جست‌وجوی استاد و پژوهشگر

• Cyber-Physical Systems
• Internet of Things
• Data Analytics
• +۵ مورد دیگر

Mahima Agumbe Suresh serves as an Associate Professor in the Department of Computer Engineering at San Jose State University's Charles W. Davidson College of Engineering, a promotion effective August 2025 following tenure approval. Previously, she was an Assistant Professor at SJSU after serving as a Visiting Assistant Professor at Texas A&M University and a postdoctoral researcher at Xerox Research Center India. Her educational foundation includes a Ph.D. in Computer Science and Engineering from Texas A&M University (2015) and a B.Tech in Computer Engineering from India's National Institute of Technology Karnataka. Research interests span Cyber-Physical Systems, Internet of Things, Smart City Data Analytics, and Augmented Reality applications for safety and education, with emphasis on practical implementations in urban infrastructure and educational technology. Her publication trajectory (2020-2025) reveals three dominant research thrusts: (1) Cyber-physical security systems for critical infrastructure like water networks and distributed controls, (2) AI-driven educational innovations including specifications grading and active learning methodologies across algorithms, networks, and data science courses, and (3) Applied computer vision and NLP solutions for human trafficking detection, e-bike safety, and product design optimization. This interdisciplinary work consistently bridges theoretical advances with real-world societal impact. Recognition includes: Quantum Faculty Fellow Award (2025) Quantum Faculty Fellow Grant (2024) VPRI Teaming Award (2024) Faculty Excellence in Teaching Service Award (2023) She directs an active research program funded by an NSF grant for mixed reality/edge computing in human-robot interaction (2024) and mentors MS thesis students in computer engineering. Service roles include Assessment Coordinator and ABET Report author for SJSU's Computer/Software Engineering programs, Chair of the Student Fairness Committee (2022-2024), and College of Engineering Mace Bearer at 2023 commencement. Her collaborative research ecosystem integrates academic-industry partnerships with Xerox Research and government entities, focusing on edge computing architectures, cyber-physical security frameworks, and educational technology innovations that address urban infrastructure challenges and pedagogical transformation in STEM education.

• Artificial Intelligence
• Human-Computer Interaction
• Brain-Computer Interfaces
• +۵ مورد دیگر

Dr. Thomas Do is a Senior Lecturer at the School of Computer Science, University of Technology Sydney (UTS), where he also serves as Co-Director of the Human-centric AI (HAI) Centre since March 2023. His academic appointments at UTS include Senior Lecturer (November 2023-present), Research Fellow (November 2021-October 2023), and Research Associate (October 2020-October 2021). He is a member of the Faculty of Engineering and Information Technology, the Australian Artificial Intelligence Institute (AAII), and the School of Computer Science at UTS. Dr. Do's educational background includes: PhD in Computer Science from the University of Technology Sydney (2020) Master's degree in Human-Computer Interaction and Robotics from the Korea Institute of Science and Technology (2016) Dr. Do's research spans the intersection of artificial intelligence, neuroscience, and human-computer interaction. His primary focus is on Brain-Computer Interfaces (BCIs), where he investigates how to integrate BCI technology into everyday applications. He explores the convergence of AI, data science, neural engineering, and human-computer interaction to unlock the full potential of BCIs. His current projects include Human-centric AI technologies (funded by GrapheneX), Brain-Robot Interfaces (funded by Australian Defence), and Human-AI teaming (funded by ARC DP). His work often involves EEG analysis, spatial navigation studies, and object recognition systems. Dr. Do's recent publications demonstrate a strong focus on advancing Brain-Computer Interface technology through innovative approaches to signal processing, multimodal integration, and practical applications. His work spans from fundamental neuroscience research on brain connectivity patterns to applied AI systems for real-world problems in healthcare, transportation, and security. A notable trend in his research is the integration of explainable AI methods with neuroscience to create more transparent and trustworthy BCI systems. Dr. Do is actively involved in teaching and supervision at UTS, where he has taught courses including Data System, AI/Analytics Capstone Project, Technology Research Methods, and Interactive Media. His current research is supported by an ARC Discovery Project grant titled "Mind-reading AI to translate silent speech into words" (2025-2027). Dr. Do maintains active collaborations with institutions worldwide, having previously worked with the Technical University of Berlin, Germany, the University of California San Diego, USA, and the US Army Research Lab. His research has practical applications in medical, defense, and consumer technology sectors.

• Computational Intelligence
• Machine Learning
• Time Series Forecasting
• +۵ مورد دیگر

Dr. Hai Yan Lu is an Associate Professor at the School of Computer Science , Faculty of Engineering and Information Technology , University of Technology Sydney (UTS) . She serves as Head of Discipline for Data Analytics and is a core member of the Decision Systems and e-Service Intelligence Lab within the Centre for Artificial Intelligence at UTS. Her academic journey includes a PhD in Engineering (2002) from UTS, Master of Engineering (1988) and Bachelor of Engineering (1985) from Harbin University of Science and Technology , China. She transitioned from a Lecturer at Yanshan University to UTS in 1994. Research Interests: Computational Intelligence, Time Series Forecasting, Data Analytics, Electromagnetic Material Modeling, 3D Human Pose Estimation, Smart Energy Systems Key Projects: Smart Power Grids, Intelligent Multi-Agent Systems, Predictive Analytics for Renewable Energy, Electromagnetic Device Optimization Technical Expertise: Machine Learning, Deep Learning, Federated Learning, Multi-Objective Optimization, Granular Computing Professional Affiliations: Senior Member, IEEE Member, ACM Vice Chair, IEEE NSW Section Women in Engineering Secretary/Treasurer, IEEE NSW Computational Intelligence Chapter Her recent publications demonstrate cutting-edge work in: Regularized Deep Learning for Financial Risk Modeling GAN-based Environmental Forecasting Hybrid Forecasting Systems for Solar Energy Advanced Magnetic Material Characterization Explainable AI for Industrial Applications

• Computer Vision
• Machine Learning
• Robotics
• +۶ مورد دیگر

Dr Mehdi Hosseinzadeh is a Postdoctoral Research Fellow at the Australian Institute for Machine Learning (AIML) within the University of Adelaide . His research sits at the intersection of Computer Vision , Machine Learning , and Robotics , with a focus on self-supervised multi-modal learning for robotics and autonomous driving applications. He earned his PhD from the University of Adelaide, where he conducted pioneering work in real-time structure and object-aware semantic visual SLAM through the Australian Centre for Robotic Vision . His research integrates advanced 3D scene understanding and object detection to enhance traditional geometric SLAM frameworks. Currently, he supervises Master’s students in the Artificial Intelligence and Machine Learning program and contributes to projects advancing robotic vision technologies. His lab affiliations include the Australian Institute for Machine Learning, located in the AIML Building (Lot 14) on the University of Adelaide campus.

• Deep Learning
• Medical Multimedia
• Explainable AI
• +۵ مورد دیگر

Steven Hicks is a Senior Research Scientist in the Holistic Systems department at Simula Metropolitan. His work focuses on applying artificial intelligence techniques to medical and multimedia applications, with particular emphasis on explainability and transparency in AI systems. Dr. Hicks' research spans multiple domains including medical imaging, particularly in gastrointestinal endoscopy and reproductive medicine. His primary interests include Deep Learning , Medical Multimedia , Explainable AI , and Computer Vision . His work has significant applications in polyp detection, sperm analysis, ECG interpretation, and medical image segmentation, contributing to advancements in diagnostic accuracy and clinical decision support systems. Analysis of Dr. Hicks' recent publications reveals a strong focus on medical applications of AI with emphasis on transparency and explainability. His work frequently addresses challenges in gastrointestinal endoscopy, reproductive medicine, and cardiology. A notable trend is his involvement in creating benchmark datasets like Kvasir-VQA and VISEM-Tracking, and organizing challenges through MediaEval and ImageCLEF to advance the field of medical AI and establish standardized evaluation frameworks. Dr. Hicks actively collaborates with medical professionals and researchers across multiple institutions, contributing to the development of practical AI solutions for real-world medical problems. His work bridges the gap between theoretical AI research and clinical applications, with significant contributions to both technical innovations and the creation of essential resources for the medical AI community.

• Image Processing
• Pattern Recognition
• Multi-media
• +۳ مورد دیگر

Seiichiro Kamata is a Professor at Waseda University's Faculty of Science and Engineering, specifically within the Graduate School of Information, Production, and Systems. He holds a Doctor of Engineering degree from Kyushu Institute of Technology and maintains an active research laboratory focused on advanced image processing techniques. His professional affiliations include the Institute of Electrical and Electronics Engineers, Institute of Image Information and Television Engineers, Information Processing Society in Japan, and Institute of Electronics, Information and Communication Engineers. Professor Kamata's research spans image processing, pattern recognition, multi-media systems, and signal processing with particular emphasis on deep learning applications. His work bridges theoretical advancements with practical implementations across medical imaging, hyperspectral analysis, and real-time computer vision systems. Recent publications demonstrate his leadership in developing novel neural network architectures for medical diagnostics, video enhancement, and document analysis. His publication record shows consistent high-impact contributions, particularly in medical image analysis (including Alzheimer's disease classification, ADHD diagnosis, and skin lesion detection) and computer vision techniques (including real-time semantic segmentation, visual tracking, and hyperspectral image classification). The research demonstrates a clear trajectory from foundational image processing algorithms toward sophisticated deep learning frameworks. Best Paper Award at ICIPRoB2020 (2020) - Constant-Time Gaussian Filtering Best Paper Award at ICSIPA2019 (2019) - Edge-guided Hierarchically Nested Network Best Paper Award at ICIEV2018 (2018) - Deep Neural Networks with Mixture of Experts Best Paper Award at Image Media Processing Symposium (2017) Best Paper Award at ICARCV2010 (2010) - Fast Polar Harmonic Transforms Professor Kamata's research group maintains a strong publication record with 266 papers and 1,622 citations (h-index: 21) as of September 2025. His work demonstrates consistent innovation in both theoretical foundations of image processing and practical applications across medical diagnostics, robotics, and cultural heritage preservation. The laboratory continues to produce cutting-edge research with recent focus on transformer architectures, multi-modal fusion techniques, and medical AI applications.

Anna Wessmanمشاهده پروفایل

استاد

• Iron Age Archaeology
• Scandinavian Archaeology
• Finnish and Baltic Prehistory
• +۸ مورد دیگر

Anna Wessman is a Professor of Iron Age Archaeology at the Department of Cultural History, University Museum of Bergen. Her research spans Late Iron Age Finland and the Baltic region, burial archaeology, metal-detecting cultures, citizen science, and museum studies. She has led major projects like the Levänluhta water burial analysis and co-developed the semantic portal FindSampo , integrating public finds into open-access databases. PhD in Archaeology (University of Helsinki, 2010) with a focus on Iron Age burial rituals Acting University Lecturer in Museum Studies, University of Helsinki (2015-2017) Adjunct Professor in Iron Age Studies, University of Turku (2022 onwards) Her research combines ethnographic methods with isotopic analysis, examining topics like the Finnar in sagas, cremation practices, and the role of citizen scientists in archaeology. She collaborates across disciplines, including genetics (e.g., Levänluhta isotope studies) and digital humanities ( FindSampo platform). Recent articles highlight her work on metal-detected artifact networks (2025), Viking Age magic interpretations (2025), and cremation studies (2024). Her projects have been funded by the Kone Foundation, Academy of Finland, and Emil Aaltonen Foundation, focusing on community archaeology and digital heritage platforms. Wessman actively bridges academic and avocational communities, advocating for structured collaboration with metal-detectorists to enhance archaeological data. Her publications address ethical dilemmas in displaying human remains (2021) and methodological innovations like 'archaeological object interviews.' Key affiliations: University of Bergen (current), University of Helsinki (PhD), University of Turku (Adjunct Professor) Founding member of the European Public Finds Recording Network and Cultural Heritage Crime Network

• Computer System Software
• Image Processing
• Programming Languages
• +۳ مورد دیگر

Murat IŞIK is a Lecturer in the Department of Computer Engineering at the Faculty of Engineering and Architecture, Kırşehir Ahi Evran University. He has been serving as a full-time faculty member since 2017 and currently holds additional administrative positions as Department Head (since 2025) and Research Application Center Deputy Director (since 2024). PhD in Computer and Information Engineering, Sakarya University (2011-2017) MSc in Computer Engineering, Sakarya University (2009-2011) Bachelor's Degree from Dicle University, Batman Technical Education Faculty (2002-2006) Dr. IŞIK's research spans multiple areas in computer science and engineering, with a focus on artificial intelligence applications. His primary research interests include Computer System Software, Image Processing, Programming Languages, Computer Software and Software Engineering, Machine Learning, and Artificial Intelligence. His work demonstrates a strong interdisciplinary approach, applying computer vision and deep learning techniques to diverse fields including medical diagnostics, education technology, and accessibility solutions. His recent publications show a trend toward practical applications of AI in education, healthcare, and everyday life challenges. He has developed innovative solutions such as real-time assessment systems for primary schools, assistive technology for visually impaired individuals, and classification systems using deep learning approaches. Research World Medal and Research World Excellent Paper Award (2016) Dr. IŞIK has successfully supervised multiple master's students and is actively involved in numerous research projects funded by higher education institutions. His projects range from BCI-supported deep learning models for motor/facial movement detection to AI-assisted insole production and intelligent test systems for primary school students. He has secured funding for projects extending through 2027, demonstrating sustained research productivity. He leads the Computer Engineering department's research efforts and has established collaborations with researchers both within Turkey and internationally, particularly in Iraq. His laboratory work focuses on implementing practical AI solutions with an emphasis on accessibility and educational applications.

Martin Middendorfمشاهده پروفایل

استاد

• Swarm Intelligence
• Bioinformatics
• Genome Rearrangement Analysis
• +۷ مورد دیگر

Prof. Dr. Martin Middendorf is a faculty member at the Department of Computer Science , Faculty of Mathematics and Computer Science , Leipzig University , Germany. He leads the Swarm Intelligence and Complex Systems Group and focuses on interdisciplinary research at the intersection of computational methods and biological systems. Fields of Interest Swarm Intelligence Bioinformatics Genome Rearrangement Analysis Combinatorial Optimization Evolutionary Algorithms Task Allocation in Multi-Agent Systems His recent research emphasizes mitochondrial genome annotation , predator-prey dynamics in swarm systems , and metaheuristic algorithms for dynamic optimization . Key trends include de-Bruijn graph applications , pheromone-dependent movement modeling , and automated behavior tracking in social insects . Supervised Students Dr. Nicolas Wieseke Dr. Hoang Thanh Le Dr. Fatma Turna Tobias Jagla Carsten Seemann Prof. Middendorf's group develops tools like DeGeCI 1.1 for mitochondrial gene annotation and explores swarm-controlled emergence in ant clustering systems. They apply swarm intelligence principles to solve real-world problems in vehicle routing , sewer network design , and biomedical signal processing .

Tim Wilhelm Nattkemperمشاهده پروفایل

استاد

• Bioinformatics
• Bioimage Informatics
• Computer Vision
• +۶ مورد دیگر

Prof. Dr.-Ing. Tim Wilhelm Nattkemper leads the Biodata Mining Group at the Faculty of Engineering , Universität Bielefeld , while holding affiliations with the Center for Biotechnology (CeBiTec) and the Institute for Bioinformatics Infrastructure . His work bridges bioinformatics with marine environmental monitoring , focusing on machine learning and computer vision applications. The group specializes in multivariate bioimage analysis , developing platforms like BioIMAX for web-based high-dimensional data exploration. Research spans from MALDI imaging to deep-sea megafauna classification , integrating information visualization and web technologies . Recent projects address seafloor macrolitter monitoring , coral stress response analysis , and self-supervised learning for diatom classification. Their 15 most recent publications (2023-2025) highlight advancements in marine imaging , automated annotation systems , and AI-driven biodiversity assessment , particularly in polymetallic nodule fields. The group also tackles technical challenges like data imbalance in marine image classification and FAIR data principles implementation. As module responsible for courses like Information Visualization and Introduction to Bioinformatics , Nattkemper contributes to academic training in bioinformatics and data science . His interdisciplinary collaborations span physics , chemistry , and ecology within Bielefeld's Material World strategic research area.

Franziska Klemsteinمشاهده پروفایل

پژوهشگر

• Monument Preservation
• Digital Humanities
• GDR Architectural History
• +۳ مورد دیگر

Dr. Franziska Klemstein is an active researcher specializing in monument preservation and digital humanities, with significant contributions to understanding heritage practices in the German Democratic Republic (GDR) era. Her work critically bridges historical scholarship and computational methods, particularly through the development of specialized datasets for document analysis and image retrieval in cultural heritage contexts. Her research focuses on: Monument preservation systems under socialist regimes Ethical implementation of AI in heritage documentation Diversity and inclusion challenges in conservation practices Classification methodologies for historical inventories Digital reconstruction of contested heritage sites Gender dynamics in preservation professions Analysis of Klemstein's recent publications reveals a methodological evolution from historical analysis toward technical innovation, with increasing emphasis on dataset creation (DHMTIC, DHREAAL, TexBiG) and evaluation frameworks for heritage computing. Her work consistently addresses the tension between digital efficiency and critical heritage values, particularly regarding sustainable AI implementation and inclusive documentation practices. While specific awards remain undocumented in the provided materials, her editorial leadership in significant volumes like "On the Way to a Place of Remembrance" (2024) demonstrates recognized expertise. Klemstein's collaborative projects with researchers like Tschirschwitz and Schmidgen indicate active participation in interdisciplinary teams focused on digital heritage solutions, though institutional affiliations and grant details aren't specified in the current text.

Touradj Ebrahimiمشاهده پروفایل

استاد

• Image and Video Compression
• Media Security
• Multimedia Signal Processing
• +۵ مورد دیگر

Touradj Ebrahimi is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he heads the Multimedia Signal Processing Group (MMSPG) within the School of Engineering's Institute of Electrical Engineering. He holds a Ph.D. in Electrical Engineering from EPFL and serves as the Convenor of the JPEG standardization committee. His research spans AI-powered imaging, multimedia signal processing, and media security. Research Interests: Professor Ebrahimi's work focuses on next-generation image/video compression (including JPEG AI and DNA-based storage), quality of experience in immersive media, trustworthy media authentication (JPEG Trust), and explainable AI for biometric systems. His group pioneers methods for point cloud compression, deepfake detection, and privacy-preserving multimedia. Publication Trends: Recent articles emphasize AI-driven compression standards, quality assessment for emerging media (point clouds, HDR), and explainability in computer vision. Key themes include JPEG AI standardization, DNA storage for visual data, and robustness evaluation of deepfake detectors. Awards & Honors: IEEE Star Innovator Award in Multimedia IEEE and Swiss National ASE Awards ISO Certificates for MPEG-4/JPEG 2000 contributions Best Paper Award (IEEE Trans. Consumer Electronics) Fellow of IEEE and SPIE Leadership: He founded RayShaper SA, Genista SA, and Emitall SA. Represents Switzerland in ISO/IEC JTC1/SC29 and ITU, advising European Commission initiatives and venture capital firms on multimedia technologies. Facilities: Leads the MMSPG lab at EPFL, focusing on intelligent multimedia systems. The group collaborates globally on standardization (JPEG, MPEG) and EU projects.

• Multi-stable Perception
• Perceptual Inference
• Cognitive Neuroscience
• +۷ مورد دیگر

Dr. Alexander Pastukhov is an Assistant Professor at the Department of General Psychology and Methodology, University of Bamberg. His research focuses on multi-stable perception , perceptual inference , and the interplay between attention and microsaccades . He develops open-source tools like saccadr , eyelinkReader , and bistablehistory for analyzing eye movements and perceptual dynamics. Research Interests : Multi-level mechanisms of perception and memory History effects in bistable perception Attentional allocation via microsaccade analysis Bayesian modeling of perceptual decision-making Scientific Awards : 2013 'Best of the Year' award from the Psychonomic Society for work on sensory memory and adaptation in structure-from-motion displays His recent software development includes probabilistic programming tools for 2D/3D transformations and eye-tracking analysis. He also contributes to applied data analysis teaching materials using R and Python. Collaborative projects with industry partners (e.g., BMW) and institutions (e.g., Fraunhofer, Orange Labs) highlight his research transfer efforts.

• Computer vision
• Robotics
• Machine perception
• +۳ مورد دیگر

Christopher Hollitt is a Senior Lecturer and Programme Director of Electrical Engineering & Mechatronics at the School of Engineering and Computer Science, Victoria University of Wellington. His research focuses on developing autonomous mobile robots capable of interacting with dynamic environments through advanced visual perception systems. Dr. Hollitt holds a BE(Elec), BSc(Hon), and PhD from the University of Adelaide, Australia. His academic journey has led him to specialize in machine perception and robotic control systems. Dr. Hollitt's research interests center on machine perception, particularly machine vision, and the control of robotic and space systems. He aims to develop artificial robot 'minds' capable of perceiving and modeling the world in real time. His specific expertise includes active vision with time constraints, statistical underpinnings of active perception, vision-based estimation of robot pose, and multi-modal active perception systems. His work bridges the gap between theoretical computer vision and practical robotic applications, with emphasis on real-world implementation challenges. His publication record shows a clear progression from fundamental visual attention models to sophisticated context-aware object detection systems. Early work focused on color spaces and basic feature extraction, while recent research explores top-down contextual information integration and uncertainty modeling for improved target object detection. His approach increasingly incorporates evolutionary algorithms and probabilistic methods to create adaptive visual systems that can handle the complexities of real-world environments. Dr. Hollitt actively supervises graduate students, with current doctoral projects examining 'Low cost analysis of milk contaminants' and 'Droplet evaporation of complex fluids.' He previously supervised Jane, Celine's Master's research on spacecraft magnetic disturbance rejection. His grant history includes the 'Generic Shoulder Relief Appliance' project funded by the New Zealand Artificial Limb Service. Within the university, Dr. Hollitt contributes significantly to the academic community through his role as Programme Director and his teaching of advanced engineering courses, helping to shape the next generation of engineers and computer scientists.

• Social Cognition
• Early Childhood Cognition
• Social Development
• +۵ مورد دیگر

Dora Kampis serves as a Tenure Track Assistant Professor in the Department of Psychology within the Faculty of Social Sciences at the University of Copenhagen. Her research focuses on the cognitive mechanisms underlying human social nature, spanning from infancy through adulthood. Her primary research interests include Social Cognition , Early Childhood Cognition , Social and Cognitive Development , and Developmental Cognitive Neuroimaging . Using behavioral methods (eye-tracking, looking time studies, interactive paradigms) and neuroimaging (primarily EEG), she investigates how social cognitive abilities emerge and develop, examining their interactions with perception and memory systems. Her work bridges ontogenetic and phylogenetic perspectives to understand the foundations of adult human cognition. Analysis of her recent publications (2021-2024) reveals a strong focus on infant social cognition, particularly perspective-taking abilities, self-other distinction, memory development, and semantic processing in preverbal children. Her research employs rigorous experimental designs with infants aged 14-36 months, often involving collaborative international studies. Key trends include investigating altercentric biases, cognitive flexibility in changing contexts, and the emergence of theory of mind precursors. Dr. Kampis maintains active research collaborations across multiple countries, as evidenced by her international co-authorship patterns. Her work has gained significant attention in the scientific community, with several publications covered by news outlets and discussed on academic social media platforms.