Jian Kang is a Professor and Associate Chair for Research at the University of Michigan School of Public Health , specializing in Biostatistics . His work focuses on developing advanced statistical methods for large-scale biomedical data , with applications to precision medicine , neuroimaging , and genomics . Education: PhD in Biostatistics, University of Michigan (2011) MS in Mathematics (Statistics), Tsinghua University (2007) BS in Statistics, Beijing Normal University (2005) Research Interests include Bayesian nonparametric methods , deep learning for medical imaging , ultra-high-dimensional variable selection , and graphical models for network inference . His 2025-2023 publications demonstrate expertise in Bayesian hierarchical modeling , spatial statistics , and machine learning for healthcare . Scientific Awards : Michigan SPH Excellence in Research Award (2025) ICSA President's Citation Award (2024) Statistics in Biopharmaceutical Research Best Paper (2023) Best Paper in Biometrics by IBS Member (2022) Fellow, American Statistical Association (2021) Grants include NSF-IIS (2021-2025) for BCI statistical learning , NIGMS (2020-2022) for metabolomics biomarker selection , NIDA (2020-2025) for imaging data analysis , and NIMH (2014-2025) for multidimensional neuroimaging methods . Labs and Teams develop Bayesian computational tools for neuroimaging and spatial transcriptomics , collaborating with institutions like Emory University and University of North Carolina.
Ida Scheel is an Associate Professor in Statistics and Data Science at the University of Oslo , Department of Mathematics. She specializes in Bayesian hierarchical modeling, recommendation systems, and stochastic processes on networks. Her research interests include: Bayesian statistics and model diagnostics Data science applications in environmental and health domains Network-based machine learning Uncertainty quantification in predictive modeling Recent publication trends show a focus on Bayesian model validation, machine learning for product adoption prediction, and real-estate analytics. She contributes to interdisciplinary projects like BigInsight and CELS . Scientific awards : Sverdrup Prize for Young Researchers (2011) Advising : Supervised 8 PhD students (main/co-supervisor) in areas spanning Bayesian causal effects, neural network survival analysis, and model conflict detection. Key grants include participation in the Data Science@UiO and Integreat projects. Labs/teams : Active member of the Center for Computational Inference in Evolutionary Life Science (CELS) and the BigInsight center.
Professor Ben Horan is the Head of School of Engineering at Deakin University , Faculty of Science Engineering and Built Environment. He holds a Doctor of Philosophy and Bachelor of Engineering from Deakin University, with expertise in electrical engineering , control systems , and human-centred computing . As a leading researcher in virtual reality (VR) applications, he focuses on safety training, aged care, and extended reality (XR) systems. PhD in Electrical Engineering (Deakin University) Graduate Certificate of Higher Education (Deakin University) Bachelor of Engineering (Deakin University) His research spans VR for electrical safety training , automated vehicle interactions , and XR applications in museums . His work includes grants from the Department of Health, Melbourne Water Corporation, and City of Greater Bendigo. Recent publications analyze 360° video realism, cognitive load in virtual workplaces, and AR for Industry 5.0. Professor Horan supervises PhD candidates exploring topics like autonomous vehicle pedestrian interactions , VR stress mitigation , and industrial XR systems . He has completed supervision of 10+ PhD and Master’s students.
Deliang Fan is an Associate Professor at the School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ. His research focuses on AI hardware, in-memory computing, and neuromorphic systems. He received his MS and PhD from Purdue University under Prof. Kaushik Roy. Education: PhD, Purdue University (2015) Research Interests: AI Hardware, In-Memory Computing, Adversarial AI, Neuromorphic Computing His work spans cross-layer co-design for AI applications, including deep learning, bioinformatics, and graph processing. He has authored 170+ peer-reviewed papers and developed hardware solutions for spintronic and memristor-based systems. Recent publications emphasize efficient architectures for transformers, federated learning, and robust neural networks. Awards include the NSF Career Award and multiple best paper recognitions. He serves in editorial and organizational roles for leading conferences like DAC, ISQED, and GLSVLSI.
Professor Scott Anthony Sisson is a leading academic at the University of New South Wales (UNSW) , holding the position of Professor of Statistics and Data Science in the School of Mathematics and Statistics . He serves as Director of the UNSW Data Science Hub (uDASH) and Deputy Director of the UNSW AI Institute (UNSW.ai) . Previously, he was Deputy Director of the Australian Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) and held leadership roles in the Australasian Society of Bayesian Analysis and Statistical Society of Australia . PhD in Statistics (Bristol University, 2002) MSc in Environmental Statistics and Systems (Lancaster University, 1997) BSc in Mathematics and Statistics (Lancaster University, 1996) His research focuses on computational statistics and Bayesian inference , with expertise in machine learning , extreme value theory , and high-dimensional data analysis . He develops simulation-based algorithms for complex statistical problems and applies these to diverse scientific challenges like seagrass decline, urban flood modeling, and drug delivery systems. His recent work spans quantum computing for statistics, graphon modeling, and synthetic likelihood methods. Scientific awards include: 2024 Fellow of the International Society of Bayesian Analysis 2023 Fellow of the Institute of Mathematical Statistics 2017 ARC Future Fellowship 2010 Queen Elizabeth II Research Fellowship 2006 John Yu Fellowship His advising team has mentored students in statistical modeling, Bayesian computation, and applied data science. Grants from the Australian Research Council and industry collaborations support his research in government and scientific applications. He contributes as Associate Editor for Journal of Computational and Graphical Statistics and Statistics and Computing .
Christian Müller is a Researcher at the Agents and Simulated Reality unit of the German Research Center for Artificial Intelligence (DFKI), focusing on robust artificial intelligence applications for cybersecurity and perception systems in autonomous environments. His work bridges collaborative AI models with security-critical domains like vehicular communication (V2X) and 3D object detection. Projects: BERTHA (Behavioral Replication for Autonomous Vehicles), B5GCyberTestV2X (Cybersecurity Testing for V2X), MOMENTUM (Hybrid AI Trustworthiness), BSI_SiKI2 (Symbolic AI Security), KAI (AI Interior Development Tool). His research emphasizes adversarial training, consensus mechanisms, and hybrid AI architectures to enhance system reliability. Recent publications span topics including semi-supervised learning, high-definition voxel grids, and V2X security frameworks.
Jaechun No is a Professor at the Department of Computer Science and Engineering, College of Engineering, Sejong University. With a Ph.D. from Syracuse University (1999), he previously served as a Researcher at Argonne National Laboratory (1999-2001) and Hewlett Packard HPDC Laboratory (2001-2003) before joining Sejong University in 2003. Education: B.S., Ewha Womans University (1985) M.S., Western Illinois University (1993) Ph.D., Syracuse University (1999) His research focuses on Cloud/Edge computing , NVMe SSD technologies , and large-scale distributed/parallel storage systems . Key achievements include optimizing KVM/QEMU and Docker I/O virtualization, developing machine learning-based server failure prediction systems, and advancing NVMe/NAND flash memory I/O caching mechanisms for hybrid file systems. Recent publications highlight his work on virtualized I/O performance control (L-DTC, 2025), GPU Direct I/O classification (e-CLAS, 2024), Kubernetes resource provisioning (2024), and virtual storage resource redistribution (vThrot, 2024). These reflect trends in virtualization optimization, machine learning integration, and distributed resource management. Jaechun No's research has been cited extensively, with 148 Scopus h-index and over 8,000 citations. His collaborations span multiple countries and institutions, focusing on I/O virtualization, storage technologies, and distributed computing environments. Professional Affiliations: Current Professor at Sejong University (2003-present) Researcher at Argonne National Laboratory (1999-2001) Researcher at Hewlett Packard HPDC Laboratory (2001-2003)
Michael Bronstein is a Professor at Università della Svizzera italiana (USI Lugano) in Switzerland and Imperial College London in the UK, where he holds the Chair in Machine Learning and Pattern Recognition. He serves as Head of Graph Learning Research at Twitter following the acquisition of his startup Fabula AI, and maintains a principal engineer position at Intel Perceptual Computing. His research focuses on the interplay between geometry, machine learning, and computer vision, with particular emphasis on non-Euclidean structured data. Professor Bronstein received his Ph.D. with distinction in Computer Science from the Technion in 2007. He has held visiting appointments at Stanford University, MIT, Harvard University (as a Radcliffe Fellow), and Tel Aviv University, and has been affiliated with multiple Institutes for Advanced Study including TUM-IAS where he was a Rudolf Diesel Industry Fellow (2017). He is a Fellow of IAPR, Senior Member of the IEEE, and a member of the Young Academy of Europe. His research program centers on theoretical and computational methods in spectral and metric geometry applied to computer vision, pattern recognition, and machine learning. He pioneered the field of geometric deep learning, developing novel neural network architectures that process non-Euclidean data structures like graphs and manifolds. His work spans from theoretical foundations to practical applications, with over 100 publications in top scientific journals and conferences, and has been featured in international media including CNN. Analysis of his recent publications reveals a strong trajectory in geometric deep learning with applications spanning computer vision, 3D shape analysis, social network analysis, and bioinformatics. His research consistently bridges theoretical innovation with real-world applications, developing novel neural architectures for processing complex data structures. The work demonstrates increasing interdisciplinary reach, connecting machine learning with fields from particle physics to molecular biology. Dalle Molle Prize (2018) Royal Society Wolfson Research Merit Award (2018) ERC Proof of Concept Grant (2018) Amazon AWS Machine Learning Research Award (2018) Fellow, International Association for Pattern Recognition (IAPR) Google Faculty Research Award (2017) Radcliffe fellowship, Harvard University (2017) Rudolf Diesel industrial fellowship, TU Munich (2017) ERC Consolidator Grant (2016) World Economic Forum Young Scientist (2014) Professor Bronstein has secured multiple ERC grants (Starting Grant 2012, Proof of Concept Grants 2016 and 2018, Consolidator Grant 2016) and has mentored numerous students who have contributed to over 30 granted patents. He has chaired more than a dozen conferences and workshops in his field and served as area chair at major computer vision conferences including ECCV 2016 and ICCV 2017. His research group at USI Lugano collaborates extensively with industry partners including Intel and Twitter. As a serial entrepreneur, Professor Bronstein co-founded Novafora (2005-2009) developing large-scale video analysis, Invision (2009-2012) which created low-cost 3D sensors and was acquired by Intel, and Fabula AI (2018-2019) focused on fake news detection which was acquired by Twitter. His work bridges theoretical research with commercial applications, with his technology contributing to Intel RealSense and Twitter's graph learning infrastructure.
Tobias Batik is a Researcher in the Department of Virtual and Augmented Reality at Technische Universität Wien . His work focuses on haptic devices for virtual reality and mixed metro map visualization, contributing to projects like Action-Origami Inspired Haptic Devices for Virtual Reality (2023) and Shiftly: A Novel Origami Shape-Shifting Haptic Device for Virtual Reality (2025). His research spans Human-Computer Interaction , Computer Graphics , and Interactive Systems , with a particular emphasis on origami-inspired design and shape-shifting interfaces. Recent publications highlight trends in Virtual Reality and Data Visualization , including metro map layout algorithms and user-specified motifs. Contact: tobias.batik@tuwien.ac.at .
Stefan Hagel is a Research Fellow at the Austrian Academy of Sciences and Privatdozent at the University of Vienna, specializing in ancient Greek and Roman music. His work bridges archaeology, philology, and digital reconstruction to analyze ancient musical instruments, notation systems, and performance practices. His research focuses on: Reconstruction of auloi (double-pipes) and hydraulis (water organs) Computational analysis of instrument acoustics Cross-cultural transmission of musical technologies Ancient harmonic theory and notation decipherment He leads major projects including Ancient Music Beyond Hellenisation (ERC Advanced Grant), Digitising Aspects of Graphical Representation in Ancient Music (FWF), and the European Music Archaeology Project . Hagel has developed specialized software for instrument analysis and serves as editor for the Journal of Music Archaeology . His publications demonstrate consistent focus on technical aspects of ancient music through interdisciplinary methodologies.
Daniel F. Keefe is a Professor in the Department of Computer Science & Engineering at the University of Minnesota, Twin Cities, where he directs the Interactive Visualization Lab (IV/LAB). He is also recognized as a Distinguished University Teaching Professor, reflecting his excellence in both research and education. His work bridges computer science, art, and design, with a focus on ethical and creative approaches to data interaction in extended realities. Education: Ph.D. in Computer Science from Brown University (2007) Bachelor of Science in Computer Engineering summa cum laude from Tufts University (1999) Additional training at the Rhode Island School of Design and School of the Museum of Fine Arts at Tufts University Professor Keefe's research centers on ethical, just, and creative human-data interaction in computer-mediated extended realities (AR/MR/VR). His work addresses high-stakes societal needs including human-in-the-loop data-driven medical decision making, Indigenous cultural revitalization, climate discourse, natural resource management, and computer-mediated creative work. His technological approaches span interactive 3D computer graphics, digital 3D drawing, multimodal sensing, spatial displays, digital fabrication with sustainable materials, and traditional craftsmanship. His research group, the Interactive Visualization Lab, is known for pioneering work at the intersection of art, science, and technology, producing over 80 research papers with numerous best paper awards at top ACM and IEEE venues. Scientific Awards: National Science Foundation CAREER Award (2010) Best Paper Award at IEEE VIS 2024 Best Paper Award at IEEE VIS 2015 Best Paper Honorable Mention at IEEE VIS 2013 ACM I3D 2011 Best Paper (Honorable Mention) IEEE VisWeek 2010 Best Panel Award 3M Nontenured Faculty Award McKnight Land-Grant Professor (2012-2014) Horace T. Morse-University of Minnesota Alumni Association Award for Outstanding Contributions to Undergraduate Education (2019) Bowers Faculty Teaching Award (2021) Professor Keefe has mentored eight Computer Science Ph.D. students and one Cognitive Science Ph.D. student to graduation, with his former students now holding positions as professors at institutions like Gonzaga, Macalester, Carleton, and UMN, as well as roles at companies including Google, 3M, and Abbott. He has also mentored more than 50 undergraduate students and advised over 20 undergraduate and master's theses. His research program is primarily funded by the National Science Foundation, with additional support from the National Institutes of Health, National Academies Keck Futures Initiative, Mayo Clinic, US Department of Agriculture Forest Service, cities of Minneapolis and Saint Paul, and corporate partners in the medical and computer industries. The Interactive Visualization Lab (IV/LAB) serves as the hub for Professor Keefe's transdisciplinary research. The lab is known for its inclusive culture and collaborative approach, working closely with Indigenous scholars and communities, artists, designers, and scientists across disciplines. Recent projects include Sculpting Vis (an NSF-sponsored project on artistic design for scientific visualization), Back to Indigenous Futures, and various initiatives exploring the intersection of data, art, and social justice. The lab has also been involved in creating public art installations like Orbacles in downtown Minneapolis and the Augmented Paafu Mat exhibited at the Queensland Art Gallery. Professor Keefe is deeply committed to diversity, equity, and inclusion, having founded and chaired the CS-IDEA committee and co-designed the department's Broadening Participation in Computing Plan.
Dr. Miguel Mascaró Portells is a Senior Lecturer at the University of the Balearic Islands in the Department of Mathematics and Computer Science. He holds a PhD in Computer Science and actively contributes to research groups focused on computer graphics, AI, and multimedia technologies. Research Focus: His work encompasses web development, cloud computing, Big Data applications, neural vision systems, multimedia content management, and geolocation technologies. Specific interests include: Object-Oriented Programming (OOP) and SOA services Mobile device programming and TDT visualization Cloud-based multiprocessing systems Home automation and control systems Teaching: Current courses include: Advanced Algorithms Programming - Computer Science I Final Degree Project supervision SOA solutions for tourism Affiliations: Active member of: Computer Graphics, Vision and AI Unit (UGIVIA) Multimedia Information Technology (TIM) Research Group
Marco L. Della Vedova is a Senior Lecturer in Applied Artificial Intelligence at Chalmers University of Technology, Sweden. He works in the Vehicle Engineering and Autonomous Systems division within the Department of Mechanics and Maritime Sciences, as part of Prof. Mattias Wahde's research group. Since 2025, he has served as Director of the Data Science and AI master's programme (MPDSC) at Chalmers, where he teaches courses including Introduction to Artificial Intelligence and Digitalization in Sports. Dr. Della Vedova earned his academic foundation at the University of Pavia, Italy, where he completed his BSc (2006), MSc (2009), and PhD (2013) in Computer Engineering. His doctoral research focused on "Real-Time Physical Systems and Electric Load Scheduling" under Prof. Tullio Facchinetti. During his PhD studies, he spent a year at U.C. Berkeley hosted by Prof. Francesco Borrelli at the Model Based Predictive and Distributed Control Lab. His research spans multiple AI domains with a strong emphasis on interpretability. Dr. Della Vedova develops interpretable methods for conversational AI, naturalness evaluation of forests using canopy height models, and geospatial applications. His work bridges theoretical AI with practical societal benefits, particularly in environmental monitoring, transportation systems, and orienteering. He has previously contributed to cloud computing, hate speech detection, and cyber-physical energy systems, demonstrating his interdisciplinary approach to AI research. Dr. Della Vedova's publication record reveals a consistent trajectory of impactful research across multiple domains of artificial intelligence. His recent work shows a strong focus on interpretability in AI systems, with significant contributions to natural language processing, geospatial analysis, and causal inference. The research demonstrates both theoretical depth and practical applications, particularly in environmental monitoring and social media analysis. His methodology often combines traditional machine learning approaches with novel interpretability techniques, creating bridges between complex AI systems and human understanding. Dr. Della Vedova has received several prestigious recognitions for his work: Best PhD thesis award from the Order of the Engineers of Bergamo (2013) Italian champion of Il Cervellone (2012) Top Italian performer in IEEEXtreme 6.0 programming competition (148th overall globally, 2012) Premio Arturo Schena award from Fondazione Credito Valtellinese (2010) With over 50 students supervised through bachelor's and master's theses, Dr. Della Vedova has established himself as a dedicated mentor in the AI community. His current PhD students include Minerva Suvanto working on interpretable NLP and Vivien Lacorre developing AI for railway infrastructure inspection. His supervision spans diverse topics from forest naturalness evaluation to hate speech detection and transportation optimization. Beyond formal supervision, he actively contributes to educational initiatives including serving as Director of Chalmers' Data Science and AI master's program and developing innovative teaching methods that connect theoretical concepts with real-world applications. Dr. Della Vedova is deeply embedded in both academic and professional communities. He leads the Applied Artificial Intelligence research group at Chalmers while maintaining strong connections with European research networks through projects like the ERASMUS+ EUrienteering initiative. His interdisciplinary approach is reflected in collaborations across computer science, environmental science, and social sciences. Notably, he applies his AI expertise to orienteering both as a researcher developing localization methods and as a licensed Event Advisor for the International Orienteering Federation, demonstrating how his professional and personal interests converge in innovative ways.
Simone Paolo Ponzetto is an Assistant Professor (Juniorprofessor) at the University of Mannheim since 2013, affiliated with the Research Group Data and Web Science. His research focuses on Semantic Web technologies, Natural Language Processing (NLP), and knowledge acquisition, particularly leveraging collaboratively built resources like Wikipedia. Prior to Mannheim, he held postdoctoral roles at Sapienza University of Rome and research positions at the University of Heidelberg and Stuttgart. His work includes pioneering projects like BabelNet, a multilingual semantic network. Ponzetto earned his PhD in Computational Linguistics from the University of Stuttgart, with interdisciplinary contributions to coreference resolution, semantic relatedness, and ontology learning. Research Interests: Unsupervised/weakly-supervised knowledge extraction Multilingual ontology learning and semantic networks Lexical semantics (word sense disambiguation, semantic similarity) Discourse semantics (coreference resolution, coherence modeling) Professional Contributions: Guest editor for a Artificial Intelligence Journal special issue on AI and Wikipedia Area chair for EMNLP-CoNLL 2012 and EACL 2014 Program committee member for ACL, AAAI, and other top conferences Lab/Team: Active in the Research Group Data and Web Science at Mannheim, advancing AI and NLP applications in collaborative knowledge systems.
Dr. Stuart Marshall serves as Associate Dean of Academic Development in the Faculty of Science and Engineering at Victoria University of Wellington - Te Herenga Waka, where he also holds the academic rank of Senior Lecturer. Previously, he served as Head of School for the School of Engineering and Computer Science from 2015-2021 after joining the institution as a Lecturer in 2003. His academic journey includes completing his BSc, MSc, and PhD in Computer Science at Victoria University of Wellington, with his doctoral research focusing on software reuse. Marshall's research interests center on information and data visualization, particularly exploring how visualization can function as collaborative tools in team environments rather than single-user applications. He also investigates explainable AI and mobile user interface design with emphasis on promoting healthy device usage patterns. His work bridges theoretical computer science with practical applications in education and environmental analysis. Analysis of his publication record reveals a consistent focus on visualization techniques, with recent work shifting toward immersive analytics and virtual reality applications for complex data analysis, particularly in ecosystem services. Earlier publications concentrated on mobile learning applications grounded in transactional distance theory, software visualization for large codebases, and graph layout algorithms. Marshall has supervised six PhD students and eight Masters students to completion, with current doctoral supervision spanning immersive environments for reading assistance, medical diagnosis interfaces, and cardiac procedure simulation. His grant portfolio includes multiple ACM ISS 2022 projects with industry partners including Sensor Holdings Limited, Niantic, Autodesk, and Northeastern University, along with an Australian Research Council grant for digital games preservation. Teaching responsibilities encompass foundational programming, safety-critical systems, human-computer interaction, and data visualization across undergraduate and graduate courses. He teaches subjects including SWEN326 (Safety-Critical Systems), CGRA151 (Introduction to Computer Graphics and Games), and DATA301 (Data Science in Practice), with teaching assignments scheduled through 2026.
