Julia Kurz is a Researcher at TU Dortmund University's Department of Sociology of Science and Technology, Faculty of Social Sciences. She leads the 'Visually Integrated Clinical Collaboration' project within the Collaborative Research Center (SFB) Media of Cooperation. Her work spans healthcare informatics, educational research, and disability studies. Education: M.A. in Sociology from the University of Siegen (2011), with prior studies at Philipps-University Marburg. Professional experience includes research roles at the University of Siegen (2012–2023) and support work with disabled populations (since 2001). Research focuses on information workflows in hospitals, collaborative data practices, and participatory technology design. Recent talks address topics like disempowerment in participation frameworks and challenges in transdisciplinary research. Her publications explore medical visualization tools, mentoring impacts, and role-playing pedagogy. Award recognition details are not explicitly listed, but her grants include SFB funding for the Media of Cooperation project. Advising activity is not documented here. She contributes to seminars on competence, knowledge theory, and educational innovation.
Miguel Nacenta is a Professor in the Department of Computer Science at the University of Victoria (UVic), Canada, and a founding member of the Victoria Interactive eXperiences with Information (VIXI) research group. Previously affiliated with the University of St Andrews (UK), his work bridges Human-Computer Interaction (HCI), Information Visualization, and Cognitive Science. He specializes in designing interactive systems that enhance human cognition, with a focus on Infotypography (using typography to encode data), collaborative problem-solving tools, and perceptual input/output devices. Research Interests: His key areas include cognitive augmentation, visualization techniques for complex tasks, multi-display environments, and tools for constraint problem-solving. Notable projects include the WriteReason tool for essay writing, InfoTypography studies on perceptual typographic parameters, and Solvi for visual constraint modeling. Grants & Collaborations: He collaborates internationally, including with the University of St Andrews on PhD scholarship programs. His work is supported by grants focusing on HCI innovations and accessibility. He actively mentors students (e.g., Adam Binks, Johannes Lang) and supervises postdoctoral researchers. Affiliations: Member of the VIXI group,他曾是St Andrews计算机科学学院的教授, 并参与多个学术服务活动, including conference program committees and journal reviews. Labs & Teams: Leads the VIXI lab at UVic, focusing on interactive technologies for cognitive tasks. Collaborates with industry partners on projects like TypoCartographer for infoTypographic maps and HaptiQ for accessible graph exploration.
John R Anderson is the Richard King Mellon University Professor of Psychology and Computer Science at Carnegie Mellon University (CMU), affiliated with the Department of Psychology within the Dietrich College of Humanities and Social Sciences. His research focuses on understanding higher-level cognition, particularly mathematical problem-solving, through the development of the ACT-R cognitive architecture—a computational framework simulating human cognitive processes. This architecture integrates behavioral, neural, and educational data to model learning and decision-making. Anderson’s work bridges cognitive science, neuroscience, and educational technology. He investigates how brain imaging (e.g., fMRI, EEG) can reveal the temporal dynamics of cognitive processes and improve instructional methods. His research emphasizes analyzing brain activity time courses to uncover underlying mechanisms of problem-solving and skill acquisition. Key Research Themes: Cognitive architectures, neural correlates of learning, computational models of memory, and intelligent tutoring systems. Notable Contributions: Development of the ACT-R architecture, integration of neuroimaging with cognitive modeling, and studies on skill transfer and learning strategies. Anderson’s publications include seminal books like Cognitive Psychology and Its Implications and How Can the Human Mind Occur in the Physical Universe? His work has advanced understanding of associative memory, strategic decision-making, and the application of cognitive models in educational technology. His lab, the ACT-R Research Group, collaborates across disciplines to model complex cognitive tasks and their neural foundations. Current projects analyze real-time brain activity to refine educational interventions and improve human-machine interaction.
Amitai Shenhav is an Associate Professor at the University of California, Berkeley, specializing in Cognitive Neuroscience. His research explores the neural and computational mechanisms underlying motivation, affect, decision-making, and cognitive control, as detailed on the Shenhav Lab website . Ph.D., Harvard University Key research themes include: Explaining motivated behavior through affective gradients Modeling decision-making with mutual inclusivity and value integration Investigating cognitive control allocation under varying motivational contexts Understanding neural dynamics in target-distractor interactions Recent publications (2025–2024) highlight his work on value-based decision-making, effort allocation, and computational models of cognitive control. These studies often bridge behavioral experiments with neural recordings and theoretical frameworks. Scientific contributions include: NSF CAREER Award (2021) for research on motivation in cognition He mentors students and collaborators in his lab, focusing on psychophysiological experiments, computational modeling, and neuroeconomic paradigms. His work intersects with psychology, neuroscience, and artificial intelligence, particularly in attention training applications.
William B Gartner serves as the Bertarelli Foundation Distinguished Professor of Family Entrepreneurship at Babson College's F.W. Olin Graduate School of Business, where he also directs research at the Bertarelli Institute for Family Entrepreneurship. Having joined Babson's faculty in 2017, Gartner brings over 40 years of experience studying entrepreneurial phenomena, with particular expertise in family entrepreneurship dynamics. He previously held distinguished chairs at the University of Southern California and Clemson University, establishing himself as a leading scholar in the field. Gartner earned his PhD, MBA, and BBA from the University of Washington, forming the foundation for his extensive research career. His academic journey began during entrepreneurship's early days in the late 1970s when he took one of the first entrepreneurship courses as an MBA student at the University of Washington, when only six students initially enrolled in the class. Gartner's research primarily explores how families act entrepreneurially, examining topics including entrepreneurial legacy, intergenerational knowledge transfer, and the transformation of family businesses across generations. His work bridges entrepreneurship theory with practical applications, particularly focusing on how entrepreneurial legacies can drive innovation in family businesses. He is particularly interested in the linguistic innovations entrepreneurs employ and how storytelling shapes entrepreneurial identity and practice. His scholarship has evolved to incorporate humanities perspectives, recognizing that literature, philosophy, and history provide valuable insights into entrepreneurial thinking and behavior. Analysis of Gartner's recent publications reveals a strong emphasis on family entrepreneurship as an ongoing process rather than a static state, with increasing attention to methodological innovation in entrepreneurship research. His work demonstrates growing interest in the role of narrative, visual methods, and qualitative approaches to understand complex entrepreneurial phenomena. The research shows consistent focus on family business succession as transformation rather than simple transfer, with increasing attention to adolescent entrepreneurial development and the influence of parenting styles. 2025 — Top 1% of Scholars in all subjects, Stanford/Elsevier's Top 2% Scientist Rankings 2024 — The FamCap25 – The Top FamilyEnterprise Academics, FAMILY CAPITAL MAGAZINE 2022 — Justin G. Longnecker Fellow, United States Association of Small Business and Entrepreneurship 2016 — Dedication to Entrepreneurship Award, Entrepreneurship Division, Academy of Management 2013 — Foundational Paper Award, Entrepreneurship Division, Academy of Management 2005 — International Award for Entrepreneurship and Small Business Research Gartner actively mentors doctoral students and early-career researchers, frequently collaborating on publications addressing complex family business dynamics. His editorial roles as Special Issue Editor for Entrepreneurship and Regional Development and Academy of Management Perspectives demonstrate his influence in shaping research agendas. He has secured significant research funding supporting studies on entrepreneurial legacy, family business succession, and adolescent entrepreneurial development, though specific grant amounts aren't detailed in the available materials. As Director of Research at the Bertarelli Institute for Family Entrepreneurship, Gartner leads initiatives connecting academic research with practical applications for family businesses. His work emphasizes the transformative potential of entrepreneurship within family contexts, challenging traditional notions of family business succession as mere transfer of ownership. Through workshops and seminars, he helps families explore how entrepreneurial legacies can lead to new innovations while preserving core values across generations.
Andrzej Majkowski is an Associate Professor at the Institute of the Theory of Electrical Engineering, Measurement and Information Systems, Faculty of Electrical Engineering, Warsaw University of Technology. His career spans over two decades of research in biomedical engineering, focusing on brain-computer interfaces, signal processing, and emotion recognition. Active in both teaching and research, he contributes to advancing methodologies in electrophysiological signal analysis. Warsaw University of Technology Institute of the Theory of Electrical Engineering, Measurement and Information Systems Faculty of Electrical Engineering Specializing in biomedical engineering , Majkowski's research bridges control systems and information technologies with neuroscience applications. His work explores brain-computer interfaces , EEG/EMG signal processing , and emotion recognition using multimodal physiological data. Recent studies focus on deep learning architectures for artifact removal and classification tasks. Recent publications highlight trends in CNN-LSTM hybrid models for signal denoising, convolutional networks for seizure detection, and machine learning applications in visual evoked potential analysis. His work spans both clinical applications (epilepsy monitoring) and human-computer interaction (emotion recognition, sign language detection). With over 98 documented publications and significant bibliometric indicators (h-index 13 in Scopus), Majkowski has supervised 95 promoted theses. His research includes one funded project and collaborations in biomedical instrumentation, though specific award details remain unspecified in available records.
Norman Sadeh is a Professor in the School of Computer Science at Carnegie Mellon University (CMU), where he has made significant contributions to cybersecurity, privacy, and AI research. He has co-founded and co-directed several groundbreaking graduate programs at CMU, including the Privacy Engineering Program (2012-present), the Ph.D. Program in Societal Computing (2003-2013), and the MBA track in Technology Strategy and Product Management (2005-2017). Carnegie Mellon University, School of Computer Science Software and Societal Systems Department CyLab Security and Privacy Institute Manufacturing Futures Institute Dr. Sadeh received his Ph.D. in Computer Science at CMU with a major in Artificial Intelligence and a minor in Operations Research. He holds an M.Sc. in computer science from the University of Southern California and a BS/MS degree in electrical engineering and applied physics from the Free University of Brussels (Belgium) as 'Ingénieur Civil Physicien.' Professor Sadeh's research spans cybersecurity, online privacy, Human-AI Interaction, AI governance, mobile computing, the Internet of Things, user-oriented machine learning, and language technologies. He is particularly known for his pioneering work on AI-based privacy enhancing technologies, including privacy assistants, automated privacy compliance tools, and NLP-based privacy solutions. His work has influenced the design of privacy features at major technology companies including Apple, Google, and Facebook/Meta, as well as privacy policies at regulatory agencies like the Federal Trade Commission and the California Office of the Attorney General. Analysis of his recent publications shows a strong focus on practical privacy solutions, particularly in mobile and IoT contexts, with an emphasis on making privacy more usable and understandable for end users. His work bridges technical innovation with policy implications, addressing both the technological and human aspects of privacy protection. 2018 Outstanding Entrepreneur of the Year award from the Pittsburgh Venture Capital Association Test of time award by the AAAI Conference on Web and Social Media (ICWSM) Gartner Group's Magic Quadrant leader in Security Awareness Computer-Based Training for 4 consecutive years Deloitte's Technology Fast 500 recognition for 3 consecutive years Professor Sadeh has advised numerous students, including PhD candidates like Aerin (Shikhun) Zhang, whose dissertation focused on understanding diverse privacy attitudes. His research has been funded through various grants, including NSF SaTC projects, and has resulted in technologies that protect tens of millions of users worldwide. He also founded Wombat Security Technologies, which was acquired by Proofpoint in 2018 and whose technologies are used by over 75% of Fortune 100 companies. Professor Sadeh leads several research initiatives including the Privacy Engineering Program, the Usable Privacy Policy Project, the Personalized Privacy Assistant Project, and CMU's Privacy Infrastructure for the Internet of Things. His Mobile Commerce Lab and E-Supply Chain Management Lab have produced influential research that has been commercialized by major organizations including IBM, Raytheon, Boeing, and the U.S. Army.
Aswin Sankaranarayanan is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU) , where he leads the Image Science Lab . His research focuses on computational photography , 3D shape estimation , and novel imaging system design . He earned his Ph.D. in Electrical and Computer Engineering (2009) from the University of Maryland and completed a postdoctoral fellowship at Rice University (2012) . Research Themes: Developing imaging systems that exploit low-dimensional signal models to overcome traditional sensing limitations Co-design of optics and processing algorithms for efficient sensing Application of non-linear signal models to high-dimensional data Advancing compressed sensing and big data processing techniques Scientific Recognition: SIGGRAPH 2023 Best Paper Award (Split-Lohmann Multifocal Displays) CVPR 2019 Best Paper Award (Fermat Paths for NLOS Reconstruction) NSF CAREER Award (2017) Dean’s Early Career Fellowship (2018-2021) Herschel Rich Invention Award (2016) Technical Contributions: His recent publications reveal expertise in non-line-of-sight shape reconstruction , VR/AR display systems , and biomedical imaging . Collaborations span institutions like University College London and University of Toronto.
M. Tamer Özsu is a University Professor of Computer Science at the David R. Cheriton School of Computer Science, University of Waterloo, where he holds a Cheriton Faculty Fellowship. He also serves as a Distinguished Visiting Professor at Tsinghua University and is the Founding Director of Waterloo-Huawei Joint Innovation Laboratory since 2018. His extensive contributions to computing have earned him numerous prestigious awards including the 2024 ACM Presidential Award for long-standing and significant contributions to the computing field. Professor Özsu's research focuses on data engineering aspects of data science, particularly addressing data management issues with two main foci: management of non-traditional data and large-scale distributed data management. He is renowned for his seminal book "Principles of Distributed Database Systems" (co-authored with Patrick Valduriez), now in its fourth edition, and the "Encyclopedia of Database Systems" (co-edited with Ling Liu), in its second edition. His work bridges theoretical foundations with practical system implementations, targeting grand societal challenges through computational approaches. His recent publications reveal a strong trend toward graph analytics, streaming data processing, and the integration of large language models with vector data management. The research shows increasing focus on GPU-accelerated graph processing, RDF query optimization, and multimodal data analysis, reflecting the evolution of data management challenges in the era of big data and AI. His work continues to address fundamental challenges in distributed data systems while adapting to emerging technologies and application domains. Scientific Awards and Fellowships ACM Presidential Award (2024) IEEE TCDE Education Award (2024) IEEE Innovation in Societal Infrastructure Award (2022) CS Can | Info Can Lifetime Achievement Award (2018/2019) ACM SIGMOD Test-of-Time Award (2015) ACM SIGMOD Contributions Award (2006) The Ohio State University College of Engineering Distinguished Alumnus Award (2008) Fellow of the Royal Society of Canada Fellow of the American Association for the Advancement of Science (AAAS) Life Fellow of the Association for Computing Machinery (ACM) Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) Elected member of the Science Academy, Türkiye Professor Özsu has been deeply involved in academic leadership and community building. As Founding Editor-in-Chief of ACM Books (2013-2019), he launched a series that by 2019 had published 28 major books with another 30 under contract. His service to ACM, particularly through SIGMOD, has been exemplary and widely recognized. He directs the Waterloo-Huawei Joint Innovation Laboratory, which focuses on cutting-edge research in data management and distributed systems, fostering strong industry-academia collaboration.
Doç. Dr. Muhammed Aras is an Associate Professor in the Department of Mechanical Engineering at Baskent University (Başkent Üniversitesi), with a research focus on sustainable machining processes, tool wear monitoring, and surface roughness optimization. His work spans advanced manufacturing technologies, energy storage systems, and biomedical device design. PhD in Manufacturing Engineering (2018), Gazi Üniversitesi MSc in Mechanical Education (2013), Gazi Üniversitesi BSc in Mechanical Engineering (2010), Tabriz Islamic Azad University Research interests include sustainable machining (dry/hard turning, cooling-lubrication strategies), tool wear analysis (CBN, ceramic and coated inserts), and surface integrity optimization using AI-based methods (firefly algorithm, TOPSIS, Grey Relational Analysis). His 15 most recent articles (2017-2024) examine topics like: Surface roughness prediction in dry hard turning Energy storage technology viability assessments Cutting parameter optimization for various steels Acoustic/vibration monitoring in machining He has received scientific awards including the Teşvik Ödülü (Encouragement Award, 2015). His patent on an automatic orthognathic surgery articulator and book chapters on tool monitoring systems demonstrate his multidisciplinary impact.
Istvan Albert is a Research Professor of Bioinformatics at Pennsylvania State University , affiliated with the Department of Biochemistry and Molecular Biology . He leads the Bioinformatics Consulting Center and teaches BMMB 852: Applied Bioinformatics . Research Interests: Specializing in bioinformatics, large-scale biological data analysis, microarray and sequence analysis, scientific programming, algorithm development, and database-driven web development. His work spans gene ontology visualization , RNA-Seq analysis , and coronavirus research . Software Development: Created GeneScape for gene function visualization and bio for bioinformatics workflows. Maintains the Biostar Handbook series and the Biostars Q&A Forum , a leading bioinformatics resource.
Mohamed F. Mokbel is a Distinguished McKnight University Professor in the Department of Computer Science and Engineering at the University of Minnesota - Twin Cities , where he also serves as the Director of Graduate Studies. He is recognized as an IEEE Fellow and ACM Distinguished Member for his contributions to spatially- and privacy-aware systems. His research focuses on database systems , spatial data management , and GIS (Geographic Information Systems) , with significant work in spatiotemporal data, location-based services, and machine learning for spatial applications. His most recent publications address scalable BERT-based trajectory imputation , spatial logistic regression frameworks , and spatiotemporal big data decay techniques . Key Awards: Distinguished McKnight University Professor (2023) ACM SIGSPATIAL 10-Year Impact Award (2022) IEEE Fellow (2020) ACM Distinguished Member (2017) NSF CAREER Award (2010) Selected Conference Papers: Recathon (2015, IEEE MDM Best Paper) ST-Hadoop (2017, SSTD Best Paper) KAMEL (2023, ACM SIGMOD Demo) Academic Service: Editor-in-Chief, ACM Transactions on Spatial Algorithms and Systems (2024–) General Co-Chair, ACM SIGSPATIAL 2025 Past Chair, ACM SIGSPATIAL (2014–2017)
Ruohan Gao is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park , with affiliate appointments at the University of Maryland Institute for Advanced Computer Studies (UMIACS) , Maryland Robotics Center (MRC) , and Artificial Intelligence Interdisciplinary Institute at Maryland (AIM) . His research focuses on Computer Vision and Machine Learning , emphasizing Multisensory Machine Intelligence that integrates sight, sound, and touch . He aims to enable machines to perceive, understand, and interact with the world as humans do, with applications in robotic manipulation , audio-visual localization , and differentiable rendering . Article Trends : Span 2018–2025 , centering on audio-visual perception , multisensory datasets , and robotics . Recurring themes include object-centric learning , sound synthesis , and cross-modal consistency . Scientific Awards : Michael H. Granof Award (UT Austin’s Top 1 Doctoral Dissertation, 2021) Best Paper Award Runner-Up (BMVC 2021) Best Paper Award Finalist (CVPR 2019) Highlight Paper (CVPR 2023) He leads the UMD Multisensory Machine Intelligence Lab and collaborates with institutions like Stanford and The University of Texas at Austin . Contact: rhgao@umd.edu .
Ravid Shwartz-Ziv is an Assistant Professor and Faculty Fellow at NYU's Center for Data Science, with a dual role as Senior Research Scientist at Wand AI. His research bridges theoretical foundations and practical applications in artificial intelligence, focusing on Large Language Models (LLMs), information theory, and neural network interpretability. Ph.D. in Computational Neuroscience, Hebrew University of Jerusalem (2021) B.Sc. in Computer Science and Computational Biology, Hebrew University of Jerusalem (2014) His research spans: Developing min-p sampling for LLM text generation Preventing representation collapse in Transformers Creating contamination-free LLM benchmarks like LiveBench Advancing information-theoretic frameworks for neural networks Exploring representation learning and model adaptation Recent publications demonstrate expertise in LLM efficiency, self-supervised learning, and multi-agent systems. Notable awards include the Google PhD Fellowship, Moore-Sloan Fellowship, and multiple best paper recognitions. He has led research initiatives at Intel and Google AI, focusing on neural network compression, XGBoost comparisons for tabular data, and innovative benchmarking frameworks.
Oisin Mac Aodha is a Reader (Associate Professor) in Machine Learning at the School of Informatics, University of Edinburgh. He is also an ELLIS Scholar and founder of the Turing interest group on biodiversity monitoring and forecasting, having previously served as a Turing Fellow from 2021-2025. Mac Aodha completed his undergraduate degree in electronic engineering from the University of Galway in Ireland, followed by his MSc and PhD at University College London (UCL). His academic journey includes postdoctoral positions at UCL (2013-2016) working with Prof. Gabriel Brostow and Prof. Kate Jones, and at Caltech (2016-2019) in Prof. Pietro Perona's Computational Vision Lab as part of the Visipedia team. His research centers on computer vision and machine learning with emphasis on 3D understanding, human-in-the-loop methods, and AI for conservation and biodiversity monitoring. He has made significant contributions to monocular depth estimation (including the influential Monodepth2 paper), fine-grained visual categorization, and biodiversity monitoring systems. His work bridges theoretical machine learning with practical ecological applications, developing tools for species identification, range estimation, and conservation efforts. Recent publications reveal a strong trend toward ecological applications while maintaining fundamental contributions to 3D vision and representation learning. His major scientific achievements include: Turing Fellow (2021-2025) ELLIS Scholar Founder of the Turing interest group on biodiversity monitoring and forecasting Co-organizer of the Fine-Grained Visual Categorization (FGVC) workshop series at major vision conferences Mac Aodha advises multiple PhD students and postdocs working on computer vision for biodiversity monitoring, 3D understanding, and human-in-the-loop learning. His team has developed practical tools like Whombat (an open-source annotation tool for bioacoustics) and contributed to field-deployed biodiversity monitoring systems. He has served as Area Chair for top conferences including NeurIPS, CVPR, ICCV, and ICML, demonstrating his standing in the computer vision community. His research group collaborates extensively with ecologists at University College London, particularly with Prof. Kate Jones' team, bridging machine learning expertise with ecological domain knowledge. The Vision at Edinburgh group he contributes to focuses on developing practical AI tools that address real-world conservation challenges while advancing fundamental computer vision research.
