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Shuvra S. BhattacharyyaView profile

Professor

• Signal Processing
• Embedded Systems
• Biomedical Engineering
• +3 more

Shuvra S. Bhattacharyya is a Professor at the University of Maryland, College Park (UMD), Department of Electrical and Computer Engineering, with a joint appointment in UMIACS. He is also a part-time visiting professor at Tampere University (Finland) and INSA Rennes (France). His research focuses on signal processing systems, embedded systems, biomedical engineering, and machine learning. He has authored 7 books and over 300 papers, and directs the Maryland DSPCAD Research Group. He holds an NSF Career Award and is an IEEE Fellow. His honors include the Fulbright Senior Specialist and FiDiPro Professorship. He advises graduate students and leads projects in neural decoding, cyber-physical systems, and criminal justice AI. Education: B.S., University of Wisconsin-Madison Ph.D., University of California, Berkeley Research Interests: Signal processing architectures, biomedical circuits, embedded software, hardware/software co-design, and AI for healthcare and criminal justice. His work emphasizes real-time systems, energy efficiency, and interdisciplinary applications. Grants & Awards: NIH Grant for real-time neural decoding (2023) IEEE Fellow (2011) Jimmy Lin Entrepreneurship Award (2012) Labs & Teams: Maryland DSPCAD Research Group, collaborating on dynamic data-driven systems, neural interface technologies, and AI ethics in criminal justice. His work integrates academic and industrial partnerships, including roles at Hitachi America and Kuck & Associates.

Manuel Gonzalez RiveroView profile

Research Fellow

• Marine Ecology
• Climate Change
• Coral Reef Monitoring
• +6 more

Dr. Manuel Gonzalez Rivero is a Research Fellow and Team Leader of Reef Process Dynamics at the Australian Institute of Marine Science (AIMS). His work focuses on multidisciplinary approaches to understand coral reef ecosystem dynamics, combining ecological theory, mathematical modeling, and advanced technologies like AI and 3D mapping. He leads initiatives integrating global coral reef monitoring with climate change impact assessments. Education: PhD in Biological Sciences (University of Exeter, UK, 2012), BSc in Biological Sciences (Universidad Central de Venezuela, 2005). Research Interests: Spatial and disturbance ecology of coral reefs Climate change effects on reef resilience Automated coral reef monitoring using AI and autonomous systems Long-term trajectories of reef communities Technological advancements in habitat mapping and 3D modeling Publications highlight contributions to coral reef recovery dynamics, AI-driven monitoring, and global bleaching impacts. His work emphasizes scalable solutions for reef conservation amid climate pressures. Grants and Labs: Active in AIMS-led projects, including XL Catlin Seaview Survey and Reef 2050 monitoring. Collaborates with institutions like the University of Queensland and UNESCO.

Tim WelshView profile

Professor

• Perception
• Cognition
• Cognitive Neuroscience
• +6 more

Dr. Timothy Welsh is a full-time Professor in the Action & Attention Lab at the University of Toronto's Faculty of Kinesiology & Physical Education , where he also serves as Associate Dean, Research since 2023. With cross-appointments from the Faculty of Physical Education and Health, his work bridges cognitive neuroscience and motor behavior research. Academic Background Ph.D. in Human Biodynamics (McMaster University, 2002) M.Sc. in Human Biodynamics (McMaster University, 1999) B.P.H.E. in Physical and Health Education (University of Toronto, 1997) Dr. Welsh's research explores neural mechanisms of goal-directed actions across typical and neurodiverse populations. His lab investigates joint action dynamics , self-objectification effects on motor performance , and cross-reality sensorimotor adaptation , with significant focus on autism and developmental coordination disorder. Recent publications (2024-2025) reveal systematic differences in motor imagery between autistic and non-autistic adults , gender-specific learning patterns , and cross-modal sensory integration challenges in mixed reality environments . These studies demonstrate his lab's expertise in combining behavioral experimentation , EEG analysis , and computational tool development like TAT-HUM. Scientific Recognition Franklin Henry Young Scientist Award Early Distinguished Career Award (NASSP) Outstanding Graduate Supervision Award As principal investigator on 15 active grants (2015-2027), including projects on digital health platforms for concussion rehabilitation and AI-assisted VR learning modules , Welsh's work impacts both basic science and applied interventions. His lab trains students at all levels through KPE 362 (undergraduate) and KIN 5514 (graduate) courses.

• Optimization methods
• Metaheuristics
• Heuristics
• +12 more

Mina Mirhosseini is a Research Fellow at the Faculty of Logistics, Molde University College, Norway. She holds a PhD in Computer Science from Shahid Beheshti University of Tehran, Iran, and has prior academic experience as a faculty member and lecturer in Iran and as a remote teaching assistant at the University of Hertfordshire, UK. Her primary research interests include Optimization Methods, Metaheuristics, Heuristics, Linear Integer Programming, Parallel Processing, Machine Learning, Artificial Intelligence, and Logistics. She has made significant contributions to solving complex computational problems such as the n-similarity problem and Mixed Integer Linear Programming (MILP) models using hybrid and parallel algorithms, particularly in the context of high-level synthesis and wireless sensor networks. The analysis of her recent publications reveals a strong focus on developing and applying advanced optimization techniques, especially quantum-inspired gravitational search algorithms and parallel genetic algorithms, to real-world engineering and computational challenges. Her work consistently emphasizes performance improvement, scalability, and load balancing in distributed and heterogeneous computing environments. Mina Mirhosseini has an extensive publication record in high-impact journals such as IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel and Distributed Computing, Journal of Supercomputing, and Computers and Electrical Engineering. Her research has been published across a range of venues, reflecting interdisciplinary work at the intersection of computer science, electrical engineering, and applied optimization. She has actively contributed to the academic community through roles such as program committee member and executive committee member for conferences on fuzzy systems, swarm intelligence, and evolutionary computation. Her academic journey includes teaching and research roles in Iran, demonstrating a sustained commitment to higher education and scientific inquiry. Mina Mirhosseini is part of the research group focused on Planning, Optimization and Decision Support at Molde University College. Her current work continues to advance the state-of-the-art in parallel and metaheuristic optimization methods, with applications in logistics, synthesis, and sensor network design.

• Library and Information Science
• Bibliometrics
• Information Behavior
• +5 more

Brady D. Lund is an active researcher in Library and Information Science with significant contributions to bibliometrics, information behavior, and the ethical implications of artificial intelligence in scholarly communication. His research spans multiple domains including data science ethics, information literacy, and public library services, with particular attention to how digital technologies impact information practices across diverse populations. Lund's research interests focus on the intersection of information science and emerging technologies, particularly examining how AI systems affect scholarly publishing practices, research ethics, and information behavior. His work on ChatGPT's impact on academic writing and AI authorship policies has been particularly influential in library and information science circles. He also investigates information literacy among vulnerable populations, including rural older adults during the pandemic, highlighting the critical role of libraries in community information ecosystems. Analysis of Lund's recent publications reveals a clear trajectory toward examining the ethical dimensions of AI in information contexts, with increasing focus on transparency frameworks, data privacy, and the practical implementation of AI systems in professional settings. His collaborative work spans multiple disciplines, connecting information science with healthcare, cybersecurity, and public policy. Lund demonstrates strong collaborative patterns, frequently working with Ting Wang and Nishith Reddy Mannuru across multiple publications. His research shows methodological diversity, employing both quantitative bibliometric approaches and qualitative investigations of information behavior. While specific grant funding isn't detailed in the publication records, his work addresses significant societal challenges related to information access, digital equity, and ethical technology implementation.

• Developmental Psychology
• Cognitive Neuroscience
• Memory Processes
• +3 more

Myriam C. Sander is a faculty member at the International Max Planck Research School on the Life Course (LIFE) and the Berlin School of Mind and Brain, affiliated with the Max Planck Institute for Human Development. Her research focuses on lifespan changes in memory processes, neural oscillations, and cognitive aging. PhD in Natural Sciences (2011), Humboldt University Berlin MSc in Psychology (2007), Humboldt University Berlin Her research interests include: Development of perception and memory across the lifespan Working and episodic memory mechanisms Neuronal correlates of aging (oscillations and activity patterns) Multivariate MRI/EEG methods Her recent publications analyze age-related differences in: Theta-gamma coupling precision Neural pattern similarity Alpha/beta desynchronization Memory consolidation during sleep Cortical and hippocampal structural integrity She advises doctoral students including Charlotte Pauley and Anna E. Karlsson.

• Developmental Psychology
• Language Acquisition
• Cognitive Neuroscience
• +5 more

Claudia Friedrich is a Professor of Developmental Psychology at the University of Tübingen, Faculty of Mathematics and Natural Sciences, Department of Psychology, where she has held her position since April 2013. Her research focuses on the cognitive processes underlying language development in children, with particular emphasis on speech processing, word recognition, and the acquisition of linguistic structures. Her educational background includes a doctorate in Psychology from the University of Leipzig (2003) and undergraduate studies in Psychology at the Technical University of Berlin (1993-1999). Professor Friedrich's research examines how children process speech sounds, word stress patterns, and develop the ability to understand language in context. Her work investigates cognitive and neural mechanisms involved in language acquisition across different age groups, with special attention to how first and second language learners process linguistic information. Through eye-tracking, ERP, and behavioral experiments, her research explores how children develop the ability to take perspectives during communication, process prosodic features of speech, and build lexical representations. She has made significant contributions to understanding how literacy acquisition shapes speech processing and how cross-modal influences affect language development. Her recent publications reveal a consistent focus on developmental trajectories in language processing, with increasing attention to cross-linguistic comparisons and multimodal aspects of language acquisition. The research demonstrates sophisticated methodological approaches combining behavioral, eye-tracking, and neurophysiological measures to investigate language processing from infancy through childhood. Professor Friedrich leads several major research projects including Project A6 "The Development of Common Ground in First and Second Language Acquisition" (2025-2029), a DFG-funded project on "Stress processing at the word level in children with different language backgrounds" (2024-2027), and previously led Project B1 on modal and amodal cognition (2020-2023). Her research has been consistently supported by prestigious funding bodies including the European Research Council (ERC) and the German Research Foundation (DFG). She heads the Developmental Psychology workspace at the University of Tübingen, where her research group investigates cognitive prerequisites for perspective-taking in language comprehension and the influence of perspective taking during language processing through memory experiments and eye movement studies.

Alistair PooreView profile

Professor

• Marine Ecology
• Evolutionary Biology
• Coastal Marine Ecosystems
• +3 more

Professor Alistair Poore serves as Head of the School of Biological, Earth and Environmental Sciences at the University of New South Wales. With a PhD from UNSW (1999) and BSc(Hons) from Monash University (1992), he has established himself as a leading marine ecologist focusing on coastal ecosystems. His research program spans multiple interconnected themes: Ecology and evolution of interactions among marine organisms Impacts of herbivores on plant communities Human impacts on coastal marine ecosystems Adaptation of marine organisms to environmental stress Use of citizen science data for biodiversity monitoring Professor Poore's recent publications demonstrate a strong focus on threatened seagrass ecosystems, particularly Posidonia australis, examining responses to climate change, tropicalization, and physical disturbance. His work increasingly integrates citizen science approaches, using recreational diver photographs and other unconventional data sources for ecological monitoring. The research shows a progression from fundamental ecological theory toward applied conservation solutions, with extensive international collaborations across Australian, Pacific, and global marine environments. Scientific recognition includes: Elected Fellow of the Royal Society of New South Wales (2020) Provasoli Award from the Phycological Society of America (2014) Citation for Outstanding Contributions to Student Learning (2013) UNSW Vice Chancellor's Award for Teaching Excellence (2012) Professor Poore actively supervises PhD students working on diverse marine topics including amphibian detection using DNA, bluebottle behavior, artificial light impacts, seagrass restoration, and reef monitoring. His teaching portfolio includes BEES2041 Data Analysis for Life and Earth Scientists, BIOS2031 Biology of Invertebrates, and BIOS3091 Marine and Aquatic Ecology, complemented by freely available Environmental Computing tutorials. His research bridges fundamental ecological questions with practical conservation applications, particularly in Australian coastal environments, with growing emphasis on how climate change reshapes marine biodiversity and ecosystem function through processes like tropicalization and habitat fragmentation.

Ilker HacihalilogluView profile

Associate Professor

• Artificial Intelligence in Healthcare
• Medical Image Processing
• Machine Learning Applications
• +8 more

Dr. Ilker Hacihaliloglu is an Associate Professor in the Department of Radiology and Medicine at the University of British Columbia's Faculty of Medicine. He holds a PhD from UBC (2010) and has held faculty positions at Rutgers University (2014-2021). His research focuses on applying machine learning to medical imaging, aiming to bridge clinical and engineering research for practical healthcare solutions. Key areas include AI-driven diagnostics, ultrasound imaging, and personalized medicine. Education: B.Sc. and M.Sc., Istanbul Technical University (2001, 2004) Ph.D., University of British Columbia (2010) Postdoctoral Fellow, Vancouver General Hospital (2010-2013) Research Interests: Artificial Intelligence in Medical Imaging Ultrasound-Based Diagnostics Image-Guided Surgery Computer-Aided Diagnostics for Orthopedics and Neurosurgery His work emphasizes explainable AI, standardization in machine learning metrics, and clinical translation of technologies. Scientific Contributions: Over 35 peer-reviewed articles (2019-2025) highlight innovations in medical image segmentation, AI-driven radiomics, and cross-modality imaging. Recent trends focus on synthetic data augmentation, diffusion models for medical imaging, and curriculum development in AI for healthcare. Grants and Advising: No explicit grants mentioned, but active mentorship in interdisciplinary research teams. Collaborations span clinicians, engineers, and computer scientists to address real-world healthcare challenges. Labs/Teams: Engaged in computational ultrasound and AI-driven diagnostics initiatives, though specific lab names are not provided in the text.

Richard GuestView profile

Professor

• Biometric Systems
• Forensic Technologies
• Applied Artificial Intelligence
• +3 more

Richard Guest is a Professor of Biometric Technologies at the University of Southampton's School of Electronics and Computer Science. He holds an Honorary Professorship at the University of Kent and a Visiting Researcher position at Purdue University. His expertise spans biometric systems, forensic technologies, and ethical AI applications. Previously, he served as Deputy Divisional Director and Head of Engineering at the University of Kent before joining Southampton in 2024. Education: BEng (Hons) in Computer Science (University of York, 1995), PhD in Electronic Engineering (University of Kent, 2000). Research focuses on applied AI, biometric security, forensic image analysis, and privacy-preserving techniques. He leads ISO/IEC standardization efforts for biometric data interchange and advises the UK Home Office on biometric ethics. His work includes mobile facial recognition systems and explainable AI for legal settings. Publications span mobile biometric verification, forensic verification tools, and transformer-based AI models. He has secured over £5M in research funding and supervises PhD students in Computer Science. Awards include Fellowships from the British Computer Society and IET, Chartered Engineer status, and membership in EPSRC's Peer Review College.

Sharib AliView profile

Assistant Professor

• Biomedical Image Analysis
• Computer Vision
• Machine Learning
• +12 more

Dr. Sharib Ali is a Lecturer (Assistant Professor) in the School of Computer Science at the University of Leeds, Faculty of Engineering and Physical Sciences. He is affiliated with the Leeds Cancer Research Centre and actively contributes to research in biomedical image analysis and computer vision. His work bridges cutting-edge AI with clinical applications, particularly in endoscopy and surgical technologies. PhD in Medical Image Analysis, University of Lorraine, France MSc in Computer Vision (by research), University of Burgundy, France Dr. Ali's research focuses on biomedical image analysis , computer vision , and machine learning , with applications in early cancer detection , computational endoscopy , and 3D reconstruction . He develops robust algorithms for segmentation, registration, depth estimation, and mosaicking, using both classical mathematical models and deep learning. His work emphasizes translational research and generalisability in real-world clinical settings. The recent publications highlight a strong trend in generalisability assessment , multi-modal data fusion , and AI benchmarking in endoscopy. His work spans from foundational algorithm development to clinical deployment, including federated learning , mixed reality in surgery , and multi-centre datasets , addressing key challenges like bias, data imbalance, and privacy. Dr. Ali has co-supervised multiple DPhil/PhD students and currently supervises several PhD candidates at the University of Leeds, University of Oxford, and Tec de Monterrey. He is actively involved in securing research funding and leading projects such as Leveraging multi-modality data for targeted biopsy and Federated learning in healthcare . He is a founding member of NAAMII, Nepal, where he volunteers to train students from LMICs. He also organizes international research initiatives including the EndoCV and P2ILF challenges at MICCAI, and serves on program committees and as a reviewer for journals like Nature Communications and Medical Image Analysis . His research is conducted within interdisciplinary teams, collaborating with clinicians from Oxford NHS University Hospitals, neuroscientists at Forschungszentrum Jülich, and engineers across Europe. He leads the development of open tools and datasets to advance the field of endoscopic computer vision.

Georgios BardisView profile

Assistant Professor

• Intelligent Computer Graphics
• Declarative Modeling
• Multicriteria Decision Analysis
• +4 more

Georgios Bardis is a permanent Assistant Professor at the Department of Informatics and Computer Engineering , School of Engineering , University of West Attica . He holds a PhD in Informatics from University of Limoges (2006), an MSc in Software Systems from University of California, Santa Barbara (1994), and a Diploma in Computer Engineering & Informatics from University of Patras (1992). His career spans multiple academic roles, including Lecturer at University of West Attica (2018-2021) and Professor of Applications at TEI of Athens (2010-2018). Research Interests : Focus on Intelligent Computer Graphics , Declarative Modeling , and Multicriteria Decision Analysis . His work integrates AI into 3D scene synthesis, urban planning, and semantic decision systems. Awards : Master Microsoft Office Specialist (MOS), 2003 NAT Scholarships for Academic Excellence (1989-1992) 1984 Monetary Prize from Hellenic Mathematical Society Leadership : Member of AKIIS Research Lab (University of West Attica), Editorial Board of International Journal of Systems Biology and Biomedical Technologies , and Reviewer for International Journal of Digital Earth . Publications : 5 peer-reviewed journals, 2 books, 8 book chapters, and 22 conference papers. Key areas include WebGL avatars, urban data analysis, and 3D modeling with AI.

Jason RaymondView profile

Academic

• Therapeutic Ultrasound
• Focused Ultrasound Surgery
• High-Intensity Focused Ultrasound (HIFU)
• +12 more

Jason Raymond, Ph.D., is a Research Assistant Professor at the Fralin Biomedical Research Institute (FBRI) at Virginia Tech-Carilion, where he also serves as the Focused Ultrasound Core Manager. He leads advanced research in therapeutic ultrasound and manages state-of-the-art facilities including MRI-guided focused ultrasound systems and a 9.4T small-bore MR-imaging platform. His work supports both preclinical and clinical trials in focused ultrasound applications. Ph.D. in Biomedical Engineering, University of Cincinnati Postdoctoral Research Fellow, University of Oxford Lecturer and Senior Research Associate, Department of Engineering Science, University of Oxford Junior Research Fellow, Kellogg College, Oxford B.S. and M.S. in Engineering Acoustics and Mechanical Engineering, Boston University Dr. Raymond’s research focuses on biomedical therapeutic ultrasound, with applications in drug delivery, blood-brain barrier opening, high-intensity focused ultrasound (HIFU) ablation, and acoustic cavitation. His expertise also extends to ultrasound contrast agents, photoacoustic imaging, and the physical interactions of sound and light in biological tissues. He has pioneered work in sonochemistry, microbubble dynamics, and non-invasive neuromodulation. His recent publications reveal a strong trend in leveraging ultrasound for chemical and biological applications, including sonochemical degradation, hydroxyl radical monitoring, and genetic engineering of biofilms. His work integrates acoustics, chemistry, and biomedical engineering to develop novel therapeutic and diagnostic tools. 38th F.V. Hunt Postdoctoral Research Fellowship, Acoustical Society of America Whitaker International Fellowship, Thoraxcenter–Erasmus Medical Center Junior Research Fellowship, Kellogg College, Oxford Dr. Raymond has been instrumental in establishing physical acoustics laboratories and has contributed to major advancements in focused ultrasound technology. He actively mentors researchers, supports collaborative projects across Virginia Tech, and provides technical guidance for industry and academic partners. His leadership in core facilities enables broad access to cutting-edge ultrasound technologies. He manages the Focused Ultrasound Technical Facilities at FBRI, which include a clinical transcranial MRI-guided focused ultrasound system (InSightec and Siemens) and a Bruker 9.4T MR system for small animal studies. These labs support research in non-invasive surgery, hyperthermia, ablation, and blood-brain barrier modulation.

• Computer Vision
• Pattern Recognition
• Facial Analysis
• +5 more

Jorge Manuel M. C. Pereira Batista is an Associate Professor at the Department of Electrical and Computer Engineering , University of Coimbra, Portugal. He serves as a senior researcher at the Institute for Systems and Robotics (ISR-UC) and leads the Computer Vision Group. His career spans academic roles, research coordination, and industry collaboration. Academic Affiliation: University of Coimbra (Electrical & Computer Engineering Department) Research Institute: Institute for Systems and Robotics (ISR-UC) His research focuses on Computer Vision , Pattern Recognition , and applications of Differential Geometry in these fields. Key subdomains include facial analysis, visual surveillance, real-time vision systems, and machine learning integration. From 2010–2023, his recent publications highlight advancements in probabilistic models , transformer architectures , multi-branch learning , and biomimetic robotics . He has coordinated multiple funded projects such as: STORK : Avian protection systems NeuroCity : Intelligent street lighting 4D Facial Dynamics : Identity recognition iTRAFFIC : BRISA highway traffic monitoring His work bridges theoretical innovation (e.g., Riemannian manifold applications) with practical deployments in transportation, energy, and healthcare sectors.

John Darrell Van HornView profile

Professor

• Human Neuroimaging
• Brain Health and Disease Biomarkers
• Computational Neuroscience
• +12 more

John Darrell Van Horn is a Professor at the University of Virginia, holding dual appointments in the School of Data Science and the Department of Psychology . He joined UVA in 2019 and is actively engaged in research and teaching, including mentoring capstone projects in the M.S. in Data Science program and teaching brain mapping with MRI. Education: Ph.D. in Psychology, University of London M.S. in Electrical and Computer Engineering, University of Maryland, College Park B.A. in Psychology, Eastern Washington University Dr. Van Horn's research centers on human neuroimaging and the application of data science to understand brain health and disease. His work integrates high-performance computation , multivariate statistics , network theory , and image processing to extract meaningful patterns and biomarkers from brain imaging data. He is a strong advocate for FAIR data principles and open science , promoting transparency and reproducibility in neuroscience research. His publications span top journals including Science , Nature , Nature Neuroscience , NeuroImage , and Biological Psychiatry , reflecting a sustained impact in the fields of neuroscience and data science. The body of work demonstrates a consistent focus on computational modeling of brain structure and function, with applications in psychiatry and cognitive neuroscience. Scientific Recognition: Fellow, Organization for Human Brain Mapping (OHBM) Dr. Van Horn has served as a mentor for capstone research projects in the M.S. in Data Science program. While specific grant funding is not detailed, his research has been widely disseminated and featured in both academic and popular media, indicating successful funding and recognition. His prior academic roles at UCLA, USC, Dartmouth, and NIH fellowships underscore a distinguished career trajectory. He is affiliated with the broader neuroscience and data science communities through publications, open science advocacy, and professional recognition. His lab or research group is not explicitly named, but his work clearly involves advanced computational analysis of neuroimaging datasets, likely involving collaborative teams focused on brain mapping and data sharing initiatives.