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Fangneng ZhanView profile

Research Fellow

• Computer Science
• Artificial Intelligence
• Neural Rendering
• +7 more

Currently a Research Fellow at Harvard University & MIT , Fangneng Zhan specializes in Neural Rendering and Generative AI . His research focuses on developing evolutive rendering frameworks, 3D-aware generative models, and multimodal synthesis techniques. Previously, he was a postdoctoral researcher at the Max Planck Institute for Informatics under Prof. Christian Theobalt. He earned his Ph.D. in Computer Science & Engineering from Nanyang Technological University, Singapore and a Bachelor's in Communication Engineering from the University of Electronic Science and Technology of China . His work spans 3D reconstruction, robotics applications , and lighting estimation , with significant contributions to SIGGRAPH , NeurIPS , and CVPR conferences. Recent research highlights include evolutive gauge transformations for neural fields, generalizable 3D style transfer via Gaussian splatting, and multimodal synthesis frameworks leveraging pre-trained models like CLIP and Stable Diffusion. He has co-authored Top50 Popular Paper in TPAMI 2023 and organized workshops at CVPR 2024 on generative models. Scientific Awards: Top50 Popular Paper, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2023 Collaborative Network: Mentored students at institutions like Harvard, NTU, and ETH Zurich. His projects include datasets for lighting estimation and real-time scene text detection systems.

• Indoor Localization
• Wireless Networks
• Pervasive Computing
• +11 more

Shueng-Han Gary Chan is a faculty member in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), within the College of Engineering. He is actively engaged in research and mentoring, with a strong publication record in mobile computing, indoor localization, and AI for pervasive systems. His research focuses on indoor localization using Wi-Fi, geomagnetic, and inertial signals , sensor fusion , crowd counting with deep learning , domain adaptation , and efficient mobile AI systems . His work bridges theoretical innovation with real-world deployment, as seen in systems for missing person search and indoor navigation. Recent publications (2023–2025) show a consistent trend toward self-supervised and domain-agnostic learning , efficient model design for mobile devices , and robust signal fusion in noisy environments . His team leverages transformer architectures, graph neural networks, and novel optimization techniques to solve real-world challenges in urban and indoor spaces. He has advised numerous graduate students, including Jierun Chen, Zhuoxuan Peng, and Tianlang He, who have contributed as first authors to joint publications. His collaborations span institutions and include work on large-scale system deployments and mobile AI. He leads a research group focused on mobile and pervasive computing , with projects involving IoT-based contact tracing, indoor navigation (e.g., DeepNavi, SiFu), and real-time localization systems. The team emphasizes practical deployment and system robustness.

Dimitrios KarampinosView profile

Professor

• Magnetic Resonance Imaging (MRI)
• Biomedical Engineering
• Medical Imaging Technology
• +6 more

Prof. Dimitrios Karampinos is a Professor at the Technical University of Munich (TUM), leading the Experimental Magnetic Resonance Imaging group within the TUM School of Medicine and Health. He specializes in developing novel MRI techniques for quantitative biomarker discovery, focusing on musculoskeletal, metabolic, and oncological applications. His career includes a PhD from the University of Illinois (2008), postdoctoral research at UCSF (2009–2012), and leadership roles at TUM since 2012. Prof. Karampinos has pioneered advancements in MRI reconstruction, signal modulation, and biomarker validation for clinical translation. Educations: BSc in Mechanical Engineering (National Technical University of Athens, Greece), PhD in Biomedical Engineering (University of Illinois, Urbana-Champaign, 2008). Research Interests: Development of MRI measurement techniques, quantitative biomarkers for disease diagnosis, and improving therapy monitoring. Key areas include musculoskeletal disease imaging, metabolic disorder assessment, and oncology applications. His work emphasizes translating research into clinical practice through innovations like accelerated imaging, artifact correction, and AI-driven analysis. Awards: ERC Starting and Proof of Concept Grants (2015, 2019), TUM Supervisory Award (2020), ISMRM Junior Fellow (2011). Grants: Multiple ERC grants for MRI method development. Labs/Teams: Leads the Experimental Magnetic Resonance Imaging group at TUM, collaborating on clinical and technical MRI advancements.

• Computational Proximity
• Near Sets
• Rough Sets
• +13 more

James F. Peters is a faculty member in the Department of Electrical and Computer Engineering at the University of Manitoba, Winnipeg, Canada. His research lies at the intersection of computational topology, proximity theory, rough sets, and digital image analysis, with applications in computer vision, pattern recognition, and biologically-inspired computing. He has made foundational contributions to the theory of near sets and computational proximity, publishing extensively in journals and book series by Springer. His research interests include computational proximity, near sets, rough sets, digital image analysis, pattern recognition, and topological models of perception. These are evident from his numerous publications in theoretical and applied computer science, often in collaboration with researchers such as Andrzej Skowron, Sheela Ramanna, and Arturo Tozzi. His work spans mathematical foundations, computational models, and real-world applications in biomedical imaging and rehabilitation systems. The recent articles (2017–2025) show a strong trend toward integrating topology, physics, and neuroscience in the analysis of digital images and brain activity. Topics include proximal nerves, optical vortices, thermodynamics of emotions, and entropy in cosmology, indicating a broad interdisciplinary approach. His publications frequently appear in journals such as Entropy , Information Sciences , and Transactions on Rough Sets , as well as in Springer’s Lecture Notes in Computer Science and Intelligent Systems Reference Library series. He has authored or co-authored several books and special issues, notably in the Transactions on Rough Sets series, and has contributed to encyclopedic works on rough sets and computational intelligence. His editorial and collaborative roles highlight his leadership in the rough and near sets research community. Dr. Peters has advised or collaborated with several researchers, though specific student names are not listed in the provided text. He has been involved in projects related to adaptive learning, telerehabilitation gaming systems, and image classification using tolerance near sets. His work often involves grants and interdisciplinary teams, especially in computational intelligence and biomedical applications. He is associated with research groups and labs focused on computational intelligence, rough sets, and digital image analysis, often in collaboration with the University of Warsaw and other international institutions. His ongoing work continues to explore the mathematical foundations of perception and proximity in both artificial and biological systems.

• Network Routing
• Segment Routing
• Software Defined Networking
• +6 more

Marco Polverini is an active computer networking researcher with a prolific publication record spanning over a decade, with 59 publications documented from 2012 to 2025. His work primarily focuses on advanced networking technologies including Segment Routing, Software Defined Networking, and Network Function Virtualization. His research interests center around network routing optimization, traffic engineering, and network monitoring. He has made significant contributions to Segment Routing technology, developing novel behaviors for low-latency communication, black hole detection mechanisms, and traffic matrix assessment techniques. His recent work integrates artificial intelligence approaches, particularly reinforcement learning, with traditional networking protocols to create more adaptive and efficient network systems. He has also been exploring the application of Digital Twin technology for network management and optimization. Analysis of his publication trends shows a clear evolution from foundational work on energy-efficient networking and traffic engineering to more recent innovations in Segment Routing, in-band network telemetry, and AI-driven network optimization. His publications consistently appear in top networking venues including IEEE JSAC, IEEE Transactions on Network and Service Management, INFOCOM, and NOMS, demonstrating his standing within the networking research community. Throughout his career, Polverini has maintained strong collaborative relationships, particularly with Antonio Cianfrani (54 joint publications), Marco Listanti (33 publications), and Francesco Giacinto Lavacca (16 publications), suggesting he works within a well-established research group focused on next-generation networking technologies.

• Database Systems
• Operating Systems
• Distributed Systems
• +3 more

Athinagoras Skiadopoulos is a computer systems researcher at Stanford University's School of Engineering, Department of Computer Science, focusing on the intersection of database systems and operating systems. His work centers around the innovative DBOS (Database-oriented Operating System) project and large-scale machine learning infrastructure, collaborating with prominent researchers including Christos Kozyrakis and Michael Stonebraker. His primary research interests include: Database-oriented Operating Systems (DBOS) Distributed systems for large-scale machine learning Resource management and optimization in data-intensive systems Transaction processing and data governance High-performance networking for accelerated computing Fault tolerance in distributed training systems Skiadopoulos's research trajectory shows a clear evolution from foundational DBOS architecture toward applications in large-scale machine learning systems. His early publications established the DBOS framework for operating system design using database principles, while his recent work addresses critical challenges in distributed training of massive neural networks. Systems like ReCycle and SlipStream demonstrate innovative approaches to pipeline adaptation and failure recovery during distributed training. His most recent 2025 work on accelerating Mixture-of-Experts training represents the cutting edge of efficient large model training infrastructure. Through his research, Skiadopoulos has established himself in both the database and systems research communities, with publications in premier venues including SOSP, OSDI, VLDB, and CIDR. His work consistently bridges theoretical database concepts with practical systems implementations, demonstrating how database techniques can solve real-world systems challenges in modern computing environments.

Kristin KnipferView profile

Research Fellow

• Leadership Development
• Organizational Learning
• Team Dynamics
• +5 more

Dr. Kristin Knipfer is a Senior Research Fellow at the TUM School of Management and Executive Director of the TUM Institute for LifeLong Learning . Her research focuses on leadership as a catalyst for organizational learning and innovation, examining how leaders influence team dynamics in academic, entrepreneurial, and corporate settings. Leadership development in academia and business Organizational learning mechanisms Knowledge management systems Digital transformation in leadership education Her recent publications explore leader identity construal, narcissistic leadership impacts, and team reflection processes. She has received multiple awards including the TUM Digital Innovations in Management Education Award and the Richard A. Swanson Research Excellence Award nomination. She leads evidence-based leadership programs for academic leaders and has co-developed digital tools like the TUM Leadership Toolbox and EMMA digital leadership coach. Her work bridges theory and practice through collaborations with institutions like the Vienna University of Economics and Business.

Anne RemkeView profile

Professor

• Dependability
• Security
• Critical Infrastructures
• +5 more

Professor Anne Remke leads the safety-critical systems group at the Faculty of Mathematics and Computer Science at Westfälische Wilhelms-Universität Münster since October 2014. She is also affiliated with the Design and Analysis of Communication Systems group at the University of Twente, where she served as assistant professor from June 2010 and became associate professor in March 2016. Her research focuses on dependability and security in critical infrastructures, particularly electrical power systems and telecommunication networks. Her educational background includes a PhD (2008) and MSc (2004) in Computer Science from the University of Twente and RWTH Aachen respectively. Her doctoral research focused on 'Model Checking Structured Infinite Markov Chains,' for which she publicly defended her thesis in June 2008. Professor Remke's research interests center on cyber-physical systems, with particular focus on evaluation of charging strategies for local energy storage in smart homes and security of control networks (SCADA) in smart grids. Her work bridges theoretical model checking techniques with practical applications in critical infrastructure protection. She has made significant contributions to the analysis of hybrid Petri nets, stochastic models, and the development of tools for dependability evaluation. Her recent publications demonstrate a strong trend toward integrating machine learning with formal verification methods for cyber-physical systems. The research spans stochastic hybrid systems, reachability analysis, and security evaluation of smart grid infrastructures, showing consistent growth in both theoretical foundations and practical applications of dependability analysis. Veni award from Dutch Science foundation (NWO) for 'Counting on a reliable water supply' GI/ITG MMB prize for best diploma thesis in computer and communication systems Best Paper Award at Valuetools 2023 conference Best Repeatability and Artifact Evaluation Award at QEST21 Teaching award from Fachschaft FB10 (2019) Professor Remke has successfully secured multiple research grants including the DFG project 'RealyST: Reachability Analysis for Stochastic Hybrid Systems' in collaboration with RWTH Aachen. She has supervised numerous students including Katharina Sichma, Pauline Blohm, Joanna Delicaris, Verena Menzel, Mathis Niehage, Jonas Stübbe, and Lisa Willemsen. Her research group actively participates in international collaborations and standardization efforts in critical infrastructure security. The safety-critical systems group maintains several research tools including HYPEG (for simulation and analysis of hybrid Petri nets), TimeNET (a GUI for modeling hybrid Petri nets), and a Smart Neighbourhood Simulation Tool for community energy storage and trading. These tools support their research in modeling and evaluating complex critical infrastructures through both analytical methods and simulation techniques.

• Robotics
• Human-Robot Interaction (HRI)
• Artificial Intelligence
• +3 more

Marc Hanheide is a Professor of Intelligent Robotics and Interactive Systems at the University of Lincoln 's School of Computer Science. With a career spanning EU projects like VAMPIRE, COGNIRON, CogX, and STRANDS, his work focuses on long-term robotic behavior, human-robot spatial interaction, and cognitive system architectures. He has secured over 12 major grants from organizations including EPSRC, BBSRC, and the European Commission. Key Research Areas : Autonomous robotics, HRI, AI, cognitive systems, agricultural robotics Current Projects : STRANDS (long-term behavior), AgriFoRwArdS (robotics training), NCNR (nuclear robotics) Major Contributions : Human-aware navigation modules, topology optimization for robot fleets, causal analysis frameworks Scientific Awards: While no specific awards are listed, his numerous EPSRC grants and leadership in multi-institutional projects highlight his impact. He has over 172 publications and collaborates with institutions like CoR-Lab and CITEC.

Thamy PogrebinschiView profile

Researcher

• Democratic Innovations
• Citizen Participation
• Digital Technology in Democracy
• +4 more

Thamy Pogrebinschi is a senior researcher at the WZB Berlin Social Science Center and faculty member at the Berlin Graduate School of Social Sciences (BGSS) of Humboldt University Berlin, where she supervises PhD students. Her academic affiliations include the Center for Civil Society Research at WZB and multiple visiting positions at Oxford University (2022-2023), Harvard Kennedy School (2017-2018), LUISS University Rome (2015), and Goethe University Frankfurt (2013-2014). Her research centers on democratic innovations and new forms of citizen participation in Latin America, with growing focus on digital technology's impact on civil society and collective intelligence. She pioneered the LATINNO project (2015-2021), creating the largest database of democratic innovations across 18 Latin American countries, cataloging 3,600 cases involving deliberation, citizen representation, and digital engagement. Current work examines civil society responses to disinformation in polarized contexts and collective intelligence in endangered democracies. Her 2023 monograph Innovating Democracy? The Means and Ends of Citizen Participation in Latin America (Cambridge University Press) presents the first large-N cross-country study of democratic innovations, introducing a comprehensive typology. Research appears in Comparative Politics , European Journal of Political Research , and Critical Policy Studies across four languages. 50+1 Influential Researchers Whose Work Could Help Shape 21st Century Politicians (Apolitical Foundation, 2022) As principal investigator of LATINNO (funded by Open Society Foundations), she led a 30+ member team analyzing democratic innovations. Current projects on disinformation combat receive institutional support through WZB's Center for Civil Society Research. She advises PhD candidates at BGSS while maintaining active collaborations with Latin American civil society organizations. Her laboratory comprises the LATINNO network and current disinformation research consortium, coordinating researchers across Brazil, Colombia, Mexico, and Peru to analyze digital threats to democracy and develop civil society countermeasures through collective intelligence frameworks.

• Computer Science
• Networking Systems
• Software Engineering
• +5 more

Xin Wang is a Professor at Fudan University's School of Computer Science, specifically within the Department of Communication Science and Engineering and affiliated with the State Key Laboratory of ASIC and System in Shanghai, China. With 185 publications spanning two decades (2003-2025), Wang maintains an exceptionally active research profile, particularly evident in recent high-output years including 22 publications in 2019, 19 in 2021, and 13 in 2024. The research portfolio demonstrates deep collaboration networks, most notably with Yang Chen (45 co-authored papers), Yangfan Zhou, and Qingyuan Gong. Wang's research spans multiple critical areas in computer science, with significant contributions to networking systems (particularly CDN optimization, HTTP/3 implementation, and IPv6 infrastructure), software engineering (focusing on work rhythms, testing methodologies, and GUI analysis), mobile applications (including healthcare implementations and accessibility features), and security (especially account security and fraud detection in e-commerce). The interdisciplinary nature of the work is evident through applications in healthcare, e-commerce, campus safety, and IoT systems. Analysis of recent publications (2023-2025) reveals a strong trend toward practical system implementations addressing real-world challenges. The research demonstrates a consistent pattern of moving from theoretical foundations to deployable solutions, with particular emphasis on optimizing performance in networking systems, enhancing security in digital platforms, and improving user experience across diverse application domains. The work frequently incorporates machine learning techniques to solve complex system problems while maintaining practical applicability. While specific grant information isn't detailed in the publication records, the extensive collaboration network spanning multiple institutions in China and internationally suggests substantial research funding support. The consistent publication output across top venues including IEEE/ACM Transactions, INFOCOM, SIGCOMM, and ICSE indicates sustained research productivity and impact.

Rebecca SchaumbergView profile

Assistant Professor

• Organizational Behavior
• Psychological Theory in Business
• Emotions in Organizations
• +6 more

Rebecca Schaumberg is an Assistant Professor of Organizational Behavior at ESMT Berlin, joining the faculty on July 1, 2025. She is part of a diverse international faculty of 47 members from 18 countries, contributing to ESMT's interdisciplinary and globally engaged research environment. Her research lies at the intersection of psychological theory and business practice, with a focus on self-conscious emotions such as shame, guilt, pride, and embarrassment. These emotions are studied in the context of decision-making, leadership processes, and organizational culture. Her work has been published in leading academic journals including Psychological Science , Academy of Management Journal , and Organizational Behavior and Human Decision Processes . The body of her research, as reflected in the 15 most recent conceptual articles, spans themes in organizational behavior, psychology, leadership, and ethics. Key trends include the influence of emotions on accountability, status dynamics, feedback reception, and ethical behavior within organizations. Her work integrates insights from social and cognitive psychology into management science. Scientific Awards: Teaching Excellence Award at the Wharton School Advising and Teaching: Dr. Schaumberg has been recognized for her excellence in teaching, particularly in negotiation, at the Wharton School. At ESMT, she will teach organizational behavior in the master’s programs. While no formal advisees are listed, her academic mentorship is expected to grow within her new role. Labs and Research Teams: No specific labs or research teams are mentioned in the available text.

Michael SchartView profile

Professor

• Foreign Language Teaching Methodology
• Didactics of German as a Foreign Language
• Empirical Teaching Research
• +6 more

Michael Schart is Professor of Methodology and Didactics of German as a Foreign Language at Friedrich Schiller University Jena since 2021. Previously, he held academic positions at Keio University (2007-2021), University of Wuppertal (2018), University of Waterloo (2017-2018), and several Japanese institutions. His research focuses on empirical teaching research, CLIL, task-based language teaching, and reflective teacher training. Doctorate (2002) in Philosophy from Friedrich Schiller University Jena DFG Fellowship (1997-2000) at Justus Liebig University Giessen Master’s in German as a Foreign Language (Jena, 1996) Intensive Japanese Language Studies at Reitaku University (1992-1993) Schart’s research emphasizes action research, classroom interaction, and curriculum development for German language instruction. He has led projects on virtual classroom dynamics (2020/21), task-based teaching at beginner levels (2007-2009), and program evaluation at Keio University (2003-2007). His work bridges qualitative methodology with practical pedagogy. Recent publications address CLIL implementation, feedback mechanisms, and digital learning environments. He co-edits journals InfoDaF and KONTEXTE , and leads the research center FLUSS for teacher professionalization in German as a foreign language education.

Thomas KunzView profile

Professor

• Migration and Intercultural Relations
• Critical Discourse Analysis
• Social Exclusion and Inequality
• +5 more

Thomas Kunz is a Professor at the College of Social Work and Health, Frankfurt University of Applied Sciences, specializing in Social Work and Education in the Context of Social Inequality. His research focuses on migration, interculturality, critical analysis of integration concepts, societal exclusion, racism, right-wing populism, and qualitative social research methods. Education: PhD (Doctorate) from Goethe-Universität Frankfurt a. M. Professional Experience: Professor since 2011; held prior roles at Bundesamt für Migration und Flüchtlinge (2007-2008), Institute for Social Work (2004-2006), and Frankfurt’s Office for Multicultural Affairs (2000-2003). His research explores how media constructs migrant identities, challenges integration narratives, and analyzes national belonging. He contributes to academic discourse on migration policy, intercultural competence, and social exclusion. Recent articles address digitalization in post-pandemic education, language sensitivity in academia, and media portrayals of migrants. He supervises BA thesis projects and collaborates with institutions like the International Family Center and Duisburger Institut für Sprach- und Sozialforschung. His work includes editing journals, serving on integration award juries, and developing indicators for migrant integration programs.

Victor TiberiusView profile

Academic

• Strategic Management
• Strategic Foresight
• Entrepreneurship
• +7 more

Victor Tiberius is an Honorary Professor at the Faculty of Economics and Social Sciences, University of Potsdam, and a visiting scholar at the Institute for Economic Education (iföb) at Ludwigsburg University of Education. He previously served as a Senior Researcher & Lecturer at the University of Potsdam and taught Corporate Strategy at ESCP Europe. His academic work is deeply integrated with practical leadership, innovation, and strategic foresight. Doctorate in Economics (Dr. rer. pol.), Carolo-Wilhelmina University of Technology, Braunschweig Doctorate in Philosophy (Dr. phil.), Free University of Berlin Doctorate in Educational Sciences (Dr. paed.), Ludwigsburg University of Education Honorary Doctorate (Dr. hc.), Dunărea de Jos University, Galați Executive Education: Columbia Business School, LSE, INSEAD, Oxford Saïd, MIT Sloan His research focuses on strategic management, entrepreneurship, innovation, and digital transformation. With over 80 peer-reviewed publications, his work explores strategic foresight, disruptive technologies, and innovative business models. He has contributed to journals such as Journal of Business Venturing Insights , Technological Forecasting & Social Change , and The Leadership Quarterly . His recent publications emphasize agility in volatile environments, digital innovation in SMEs, and leadership in family firms, reflecting a consistent trend toward practical, future-oriented strategy. Top 2% Scientist (Stanford Ranking), Top 0.5% in 2023 AD Scientific Index 2024 Honorary Doctorate from Dunărea de Jos University Editorial Board Member: Futures & Foresight Science , Technology in Society , Review of Managerial Science Reviewer for over 100 journals and conferences Victor Tiberius has supervised over 400 bachelor’s and master’s theses and taught a wide range of courses including Strategic Leadership, Organizational Behavior, and Service Management. He is actively involved in academic networks such as the Academy of Management, Schmalenbach Society, and World Futures Studies Federation (2009–2020). He also leads the Honorary Professorship for Strategy and Entrepreneurship at the University of Potsdam, contributing to research colloquia and curriculum development. His dual expertise in academia and executive leadership bridges theory and practice in management education.