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Serina ChangView profile

Assistant Professor

• Artificial Intelligence
• Human Behavior
• Public Health
• +5 more

Serina Chang is an Assistant Professor at the University of California, Berkeley , jointly appointed in Electrical Engineering and Computer Sciences (EECS) and Computational Precision Health , with affiliations to the Berkeley AI Research (BAIR) Lab . Her research addresses AI for public health, focusing on human behavior modeling, network inference, and policy decision support. PhD in Computer Science from Stanford University (2024) Postdoctoral research in Microsoft Research's Computational Social Science group Her work spans three thematic areas: inferring human behaviors from novel data sources (search logs, mobility data), simulating behaviors with LLMs for public health applications, and AI-driven policy support through models for vaccine distribution, epidemic forecasting, and equity-focused reopening strategies. She has pioneered AI models using anonymized mobility data to analyze socioeconomic disparities in pandemic impacts. Recent publications highlight cross-disciplinary innovations: Nature (2025) perspective on AI for infectious disease modeling ACL 2025 work on human-AI interaction evaluation ICWSM 2025 paper demonstrating LLM capabilities in network generation Scientific recognition includes: Google Research Scholar Award (2025) NSF Graduate Fellowship (2024) KDD Dissertation Award (2025) Meta PhD Fellowship (2023) She advises PhD students in EECS and Computational Precision Health , emphasizing skills in ML research , interdisciplinary collaboration , and real-world impact . Her lab integrates computational methods with public health practice through partnerships with WHO , Chan Zuckerberg Biohub , and United Nations Development Programme . Current teaching includes CS 294-286: Machine Learning & Human Behavior . She explicitly invites students with interests in AI , human behavior , network science , and public health applications to apply for PhD advising.

• Machine Learning
• AI-Based Audio Signal Processing
• Digital Image Processing
• +7 more

Dr. Martin Loeser is a Professor and Program Director of Electrical and Computer Engineering at the ZHAW Zurich University of Applied Sciences, School of Engineering, Department of Information Technology, Electrical Engineering and Mechatronics. He holds a Ph.D. in Computational Physics from ETH Zurich (2008) and a Master's degree in Electrical and Computer Engineering from the Technical University of Munich (2003). His educational background includes: Ph.D. in Computational Physics, ETH Zurich (2004-2008) Master of Science in Economics and Management, University of Hagen (2010-2012) Master of Science in Electrical and Computer Engineering, Technical University of Munich (1997-2003) Study Abroad Degree in Applied Physics, University of Sydney (2001-2002) Dr. Loeser's research spans multiple interdisciplinary fields with a strong focus on Machine Learning, AI-Based Signal Processing, and Digital Image Processing . His work bridges theoretical computational methods with practical applications in healthcare, education, and optoelectronics. Recent research has focused on using machine learning and virtual reality for early detection of cognitive impairments, developing AI-based educational tools, and creating medical diagnostic devices. His earlier work centered on computational physics and optoelectronic device modeling, particularly for LEDs and solar cells. His publication record shows a clear evolution from fundamental computational physics and optoelectronics (2006-2010) toward AI applications in healthcare and education (2015-present). This transition reflects his ability to adapt core computational expertise to emerging technological challenges. His recent work demonstrates strong interdisciplinary collaboration, particularly with medical researchers for cognitive impairment detection and with educational specialists for AI-enhanced teaching tools. Among his recognitions is being named "Best graduate of the year in the Master's degree program in Management" from the University of Hagen in June 2012. Dr. Loeser maintains active research collaborations across disciplines and institutions, as evidenced by his diverse publication record spanning computer science, healthcare technology, education, and traditional engineering fields. His current research program focuses on edge AI applications, particularly in healthcare diagnostics and educational technology.

Olivier CanévetView profile

Lecturer

• Computer Vision
• Machine Learning
• Deep Learning
• +3 more

Olivier Canévet is a Lecturer at EPFL, affiliated with both the School of Engineering (STI) and the School of Life Sciences (VPA-AVP-DLE) divisions. He holds a dual role as a Research Engineer at the Idiap Research Institute and serves as a Lecturer in the Department of Electrical Engineering (IEM) and the EDEE-ENS teaching unit. His primary affiliations include the Learning and Information Systems Laboratory (LIDIAP) at EPFL. Education: PhD in Electrical Engineering from EPFL (2012–2016), supervised by Dr. François Fleuret. Engineering diploma from Télécom Bretagne (2007–2012). Additional experiences include a CNES internship on satellite imagery and CERN projects on Invenio ranking systems. He also spent a year managing geophysical observatories in the French Southern and Antarctic Lands. Research Interests: Focuses on Computer Vision, Machine Learning, and Deep Learning applications in human-robot interaction, security systems, and real-time perception. Specializes in depth-based networks for human pose estimation, multi-person tracking, and socially intelligent robotic systems. Article Trends: Recent work emphasizes real-time CNN architectures for depth-based human pose estimation, multi-party human-robot interaction datasets (MuMMER), and security systems leveraging depth maps (UNICITY database). Prior contributions include efficient sample mining techniques and domain adaptation methods. Grants & Funding: His PhD was funded by the Swiss National Science Foundation (DASH project). Current research benefits from EPFL's interdisciplinary infrastructure and Idiap's resources in machine learning and robotics. Labs & Teams: Leads projects at LIDIAP lab (EPFL) focusing on computer vision applications, collaborates with Idiap on security systems, and contributes to teaching initiatives in the EDEE-ENS unit.

• Data Science
• Machine Learning
• Privacy-Preserving Technologies
• +3 more

Valentin Hartmann is a Researcher at the Data Science Laboratory (DLAB) within the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL). His work focuses on advanced topics in data science, including differential privacy, optimal transport theory, and privacy-preserving machine learning. He holds a postdoctoral position and contributes to cutting-edge research in computational statistics and secure distributed learning. His research interests span multiple domains: (1) Development of privacy-preserving techniques for machine learning and data analysis, (2) Optimization of transport problems with applications in statistics and geometry, (3) Design of secure algorithms for distributed systems, and (4) Implementation of robust statistical methods through R packages like 'transport'. Valentin actively publishes in top-tier venues, with recent contributions addressing neural network behavior analysis, privacy risks in data distribution inference, and novel cryptographic approaches for collaborative learning. His work bridges theoretical foundations with practical software solutions, as evidenced by his development of the 'transport' R package for optimal transport computations. He is affiliated with the DLAB research group at EPFL's INN building (Office INN 315), contributing to interdisciplinary projects at the intersection of computer science, mathematics, and privacy engineering.

• Human-Computer Interaction
• Recommender Systems
• Deep Reinforcement Learning
• +4 more

Ingram Sandy is a Professor at the Haute école d'ingénierie et d'architecture de Fribourg (HES-SO), part of the iSIS Institute for Intelligent and Secure Systems. Her research focuses on Human-Computer Interaction, Recommender Systems, and Digital Wellbeing, with a strong emphasis on applying technology to education and workplace environments. She leads and collaborates on interdisciplinary projects addressing challenges in smart environments, user experience design, and AI integration in learning platforms. Key projects include developing serious games for mental health support (Sesame Project), workplace comfort systems (SpotOn), and chatbot-enhanced educational tools. She has also contributed to frameworks like eLogBook and Graasp, aiming to bridge formal and informal learning through social software innovations. Her work spans over 15 years, with publications in top venues like IEEE, ACM, and international conferences on human-computer interaction and learning technologies. Ingram Sandy’s research interests span from foundational AI methods (reinforcement learning, deep learning) to applied domains like digital wellbeing assessment, smart office comfort modeling, and chatbot-mediated education. She actively collaborates with industry partners (e.g., OrchardAI) and academic networks across Switzerland and Europe. Her contributions highlight the intersection of technology, human behavior, and educational design.

Martin TomasikView profile

Professor

• Educational Science
• Developmental Psychology
• Quantitative Methodology
• +3 more

Martin Tomasik is a Professor at the Institute of Educational Science , University of Zurich, focusing on Research Methods in Developmental and Educational Sciences . He leads the development of digital tools for measuring learning processes and advancing mathematical models for intensive longitudinal data analysis, in collaboration with the Institute for Educational Evaluation in Zurich. Research spans quantitative methodology , mathematical modeling , and longitudinal data in education and development. His work involves statistical software development and analysis of large-scale assessments and formative feedback systems . Key research themes include: Competence trajectory modeling and psychometric software. Cross-domain longitudinal studies on social change , developmental barriers , and adaptive disengagement . Applications of virtual reality and machine learning in cognitive and educational assessments. Scientific Awards : German Study Prize/Recognition Award (Körber Foundation) for his doctoral work on developmental barriers and disengagement. Project Leadership includes the Zurich Learning Progress Survey (LEAPS) , examining learning trajectories from kindergarten to compulsory schooling, and collaborations with institutions like PH Zurich , Witten-Herdecke University , and Max Planck Institute for Human Development .

Benjamin GreweView profile

Associate Professor

• Artificial Intelligence
• Neural Networks
• Neuroscience
• +3 more

Prof. Dr. Benjamin Grewe is an Associate Professor at the Department of Information Technology and Electrical Engineering at ETH Zürich. His research focuses on the intersection of artificial intelligence, neuroscience, and neural networks, with a particular emphasis on cortical hierarchies, continual learning, and biologically plausible algorithms. He leads projects involving deep feedback control, synaptic connectivity analysis, and neural ensemble dynamics. Grewe teaches courses such as Learning in Deep Artificial and Biological Neuronal Networks and Reinforcement Learning Basics , integrating theoretical and applied perspectives. His work bridges computational models with biological insights, contributing to advancements in medical robotics, process control, and AI safety. His research interests span neural network architectures , continual learning , and biological neuronal systems . Recent projects explore synaptic plasticity in cortical microcircuits and the application of AI to surgical planning and industrial automation. Grewe’s publications reflect a multidisciplinary approach, addressing challenges in both technical and biological domains. No scientific awards are explicitly mentioned in the provided texts. His advising and grant activities remain unspecified in the available data. His lab, part of the Neural and Intelligent Systems group, focuses on developing biologically inspired algorithms and neural interfaces.

• Financial Technology (FinTech)
• Artificial Intelligence (AI)
• Machine Learning (ML)
• +5 more

Philip Treleaven is a Professor at the Department of Computer Science , University College London (UCL). His work bridges Artificial Intelligence , Blockchain Technology , and Financial Computing , with a focus on DeFi , Tokenization , and Algorithm Auditing . He leads research on AI governance , privacy-preserving data , and financial market automation . Key Research Areas AI Ethics & Governance Decentralized Finance (DeFi) Tokenization & Web3 Cybersecurity & Emerging Threats Notable Collaborations Adriano Koshiyama Jeremy Barnett Rem Sadykhov Lukasz Szpruch Nick Firoozye Giles Pavey Recent Publications "Data Assets: Tokenization and Valuation" (2023) "DeTEcT: Dynamic and Probabilistic Parameters Extension" (2024) "Optimising Large Language Models" (2025) His research drives cross-disciplinary innovation , impacting finance , law , and data governance through applied machine learning and blockchain solutions . Current projects include DeTEcT (Decentralized Token Economy Theory) and Federated Computing Infrastructure .

Marco D'AmbrosView profile

Professor

• Mining Software Repositories
• Software Evolution
• Software Visualization
• +2 more

Marco D'Ambros is Professor and Director of CodeLounge , the software research & development center within the Software Institute at Università della Svizzera italiana (USI) in Lugano, Switzerland. Since February 2018 he has led CodeLounge, bridging academic research with industrial practice. Education Ph.D. in Computer Science, Università della Svizzera italiana, 2010 – Dissertation: "On the Evolution of Source Code and Software Defects" M.Sc. (Laurea) in Computer Engineering, Politecnico di Milano, Italy, 2005 – Thesis: "Software Archaeology – Reconstructing the Evolution of Software Systems" Research Interests Marco’s work centers on mining software repositories , software evolution , and software visualization . He designs interactive visual analytics tools—such as Churrasco , BugCrawler , Evolution Radar —that help developers and researchers understand how large codebases change over time, where defects emerge, and how teams collaborate. Recent projects extend these ideas to microservices architectures and text mining of development communication (e.g., mailing lists, issue trackers). Scientific Awards Best Paper Award , IEEE Working Conference on Reverse Engineering (WCRE) 2009 Grants & Collaborations Between 2012 and 2018 Marco led high-impact data-integration projects for government and enterprise clients worldwide as a technical leader at Palantir Technologies , bringing industrial-scale challenges back to academia. At USI he continues to foster industry partnerships through CodeLounge, securing collaborative R&D funding for topics ranging from large-scale microservice evolution to AI-assisted program comprehension. Laboratories & Teams CodeLounge serves as an open laboratory where faculty, doctoral students, and industry engineers co-develop next-generation tooling for software evolution analysis. The lab hosts visiting researchers and maintains open-source releases of its research prototypes.

Jannis VamvasView profile

Lecturer

• Natural Language Processing
• Multilingual Language Models
• Machine Translation
• +5 more

Jannis Vamvas is a Lecturer and active researcher in the Department of Computational Linguistics at the University of Zurich, Switzerland. With a PhD thesis titled Model-Based Evaluation of Multilinguality , he specializes in deep learning for natural language processing, with particular emphasis on multilingual language models and their evaluation. Academic Roles Current Lecturer for Large Language Models , Text Generation , and Mathematical Foundations at UZH Postdoctoral Researcher (MUTAMUR Project, 2023) PhD in Computational Linguistics (2019-2023, supervised by Rico Sennrich, Lena A. Jäger, and Martin Volk) Education M.Sc. in Computational Linguistics (major) and Computer Science (minor) at LMU Munich (2017-2019) B.A. in Computer Science and Philosophy from University of Basel (2011-2015) His research focuses on multilingual language models, particularly SwissBERT for Swiss languages, and methods for evaluating translation quality through approaches like Contrastive Conditioning and Translation Cross-Likelihood. He explores how to adapt models to local data, detect translation errors, and improve document translation through source-primed conversation techniques. Recent publications highlight his work on: Translation direction detection Hallucination mitigation in machine translation Minimum Bayes Risk decoding Swiss multilingual language modeling Semantic difference recognition Scientific recognition includes: EAMT 2024 Highly Commended Thesis Best Paper Award at SwissText 2025 Best Scientific Paper Award at SwissText 2024 He teaches advanced courses on Large Language Models, Text Generation, and Mathematical Foundations, and has served as organizer of the Computational Linguistics Colloquium at UZH. Current projects include the InvestigaDiff initiative and machine translation for Romansh idioms.

• Human-Computer Interaction
• Privacy and Security
• Wearable Technology
• +2 more

Mauro Cherubini serves as a Professor at the University of Lausanne, where he is affiliated with the LIVES Centre (Interdisciplinary Centre for Research on Life Course and Vulnerabilities). His academic work bridges computer science and social sciences, focusing on the design and evaluation of interactive systems that address societal challenges through user-centered approaches. His research interests include: Human-Computer Interaction (HCI) and user experience design Privacy, security, and ethical implications of digital technologies Wearable technology and self-tracking for health behavior change Motivational systems and persuasive computing AI ethics and transparency in algorithmic decision-making Cherubini employs mixed-methods research, participatory design, and empirical evaluations to develop solutions that balance technical innovation with social responsibility. Analysis of his 2023-2025 publications reveals three converging trends: (1) increasing focus on AI's societal impacts, particularly cultural bias in hiring tools and model risk frameworks; (2) sustained investigation into wearable technology's psychological effects on body awareness and privacy; and (3) practical cybersecurity research addressing threats like account enumeration and second-hand device vulnerabilities. His work consistently emphasizes real-world applicability through user studies and interdisciplinary collaboration. As a core member of the LIVES Centre, Cherubini contributes to interdisciplinary projects examining life course vulnerabilities through digital lens. His team develops tools supporting autonomy in job search training, physical activity motivation, and collaborative programming education, while maintaining strong connections with Swiss cybersecurity communities and international HCI conferences like CHI.

• Service Design & Management
• Collaboration Engineering
• Digital Learning
• +7 more

Jan Marco Leimeister is a Professor and Director at the Institute of Business Administration (IBWL) at the University of Kassel. His research focuses on service design & management, collaboration engineering, and digital learning innovations. He leads interdisciplinary projects exploring conversational AI, generative AI applications, and ethical AI system design. Key research areas include service innovation ecosystems, hybrid human-AI collaboration frameworks, and digital transformation strategies. His work emphasizes practical applications in areas like financial fraud detection (via synthetic data ecosystems) and adaptive learning systems for argumentation skill development. He actively contributes to advancing methodologies for structured literature reviews in business research and integrating generative AI into scholarly practices. Recent publications (2024-2025) highlight contributions in AI ethics, metaverse platform analysis, and process mining for IT service management. His work bridges theory and practice, addressing challenges in data fairness, conversational agent branding, and scaling digital service innovations. Leimeister's academic leadership includes directing the IBWL's master's programs in Digital Business and Sustainable Management. He collaborates internationally, emphasizing design science research and technology-driven solutions for modern organizational challenges.

Fernando Porté-AgelView profile

Professor

• Environmental Fluid Mechanics
• Turbulence
• Computational Fluid Dynamics
• +3 more

Fernando Porté-Agel is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL), leading the Wind Engineering and Renewable Energy Laboratory (WIRE) within the School of Architecture, Civil and Environmental Engineering (ENAC). His expertise spans Environmental Fluid Mechanics, Turbulence, and Wind Energy. He holds a Ph.D. from Johns Hopkins University (1999) and has held academic positions at the University of Minnesota before joining EPFL in 2010. Education: Ph.D., Environmental Engineering, Johns Hopkins University (1999) M.Sc., Hydrologic Engineering, IHE Delft (1995) B.S., Civil Engineering, Universidad Politécnica de Cataluña (1992) Research Interests: Large-eddy simulation of atmospheric boundary layers Wind turbine wakes and wind farm optimization Environmental fluid dynamics and turbulence modeling Wind energy systems and renewable energy technologies Awards: McKnight Presidential Fellow (2006–2009) NASA Young Investigator Award (2001–2004) NSF CAREER Award (2001–2006) Teaching & Advising: PhD Students: Duan Guiyue, Jamshidmofid Mohammad, and others Courses: Fluid Mechanics (for SIE), Environmental Transport Phenomena Labs & Teams: Directs the WIRE lab, collaborating on projects involving wind farm layout optimization, lidar-based wake measurements, and turbulence modeling.

Niao HeView profile

Associate Professor

• Machine Learning
• Optimization
• Reinforcement Learning
• +4 more

Prof. Niao He is an Associate Professor in the Department of Computer Science at ETH Zürich. His research focuses on optimization theory, reinforcement learning, stochastic systems, and their applications in machine learning and multi-agent systems. He holds an academic position at one of the world's leading technical universities, contributing to both theoretical advancements and practical algorithmic solutions. His research interests span optimization theory (e.g., convex/non-convex optimization, stochastic optimization), reinforcement learning (policy gradient methods, multi-agent systems), and statistical learning (risk-averse methods, entropy regularization). He has also explored applications in data science, network revenue management, and deep reinforcement learning for complex systems. Recent work emphasizes algorithmic reproducibility, robustness under model uncertainty, and efficient methods for large-scale problems. Notable contributions include novel approaches to minimax optimization, mean-field games, and adaptive learning frameworks. His articles consistently address theoretical guarantees while maintaining practical relevance for real-world computational challenges.

Johannes LugerView profile

Professor

• Strategic Management
• Decision Making
• Quantitative Methods
• +3 more

Prof. Dr. Johannes Luger is a Professor in the Department of Business Administration at the University of Zurich, leading the Evidence-Based Strategic Management chair. His research focuses on strategy, decision-making processes, and the integration of quantitative and experimental methods. He explores topics such as AI-human collaboration, organizational structure impacts, and strategic responses to exogenous shocks. He was recently appointed as a Program Director and leads a team including members like Robert Janjic and Yuqi Zheng. His work bridges theory and practice through courses on evidence-based strategy and strategic decision-making. Notable research trends include analyzing the digital transformation's strategic implications, organizational learning dynamics, and the evolving role of leadership in uncertain environments. His experimental approach often involves field studies and large-scale data analysis to validate hypotheses about managerial behavior and organizational outcomes. While no formal awards are listed, his contributions to strategic management and digitalization research are evident through his prolific publication record. Advising and team leadership are central to his role, though specific grants or lab affiliations are not detailed here. His research emphasizes practical applications, such as optimizing organizational decision-making frameworks in the face of rapid technological change.