جست‌وجوی استاد و پژوهشگر

• Signal Analysis
• Biomedical Engineering
• Power Systems
• +۷ مورد دیگر

Marta Molinas is a Professor in the Department of Engineering Cybernetics at NTNU, affiliated with the Faculty of Information Technology and Electrical Engineering. She holds dual professorships in Industrial Electrotechnics and Power Electronics for Power Systems. She is also an affiliated scientist at the AMOS Center of Excellence. Her research focuses on non-linear/non-stationary signal analysis in both biological systems (EEG) and electrical grids, emphasizing synchronization mechanisms. Key projects include the 'David & Goliath' initiative and 'The Girl Effect' (2019-2020). Education: Dr. Eng. from Tokyo Institute of Technology (2000), Master of Engineering from University of the Ryukyus (1997). Teaching includes courses like TTK4240 Industrial Electrotechnics and TTK7 Adaptive Data Analysis. She supervises numerous PhD students and postdocs in areas like EEG signal analysis and grid stability. Research interests span EEG-based brain-computer interfaces, grid stability, and biomarker identification. Notable experiments include translating EEG signals into drone control and developing low-density EEG systems. Awards: Not explicitly listed, but recognized through prolific publications and grants.

Sarah Fortuneمشاهده پروفایل

استادیار

• Marine Mammal Conservation
• Biological Oceanography
• Zooplankton Ecology
• +۵ مورد دیگر

Sarah Fortune is an Assistant Professor in the Department of Oceanography at Dalhousie University, holding the Canadian Wildlife Federation Chair in Large Whale Conservation. She leads the Fortune Whale Lab, focusing on interdisciplinary research at the intersection of biological and physical oceanography, ecology, and physiology to address conservation challenges for large whales. Her work emphasizes understanding how environmental variability impacts whale behavior, prey availability, and ecosystem health, particularly in Arctic and temperate regions. Education: BAH from Queen’s University, MSc and PhD from the University of British Columbia, with additional research at the Woods Hole Oceanographic Institution. Her research explores whale movement ecology, foraging strategies, and responses to climate change, often collaborating with Indigenous communities, government agencies, and industry partners. She has conducted fieldwork in the Canadian Arctic, Cumberland Sound, and the Gulf of St. Lawrence. Research Interests: Marine mammal conservation, zooplankton ecology, Arctic ecosystem changes, whale behavior and energetics, and the impacts of human activities on marine life. Her lab employs advanced technologies like multi-scale tags and satellite telemetry to study whale behavior and habitat use. Awards: Recipient of multiple prestigious fellowships including the W. Garfield Weston Postdoctoral Fellowship and the Emily Gallagher Memorial Award. Her work has been recognized for bridging ecological theory with applied conservation outcomes. Advising & Grants: While no specific student names are listed, her lab supports graduate research on whale ecology. Funding sources include the W. Garfield Weston Foundation and NSERC grants. She contributes to conservation initiatives like the COSEWIC Status Report on North Atlantic right whales. Labs/Teams: Director of the Fortune Whale Lab, collaborating with universities, government bodies, and Indigenous groups to advance whale conservation through empirical and model-based research. Current projects include assessing vessel-whale interactions in Arctic shipping lanes and quantifying prey thresholds for bowhead whales.

Ricardo Betancurمشاهده پروفایل

دانشیار

• Ichthyology
• Evolutionary Biology
• Phylogenomics
• +۶ مورد دیگر

Ricardo Betancur is an Associate Professor of Marine Biology at the Scripps Institution of Oceanography, UC San Diego. His research focuses on Ichthyology, Evolutionary Biology, and Phylogenomics, with a particular emphasis on understanding the evolutionary processes shaping fish diversity. His work integrates genomic, morphological, and ecological data to explore topics such as adaptive radiation, biogeography, and phylogenetic relationships in marine and freshwater fish species. Education: PhD Biological Sciences, Auburn University (2009) M.Sc. Marine Biology, Universidad Nacional de Colombia (2003) B.Sc. Marine Biology, Universidad Jorge Tadeo Lozano (2000) Research interests include phylogenetic comparative methods, population genetics, and the application of genomics to resolve evolutionary questions in fish lineages. His projects often involve genomic sequencing of diverse fish species, such as sea catfishes and anglerfishes, to uncover patterns of diversification and adaptation. He has contributed significantly to the classification and evolutionary understanding of ariid catfishes and other marine taxa. His research also addresses the impact of environmental factors, such as ancient climate changes, on fish evolution and distribution. He leads the Fish Phylogeny Research Group and maintains an active website ( fishphylogeny.org ) dedicated to advancing fish phylogenetics. Advising and grants: While no student advisees are listed here, Betancur’s lab focuses on training researchers in phylogenomics and evolutionary biology. His grants support large-scale genomic projects and collaborative initiatives in marine biodiversity. Labs/Teams: He directs the Fish Phylogeny Research Group at Scripps, emphasizing interdisciplinary approaches to fish systematics and evolutionary genomics.

Kirsten Mølgaard Nielsenمشاهده پروفایل

دانشیار

• Control Systems
• Automation
• Renewable Energy
• +۸ مورد دیگر

Kirsten Mølgaard Nielsen is an Associate Professor in the Department of Electronic Systems at Aalborg University, under The Technical Faculty of IT and Design. She is affiliated with the Automation & Control Green Lab, focusing on sustainable automation and control systems for energy and environmental applications. University: Aalborg University School: The Technical Faculty of IT and Design Department: Department of Electronic Systems Research Lab: Automation & Control Green Lab Email: kmn@es.aau.dk Phone: +4599408761 ORCID: https://orcid.org/0000-0002-2552-1764 Her research interests include control systems, automation, renewable energy (particularly wave and wind), digital hydraulics, smart grids, and wastewater treatment. She has contributed extensively to projects involving energy storage, process control, and sustainable engineering solutions. Her recent publications (2018–2022) demonstrate a strong trend in environmental and energy control systems, particularly in wastewater treatment plant optimization, sewer flow management, and refrigeration system control. These works highlight her expertise in applying advanced control strategies to real-world engineering challenges. She has participated in multiple research projects such as 'Digital Hydraulic Power Take Off for Wave Energy', 'IFIV: Intelligent Remote Control of Individual Heat Pumps', and 'Plug and Play Process Control (P³C)', showcasing her collaborative and interdisciplinary approach. Kirsten Mølgaard Nielsen has not received any explicitly mentioned scientific awards in the provided texts. There is no indication of student advising or grants, though her role in major research projects suggests leadership and mentorship within her team. She is actively involved in research and development within the Automation & Control Green Lab, contributing to advancements in sustainable energy and environmental control technologies.

• Computer Vision
• Machine Learning
• Probabilistic Graphical Models
• +۷ مورد دیگر

Jacobus J Barnard is a Professor in the Department of Computer Science at the University of Arizona, College of Engineering. He is actively involved in teaching and research, with a focus on computer vision, machine learning, and interdisciplinary applications in emotion dynamics, biology, and planetary science. Department: Computer Science School: College of Engineering University: University of Arizona Academic Rank: Professor His research interests are centered on Computer Vision , Probabilistic Modeling , and Machine Learning , with applications in emotion regulation, biological structure analysis, and remote sensing. He develops statistical models for understanding indoor scenes, tracking biological motion, and analyzing social and emotional dynamics. His work integrates vision and language, with generative models linking image regions to semantic concepts. The recent publications reflect a strong trend in deep learning for image restoration (e.g., fiber bundle imaging), Bayesian modeling in planetary science (crater shape analysis), and interdisciplinary AI (emotion dynamics, gene expression). His work spans computer science, psychology, biology, and engineering, often combining statistical inference with real-world data from microscopy, aerial imaging, and human behavior studies. Scientific Awards: No explicit awards listed in the provided text. Barnard has advised numerous students, including PhD candidates and honors undergraduates, working on topics such as gesture authentication, pollen tube tracking, and emotion modeling. He has not received mention of major grants, but his sustained publication record and interdisciplinary collaborations suggest active funding. He teaches core courses like Intro to Computer Vision and Probabilistic Graphical Models , and supervises dissertation and independent study research. He is involved in developing computational models for understanding biological structures (e.g., Alternaria fungus, Drosophila neurons), emotion regulation in couples, and automated trail detection in aerial imagery. His lab appears to focus on statistical vision, with strong ties to cognitive science and bioinformatics.

• Machine Learning
• Bioengineering
• Medical Imaging
• +۵ مورد دیگر

Wei Qu, M.D., Ph.D., M.S., is a Senior Biologist at the National Toxicology Program (NTP) within the National Institute of Environmental Health Sciences (NIEHS). His work focuses on integrating machine learning, bioengineering, and neuroimaging to advance toxicology and medical research. He leads projects in graph neural networks, functional connectivity modeling, and biomedical image analysis. Qu’s research bridges computational methods with biological systems, addressing challenges in neurodegenerative diseases, biosensing interfaces, and automated medical diagnostics. Key research areas include graph-based representation learning, dynamic functional connectivity analysis, and 3D microscopy image processing. His contributions span neuroimaging data interpretation, biosensor design, and cervical cancer screening algorithms. Qu collaborates across interdisciplinary teams to develop replicable machine learning frameworks for environmental health studies.

Kaie Kubjasمشاهده پروفایل

دانشیار

• Algebraic Statistics
• Nonlinear Algebra
• Matrix and Tensor Decompositions
• +۵ مورد دیگر

Kaie Kubjas is an Associate Professor at Aalto University in the Department of Mathematics and Systems Analysis, School of Science. Since 2024, she has held a tenured position, following a tenure-track role from 2017–2024. She earned her PhD in Mathematics at Freie Universität Berlin (2013) under Professors Christian Haase and Klaus Altmann, with postdoctoral research at institutions including the Max Planck Institute and MIT. Her research focuses on applied nonlinear algebra, algebraic statistics, and their applications in biology (e.g., phylogenetics and 3D genome reconstruction), as well as matrix/tensor decompositions. She has organized major events like the European Women in Mathematics General Meeting 2022 and the 2025 workshop on Algebraic Statistics and Multistate Models. Kubjas serves on editorial boards of journals like SIAM Journal on Applied Algebra and Geometry and Annales Fennici Mathematici . Recent work includes advances in log-concave maximum likelihood estimation, 3D genome reconstruction, and structured matrix decompositions. Her students, such as Olga Kuznetsova (Second Place MEGA 2021 Poster Award winner), have contributed to these areas. She regularly contributes to seminars like the Algebra and Discrete Mathematics at Aalto, fostering interdisciplinary collaboration.

Brian Taylorمشاهده پروفایل

استادیار

• Magnetoreception
• Multimodal Sensation
• Sensory Processing
• +۳ مورد دیگر

Brian Taylor is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Case Western Reserve University's Case School of Engineering. His research focuses on understanding how animals use magnetoreception and sensory processing for navigation, with the goal of applying these principles to develop robust, low-cost autonomous systems. He leads the WAKANDA lab, which explores biomimetic solutions for navigation challenges in environments where GPS is unavailable or unreliable. Dr. Taylor earned his BS, MS, and PhD in Mechanical Engineering from Case Western Reserve University (2005, 2009, 2012). His work bridges biomechanics, computational neuroscience, and robotics, emphasizing the integration of animal-inspired mechanisms into engineered systems. Recent projects include modeling magnetic field navigation in leeches and developing bioinspired algorithms for marine robotics and space exploration. His research emphasizes interdisciplinary approaches, combining field studies, computational modeling, and hardware prototyping. Key themes include distributed sensing, neural adaptation to environmental cues, and energy-efficient navigation strategies observed in migratory animals.

Patrick Barclayمشاهده پروفایل

استاد

• Evolutionary Psychology
• Cooperation
• Altruism
• +۷ مورد دیگر

Patrick Barclay is a Professor of Psychology at the University of Guelph, cross-appointed with the Bachelor of Arts & Science (BAS) program. His research focuses on the evolution of human cooperation and risk-taking, integrating evolutionary biology, game theory, and experimental economics. He investigates topics like altruism, reputation systems, costly punishment, biological markets, and intergroup competition. His work has been recognized with awards including the Early Career Award for Distinguished Scientific Contribution (HBES, 2015). He teaches evolutionary psychology and critical thinking courses, and actively involves students in research projects. Research Interests: Evolution of cooperation, altruism, reputation, costly punishment, biological markets, partner choice, risk-taking, intergroup threats, and experimental economics. Awards: New Investigator Award (HBES 2003, 2005) Early Career Award for Distinguished Scientific Contribution (HBES 2015) Teaching: Evolutionary Psychology, Critical Thinking, and interdisciplinary BAS program courses bridging arts and sciences.

Xue Liuمشاهده پروفایل

استاد

• Intelligent Computing and Communications Systems
• Sustainable Computing
• AI and Machine Learning Applications
• +۳ مورد دیگر

Xue Liu is a Professor and William Dawson Scholar at McGill University's School of Computer Science, affiliated with the Department of Mathematics and Statistics (courtesy appointment) and the Department of Electrical and Computer Engineering. He holds positions as Vice President R&D and Chief Scientist at Samsung AI Center Montreal, and serves as Chair (2021-2023) of ACM SIGBED. His research focuses on intelligent computing, cyber-physical systems, sustainable computing, AI/ML applications, IoT, and blockchain technologies. Dr. Liu has received multiple Best Paper Awards from conferences such as IEEE GLOBECOM, IEEE HPCC, and IEEE/ACM IWQoS. He is a Fellow of the Canadian Academy of Engineering (FCAE) and IEEE (FIEEE). His work bridges academia and industry, with entrepreneurial ventures including advisory roles at Infinity Stones Inc., TandemLaunch, and Aerial Technologies. He co-edits the Journal of Blockchain Research and serves on editorial boards of major journals like ACM Transactions on Cyber-Physical Systems and IEEE Transactions on Networking. Key research contributions include innovations in blockchain infrastructure, smart grid optimization, and AI-driven systems. His labs, including the Cyber-Physical Intelligence Lab and MILA affiliation, advance interdisciplinary research in machine learning, robotics, and networked systems. Dr. Liu mentors startups and advises initiatives like Learnable Inc., integrating AI into education and healthcare. Education Affiliations: McGill University School of Computer Science (prestige rankings include top 25 globally per QS 2023) Awards: Multiple Best Paper Awards, FCAE & FIEEE Fellowships Labs: Cyber-Physical Intelligence Lab, MILA, CIM, SYTACom Publications: Over 150 peer-reviewed articles, including books on cloud computing, smart grids, and cyber-physical systems

David Hunterمشاهده پروفایل

مدرس

• Computer Vision
• Image Processing
• 3D Shape Retrieval
• +۳ مورد دیگر

David Hunter is a Lecturer in Computer Science at the Department of Computer Science, Aberystwyth University. His research spans computer vision, binocular vision, and educational identity studies. He has contributed to projects involving 3D shape retrieval, natural image statistics, and protein domain analysis through initiatives like SHREC challenges. Research Interests: Computer Vision: Focusing on binocular disparity, depth perception, and natural image processing. 3D Shape Retrieval: Developing novel algorithms for nonrigid and protein surface comparisons. Educational Studies: Exploring student and graduate identity transitions in higher education. His publications highlight contributions to journals like Royal Society Open Science and conferences such as CSCI. He manages the Tobii Pro Eye Tracking Glasses facility, aiding visual research. Supervised graduate students include Dave Davies (MPhil) and Muhammad Iftikhar (PhD).

• Neuromorphic Computing
• Reservoir Computing
• Reinforcement Learning
• +۳ مورد دیگر

Prof. Abigail Morrison is a Professor and Group Leader of the Computation in Neural Circuits group at the Institute for Advanced Simulation (IAS-6), part of Forschungszentrum Jülich. Her work focuses on advancing neuromorphic computing, reservoir computing, and spiking neural network simulations. She leads development of the NEST simulator and NESTML modeling language, tools critical for large-scale brain modeling. Key research areas include: Neuromorphic hardware-software co-design Biophysically plausible neural network models GPU-accelerated simulation frameworks Multi-scale brain co-simulation techniques Applications in embodied AI and neuroscience Her lab contributes to the European EBRAINS infrastructure and has pioneered methods for parallel GPU-based network construction. Recent work explores topographic neural circuit architectures for signal processing and reinforcement learning in neuromorphic systems. Notable achievements include: Development of NESTML (v8.0+) ODE-toolbox for ODE solver selection Co-design of HNC neuromorphic compute nodes Advances in dendritic computation modeling Current efforts emphasize exascale computing readiness, neuromorphic system validation, and bridging biological plausibility with computational efficiency in neural network simulations.

Jakub Nedbalمشاهده پروفایل

پژوهشگر

• Fluorescence lifetime imaging
• Microscope development
• SPAD Arrays
• +۳ مورد دیگر

Jakub Nedbal is a Researcher at King’s College London’s Department of Physics within the Faculty of Natural, Mathematical & Engineering Sciences. He holds a Master’s in Physics from Masaryk University (Czech Republic) and a Master’s in Immunology from King’s College London, alongside a PhD focused on imaging techniques in B cell biology. His research specializes in developing advanced fluorescence-based imaging technologies, particularly leveraging SPAD arrays and time-correlated single-photon counting (TCSPC) techniques. Notable contributions include creating the first fluorescence lifetime flow cytometer using TCSPC and co-developing a fast TCSPC microscope with parallelized SPAD array detectors. Current work focuses on spatially resolved TCSPC detectors for thick-sample imaging and low-phototoxicity techniques. Outside academia, Jakub served as Product Development Lead for a startup commercializing TCSPC array cameras until 2018. His innovations have been applied to art conservation via fluorescence lifetime imaging (FLIM) for varnish analysis and have advanced microalgae cultivation bioreactor designs. His research interests span fluorescence lifetime imaging, microscope development, SPAD arrays, and photosynthesis studies. Recent work highlights include a novel dual-excitation fluorometer for photosynthesis research and a SPAD array camera for lightsheet FLIM. His techniques address challenges in live-cell imaging phototoxicity and high-throughput FRET screening.

Eliseo Ferranteمشاهده پروفایل

استاد مهمان

• Swarm Robotics
• Complex Systems
• Machine Learning
• +۷ مورد دیگر

Eliseo Ferrante is a Visiting Professor of Computer Science at New York University Abu Dhabi and a tenured Assistant Professor at Vrije Universiteit Amsterdam. He holds a PhD in Applied Sciences from Université Libre de Bruxelles and dual MSc degrees in Computer Science and Artificial Intelligence from Politecnico di Milano and the University of Illinois at Chicago. His academic work spans robotics, artificial intelligence, and complex systems, with a focus on swarm robotics and statistical physics. MSc, Computer Science and Artificial Intelligence – Politecnico di Milano MSc, Computer Science and Artificial Intelligence – University of Illinois at Chicago PhD, Applied Sciences – Université Libre de Bruxelles His research interests include swarm robotics, complex systems, machine learning, artificial intelligence, non-equilibrium statistical physics, and evolutionary and theoretical biology . Ferrante’s work bridges fundamental and applied research, aiming to develop scalable, controllable swarm systems that operate without centralized control or man-made infrastructure. He has contributed to over 80 publications across robotics, physics, and computer science. The 15 most recent articles reflect a consistent focus on decentralized control, emergent behavior in swarms, bio-inspired algorithms, and the application of statistical physics to robotics . Topics include UAV swarms, task allocation, energy efficiency, and human-swarm interaction, demonstrating both theoretical depth and real-world applicability. Eliseo Ferrante actively contributes to the academic community through editorial roles and conference organization, including serving as a co-organizer for IROS 2024 in Abu Dhabi. He has taught core computer science courses such as Data Structures , Machine Learning , Operating Systems , and the Capstone Project , mentoring students in research and development. His teaching spans from algorithm design to advanced machine learning and system-level programming. Ferrante leads research on large-scale robotic swarms, focusing on achieving a balance between scalability and controllability. His lab explores autonomous navigation in GPS-denied environments, swarm intelligence, and robust coordination in dynamic settings. He collaborates across disciplines, integrating insights from biology and physics into engineering solutions for real-world challenges.

Asieh Abolpour Mofradمشاهده پروفایل

پژوهشگر

• Machine Learning
• Neural Networks
• Associative Memory
• +۷ مورد دیگر

Asieh Abolpour Mofrad is a researcher at the Department of Informatics, University of Bergen (UiB), with a dual PhD background in behavioral sciences and informatics. She is currently engaged in the Retail Fresh project, leveraging machine learning to reduce food waste in the retail sector in collaboration with Link Retail. Previously, she contributed to the DRONE (Drug Repurposing for Neurological Diseases) project at the Department of Global Health and Public Health, UiB, where she applied machine learning to Norwegian health registry data to investigate Parkinson's disease treatments. Her academic credentials include two doctoral theses: one from OsloMet (2021) on the integration of behavior analysis and machine learning for modeling stimulus equivalence, and another from UiB (2021) on clique-based neural associative memories. Her research bridges artificial intelligence, cognitive modeling, neural networks, and public health applications. Her research interests span machine learning, neural associative memory, reinforcement learning, cognitive modeling, health informatics, and drug repurposing. She employs computational frameworks such as projective simulation and tournament-based neural networks to model complex cognitive and biological phenomena. The analysis of her recent publications reveals a consistent focus on machine learning applications in both cognitive science and healthcare. Her work includes modeling stimulus equivalence, designing neural memory architectures, solving stochastic optimization problems, and analyzing large-scale health data for neurological disease risk. These efforts reflect interdisciplinary innovation, combining theoretical computer science with real-world applications in psychology and medicine. Scientific Contributions: Developed the Enhanced Equivalence Projective Simulation (E-EPS) framework for modeling derived relations in behavior analysis. Designed tournament-based neural networks for efficient sequence storage and bidirectional retrieval. Applied machine learning to large-scale health registries to identify drug classes associated with Parkinson’s disease risk. Contributed to adaptive learning systems based on flow theory for educational optimization. She has advised no publicly listed students but collaborates extensively with researchers across institutions. Her work is supported by interdisciplinary research projects and she actively disseminates code via GitHub, particularly in Jupyter notebooks for reproducibility. She is affiliated with research teams in informatics and global health at UiB, contributing to both theoretical and applied machine learning initiatives. Laboratories and Research Teams: Department of Informatics, University of Bergen – Machine Learning and Neural Systems group. DRONE Project – Interdisciplinary team on drug repurposing using AI and health data. Retail Fresh Project – AI for sustainable retail, in collaboration with industry partners.