جست‌وجوی استاد و پژوهشگر

• Climate Change Economics
• Environmental Policy
• Sustainable Development
• +۴ مورد دیگر

Mathias Kirchner is a Researcher at the University of Natural Resources and Life Sciences, Vienna (BOKU), affiliated with the wZGWN Centre for Global Change and Sustainability. Currently on parental part-time status (available Mondays and Wednesdays), he contributes to teaching and research in climate change economics, with a focus on policy-relevant modeling for Austrian sustainability transitions. His research spans : Climate Change Economics (CO2 pricing, decarbonization pathways) Environmental Policy (land use, stakeholder integration) Social Equity (distributional impacts, poverty assessments) Integrated Assessment Modeling (system dynamics, multi-model approaches) Work emphasizes Austrian contexts while addressing global challenges like SDG implementation and climate justice. Recent publications (2022-2025) reveal three dominant trends : (1) Economic modeling of carbon taxation with focus on revenue recycling and triple dividends, (2) Social equity analyses of decarbonization in housing/mobility sectors, and (3) Stakeholder-driven methodologies for SDG pathway validation. Key subfields include distributional impacts across income groups, land-use change under climate scenarios, and causal loop diagram applications. Dr. Kirchner operates within the wZGWN Centre for Global Change and Sustainability , an interdisciplinary hub at BOKU addressing climate adaptation, resource management, and policy coherence through integrated modeling approaches that bridge natural and social sciences.

• Electromagnetics
• Numerical Methods in Electrical Engineering
• Measurement Uncertainty Analysis
• +۳ مورد دیگر

Srđan Divac is a Teaching Assistant at the Department of General Electrical Engineering and Electronics within the Faculty of Technical Sciences, University of Kragujevac . He earned his PhD in 2025 (average grade: 9.88) after completing his master's (2018, grade: 10) and bachelor's (2017, grade: 9.67) studies at the same institution. His research focuses on: Numerical methods in electromagnetics Measurement uncertainty analysis for electrical machines Dynamic hysteresis modeling in ferromagnetic materials Harmonic interpolation techniques for nonlinear circuits Instantaneous magnetisation power analysis Equivalent circuit parameter estimation Key article trends include simulation of magnetic hysteresis loops, electromagnetic computation for power systems, and advanced modeling of ferromagnetic and permalloy materials under varying excitation frequencies. His work appears in journals like Applied Sciences and Energies , as well as conferences such as IcETRAN and PES .

Ivan Bratkoمشاهده پروفایل

استاد

• Machine Learning
• Knowledge-Based Systems
• Qualitative Modelling
• +۵ مورد دیگر

Ivan Bratko is a Professor of Computer Science at the University of Ljubljana's Faculty of Computer and Information Science. He founded the Artificial Intelligence Laboratory in 1985 and served as its head until 2017, remaining an active member. Until 2002, he also directed the AI group at the Jožef Stefan Institute. His academic journey includes B.Sc., M.Sc., and Ph.D. degrees in electrical engineering and computer science, all from the University of Ljubljana. Bratko's research spans machine learning, knowledge-based systems, qualitative modeling, intelligent robotics, heuristic programming, and computer chess. His work focuses on learning from noisy data, combining learning with qualitative reasoning, constructive induction, Inductive Logic Programming, and applications in medicine and dynamic system control. He has authored over 200 scientific papers and influential books including Prolog Programming for Artificial Intelligence (third edition, 2001), KARDIO: A Study in Deep and Qualitative Knowledge for Expert Systems (MIT Press, 1989), and Machine Learning and Data Mining: Methods and Applications (Wiley, 1998). His publication portfolio demonstrates consistent contributions to AI, with recent work emphasizing argument-based machine learning, qualitative modeling applications, and medical AI systems. These publications reveal strong interdisciplinary connections between theoretical AI and practical applications in environmental science, healthcare, and robotics. Fellow of the European Coordinating Committee for Artificial Intelligence (ECCAI) Member of the Slovene Academy of Arts and Sciences (SAZU) Former editorial board member of Artificial Intelligence , Machine Learning , Journal of AI Research , and other leading journals Co-founder and first chairman of the Slovenian AI Society (SLAIS) Bratko has secured numerous research projects including ARRS programs on artificial intelligence (2009-2020), the PARKINSCHECK project for Parkinson's disease detection, and European projects like X-MEDIA and XPERO. His laboratory serves as the central hub for AI research at the University of Ljubljana, fostering collaborations across medical, environmental, and industrial domains. He has mentored numerous researchers and maintained active collaborations through visiting positions at institutions including Edinburgh University, University of New South Wales, and Delft University of Technology.

• Coastal Adaptation
• Climate Resilient Design
• Urban Ecology
• +۳ مورد دیگر

Kees Lokman is an Associate Professor and Chair of Landscape Architecture at the University of British Columbia (UBC) School of Architecture and Landscape Architecture. His research addresses design challenges related to sea level rise adaptation, water/food shortages, and energy transitions, with a focus on community values. He directs the UBC Coastal Adaptation Lab and leads the transdisciplinary project Living with Water , funded by the Pacific Institute for Climate Solutions. Education: MDesS, Harvard Graduate School of Design MSc, Wageningen University BSc, Wageningen University His work intersects coastal adaptation, urban ecology, and climate-resilient design, emphasizing collaboration with Indigenous communities and governmental stakeholders. Research outcomes have been published in journals like Journal of Landscape Architecture and Landscape Research . Scientific Trends: Recent articles explore fluid geographies, flood-resilient landscapes, and integrating Indigenous knowledge into coastal adaptation. Key subfields include design frameworks for uncertainty, GIS-based risk visualization, and urban metabolism applications. Awards & Funding: CELA 2019 Excellence in Research and Creative Work Award (Junior Level) Natural Resources Canada Social Sciences and Humanities Research Council Creative Industries Fund NL Hampton New Faculty Grant Professional Practice: He has practiced at Piet Oudolf’s office in the Netherlands and Terry Guen Design Associates in Chicago, and founded Parallax Landscape . His lab collaborates with Squamish and Tsleil-Waututh First Nations on coastal flooding solutions.

• Robotics
• Simultaneous Localization and Mapping (SLAM)
• Sensor Fusion
• +۵ مورد دیگر

Maurice Fallon is a Professor of Engineering Science at the University of Oxford and a Royal Society University Research Fellow, leading the Dynamic Robot Systems Group (Perception) at the Oxford Robotics Institute. His research focuses on robust probabilistic methods for localization and mapping in challenging environments through advanced sensor fusion. Education: Electronic Engineering, University College Dublin PhD in Acoustic Source Tracking, University of Cambridge Research Interests: Dr. Fallon specializes in probabilistic state estimation , legged robot navigation , and dynamic motion planning for autonomous systems operating in vision-denied or complex natural environments. His work emphasizes robustness through multi-sensor integration , with applications spanning disaster response, forestry, and industrial inspection. Key innovations include terrain-aware locomotion and long-term autonomy frameworks. Publication Trends: Recent work (2024-2025) demonstrates a strategic shift toward forest robotics and long-term industrial inspection , leveraging legged and aerial platforms. There is strong emphasis on vision foundation models for place recognition, scalable 3D reconstruction using neural radiance fields, and open-vocabulary scene understanding . The research consistently addresses real-world challenges like lighting variations, sensor dropout, and environmental dynamics. Scientific Awards: Royal Society University Research Fellowship 4x Best Paper Awards at ICRA Nominations at Intelligent Vehicles, AAAI, and Humanoids conferences Advising and Grants: Dr. Fallon has secured major funding as PI/Co-I for EU/UK projects including ORCA, RAIN, THING, MEMMO, and the DARPA SubT-winning CERBERUS team. Current initiatives include the Horizon Europe DigiForest project and UKAEA collaborations. He mentors PhD students and postdocs in robotics systems development, though specific advisees aren't listed in source materials. Labs and Teams: He directs the Dynamic Robot Systems Group, which achieved global recognition through DARPA Robotics Challenge participation and SubT Challenge victory. The team operates specialized facilities for legged robot testing and maintains partnerships with nuclear energy and forestry sectors for field deployment.

• Collaborative Learning
• Uncertainty Management in STEM
• Feedback Mechanisms
• +۱۲ مورد دیگر

Chandan Dasgupta is an Assistant Professor in the Educational Technology department at Indian Institute of Technology Bombay. He holds a Ph.D. in Learning Sciences from the University of Illinois at Chicago and a Master’s in Human-Computer Interaction from Georgia Institute of Technology. His research focuses on collaborative learning, uncertainty management in STEM, and distributed creativity in makerspaces. Education: Ph.D. in Learning Sciences, University of Illinois at Chicago M.S. in Human-Computer Interaction, Georgia Institute of Technology Dasgupta designs technology-enhanced environments for STEM education, emphasizing embodied learning, inquiry-based pedagogy, and artificial intelligence integration. His recent work explores AR/VR applications for spatial reasoning and critical action curricula in engineering education. Analysis of his 88 research outputs reveals trends in collaborative learning technologies (19 publications in 2023-2025), uncertainty management frameworks (13 papers), and makerspace creativity (7 studies). Key technologies include augmented reality (GeoSolvAR, MathReality), virtual reality conversational agents, and adaptive learning systems. Dasgupta actively contributes to EdTech policy discussions in India, advocating for contextualized solutions aligned with UN Sustainable Development Goals. His collaborations span institutions in the USA (Purdue, Georgia Tech) and India (IIT Bombay, Tulna partnership).

Wei Liuمشاهده پروفایل

دانشیار

• Adversarial Machine Learning
• Generative AI
• Cybersecurity
• +۶ مورد دیگر

Wei Liu is an Associate Professor in Machine Learning and Director of the Future Intelligence Research Lab at the University of Technology Sydney's School of Computer Science. He holds a PhD in Machine Learning from the University of Sydney and maintains active roles as a senior IEEE member and area chair for top AI conferences including KDD, AAAI, and ICDM. Education: PhD in Machine Learning, University of Sydney His research focuses on adversarial machine learning, generative AI, cybersecurity, and multimodal learning, with particular emphasis on AI security, robustness of algorithms, and model fairness. Liu's work addresses critical challenges in developing next-generation AI systems that can withstand cyber attacks while maintaining performance with multi-modal data and balanced outcomes despite data imbalances. Analysis of his recent publications reveals a strong trend toward securing large language models against novel attack vectors while advancing multimodal learning techniques. His work spans both theoretical contributions in adversarial frameworks and practical applications in cybersecurity, transportation, and industrial systems. Scientific Awards: 3 Best Paper Awards Most Influential Paper Award at PAKDD Nominee for NSW Premier's Prizes for Early Career Researcher (2017) Liu actively supervises numerous PhD students working on adversarial attacks, robust AI models, and agricultural applications. He has secured substantial funding including ARC Discovery Projects, government grants, and industry partnerships with organizations including Agriwebb, CSIRO Data61, and AVEVA. His Future Intelligence Research Lab specifically targets three emerging challenges: AI security against cyber attacks, robustness with multi-modal data, and model fairness with imbalanced datasets. The Future Intelligence Research Lab produces next-generation AI algorithms addressing AI security vulnerabilities, multi-modal robustness challenges, and fairness issues in real-world deployment scenarios, with multiple representative papers demonstrating practical applications in each domain.

• Robotics
• Autonomous Systems
• Spatial Cognition
• +۶ مورد دیگر

Patric Jensfelt is a full-time Professor at the Robotics, Perception and Learning (RPL) division within the School of Electrical Engineering and Computer Science (EECS) at KTH Royal Institute of Technology . He contributes to robotics and autonomous systems through research, teaching, and industry collaboration. Academic Rank: Professor Department: Robotics, Perception and Learning (RPL) School: School of Electrical Engineering and Computer Science (EECS) University: KTH Royal Institute of Technology His research focuses on robotics and autonomous systems , particularly spatial cognition for enabling robots to understand and model their environments. He integrates sensor data with high-level symbolic reasoning to advance navigation, mapping, and system integration. Recent work includes applications in service robotics, 3D foot scanning (via the company Volumental ), and drone-based perception. The 15 most recent articles highlight advancements in scene flow estimation, autonomous driving, LiDAR processing, and domain adaptation. They emphasize self-supervised learning , dynamic environment modeling , and robust spatial representations for autonomous systems. Patric supervises Master’s and PhD students, particularly in project courses like DD2410 Introduction to Robotics , DD2419 Project Course in Robotics and Autonomous Systems , and the doctoral course FDD3356 System Integration for Robotics . He is actively involved in the WASP Graduate School , teaching Autonomous Systems annually. He leads the Robotics, Perception and Learning (RPL) division, which fosters innovation in robotics through experimental projects and industry partnerships like Intelligent Machines . The division supports internships (unpaid) and encourages applicants to demonstrate prior engagement with his research.

Andi Zhangمشاهده پروفایل

عضو هیئت علمی

• Machine Learning
• Bayesian Deep Learning
• Deep Learning Robustness
• +۲ مورد دیگر

Dr. Andi Zhang is a Machine Learning Research Associate at the Centre for AI Fundamentals, University of Manchester, specializing in out-of-distribution detection and probabilistic modeling. His research develops methods for improving model robustness and uncertainty quantification in deep learning systems. Research Contributions: Pioneered techniques for using fine-tuned LLMs as OOD detectors without architectural modifications. Developed novel approaches for semantic-aware adversarial example generation through probabilistic frameworks. Contributed fundamental critiques of misconceptions in OOD detection literature. Technical Expertise: Bayesian optimization, neural architecture analysis, and generative modeling. Maintains active GitHub repositories documenting experimental frameworks for OOD detection and adversarial robustness.

• Mechanical Engineering
• Artificial Intelligence
• Tribology
• +۳ مورد دیگر

Hassan Shirvani is a Professor of Engineering Design and Simulation at the School of Engineering and the Built Environment, Anglia Ruskin University. He serves as Director of the Engineering Analysis Simulation and Tribology (EAST) Research Group, focusing on industry collaborations to solve engineering challenges. PhD in Mechanical Engineering, University of Bath MSc in Mechanical Engineering, University of Birmingham Member, Institute of Mechanical Engineers (IMechE) His research spans mechanical engineering, artificial intelligence, and biomedical applications, including: Thermal system optimization Machine learning in clinical decision-making Composite metal foil manufacturing Virtual reality medical training systems Flow dynamics in heat exchangers and nozzles AI-assisted diagnostics Hybrid manufacturing processes Hassan's publications reflect expertise in computational modeling, multi-physics simulations, and industrial applications. Notable areas include deep learning for suicide prediction, thermodynamic analysis of sustainable energy systems, and tribology in mechanical components.

• Supply Chain Management
• Lean Management
• Business Networks
• +۴ مورد دیگر

Ann Charlott Pedersen is a Professor at the Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology (NTNU), within the Faculty of Economics and Management. Her work focuses on supply chain management, lean strategies, and inter-organizational networks. She holds a doctoral degree from NTNU (1996). Her research explores business network dynamics, strategic supplier development, and the integration of digital technologies with lean practices. Key themes include resource heterogeneity management, network foresighting, and triadic relationship analysis in manufacturing contexts. Over 30 years of academic contributions span journals like Industrial Marketing Management and Journal of Business Research . Pedersen collaborates widely, with frequent co-authorship on topics such as supplier network initiatives, lean implementation, and start-up customer relationship strategies. Her teaching includes advanced courses on strategic supply chain management and business network leadership at the master’s level.

• Internet of Things
• RFID and antenna design
• Wireless sensor networks
• +۵ مورد دیگر

Turke Althobaiti is an active researcher and faculty member whose recent work is concentrated in electrical and computer engineering, with strong interdisciplinary links to computer science and biomedical informatics. Based on co-author affiliations and publication scopes, he is associated with King Saud University, College of Engineering, Department of Electrical Engineering . Research Interests: Design of UHF RFID antennas and Internet-of-Things sensing systems. Localization and communication in smart cities, including non-line-of-sight mitigation and 5G/6G networks. Machine-learning-driven healthcare applications—ranging from COVID-19 detection via chest X-rays to arrhythmia and pneumonia screening. Assistive technologies for the visually impaired, employing contactless RF sensing and AI-based navigation aids. Cloud-security solutions, specifically ensemble intrusion-detection systems against flash-crowd attacks. Cross-disciplinary forays into metabolomics biomarkers and human-animal affective computing. Across 15 recent publications (2019-2025), Althobaiti demonstrates a clear trajectory toward AI-enabled sensing and communication . Workflows combine hardware-level innovations (antennas, RFID tags, USRP radios) with data-level advances (deep learning, ensemble methods, privacy-preserving techniques) to address real-world problems in healthcare, smart cities, and assistive living. Scientific Awards & Recognition: No specific awards are listed in the provided text. Advising & Grants: While no explicit list of students or funded projects is given, the high volume of multi-institutional collaborations and senior-author positions suggest active supervision of graduate researchers and participation in funded projects, most likely supported by the Deanship of Scientific Research at King Saud University or similar Saudi funding bodies. Laboratories & Teams: Though no formal laboratory names are provided, the breadth of hardware prototyping, RF experimentation, and AI model development implies access to well-equipped laboratories in RF/microwave engineering, embedded systems, and computational intelligence.

• Clinical Psychology
• Anxiety Disorders
• Experimental Psychopathology
• +۴ مورد دیگر

Vera Bouwman is a dedicated researcher and PhD candidate at Utrecht University's Department of Clinical Psychology within the Social and Behavioural Sciences faculty since September 2020. Her work is centered at the Martinus J. Langeveld Building (Heidelberglaan 1, Room H1.29) where she conducts cutting-edge research on anxiety disorders and treatment response prediction. Her educational background includes a Master's degree in Clinical Psychology (2016) and a Research Master's degree in Social and Health Psychology earned cum laude (2018), both from Utrecht University. Following her studies, she gained valuable research experience as a research assistant (2018-2019) in the Experimental Psychopathology lab. Bouwman's research focuses on implementing experimental approaches to predict anxiety-related treatment response through dynamical system models. Her work aims to identify dynamic early warning signals that indicate individual readiness for change, with the ultimate goal of understanding why and when psychological change is most likely to occur in clinical settings. This innovative approach bridges theoretical models with practical clinical applications. Her publication record demonstrates consistent focus on fear generalization, anxiety disorders, and emotional memory modification. Her research shows a clear trajectory toward developing predictive models for treatment response, with increasing sophistication in methodology from her early work on memory modification through to her current focus on dynamical systems approaches to anxiety treatment. Bouwman's research is supported by significant funding, currently working on a NWO VIDI grant awarded to Dr. Elske Salemink for the project 'Time for change. Sensitivity to experimental perturbations as a predictor of anxiety improvement in treatment'. Previously, her work contributed to a NWO VICI grant awarded to Prof. Dr. Iris Engelhard. She is supervised by an esteemed team including Dr. Elske Salemink (primary supervisor), Prof. Dr. Iris Engelhard, and Dr. Lynn Mobach. Her work is closely associated with the Experimental Psychopathology lab at Utrecht University, where she continues to develop her expertise in experimental approaches to understanding and treating anxiety disorders.

Maria A. Martinمشاهده پروفایل

پژوهشگر

• Climate Change Modeling
• Glaciology
• Numerical Simulation
• +۳ مورد دیگر

Dr. Maria A. Martin has been affiliated with the Potsdam Institute for Climate Impact Research (PIK) since 2008, initially as a doctoral student and later transitioning to a post-doctoral researcher. Since 2014, she has served as a Research Analyst within the Scientific Directors' Staff department. Her academic background includes a physics degree from the University of Potsdam and Université Pierre-et-Marie-Curie in Paris, where her doctoral thesis focused on synchronization in quantum-optical resonators. At PIK, she co-developed the Parallel Ice Sheet Model and conducted pioneering research on Antarctic ice-flow dynamics using numerical modeling techniques. Dr. Martin's research interests span climate modeling, glaciology, and environmental science, with a particular emphasis on understanding the mechanisms driving ice-sheet dynamics and their implications for sea-level rise. Her work integrates advanced computational models with observational data to address critical questions in climate science. Beyond technical contributions, she has engaged in science communication through initiatives like the children's podcast Pinselohr . Her professional network and collaborations are reflected in her participation in interdisciplinary projects at PIK, including contributions to policy advice and software development related to climate impact research. She holds an ORCID identifier (0000-0002-1443-0891) and maintains active involvement in academic discourse through peer-reviewed publications and institutional outreach activities.

Ralph Etienne-Cummingsمشاهده پروفایل

استاد

• Neuromorphic Engineering
• Neural Prosthetics
• Biomedical Robotics
• +۵ مورد دیگر

Ralph Etienne-Cummings is the Julian S. Smith Professor of Electrical and Computer Engineering at Johns Hopkins University (JHU), where he also serves as Vice Provost for Faculty Affairs. He holds secondary appointments in Computer Science and is affiliated with JHU's Applied Physics Lab. His work spans three decades, pioneering advancements in neuromorphic engineering, neural prosthetics, and biomorphic robotics. Etienne-Cummings leads the Computational Sensory Motor Systems Laboratory and has developed systems for closed-loop neural interfaces, prosthetics, and biomedical sensors. Education: BSc in Physics (1988), Lincoln University MSEE (1990) and PhD (1994) in Electrical Engineering, University of Pennsylvania Research Interests: His work focuses on neuromorphic systems, bio-inspired algorithms, and neural prosthetics. Key areas include spinal cord stimulation for mobility restoration, wearable health monitoring, and ultrasonic imaging for infertility treatment. He has contributed to silicon Central Pattern Generators (CPGs) for bipedal robotics and developed the first large-scale neural computer using VLSI chips. His lab explores organoid intelligence and biohybrid systems, blending neuroscience with engineering. Impact & Recognition: Named Fellow of AIMBE (2021) and IEEE (2012) Recipient of JHU Discovery Awards (2018–2019) and NSF CAREER Award (1996) Developed the 'Microbead'—a 0.009mm³ wireless neural stimulator Industry & Outreach: Served as founding director of JHU's Institute of Neuromorphic Engineering and advised firms like Panasonic and Avago. Testified in federal court on intellectual property disputes. Recognized as a 'ScienceMaker' in the HistoryMakers Archive for contributions to African American STEM leadership. Labs & Collaborations: Directs the Computational Sensory Motor Systems Lab. Collaborates with DARPA on prosthetics and the NIH on bioelectronic medicine. His work bridges academia and industry, emphasizing practical applications of neural engineering.