Find Academic Supervisors

• Robotics
• Computer Vision
• Human-Machine Interaction
• +3 more

Gregory D. Hager is the Mandell Bellmore Professor of Computer Science at Johns Hopkins University, with joint appointments in Electrical and Computer Engineering, Mechanical Engineering, and the Department of Surgery at the School of Medicine. He serves as the head of the NSF's Computer and Information Science and Engineering Directorate (as of 2024) and is the founding director of the Johns Hopkins Malone Center for Engineering in Healthcare. Previously, he chaired the Department of Computer Science from 2010-2015 and served as deputy director of the NSF Engineering Research Center for Computer-Integrated Surgical Systems and Technology. Hager's research focuses on collaborative and vision-based robotics, time-series analysis of image data, and medical applications of image analysis and robotics. His work spans surgical robotics, human-machine collaboration, and computer vision with applications in healthcare. As director of the Computational Interaction and Robotics Lab (CIRL), he investigates dynamic spatial interaction at the intersection of imaging, robotics, and human-computer interaction. His research has led to real-world applications in surgical training, medical imaging, diagnostics, and computer-enhanced interventional medicine. Hager's publications demonstrate consistent advancement in surgical data science, with recent work focusing on 3D reconstruction from endoscopic video, surgical skill assessment using AI, and robotic assistance in neurosurgery. His research trajectory shows increasing integration of deep learning with surgical robotics, particularly in real-time guidance systems and objective skill assessment metrics. IEEE Fellow MICCAI Fellow ACM Fellow AIMBE Fellow AAAS Fellow MICCAI Best Paper Award (2006) Fulbright Junior Faculty Award (1988) Morris Ruben Outstanding Dissertation Award (1988) Hager has advised numerous PhD students who have become leaders in computer vision and medical robotics. His lab has secured significant research funding, including NSF Engineering Research Center support. He co-founded two successful startups: Clear Guide Medical (ultrasound-guided procedures) and Ready Robotics (industrial robot usability). As chair of the Computing Community Consortium and member of the International Federation of Robotics Research board, he has shaped national research agendas in computing and robotics. Hager leads the Computational Interaction and Robotics Lab (CIRL), which is associated with the NSF Engineering Research Center for Computer-Integrated Surgical Systems and Technology (ERC-CISST) and the Laboratory for Computational Sensing and Robotics (LCSR). His team collaborates extensively with clinicians at Johns Hopkins Hospital to translate robotics research into clinical practice.

Vincent FortuinView profile

Assistant Professor

• Bayesian Deep Learning
• Uncertainty Quantification
• Probabilistic Inference
• +5 more

Dr. Vincent Fortuin is a tenure-track Assistant Professor at the Technical University of Munich (TUM) and a research group leader at Helmholtz AI in Munich. He leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group and holds multiple prestigious fellowships including the Branco Weiss Fellowship. His academic affiliations include the TUM School of Computation, Information and Technology, the Konrad Zuse School of Excellence in Reliable AI, and the Munich Center for Machine Learning. Dr. Fortuin earned his BSc in Molecular Life Sciences from the University of Hamburg (2012-2015), followed by an MSc in Computational Biology and Bioinformatics from ETH Zürich (2015-2017), where he received the ETH Excellence Scholarship and the Willi Studer Prize. He completed his PhD in Machine Learning at ETH Zürich (2017-2021) under the supervision of Gunnar Rätsch and Andreas Krause, supported by a Swiss Data Science Center PhD Fellowship. Prior to joining TUM, he was a Research Fellow at St. John's College, University of Cambridge (2022-2023). His research focuses on the intersection of Bayesian statistics and deep learning, specifically developing methods for more robust, data-efficient AI systems with reliable uncertainty estimates. His work addresses critical limitations in standard deep learning approaches, particularly their tendency to be overconfident in predictions and require large datasets for training. He investigates better priors and more efficient inference techniques for Bayesian deep learning, deep generative modeling, meta-learning, and PAC-Bayesian theory, with applications in scientific and biomedical domains. Dr. Fortuin's recent publications demonstrate a consistent focus on improving uncertainty quantification in deep learning systems, with increasing emphasis on practical applications in scientific contexts. His work spans from theoretical foundations of Bayesian deep learning to practical implementations in protein design, materials science, and medical applications. A notable trend is his exploration of how to make Bayesian methods more scalable and applicable to modern large-scale AI systems while maintaining theoretical guarantees. Branco Weiss Fellowship (2023) St John's College Research Fellowship (2022) Swiss National Science Foundation Postdoc.Mobility Fellowship (2022) Swiss Data Science Center PhD Fellowship (2018) ETH Excellence Scholarship (2015) Willi Studer Award (2018) Dr. Fortuin actively supervises PhD and Master's students through his ELPIS research group at Helmholtz AI. He serves as a regular reviewer and area chair for major machine learning conferences and is an action editor for TMLR. He co-organizes the Symposium on Advances in Approximate Bayesian Inference (AABI) and the ICBINB initiative, demonstrating his commitment to advancing the field through community building. His research group receives funding from multiple sources including Helmholtz AI, the Branco Weiss Fellowship, and collaborations with international institutions. Dr. Fortuin leads the Efficient Learning and Probabilistic Inference for Science (ELPIS) group at Helmholtz AI, which focuses on fundamental machine learning research motivated by real-world scientific problems. The group collaborates extensively with researchers across Helmholtz centers and international institutions, particularly in biomedical applications where reliable uncertainty estimates are crucial.

• Artificial Intelligence
• Machine Learning
• Cognitive Robotics
• +3 more

Dr. Ulrike Kuhl is a Researcher at the University of Bielefeld, serving as Project Coordinator for the AI Academy OWL at the Research Institute for Cognition and Robotics and as Scientific Project Coordinator within the Faculty of Engineering's Machine Learning Group. Her office is located at CITEC 2-412, and she can be reached at +49 521 106-12125. Dr. Kuhl's research spans several interconnected domains at the forefront of human-centered AI development: Explainable Artificial Intelligence (XAI) frameworks and their psychological impact Cognitive learning enhanced through AI technologies Counterfactual explanation methodologies and user behavior Human-AI interaction design principles Applications of machine learning in environmental monitoring and sports analytics Analysis of Dr. Kuhl's publication trajectory reveals a consistent focus on bridging the gap between sophisticated AI systems and human understanding. Her work particularly examines how different explanation types affect user trust and decision-making, with recent publications exploring counterfactual explanations in contexts ranging from water distribution networks to educational technology. She has developed experimental frameworks like the 'Alien Zoo' methodology for systematically studying explanation usability. Dr. Kuhl actively contributes to the Center for Cognitive Interaction Technology (CITEC) at the University of Bielefeld, an interdisciplinary hub where computer scientists, engineers, and cognitive scientists collaborate on next-generation interactive technologies. Through her coordination of the AI Academy OWL initiative, she facilitates regional collaboration between academic researchers and industry partners to advance artificial intelligence applications in the Ostwestfalen-Lippe region.

• Responsible Computing
• Data Protection & Governance
• Digital Well-Being
• +3 more

Asia J. Biega is a tenure-track faculty member (W2) at the Max Planck Institute for Security and Privacy (MPI-SP), where she leads the interdisciplinary Responsible Computing group. She is also a principal investigator of the Cluster of Excellence CASA and the FINDHR consortium. Her work sits at the intersection of computing and society, focusing on responsible computing, data protection & governance, and digital well-being within data-driven and AI-based systems. Dr. Biega's research spans multiple disciplines including computer science, law, philosophy, and social sciences. Her work examines how principles of responsible computing can be computationally operationalized, with particular attention to data protection frameworks, privacy technology governance, and digital well-being. She actively collaborates across disciplinary boundaries to make technical contributions while supporting research in other fields. Her approach combines theoretical rigor with practical applications, often drawing from her industry experience at Microsoft and Google. Her publication record reveals a strong focus on the intersection of fairness, privacy, and transparency in information retrieval systems. She has pioneered work on data minimization compliance, fair ranking algorithms, and user perceptions of data collection practices. Her research consistently bridges technical and legal perspectives, particularly examining how GDPR principles can be computationally implemented. Recent work shows increasing attention to generative AI governance, algorithmic hiring systems, and the relational aspects of data in recommender systems. Dr. Biega has received several prestigious awards including the Council of Europe's Rodotà Award for innovative research in data protection, the SaTML Notable Reviewer Award, the GI-DBIS Dissertation Award of the German Informatics Society, and recognition as one of the '100 Brilliant Women in AI Ethics' in 2025. She has advised numerous PhD students and postdocs who have gone on to faculty positions at institutions including the University of Trieste, Penn State, and the University of Washington. Her research is funded by the Max Planck Society, Alexander von Humboldt Foundation, and the European Union (Horizon Europe FINDHR). She serves as General Co-Chair for ACM FAccT 2025 and has held leadership roles in multiple academic conferences.

• Machine Learning
• Dynamical Systems
• Optimization
• +3 more

Michael Muehlebach leads the independent Learning and Dynamical Systems research group at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. His interdisciplinary work bridges machine learning, dynamical systems theory, and control engineering to develop algorithms for cyber-physical systems with theoretical guarantees and practical implementations. Dr. Muehlebach received his B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich in 2010 and 2013, specializing in robotics and control systems. He completed his Ph.D. at ETH's Institute for Dynamic Systems and Control under Prof. R. D'Andrea in 2018, followed by postdoctoral research with Prof. Michael I. Jordan at UC Berkeley. His research focuses on constrained optimization, reinforcement learning, and control theory with applications in robotics. He pioneered approaches that express constraints in terms of velocities rather than positions, enabling more efficient optimization algorithms. His work spans theoretical foundations to physical implementations, including the One-Wheel Cubli balancing robot and electromagnetic navigation systems. Recent publications reveal a strong trend toward physics-informed machine learning, particularly for robotics applications requiring real-time performance and safety guarantees. Dr. Muehlebach has received numerous prestigious awards: Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellowship (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) He actively mentors doctoral researchers including Hao Ma, Melis Ilayda Bal, and Onno Eberhard, with research supported by multiple grants. His group maintains strong collaborations with Bernhard Schölkopf's Empirical Inference group at the Max Planck Institute. The Learning and Dynamical Systems group develops innovative hardware and software platforms, including Floaty (a wind-harnessing flying robot), advanced electromagnetic navigation systems, and data-efficient learning methods for robotic table tennis. Their approach combines rigorous theoretical analysis with practical validation on physical systems, emphasizing the integration of known physical structure into machine learning algorithms to improve sample efficiency and ensure generalization.

Emilio FerraraView profile

Professor

• Artificial Intelligence
• Computational Social Science
• Network Analysis
• +5 more

Dr. Emilio Ferrara is a Professor of Computer Science & Communication at the University of Southern California, holding appointments in the Viterbi School of Engineering, Annenberg School for Communication and Journalism, and Keck School of Medicine. He serves as Associate Director of Applied Data Science at USC, Research Team Leader at the USC Information Sciences Institute, and Principal Investigator at the USC/ISI Machine Intelligence and Data Science (MINDS) group. His educational background includes a PhD in Machine Learning and BSc & MSc in Computer Science. Ferrara's research focuses on the intersection of artificial intelligence and computational social science, specifically using AI to model and predict human behavior in techno-social systems. His work spans social networks, machine learning, and network science with applications to understanding communication dynamics in digital environments. Ferrara has published over 150 articles in prestigious venues including Proceedings of the National Academy of Sciences, Communications of the ACM, and Physical Review Letters. His research has been widely featured in major news outlets and examines topics ranging from social media manipulation to AI ethics and online behavior. His scientific achievements have been recognized with numerous awards: 2019 Viterbi Scientific Award 2018 DARPA Director's Fellowship 2016 DARPA Young Faculty Award 2016 Complex Systems Society Junior Scientific Award 2015 IBM Watson Big Data Influencer Ferrara's research is supported by major funding agencies including DARPA, IARPA, Air Force, and Office of Naval Research. He leads the HUMANS LAB (Humans & Machines + Networks & Social Systems) which has trained numerous PhD students who have gone on to successful careers in academia and industry. His work on social bots, election integrity, and AI ethics continues to shape understanding of digital social dynamics.

Elena SimperlView profile

Professor

• Human-centric AI
• Knowledge Graphs
• Knowledge Engineering
• +7 more

Elena Simperl is a Professor of Computer Science and Deputy Head of Department for Enterprise and Engagement at King's College London's Department of Informatics. She co-directs the King's Institute for Artificial Intelligence and serves as Director of Research for the Open Data Institute. As a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study, she leads the Trustworthy Knowledge Graphs focus group and contributes to advancing human-centric AI research across European institutions. Professor Simperl obtained her doctoral degree in Computer Science from the Free University of Berlin and her diploma from the Technical University of Munich. Prior to joining King's, she held academic positions in Germany, Austria, and at the University of Southampton, and was a Turing Fellow. Her career trajectory demonstrates consistent leadership in bridging academic research with practical applications in data ecosystems. Her research sits at the critical intersection of AI and social computing, focusing on human-centric approaches to building sociotechnical systems that integrate data, algorithms, and human capabilities. She investigates how to make knowledge engineering more accessible, how to leverage collective intelligence for data quality improvement, and how to design participatory AI systems that address societal challenges like misinformation. Her work spans knowledge graphs, semantic technologies, crowdsourcing, and open data, with particular emphasis on the social dimensions of data-intensive systems and the governance frameworks needed for trustworthy AI deployment. Analysis of her recent publications reveals a strong evolution toward integrating large language models with traditional knowledge engineering practices while maintaining human oversight. There's a clear trajectory from foundational work on knowledge representation toward increasingly applied research addressing real-world challenges in media ecosystems, citizen science, and data governance, with growing attention to policy implications of AI technologies. Fellow of the British Computer Society Fellow of the Royal Society of Arts Hans Fischer Senior Fellow at TUM-IAS (2023) Ranked among top 100 most influential scholars in knowledge engineering of the last decade Included in Women in AI 2000 ranking Professor Simperl has led 14 major European and national research projects totaling millions in funding, including MediaFutures (a Horizon 2020 program tackling online misinformation), QROWD, ODINE, Data Pitch, and ACTION. She currently co-chairs the Croissant working group in ML Commons developing data standards for AI, and serves as president of the Semantic Web Science Association. Her research has directly influenced the development of data ecosystems supporting startups and citizen science initiatives across Europe, demonstrating exceptional ability to translate theoretical advances into practical impact. As Director of Research at the Open Data Institute, she oversees initiatives connecting data entrepreneurs with artists and civic organizations. Her leadership in the MediaFutures project established a data-driven innovation hub that supported 51 startups/SMEs and 43 artists through three open calls, creating a sustainable model for arts-technology collaborations addressing media challenges. Her work with the ODINE project helped create a European ecosystem for data-driven startups, demonstrating her commitment to building practical applications of open data principles.

Bernhard SchölkopfView profile

Professor

• Machine Learning
• Artificial Intelligence
• Causal Inference
• +3 more

Bernhard Schölkopf is a Professor and director at the Max Planck Institute for Intelligent Systems in Tübingen. He previously served as a director at the Max Planck Institute for Biological Cybernetics (2001-2011) and held industrial positions at AT&T Bell Laboratories, Microsoft Research, and Biowulf Technologies. He is also an honorary professor at the Technical University of Berlin since 2002. Education : Studied physics, mathematics, and philosophy in Tübingen and London; received a doctorate in computer science from the Technical University of Berlin (1997). His research spans machine learning , artificial intelligence , and causal inference , with interdisciplinary applications in computational neuroscience and theoretical computer science . He has contributed to foundational work in kernel methods, support vector machines, and causal modeling. Scientific Awards : Körber Prize 2019

Yangruibo DingView profile

Assistant Professor

• Software Engineering
• Large Language Models
• Agentic Systems
• +7 more

Yangruibo Ding is an incoming Assistant Professor in the Department of Computer Science at the University of California, Los Angeles (UCLA), and currently serves as a Postdoctoral Scientist at AWS Agentic AI. He has held significant research positions at Google DeepMind, Amazon AWS AI Labs, and IBM Research, establishing himself as a leading researcher in software engineering with a focus on large language models for code. His research focuses on developing large language models (LLMs) and agentic systems for software engineering. He specializes in training LLMs with advanced symbolic reasoning capabilities for debugging, testing, program analysis, and verification. His work aims to build efficient, collaborative agentic systems for complex software development and maintenance tasks, with particular emphasis on code generation, vulnerability detection, and execution-aware pre-training techniques. Dr. Ding's publication record reveals a strong trajectory toward enhancing code intelligence through comprehensive semantics reasoning and self-refinement approaches. His research spans multiple dimensions of software engineering including code completion, vulnerability detection, model evaluation, and cross-file context understanding, with applications across various programming languages and development environments. Dr. Ding has received numerous prestigious awards recognizing his contributions to the field: IBM Ph.D. Fellowship Award (2022-2024) ACM SIGSOFT Distinguished Paper Award (2023) IEEE TSE Best Paper Award Runner-up (2022) Ph.D. Service Award, Columbia CS (2025) NSF Student Travel Award for ESEC/FSE'23 (2023) ACM SIGSOFT CAPS Travel Grant (2023) NSF Travel Award for ICSE'22 (2022) As he establishes his research group at UCLA, Dr. Ding is actively seeking students with strong coding skills and experience in large language models, program analysis, verification, or security. He serves on program committees for major conferences including ICSE (2026), ASE (2024, 2025), and ESEC/FSE Artifacts Track (2023), and regularly reviews for top-tier conferences and journals in AI and software engineering. His research is conducted through collaborations with leading industry teams including AWS Agentic AI, Google DeepMind's Learning4Code team, and IBM Research's AI for Code team, creating a robust network of industry-academia partnerships that drive innovation in software engineering research.

• Information Systems
• Healthcare IT
• Knowledge Management
• +5 more

Atreyi Kankanhalli is a Professor at the National University of Singapore, specializing in Information Systems with a focus on knowledge management, healthcare IT, and digital innovation. Their research spans over three decades, with prolific contributions in top journals like MIS Quarterly, Journal of AIS, and Information & Management. They have co-authored over 150 papers addressing topics such as crowdsourcing, online communities, and the impact of AI on scholarly practices. Notable work includes studies on user adherence to health apps, innovation in public sector data utilization, and the ethical challenges of generative AI in peer review. Kankanhalli has also led research on global virtual teams and digital technologies' role in social justice, reflecting a commitment to both technical and societal dimensions of information systems. Education & Background: While specific degree details are not provided, their extensive publication history and academic roles imply advanced qualifications in Information Systems or related fields. They have collaborated with global researchers across institutions like NUS, University of Illinois, and Singapore Management University. Research Themes: Core areas include digital health interventions (e.g., fitness app adherence), organizational innovation via open data and crowdsourcing, and the socio-technical challenges of AI in academia. Their work often bridges theoretical frameworks with practical applications, such as healthcare decision support systems and policy-driven technology adoption. Impact & Influence: As an editorial board member and frequent conference contributor (e.g., ICIS, PACIS), Kankanhalli shapes the field's research agenda. Their recent focus on generative AI's implications highlights proactive engagement with emerging technologies' ethical and methodological challenges.

Michael GröschelView profile

Professor

• Business Process Management
• Digital Transformation
• Business Models
• +3 more

Prof. Dr. Michael Gröschel is a Professor of Business Informatics at the Faculty of Computer Science, Mannheim University of Technology. His expertise spans Business Process Management (BPMN, Process Mining), Digital Transformation, and innovative Business Models. He teaches courses on BPM, project management, and e-business, often collaborating with industry clients through student projects. As a consultant, he specializes in BPMN training and IT-driven business strategy. His work emphasizes practical applications of IT tools like RPA and low-code platforms. Recent publications focus on RPA bot performance evaluation, AI in automotive trade, and business model-IT alignment. Prof. Gröschel’s research bridges academic insights with real-world challenges, particularly in leveraging technology for business innovation. He has authored books and articles on business intelligence tools, mobile business strategies, and digital customer care. His consulting services include executive coaching for academic career advancement and enterprise IT management best practices. Office: Building A, Room 007c | Phone: +49 621 292-6764 | Professional website available for further engagement opportunities.

• Requirements Engineering
• Goal Modeling
• Conceptual Modeling
• +7 more

Sotirios Liaskos is an Associate Professor in the School of Information Technology at the Faculty of Science, York University. He is a leading researcher in requirements engineering and conceptual modeling, with a focus on goal models, empirical evaluation, and model-driven engineering. Research Interests: Requirements Engineering Goal and Conceptual Modeling Empirical Software Engineering Model-Driven Design and Automation Uncertainty and Decision-Theoretic Reasoning in Models Applications in Blockchain and Reinforcement Learning His recent work emphasizes the empirical validation of modeling constructs, the integration of decision theory into goal models, and the automated generation of secure workflows and AI training environments. He has led and co-authored numerous experimental studies on model comprehensibility and semantic quality. Publication Trends: His publications consistently appear in top venues like ER, RE, and iStar. The last five years show a strong trend toward empirical evaluation of modeling languages, decision-theoretic goal models, and applications of goal modeling in emerging domains such as blockchain and reinforcement learning simulation design. Scientific Contributions: Developed frameworks for empirical evaluation of modeling language ontologies (Peira framework). Advanced decision-theoretic approaches to goal modeling under uncertainty. Pioneered model-driven methods for generating blockchain simulators and reinforcement learning environments. Conducted foundational empirical studies on the comprehensibility of contribution links and visualization alternatives in goal models. Advising and Collaboration: He has advised several researchers including Ibrahim Jaouhar, Wisal Tambosi, Mehrnaz Zhian, and Saba Zarbaf. He maintains a highly collaborative research profile with frequent co-authorship with John Mylopoulos, Shakil M. Khan, and other international researchers. He has served as a co-editor for multiple iStar workshop proceedings, indicating leadership in the goal-oriented requirements engineering community. Labs and Teams: While no specific lab name is mentioned, his work is closely associated with research groups focused on requirements engineering and conceptual modeling, likely within York University’s software engineering research cluster. His collaborations span institutions in Canada, Europe, and beyond.

Birgit EickelmannView profile

Professor

• Digital Education
• School Pedagogy
• Educational Technology
• +6 more

Prof. Dr. Birgit Eickelmann is a Professor of School Pedagogy at the Institute of Educational Science within the Faculty of Arts and Humanities at Paderborn University. She has held this position since October 2012, initially as a W2 professor until January 2014, and then as a W3 professor (full professor) from February 2014 onward. Her research focuses on school and lesson development in the digital age, school pedagogy under digital transformation conditions, empirical school research, teacher education, and school leadership with emphasis on digital learning leadership. Her educational background includes a habilitation in Educational Science in May 2012, a PhD in Educational Science with summa cum laude in July 2009, and state examinations for teaching Mathematics and Physics in December 1996 and January 1999. Prof. Eickelmann's research centers on the digital transformation of educational systems, particularly examining how schools develop digital competencies among students and teachers. Her work emphasizes equitable access to digital learning opportunities, the role of school leadership in digital transformation, and the development of computational thinking skills. She investigates how schools can become resilient in the face of digital challenges, with special attention to organizational factors that support successful digital integration. Her publication record reveals a strong focus on international comparative studies, particularly the IEA's ICILS (International Computer and Information Literacy Study) across multiple cycles (2013, 2018, 2023). Her research spans digital literacy assessment, school-level factors influencing digital competence development, and policy implications for educational systems undergoing digital transformation. Prof. Eickelmann leads several major research initiatives including the National Research Center for the IEA Study ICILS 2023 (2021-2026), the German coordination of the Horizon-2020 project 'DigiGen' (2019-2022), and previous leadership of ICILS 2018 (2015-2021) and ICILS 2013 (2012-2015). She is actively involved in policy advising regarding digital education in Germany. She is a member of numerous scholarly organizations including the World Educational Research Association (since 2021), the Society for Empirical Educational Research (since 2016), and the German Society for Educational Science (since 2014), among others. Her work bridges academic research with practical implementation in schools through projects like 'Navigator Bildung Digitalisierung' and 'schultransformNEXT'.

• Learning Analytics
• Educational Technology
• Virtual Reality Education
• +5 more

Ulrik Schroeder is a Universitätsprofessor (Full Professor) at RWTH Aachen University, leading the Chair of Learning Technologies within the Faculty of Computer Science. His research focuses on the intersection of educational technology, learning analytics, and immersive technologies with particular emphasis on practical implementations in higher education settings. Professor Schroeder's research spans multiple interconnected domains in educational technology. His primary interests include Learning Analytics implementation (particularly using xAPI standards), Virtual Reality applications for education, Open Educational Resources development and conversion, and gamification approaches for programming education. He has developed several notable tools including convOERter for OER conversion, WebWriter for creating explorable explanations, and various xAPI-based learning analytics infrastructures. His work consistently bridges theoretical frameworks with practical educational applications, often focusing on computer science education contexts. Analysis of his recent publications reveals a strong trend toward integrating Learning Analytics with immersive technologies, particularly Virtual Reality environments. His research demonstrates a systematic approach to educational technology development, with emphasis on scalability, interoperability through standards like xAPI, and practical implementation in real educational settings. The work increasingly focuses on personalized learning paths, quality assurance for educational resources, and privacy-conscious data collection. Co-editor of 21. Fachtagung Bildungstechnologien (DELFI) (2023) Co-editor of Hochschuldidaktik der Informatik HDI 2018 Co-editor of DeLFI 2018 conference proceedings Professor Schroeder has supervised numerous doctoral and postdoctoral researchers who frequently appear as co-authors on his publications, indicating an active research group. His projects often involve interdisciplinary collaborations across computer science, education, and psychology. Current major initiatives include the AIStudyBuddy project for study path analysis and the development of VR classroom simulations for teacher training. His research group, the Learning Technologies Innovation Lab, develops open research tools that support various aspects of educational technology research and implementation.

• Networked Systems
• Distributed Systems
• Social Computing
• +5 more

Krishna Gummadi is a Scientific Director and Professor at the Max Planck Institute for Software Systems (MPI-SWS) in Germany, where he leads the Networked Systems Research Group. He also holds a professorship at the University of Saarland, demonstrating his dual commitment to research and academic instruction in computer science. His educational background includes: Ph.D. in Computer Science and Engineering from the University of Washington (2005) B.Tech. in Computer Science and Engineering from the Indian Institute of Technology, Madras (2000) Gummadi's research spans networked and distributed computer systems with a current focus on social computing systems. His work addresses critical challenges in algorithmic fairness, privacy in social media, trustworthiness of online identities, and information dissemination in social networks. He approaches these problems through interdisciplinary methods combining user-centric studies, data-centric analysis, and systems-centric design to create practical solutions that enhance fairness, transparency, and user control in online platforms. His methodology integrates large-scale observational studies, computational modeling, and system implementation to tackle complex human-computer interaction challenges at societal scale. His recent publications reveal a strong emphasis on fairness in algorithmic decision making, with significant contributions to quantifying and addressing discrimination in machine learning systems. His work bridges computer science, social science, and ethics, creating frameworks for fair classification, understanding media bias, and developing privacy-preserving techniques that maintain functionality while protecting user data. The research demonstrates a progression from technical system design to addressing societal implications of computing systems. Among his notable scientific achievements: ERC Advanced Grant in 2017 for 'Foundations for Fair Social Computing' Test of Time Awards at ACM SIGCOMM and AAAI ICWSM Casper Bowden Privacy Enhancing Technologies (PET) and CNIL-INRIA Privacy Runners-Up Awards IW3C2 WWW Best Paper Honorable Mention Multiple Best Paper awards across prestigious conferences Gummadi has advised numerous PhD students and postdoctoral researchers who have gone on to prominent positions in academia and industry. His ERC Advanced Grant has supported extensive research into fair social computing, while his leadership in major conferences (including serving as General Chair for ICWSM 2016 and Program Chair for WWW 2015) has shaped research directions in the field. His teaching portfolio includes courses on Distributed Systems, Human-Centered Machine Learning, and Social Media Analysis. He leads the Networked Systems Research Group at MPI-SWS, which has developed several publicly available systems including tools for fair classification, privacy risk assessment, trust evaluation in social media, and information diet management. The group's work bridges theoretical advances with practical implementations that address real-world challenges in social computing, with numerous software releases and datasets made available to the research community.