Find Academic Supervisors

• Databases
• Big Data Management
• Data Science
• +3 more

Michael Kaufmann is a Lecturer and Project Manager at the Lucerne School of Computer Science and Information Technology, part of Lucerne University of Applied Sciences and Arts. He has held this position since 2016 and has extensive experience in academic and industry roles, including roles as a Board Member of the FMsquare Foundation and Coordinator of the Research Team Data Intelligence. His academic journey includes a Habilitation in 2023 from FernUniversität in Hagen, Germany, focusing on 'Emergent Knowledge Engineering in Big Data Management,' and a PhD in Computer Science from the University of Fribourg in 2012. His research and teaching focus on databases, big data management, and data science, with notable contributions to database security, fuzzy logic applications, and semantic analysis. He has led numerous research projects, including IFZ FinTech, Netted Letters, and MinimalTools, exploring topics such as decision intelligence and data quality assessment. His work often bridges theoretical advancements with practical applications in industries like tourism, finance, and social media analytics. Despite his prolific output, no specific academic awards or student advisees are explicitly mentioned in the provided materials. Education highlights include a Habilitation thesis on big data management frameworks and a PhD on inductive fuzzy classification in marketing analytics. His teaching spans specialized courses like Database Modeling, NoSQL Systems, and Data Science. Professional experience prior to academia includes roles as a Business Analyst, Data Architect, and Data Warehouse Analyst at firms like PostFinance and Mobiliar. Key research projects include analyzing tourism hotspots using travel blog data, GDPR-compliant social network analysis, and developing interactive research environments. His publications emphasize practical applications of databases and machine learning, with a focus on ethical AI frameworks and scalable in-database analytics. Kaufmann’s work often integrates interdisciplinary methods, such as combining fuzzy logic with marketing analytics and natural language processing for knowledge extraction.

• Artificial Intelligence (AI) in Industry
• Machine Learning
• Natural Language Processing (LLMs)
• +4 more

Prof. Dr. Esther Gelle is a Lecturer in Digital System Design at the FHNW Institute of Business Engineering, part of the FHNW School of Engineering and Environment at the University of Applied Sciences Northwestern Switzerland. Her work focuses on AI applications in industry, machine learning, natural language processing (LLMs), constraint satisfaction problems, and digital business process modeling. She has contributed to advancements in industrial automation systems, IT asset management, and configuration frameworks. Her research spans over two decades, with notable contributions in constraint satisfaction problem solving, industrial standard adaptation (e.g., IEC 61346), and autonomous IT management. She collaborates with industry partners to bridge theoretical concepts with practical applications. Her publications emphasize optimization techniques, system modeling, and automated configuration in engineering contexts. Her articles consistently explore themes like constraint-based problem solving, industrial process control, and the integration of AI into technical systems. While no awards are explicitly listed, her extensive publication record reflects sustained academic and industrial impact. She currently holds no listed students or grants but actively engages in research teams focused on automation and digital transformation. Her affiliation with the Institute of Business Engineering positions her at the intersection of technical systems and business processes, emphasizing practical solutions for modern industrial challenges. She is based at the FHNW Campus Brugg-Windisch, contributing to both educational and applied research initiatives.

Hanna KrasnovaView profile

Professor

• Social Media
• Digital Integration
• Privacy
• +4 more

Prof. Dr. Hanna Krasnova is a Professor of Information Systems at the University of Potsdam, specializing in Social Media and Data Science. She previously held an Assistant Professorship at the University of Bern. Her research focuses on the societal impact of social media, including phenomena like 'phubbing' (smartphone-induced snubbing), digital integration of refugees, privacy concerns, and the psychological effects of social media use. Her work has been widely recognized, including a Best Paper Award at ICIS 2013 and extensive media coverage (e.g., CNN, BBC). Her key research areas include digital well-being, privacy calculus, and the sharing economy. She leads projects at the German Internet-Institute on topics like 'Digital Technologies and Well-Being' and 'Digital Integration,' exploring how technology aids refugee integration. Krasnova advises numerous PhD and Master’s students, including Dr. Katharina Baum and Dr. Olga Abramova. Her contributions extend to academic leadership, serving as Doctoral Consortium Co-Chair at ECIS 2018 and Track Chair at multiple conferences. Over 3,000 citations on Google Scholar highlight her scholarly impact. She is also noted for accessible outreach, with her findings featured in global media.

• Informatics Education
• Algorithms
• Computer Science Education
• +3 more

Regula Lacher is a Researcher at ETH Zürich’s Department of Computer Science, affiliated with the Professorship for Algorithms and Didactics. Her work focuses on advancing computer science education through innovative pedagogical methods, emphasizing algorithm design, problem-solving techniques, and the integration of computational thinking into K-12 curricula. She specializes in developing tangible, non-digital teaching tools and game-based approaches to enhance abstract concept comprehension. Her research explores how informatics education can improve general educational outcomes, particularly through constructive induction and genetic Socratic methodologies. She has contributed to curriculum design frameworks for both Swiss Maturität and international K-12 systems, advocating for cross-disciplinary learning and teacher training programs. Publications span topics like robotics-based programming, cybersecurity education, and multilingual educational materials. Key themes include bridging concrete and abstract learning, promoting equity through accessible resources, and leveraging historical pedagogical tools like LOGO in modern classrooms. Her work addresses challenges in standardizing informatics education while maintaining adaptability for diverse cultural and linguistic contexts, as seen in her French and Italian language publications.

Cansin Yaman EvrenosogluView profile

Researcher

• Electric Power Engineering
• Renewable Energy Integration
• Energy Storage Systems
• +5 more

Cansin Yaman Evrenosoglu is a Principal Expert at the Research Center for Energy Networks (FEN) at ETH Zürich, Switzerland. His academic journey includes a B.S. and M.S. in Electrical Power Engineering from Istanbul Technical University (1998–2001) and a Ph.D. in Electrical and Computer Engineering from Texas A&M University (2006). Prior to FEN, he held roles such as Assistant Professor at Virginia Tech (2011–2013) and University of Nevada Reno (2008–2011), and Principal Scientist at ABB Corporate Research (2013–2019). His research focuses on electric power grids, including design, operation, and protection of transmission/distribution systems. Key areas include secure integration of renewable energy sources, high-penetration scenarios with energy storage systems, electric vehicles, solar PVs, and heat pumps. He is a Senior Member of IEEE and served as Editor for the IEEE Transactions on Smart Grid (2014–2019). Evrenosoglu has supervised numerous students, including PhD graduates Meghana M. Reddy and Alexandra Weinberger, and has contributed to projects like the Arrangements and Drawings initiative. His work spans academic and industrial collaborations, emphasizing infrastructure planning and smart grid technologies.

• Augmented Reality
• Game Technology
• Medical Imaging
• +4 more

Dr. Fabio Zünd is a Lecturer at the Department of Computer Science, ETH Zurich, affiliated with the Game Technology Center (GTC). He is based in Zurich, Switzerland, and can be reached at fabio.zund@inf.ethz.ch . His work focuses on innovative applications of Augmented Reality (AR), Game Technology, and Human-Computer Interaction (HCI), with notable contributions to medical AR integration, facial image anonymization, and interactive storytelling systems. Research interests include AR-driven medical education, personalized clinical imaging tools, and the development of immersive technologies that bridge real and virtual worlds. His projects often address challenges in user-centered design, such as scent-based gaming interfaces and emotion-aware interaction techniques. Recent work highlights include the VerA system for clinical facial image anonymization, scent prediction in video games, and latent diffusion models for art generation. He explores interdisciplinary applications in healthcare, education, and entertainment through collaborations with institutions like the ETH Library and industry partners. No scientific awards are explicitly mentioned in the provided information. His advising record remains unspecified, though he contributes to ETH’s educational programs through courses like the Seminar on Game Technology . Ongoing projects involve the Game Technology Center’s exploration of tangible interaction systems and wearable input devices such as the DigiGlo glove technology.

Matija PiškorecView profile

Researcher

• Machine Learning
• Complex Systems
• Statistical Inference
• +5 more

Matija Piškorec is a Senior Research Associate at the Faculty of Informatics, University of Zurich. His research spans machine learning, complex systems, and blockchain technologies, with a focus on statistical inference of social influence and network analysis. Primary Affiliation: Faculty of Informatics, University of Zurich Research Interests Machine learning and complex systems Statistical inference of influence in online social networks Blockchain technologies and distributed ledger systems Information visualization and interactive web applications in computational biology Publications His recent work explores blockchain networks like Polkadot and Ethereum, analyzing their structure and consensus mechanisms. Earlier research focuses on social network influence, financial data cohesiveness, and computational biology tools. Awards No scientific awards or honors were explicitly mentioned in the text. Additional Contributions Developed web-based visualization tools (e.g., MultiNets) and applied machine learning to diverse domains, including microbiology and finance.

• Computational Linguistics
• Eye-tracking
• Machine Learning
• +5 more

Lena Jaeger is an Associate Professor of Digital Linguistics at the University of Zurich, where she leads research at the intersection of linguistics, computational cognitive science, and machine learning. She joined the Chair of Computational Linguistics at UZH in July 2020 after establishing a Machine Learning Junior Research Group at the University of Potsdam, funded by the German Federal Ministry of Education and Research. Her educational background spans multiple disciplines: she earned an MA in Chinese Language and Culture (Sinology) from the University of Freiburg im Breisgau, Tongji University Shanghai, Beijing Language and Culture University, and Université Paris 7 Denis-Diderot; followed by an MSc in Experimental and Clinical Linguistics at the University of Potsdam; and completed her doctorate in cognitive science at the same institution. Notably, she also earned a bachelor's degree in computer science during or after her doctoral studies. Professor Jaeger's research focuses on investigating cognitive mechanisms underlying human language processing using experimental psycholinguistics, computational modeling, and machine learning methods. Her current work develops machine learning techniques for analyzing eye-tracking data to understand cognitive processes reflected in eye movement behavior. This interdisciplinary approach combines insights from linguistics, cognitive science, and artificial intelligence to create models that bridge human and machine language understanding. Her recent publications reveal a strong trend toward developing eye-tracking methodologies, creating multilingual corpora, and applying machine learning to understand reading behavior and language processing. Her work spans from fundamental research on cognitive mechanisms to practical applications in educational technology, medical diagnostics, and AI development. Best student late breaking work award for Reporting Eye-Tracking Data Quality: Towards a New Standard Best short paper award for Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models Professor Jaeger actively supervises multiple PhD students across computational linguistics, machine learning, and phonetics disciplines. Her research group collaborates extensively on large-scale projects like the MultiplEYE initiative, which establishes standards for multilingual eye-tracking data collection. She has secured significant research funding, including a Machine Learning Junior Research Group grant from the German Federal Ministry of Education and Research before moving to UZH. Her laboratory work centers on eye-tracking methodologies, developing tools like pymovements for eye movement data processing, and creating comprehensive corpora such as MECO (Multilingual Eye-Movement Corpus), MultiplEYE, and CoLAGaze. These resources support cross-linguistic research on reading behavior and language processing across diverse populations.

Srikanth MadikeriView profile

Lecturer

• Automatic Speech Recognition
• Speaker Recognition
• Language Recognition
• +3 more

Dr. Srikanth Madikeri is a Lecturer and Senior Researcher at the University of Zurich's Department of Computational Linguistics. He holds a Ph.D. in Computer Science from IIT Madras (2013) and a Bachelor of Engineering from Anna University (2008). Before joining UZH, he spent 11 years at Idiap Research Institute as a Postdoctoral Researcher and Research Associate. His research focuses on low-resource speech technologies, including Automatic Speech Recognition (ASR), Speaker Diarization, Language Recognition, and Spoken Dialog Systems. He specializes in adapting models for challenging domains like air traffic control and criminal investigations. Recent publications demonstrate strong trends in multimodal systems, domain adaptation for ASR, and efficient data selection techniques. His work frequently integrates speech processing with NLP tasks and criminal network analysis. Awards: Best Paper Award in Signal Processing Track (NCC 2011) International Create Challenge 2017 Winner He teaches Speech Technology and Machine Learning for Computational Linguistics at UZH. Professional activities include serving as Area Chair for Interspeech (2021-2022) and developing open-source toolkits like pkwrap for LF-MMI training.

Abraham BernsteinView profile

Professor

• Semantic Web
• Data Mining
• Machine Learning
• +7 more

Abraham Bernstein is a Full Professor of Informatics at the University of Zurich (UZH), where he serves as Head of the Dynamic and Distributed Information Systems Group and Director of the UZH Digital Society Initiative. He leads a university-wide initiative with over 180 faculty members investigating the interplay between society and digitalization. His work bridges social science foundations (organizational psychology/sociology/economics) and technical disciplines (computer science, artificial intelligence), creating a unique interdisciplinary approach to digital transformation challenges. Education: Diploma in Computer Science from ETH Zurich Ph.D. in Management with concentration in Information Technologies from MIT's Sloan School of Management Professor Bernstein's research spans the Semantic Web, data mining/machine learning, recommender systems, crowd computing, and collective intelligence. His work uniquely integrates social science perspectives with technical computer science approaches, examining how social and technical elements interact in digital systems. Recent work focuses on explainable AI, ethical decision-making with AI systems, and the societal implications of digital transformation, reflecting his commitment to addressing both technical challenges and their broader societal context. His publication record shows a strong trajectory in multimodal information retrieval, knowledge representation, and human-AI collaboration, with increasing focus on ethical considerations and societal impact of AI technologies. The research demonstrates consistent innovation in bridging technical AI capabilities with human-centered design principles, particularly in areas like explainable recommender systems and democratic applications of AI. Scientific Recognition: Nominated Digital Shaper by Bilanz magazine (2017) Professor Bernstein has supervised over 30 PhD students whose work spans semantic technologies, data mining, recommender systems, and human-AI interaction. His research group has secured significant funding for projects related to digital society, knowledge representation, and AI ethics. As Director of the Digital Society Initiative, he coordinates cross-disciplinary research across UZH's faculties, bringing together scholars from humanities, social sciences, law, economics, and STEM fields to address complex digital transformation challenges. He leads the Dynamic and Distributed Information Systems Group at UZH, which maintains strong international collaborations and contributes significantly to both theoretical advances and practical applications in information systems. The group's work has influenced standards in semantic web technologies and continues to shape discourse on responsible AI development and deployment in society.

Sarah Degallier RochatView profile

Professor

• Human-robot interaction
• Collaborative robotics
• Digital skills training
• +5 more

Prof. Dr. Sarah Degallier Rochat serves as Head of Humane Digital Transformation at Bern University of Applied Sciences (BFH), where she is a Professor in the Department of Technology and Computer Science. She is affiliated with the Institute for Human Centered Engineering (HuCE) and specifically works within the Laboratory for Computer Perception and Virtual Reality. Her research spans multiple institutional collaborations including the Institute for Data Applications and Security (IDAS) at BFH and partnerships with the University of Fribourg on experimental studies. Professor Degallier Rochat's research focuses on human-robot interaction , collaborative robotics , and humane digital transformation . She challenges the notion of complete human replacement by technology, advocating instead for human augmentation where technology empowers rather than mechanizes workers. Her work examines how humans and robots can work together effectively in industrial settings, with particular emphasis on developing intuitive interfaces for robot programming that promote digital skills acquisition among workers through experiential learning rather than symbolic cognition. Her research has evolved from foundational work in motor primitives for humanoid robots to current applications in industrial settings. She investigates the human factors involved in digital transformation, with special attention to workers with intermediate and lower levels of education who are often overlooked in digital skills training. Her approach emphasizes embodied cognition and work experience as foundations for developing effective human-machine interfaces that leverage existing worker knowledge. Human augmentation through collaborative robotics Intuitive robot programming interfaces (visual programming) Digital skills development for industrial workers Human-centered approaches to digital transformation Ethical considerations in human-robot collaboration Workplace adaptation to new technologies Professor Degallier Rochat has secured funding from diverse sources including Innosuisse, the Gerbert Rüf Foundation, and SNF. With support from the Gerbert Rüf Foundation's First Venture program, she co-founded a spin-off company aimed at making automation profitable for SMEs through employee training. Her research demonstrates significant practical impact, with interface designs tested across 116 participants showing positive effects on programming anxiety and usability. Her laboratory develops practical solutions for human-robot collaboration in manufacturing environments, creating interfaces that workers can use without extensive technical training. Through numerous publications, conference presentations, and media engagements, she actively contributes to the discourse on ethical and human-centered technological development in industry.