جست‌وجوی استاد و پژوهشگر

• Human-Computer Interaction
• Mixed Reality
• Smart Built Environments
• +۴ مورد دیگر

Denis Gracanin is a Professor at Virginia Tech's Department of Computer Science, College of Engineering. His research spans Human-Computer Interaction, Mixed Reality, Smart Built Environments, and Generative AI, with a focus on immersive technologies, cybersecurity, and assistive systems. Research highlights include: Developing frameworks for XR-based security in zero-trust networks Advancing real-time 3D object generation in augmented reality using generative AI Designing smart environments for stress management and mobility assistance His recent work explores AI-driven design optimization, sonification in immersive analytics, and privacy-centric IoT systems. Articles emphasize applications in healthcare, cybersecurity, and smart lighting, leveraging machine learning and multimodal data. Gracanin collaborates extensively with researchers like Kresimir Matkovic, Mohamed Azab, and Majid Behravan, publishing in top venues such as IEEE Transactions on Visualization and Computer Graphics, CHI, and VR Workshops.

• Computer Graphics
• Artificial Intelligence
• Virtual Reality
• +۳ مورد دیگر

Tomasz Bednarz is a Professor at the University of New South Wales' School of Engineering and Information Technology, with affiliations at CSIRO Data61 and Kyushu University (PhD). His work bridges Artificial Intelligence , Virtual Reality , and Human-Computer Interaction , focusing on data visualization and creative computation . Key collaborations include researchers from biomedical imaging to urban analytics. University of New South Wales, Sydney, NSW CSIRO Data61, Sydney, NSW Kyushu University, Japan (PhD) His research explores immersive analytics through projects like VR-based emotion elicitation, physics-informed AI for simulations, and AI-enhanced visual storytelling frameworks. Recent work applies Generative AI to graphics and reinforcement learning for location recommendation systems. Publications highlight interdisciplinary impact across conservation , healthcare , and disaster management . Article trends show: (1) 2025 focuses on AI-driven graphics and location recommendation systems (2) 2023-2024 emphasizes VR analytics, wildfire modeling, and AI-human collaboration (3) 2018-2022 expands visualization narratives, eye-tracking, and emotion analysis (4) pre-2015 foundational work in heterogeneous computing and VR tele-operation. Major contributions include: Co-developing Gravity++ for interactive data storytelling Advancing VR for conservation and mining robotics Pioneering touchless biofilm interaction systems Establishing frameworks for 3D urban analytics and medical imaging

Mai Otsukiمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Augmented Reality
• Virtual Reality
• +۵ مورد دیگر

Mai Otsuki is an Associate Professor in the Department of Information and Computer Science at Keio University's Faculty of Science and Technology, specializing in Human-Computer Interaction with a focus on Augmented and Virtual Reality systems. Her research spans nearly two decades, with an extensive publication record in top-tier venues including CHI, UIST, VRST, and IEEE Transactions on Visualization and Computer Graphics. Dr. Otsuki's primary research interests center on developing novel interaction techniques for augmented and virtual environments, particularly focusing on remote collaboration, spatial interfaces, and haptic feedback systems. Her work addresses fundamental challenges in how humans perceive and interact with virtual elements, with practical applications in remote instruction, industrial training, sports education, and service industry skill development. She has pioneered approaches for gaze representation, semantic labeling in AR, and physical interaction with virtual objects. Her recent publications (2023-2025) demonstrate continued innovation in applying AR/VR technologies to practical domains including industrial machinery operation, shopping experiences, and assembly tasks, with growing emphasis on physiological metrics and cognitive load assessment. Her work consistently bridges theoretical HCI principles with real-world applications, showing particular strength in experimental design and user-centered evaluation methodologies. Dr. Otsuki has established a robust collaborative network with researchers across Japan and internationally, frequently working with Hideaki Kuzuoka, Asako Kimura, Fumihisa Shibata, and Hideyuki Tamura. Her research group appears to focus on developing practical AR/VR systems that address specific human performance challenges in collaborative and instructional contexts.

Werner Brannathمشاهده پروفایل

استاد

• Biostatistics
• Clinical Trials Design
• Adaptive Designs
• +۵ مورد دیگر

Prof. Werner Brannath is a Professor of Applied Statistics and Biometry at the University of Bremen's Faculty of Mathematics and Computer Science. He heads the Biometry Department at the Competence Centre for Clinical Studies Bremen (KKSB) and serves on its Board of Directors. His expertise spans statistical methodologies for clinical trials, adaptive designs, and precision medicine. He has held roles at the Medical University of Vienna and Stanford University, and currently chairs ethics committees and serves as an Associate Editor for Biometrical Journal . Research interests include machine learning in healthcare, digital health applications, and multiple testing procedures. Education - Studied Mathematics and Physics in Karlsruhe and Vienna - Research Assistant at University of Vienna and Medical University of Vienna - Research Fellowship at Stanford University's Statistics Department Research Networks - DFG Research Training Group π³ - Leibniz ScienceCampus 'Digital Public Health' - AI Center for Healthcare at University of Bremen Teaching and Supervision - Leads the Master’s program in Medical Biometry/Biostatistics - Supervises bachelor and master students in mathematics and biostatistics Professional Activities - Member of International Biometric Society (IBS-DR and ROeS) - Chair of IBS-DR's Public Relations Working Group - Member of multiple clinical trial ethics and data monitoring boards Recent Research Focus - Development of machine learning methods for clinical applications - Platform trials and adaptive design innovations - Statistical solutions for overlapping populations in clinical trials

Kevin Bauerمشاهده پروفایل

استاد

• Artificial Intelligence
• Business Informatics
• Game Theory
• +۴ مورد دیگر

Kevin Bauer is a Professor of Game Theory and Causal AI in Business and Economics at Goethe University Frankfurt, with his professorship integrated into the Hessian AI Center (Hessian.AI). He previously held a postdoctoral position at the SAFE Leibniz Institute for Financial Research and served as an Assistant Professor at the University of Mannheim in the Department of Business Informatics from 2023 to 2024. Education: Two Master's Degrees in Quantitative Economics (Applied Microeconomics focus) and Business Informatics (AI focus), Doctorate from Goethe University Frankfurt (2018). His research bridges business and technology, focusing on explainable AI, algorithmic transparency, causal machine learning, and human information processing. Recent work spans interdisciplinary topics like sustainable finance strategies, human-AI interaction dynamics, and market behavior analysis using causal methodologies. Recent publications reflect his commitment to advancing AI ethics, financial system stability, and decision-making frameworks through interdisciplinary approaches. Scientific Awards: Information Systems Research Best Paper Award, Association for Information Systems Senior Scholar Award. Kevin’s professional journey includes contributing to Frankfurt’s AI ecosystem via TechQuartier and analyzing AI’s financial system impact at SAFE Leibniz Institute. He currently leads the Bauer team within Goethe University’s Department of Business Information Systems, fostering collaboration across the Hessian AI Center and academic networks. His contact is bauer@wiwi.uni-frankfurt.de at Campus Westend, Theodor-W.-Adorno-Platz 4.

Thomas Otterمشاهده پروفایل

استاد

• Bayesian Modeling
• Decision Support Systems
• Marketing Analytics
• +۳ مورد دیگر

Thomas Otter is a Professor of Quantitative Marketing at Goethe University Frankfurt, where he has been since 2007. His research focuses on developing Bayesian models for decision support in marketing, analyzing consumer choice behavior, conjoint measurement, causal inference, and marketing efficiency metrics. He previously served as an assistant professor at the University of California, Riverside, and Ohio State University. He studied at Karl-Franzens University Graz and the Vienna University of Economics and Business. Current doctoral students include Sergey Turlo (since 2019), Matteo Fina (since 2020), and Markus Brobeil (since 2023) Former advisees include Arash Laghaie (Nova School), Max Pachali (Tilburg), Tetyana Kosyakova (Frankfurt School), and Selin Akca (University of Zurich) Contact: Room 1.224, RuW Building, Campus Westend His work combines Bayesian statistics with computational methods to address marketing problems, particularly in understanding consumer decision-making processes. Key research themes include modeling selection decisions, analyzing marketing expenditure effectiveness, and developing probabilistic frameworks for consumer insights. Recent publications focus on integrating text analytics with choice modeling, while earlier work established foundational Bayesian approaches for conjoint analysis and demand estimation. His academic advising network spans current and former doctoral researchers who now hold faculty positions at institutions like Erasmus University, Grenoble Ecole, and EDHEC Business School. Publications show strong emphasis on Bayesian methodology (14/15 articles), with applications in consumer choice (12/15), conjoint analysis (10/15), and causal inference (8/15). Methodological innovations include hierarchical modeling (9/15), handling measurement error (7/15), and integrating behavioral data (6/15). Key domains span marketing analytics (13/15), decision science (11/15), and econometrics (9/15).

• Formal Verification
• Theorem Proving
• Approximate Computing
• +۵ مورد دیگر

Sofiène Tahar is a Professor at Concordia University's Department of Electrical and Computer Engineering, affiliated with the Faculty of Engineering and Computer Science. His research focuses on formal verification, theorem proving, and their applications in cyber-physical systems, circuit design, and reliability engineering. He has authored over 300 publications in top-tier conferences and journals, including DATE, ICFEM, and FMCAD. Key research areas include formal methods for analog/digital circuits, approximate computing, stochastic systems, and safety-critical systems. His work bridges theoretical foundations (e.g., theorem proving in HOL) with practical engineering problems like circuit reliability and autonomous systems verification. Recent projects involve formal analysis of vehicular systems, energy-efficient approximators, and machine learning integration with formal verification frameworks. Tahar collaborates extensively with industry partners and holds leadership roles in international conferences, including co-chairing ICFEM 2023.

• Machine Learning
• Biomedical Engineering
• Digital Health
• +۴ مورد دیگر

Björn Eskofier is a Professor at the School of Advanced Optical Technologies (SAOT) of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU Erlangen). His research focuses on Machine Learning, Biomedical Engineering, Digital Health, and Sensor Technology with applications in stress detection, gait analysis, and radar-based biomedical monitoring. His 2025 publications demonstrate expertise in machine learning for physiological signal analysis , wearable sensor systems , and radar-based health monitoring across fields like stress response, sleep staging, and palliative care. Current work emphasizes multimodal data fusion and open-source biomedical frameworks . Key collaborations include University Hospital Erlangen , IEEE Engineering in Medicine and Biology Society , and European Health Psychology Society . His team develops EmpkinS emotion-based biofeedback systems and gaitmap open-source gait analysis ecosystem.

• Manufacturing Systems
• Production Technologies
• Factory Automation
• +۸ مورد دیگر

Prof. Dr.-Ing. Jörg Franke is a Professor at the Department of Mechanical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, leading the Institute for Factory Automation and Production Systems (FAPS). His research spans manufacturing systems, production technologies, and sustainable industrial practices, with a strong focus on electric mobility and data-driven production optimization .

Boris Kozyrevمشاهده پروفایل

پژوهشگر

• Macroeconomics
• Forecasting
• Applied Econometrics
• +۳ مورد دیگر

Boris Kozyrev is a Researcher in the Department of Macroeconomics at the Halle Institute for Economic Research (IWH) since September 2021. He holds a bachelor's degree from Lomonosov Moscow State University and a master's degree from Toulouse School of Economics. Previously, he worked as a Junior Researcher at the Russian Presidential Academy of National Economy and Public Administration. His research focuses on macroeconomic forecasting, applied econometrics, and computational economics. Key contributions include developing random subspace regression methods for forecast aggregation and applying neural networks to GDP prediction in volatile environments. His work emphasizes model interpretability and handling high-dimensional data, with applications to German and global economic trends. Recent publications explore topics such as forecast combination techniques neural network-based GDP prediction energy crisis impacts on Germany structural challenges in post-pandemic recovery These articles highlight his expertise in quantitative modeling and real-world economic analysis. His affiliations include collaborations with institutions like the IWH and prior work at the Russian Presidential Academy. Research interests intersect with policy analysis, particularly in energy and trade sectors affecting Germany's economy.

• Bioinformatics
• Computational Biology
• Marine Ecology
• +۳ مورد دیگر

Martin Zurowietz is a researcher at the Genome Informatics Group , Institute for Bioinformatics Infrastructure (BIBI) , and Center for Biotechnology (CeBiTec) at Bielefeld University . His work focuses on bioinformatics infrastructure, marine data analysis, and deep learning applications for environmental monitoring. Research Interests : Bioinformatics, computational biology, machine learning, marine ecology, and data science. Technical Contributions : Development of platforms like BIIGLE and MAIA for large-scale image annotation, FAIR data principles in marine imaging, and automated diatom taxonomy. Key Collaborations : Projects involving polymetallic nodule fields, Vazella pourtalesii assemblages, and environmental impact assessments using deep learning. Recent Publications highlight his expertise in: Deep learning for biodiversity analysis Marine imaging systems and workshops Digital microscopy methods Data management for marine ecosystems Interactive visualization tools for scientific data

• Machine Learning
• Process-Based Modeling
• Environmental Science
• +۲ مورد دیگر

Yucong Hu is a Doctoral Researcher at the Max Planck Institute for Biogeochemistry in the Biogeochemical Integration Department . Affiliated with the IMPRS-gBGC graduate school , his work focuses on integrating process-based models with machine learning to address challenges in environmental data analysis. Current projects include upscaling scarce datasets, modeling unmeasurable natural processes, and optimizing hydrological simulations through advanced neural network architectures. Education 2020-2024: MS in Ecology, University of Chinese Academy of Sciences 2018-2020: BEng in Computer Science and Technology, China Agricultural University 2016-2020: BEng in Agricultural Biological Environmental and Energy Engineering, China Agricultural University His research leverages LSTM networks for streamflow and water quality prediction, as well as storm event-based pollutant delivery process analysis. Despite limited publication visibility, his work contributes to environmental data science and hydrological modeling.

• Machine Learning
• Optimization Algorithms
• Energy Informatics
• +۷ مورد دیگر

Cristina Pop is a researcher affiliated with Babes-Bolyai University (Department of Clinical Psychology and Psychotherapy) and has contributed to interdisciplinary research spanning computer science , energy systems , and health informatics . Her work integrates machine learning and bio-inspired optimization algorithms to address challenges in renewable energy prediction , smart grid management , and health monitoring for seniors .

• Mobile Edge Computing
• Cloud Computing
• Internet of Things
• +۱۲ مورد دیگر

Junaid Shuja is a researcher with significant contributions to Mobile Edge Computing , Cloud Environments , and IoT Systems . Collaborating with scholars like Kashif Bilal , Abdullah Gani , and Ehzaz Mustafa , his work spans computation offloading, resource allocation, and security frameworks. Research Highlights 2017: Analysis of Vector Code Offloading in Heterogeneous Architectures 2021: Survey on Machine Learning for Edge Caching 2023: Reinforcement Learning for Computation Offloading in Vehicular Networks 2024: Blockchain Applications in Land Lease Systems and Employee Transfers 2025: Deep Reinforcement Learning for Resource Optimization His recent work focuses on Deep Learning and Blockchain for latency-sensitive applications in IoT and vehicular networks, published in IEEE Access , Cluster Computing , and Telecommunication Systems . Key co-authors include Faisal Rehman , Abdallah Namoun , and Muhammad Bilal .

Shweta Suranمشاهده پروفایل

پژوهشگر ارشد

• Collective Intelligence
• Collective Behaviour
• Citizen Science
• +۱۰ مورد دیگر

Shweta Suran is a Postdoctoral Fellow at the Max Planck Institute for Human Development (Berlin, Germany) with affiliations as an External Researcher at Tallinn University of Technology (School of Information Technologies, Department of Software Science) and IT University of Copenhagen (Collective Intelligence Research Group). She previously held roles at TalTech, The Open University (UK), and Krishna Engineering College. Doctor of Philosophy (2022) in Computer Science, Tallinn University of Technology Research Master's Degree (2014) in Knowledge Engineering, SRM Institute of Science and Technology Her research focuses on Collective Intelligence, Collective Behaviour, Social Network Analysis, Data Mining, and Digital Image Processing. She explores how individuals interact with online content, the impact of conversational affordances on deliberation quality, and correlations between social media preferences, media choices, and socio-economic/political factors. Her recent publications include work on a digital collaborative platform for the silver economy (2023), frameworks for collective intelligence (2020), pathfinding in medical imaging (2014), and AI applications in spatial data infrastructure (2019-2021). She co-supervised Sijo Arakkal Peious (2019 Master's thesis). Marie Skłodowska-Curie Postdoctoral Fellowship (2024) Seal of Excellence from European Commission (2023) SAP Fellowship Award (2016) Gold Medal at SRM University (2014) She contributed to projects like the EU Green Deal-aligned Waterway Safety Management program (2020-2021) and cost-effective 3D spatial data infrastructure (2019-2021), with funding totaling €1,996,000. Her work bridges ICT, collective behavior, and societal challenges.