complex Event Recognition
• +4 more

Dr. Alexander Artikis is an Associate Professor of Artificial Intelligence at the University of Piraeus and a Research Associate at the National Centre for Scientific Research (NCSR) "Demokritos". He leads the Complex Event Recognition (CER) group , focusing on symbolic and probabilistic approaches to event recognition and forecasting. University of Piraeus (2025–present) NCSR Demokritos (2017–present) Complex Event Recognition Group (2017–present) His research spans Artificial Intelligence and Distributed Systems , with a focus on: Complex Event Recognition (CER) : Developing logic-based systems for detecting events in real-time data streams Event Calculus : Creating probabilistic and incremental versions for runtime reasoning Multi-Agent Systems : Modeling norm-governed interactions Maritime Informatics : Applying CER to vessel trajectory analysis and fleet management Key publications reveal trends in: Neuro-symbolic forecasting models combining deep learning and logic-based reasoning Symbolic automata with memory for pattern detection Online learning techniques for dynamic event rule generation Tensor-based formalizations for efficient temporal reasoning Handling uncertainty in real-time maritime data streams Optimizing memory usage for scalable stream processing He contributes to open-source tools like RTEC (Run-Time Event Calculus) and holds a European patent on complex event forecasting. His work addresses challenges in: Proactive decision-making systems Knowledge Graph consistency Hybrid human-machine discovery of movement patterns Big Data analytics for time-critical applications

Mauro PezzèView profile

Professor

• Software Engineering
• Software Testing and Analysis
• Self-Adaptive Systems
• +5 more

Mauro Pezzè is a Full Professor of Software Engineering at the Università della Svizzera italiana (USI) and Università di Milano Bicocca, leading the STAR research group since 2006. He holds a laurea from the University of Pisa and a PhD from Politecnico di Milano. His research focuses on software testing, analysis, self-adaptive systems, and cloud systems. He has held editorial roles, including Editor-in-Chief of ACM Transactions on Software Engineering and Methodologies (TOSEM), and served on numerous program committees. Education: Laurea (Pisa), PhD (Politecnico di Milano). Professional roles include Dean of the Faculty of Informatics at USI (2009-2013), visiting scientist at UC Irvine and Edinburgh, and technical lead for international projects. He co-authored a seminal book on software testing (Wiley, 2007), with over 670 citations. Research Interests: Software Testing, Self-Adaptive Systems, Cloud Computing, AI in SE, Sustainable Software. Projects include work on field-based testing, failure prediction in distributed systems, and neuro-symbolic approaches for test oracles. Grants and Advising: Led STAR Lab projects in self-healing systems, GUI testing, and semantic matching. Advised numerous PhD/postdoc students (e.g., Ciniselli, Di Grazia, Qiu). Collaborations with European tech firms on R&D initiatives. Labs/Teams: STAR Group at USI/Constructor Institute, Bicocca, and Politecnico di Milano. Current members include postdocs and PhD students working on AI-driven testing and cloud reliability.