Find Academic Supervisors

• Dementia Diagnostics
• Neurodegenerative Disorders
• Cognitive Neuroscience
• +3 more

Dr. David Foxe is a post-doctoral researcher at the Brain and Mind Centre, University of Sydney, specializing in neurodegenerative diseases. His work focuses on improving diagnostic accuracy for rare dementias, particularly primary progressive aphasia (PPA), and enhancing clinical care for dementia patients. He holds a PhD from the University of Sydney, where his doctoral research investigated cognitive care for PPA patients. Research interests include dementia subtypes (e.g., Alzheimer’s, frontotemporal dementia), neuroimaging biomarkers, and neuropsychological assessment tools. Key contributions include developing the Box Task for PPA differentiation and validating the Addenbrooke’s Cognitive Examination III for clinical use. His work spans both basic neuroscience and translational clinical applications. Publications emphasize longitudinal studies of behavioral and cognitive changes in neurodegenerative disorders, with recent focus on regional dementia care in the Western Pacific. Awards include the 2025 BMC Emerging Researcher Award. Dr. Foxe collaborates widely in interdisciplinary teams, contributing to global health initiatives addressing dementia priorities. Grants include a 2023 project optimizing early dementia diagnosis and a 2025 award supporting emerging research. His lab, the FRONTIER Research Group, advances understanding of dementia mechanisms and clinical management strategies.

Svitlana KurinnaView profile

Research Fellow

• Wound Repair
• MicroRNA Research
• Epidermal Biology
• +3 more

Dr. Svitlana Kurinna is a Research Fellow in the Division of Cell Matrix Biology & Regenerative Medicine at the University of Manchester. Her work focuses on understanding microRNA (miRNA) mechanisms in wound repair and regenerative medicine. She leads projects investigating miR-29’s role in epidermal and skin regeneration, combining molecular biology with computational approaches. Education: PhD in Molecular Biology from University of Texas MD Anderson Cancer Center (2010), MSc in Biology from Taras Shevchenko National University of Kyiv (2001) Research Beacons: Manchester Regenerative Medicine Network, Digital Futures Research Interests: Dr. Kurinna’s studies explore miRNA regulation in skin repair, with emphasis on miR-29’s impact on keratinocytes and collagen production. Her team uses advanced techniques like miR-CLIP to identify novel RNA targets and employs in vivo/in vitro models to assess therapeutic potentials. Recent work integrates AI for automated wound analysis, enhancing histology interpretation. Publications highlight interdisciplinary approaches, combining molecular mechanisms with clinical implications. Key contributions include identifying laminin C2’s role in skin repair and elucidating Nrf2 pathways in cardiovascular health. Her work aligns with UN SDGs for health and innovation. Labs/Teams: Collaborates within the Manchester Regenerative Medicine Network, advancing therapies for cutaneous sclerosis and impaired wound healing.

Brendan KellyView profile

Associate Professor

• Cotton Fiber Phenomics
• Agricultural Biotechnology
• Plant Physiology
• +3 more

Brendan Kelly is an Associate Professor of Cotton Fiber Phenomics at Texas Tech University's Department of Plant and Soil Science, with a joint appointment at Texas A&M AgriLife Research. He leads the Cotton Phenomics Laboratory at the Fiber and Biopolymer Research Institute, focusing on improving cotton fiber quality through advanced data analysis and industry collaboration. His work integrates modern instrumentation and multivariate approaches to understand fiber variability and its impact on textile processing. Dr. Kelly holds a Ph.D. and bachelor’s degree in mathematics from Texas Tech University. His teaching includes foundational courses like PSS 100 (Freshman Seminar), PSS 1321 (Agronomic Plant Science), and specialized courses such as PSS 5370 (Cotton Fiber-Textile Industries). His research emphasizes cotton fiber elongation, genetic improvement, and the development of protocols for fiber quality evaluation. Key research areas include cotton fiber morphology, climate impacts on yield, and the application of artificial intelligence in phenotyping. His work bridges agricultural science with textile industry needs, aiming to enhance cotton’s utility as an industrial raw material. Collaborations with industry partners ensure practical applications of his findings. Labs and teams include the Cotton Phenomics Lab and affiliations with Texas Tech’s Fiber and Biopolymer Research Institute. Future work focuses on advancing sustainable cotton production and improving yarn quality through genetic and agronomic strategies.

• Digital Forensics
• Cybersecurity
• Blockchain Technology
• +3 more

Virginia Franqueira is a Lecturer in the Department of Electronics, Computing and Mathematics. Her research spans digital forensics, blockchain security, and trust management systems, with applications in vehicular networks, cloud computing, and cybercrime investigation. She develops analytical frameworks for emerging security challenges, including ransomware, IoT vulnerabilities, and multimedia content verification. Her publications address blockchain forensics, behavioral analysis in digital investigations, and machine learning applications for privacy verification. Recent work includes automated violence detection in video, PhotoDNA vulnerability assessments, and trust models for vehicular ad-hoc networks (VANETs). She actively contributes to forensic methodology standardization, particularly for indecent image of children (IIOC) cases. Dr. Franqueira designs educational materials on IoT prototyping and security, emphasizing practical applications for students and professionals.

Aparna BharatiView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Media Forensics
• +3 more

Aparna Bharati serves as an Assistant Professor in the Department of Computer Science and Engineering at Lehigh University's P.C. Rossin College of Engineering and Applied Science. She leads the Computer Vision Research Group and maintains critical affiliations with the AIRLab, I-DISC, and NHI Initiatives, driving interdisciplinary collaboration in artificial intelligence and data science. Education: Ph.D. in Computer Science, University of Notre Dame (2020) B.Tech. in Computer Science and Engineering, Indraprastha Institute of Information Technology, India (2015) Research Focus: Dr. Bharati pioneers work at the intersection of Computer Vision, Machine Learning, and Media Forensics, with deep specialization in Biometrics and Pattern Recognition. Her research develops advanced algorithms for detecting image manipulation, establishing media provenance, and mitigating security vulnerabilities in AI-generated content, directly addressing critical challenges in digital trust and authentication systems. Publication Trends: Analysis of her 15 most recent publications (2023-2025) reveals an intense focus on generative AI threats and defenses. Key trajectories include negation understanding in multimodal foundation models, identity-based provenance tracking for synthetic media, and perturbation-resistant image protection mechanisms. Her work consistently targets emerging vulnerabilities in deepfakes, style transfer, and neural compression while advancing forensic methodologies for next-generation image analysis. Leadership & Grants: Dr. Bharati directs the Computer Vision Research Group while mentoring graduate students in cutting-edge media forensics projects. Her research has been supported by high-impact initiatives including the DARPA MediFor program during her doctoral work at Notre Dame. She actively shapes the field through leadership roles on the IEEE Biometric Council Editorial Board and PAMI-TC Executive Committee. Research Ecosystem: Within Lehigh's collaborative infrastructure, she leverages AIRLab for AI innovation, I-DISC for data-intensive computing resources, and NHI for health informatics applications. This tripartite affiliation enables her to bridge theoretical computer vision with real-world security and healthcare implementations through cross-departmental team science.

Stephanie SchwartzView profile

Professor

• Machine Learning
• Data Science
• User Modeling
• +4 more

Dr. Stephanie Schwartz is a full-time Professor in the Department of Computer Science at Millersville University since Fall 2003. With a Ph.D. (2006) and M.S. (1993) from the University of Delaware and a B.S. (1991) from Shippensburg University, her academic background is rooted in computer science. Former industry experience at MapQuest, AMP, and Primavera Software bridges theoretical research with practical applications. Education: Ph.D. in Computer Science, University of Delaware (2006) M.S. in Computer Science, University of Delaware (1993) B.S. in Computer Science, Shippensburg University (1991) Her research focuses on machine learning , data science , user modeling , cognitive modeling , and artificial intelligence , with a strong emphasis on information graphics accessibility for visually impaired users. Recent publications highlight her work in cybersecurity (code reuse attacks, phishing detection) and visual analytics (chart interpretation, multimodal document processing). Dr. Schwartz leads cross-disciplinary collaborations through projects like the Software Productization Center , focusing on automated understanding of bar charts, line graphs, and Bayesian network applications. Her research trends since 2010 show sustained innovation in accessible visualization and machine learning for security . Scientific Awards: James Chen Annual Award for Best Journal Article (2007) She has contributed to information graphics accessibility , developing systems like Interactive SIGHT for textual summaries of charts. Her work spans educational robotics , document summarization , and user preference modeling in collaborative environments.

Anastasios MazisView profile

Researcher

• Remote Sensing
• Plant Ecophysiology
• Invasive Species Management
• +3 more

Anastasios Mazis is a Postdoctoral Researcher at the University of California, Merced, specializing in plant ecophysiology and invasive species management. His work focuses on remote sensing applications in diverse ecosystems, including forests, grasslands, and wetlands, leveraging advanced techniques like multispectral imaging, LIDAR, and high-throughput phenotyping. He holds a Ph.D. in Applied Ecology from the University of Nebraska-Lincoln and a B.S. in Crop Science and Ecology from Aristotle University of Thessaloniki. Key research interests include the use of big data analytics and programming (R, Python, MATLAB) to analyze plant responses to environmental stressors, particularly drought. His projects span topics such as invasive species impact assessment, precision agriculture, and ecosystem engineering effects in delta regions. Recent work includes studies on smooth bromegrass pasture management, soybean phenotyping via satellite imagery, and the ecophysiological effects of juniper encroachment in semiarid grasslands. Dr. Mazis collaborates on initiatives like the Nebraska SIF Campaign, integrating field experiments with remote sensing to understand ecosystem dynamics. His methodologies often combine traditional ecological approaches with cutting-edge technologies to address challenges in agricultural sustainability and environmental conservation.

Antonio NorelliView profile

Research Fellow

• Artificial Intelligence
• Deep Learning
• Explainable AI
• +3 more

Antonio Norelli is a Research Fellow at Oxford University and a PhD student in Computer Science at Sapienza University of Rome, specializing in AI and deep learning within the GLADIA research group. His academic journey includes a BSc in Physics, an MSc in Computer Science, and the SSAS interdisciplinary honors program. He has held research internships at Spiketrap (San Francisco) and Amazon Lablet in Tübingen. His research focuses on bridging the gap between human and artificial intelligence, emphasizing symbolic reasoning, explainable AI, and multimodal learning. Notable contributions include the ASIF framework for unsupervised multimodal alignment and Olivaw, an Othello-playing AI that achieves expert-level performance without human knowledge. His key research interests span artificial scientific discovery, neural network explainability, and the theoretical foundations of intelligence. Recent work explores relative representation learning and zero-shot latent space communication, with applications in cross-modal retrieval and efficient model adaptation. Norelli’s interdisciplinary approach combines insights from philosophy of science (Popperian critical rationalism) with machine learning to develop systems capable of symbolic reasoning and hypothesis generation. Publications highlight advancements in: (1) Multimodal learning without training through coupled data alignment, (2) Game AI strategies for Othello using minimal resources, (3) Neuro-symbolic architectures for interpretable decision-making. His work has been presented at NeurIPS and IEEE Transactions on Games, among others. Current efforts aim to formalize intelligence as information-processing mechanisms grounded in symbol manipulation and semantic alignment.

• Autonomous Vehicles
• Computer Vision
• Intelligent Transportation Systems
• +5 more

Jose Maria Armingol Moreno is a Full Professor at the University Carlos III of Madrid, affiliated with the Department of Systems Engineering and Automation. He serves as Director of the Master's Degree in Internet of Things: Applied Technologies and leads the Intelligent Systems Laboratory research group within the Duque de Santomauro Institute of Motor Vehicle Safety. His expertise spans autonomous systems, computer vision, robotics, and intelligent transportation infrastructure. Key research interests include vehicle safety, sensor fusion, and deep learning applications in traffic monitoring and autonomous navigation. He has authored over 100 peer-reviewed articles, with recent work focusing on 3D vehicle detection, UAV battery systems, and intelligent infrastructure for smart cities. Dr. Armingol has also led numerous projects on autonomous vehicle development, cooperative driving systems, and traffic safety technologies. His contributions include patented innovations in vehicle inspection and collision avoidance systems.

Nikolas FrancisView profile

Assistant Professor

• Auditory Neuroscience
• Cortical Processing
• Perceptual Decision-Making
• +2 more

Nikolas Francis is Assistant Professor in the Department of Biology at University of Maryland and member of the Brain and Behavior Institute. His research investigates neural mechanisms of auditory perception and decision-making using in vivo electrophysiology, 2-photon imaging, and automated behavioral paradigms in mice. He develops computational approaches to analyze cortical network dynamics during sensory-guided behavior. Education: PhD, Massachusetts Institute of Technology (2011) BA, University of Iowa (2003) His lab examines how auditory cortex represents and transforms sound information during perceptual tasks, focusing on neural coding principles, attention mechanisms, and decision processes. Recent work explores how psilocybin modulates cortical processing while preserving basic auditory representations. He has developed automated home-cage systems for longitudinal behavioral monitoring that reveal circadian patterns in task engagement. Analysis of his publications shows emphasis on information coding strategies across cortical layers, with recent work examining network-level information transfer and psychedelic modulation of sensory processing. His NIH-funded research combines neurophysiological recordings with computational modeling to understand neural sequence generation during decision-making. He teaches courses in neurobiology and mentors graduate students through UMD's Neuroscience and Cognitive Science program. His lab maintains collaborations with machine learning researchers developing novel analysis methods for neural ensemble data.

Stefan RaunserView profile

Professor

• Structural Biochemistry
• Cryo-Electron Microscopy (Cryo-EM)
• Cryo-Electron Tomography (Cryo-ET)
• +4 more

Prof. Dr. Stefan Raunser is a Director at the Max Planck Institute of Molecular Physiology, leading the Department of Structural Biochemistry in Dortmund, Germany. His research focuses on high-resolution structural studies of proteins and complexes using cryo-electron microscopy (Cryo-EM) and tomography (Cryo-ET), with major contributions to understanding toxin-mediated membrane permeation, actin cytoskeleton dynamics, and muscle sarcomere architecture. Key research areas: Cryo-EM/ET methodology, actin-myosin interactions, Tc toxin mechanisms, and in situ structural biology. His group developed widely used software tools like SPHIRE-crYOLO and TomoTwin for automated particle picking and structural data mining. Recent publications highlight structural insights into Tc toxin injection mechanisms, actin filament turnover, and sarcomere organization. His work bridges molecular structure with functional biology, particularly in muscle physiology and bacterial pathogenesis. Despite extensive collaborations, no specific student names or scientific awards are explicitly mentioned in the provided text. The department emphasizes technological innovation to advance biomedical and cellular research.

Philipp FleckView profile

Researcher

• Augmented Reality
• Virtual Reality
• Computer Graphics
• +4 more

Philipp Fleck is a researcher at the Institute of Visual Computing at Graz University of Technology (TU Graz). His work focuses on augmented reality (AR), virtual reality (VR), and computer graphics, with applications in industrial systems, medical imaging, and situated analytics. He has developed tools like CECILIA and RagRug for game content exploration and spatial data analysis. Research Highlights: AR for heavy machinery safety using laser projections Thermochromic temperature sensing for cost-efficient thermal imaging Compact World Anchors for large-scale localization Situated analytics frameworks for physical-digital integration Scientific Contributions: His publications span 3D reconstruction, SLAM, image processing, and IoT-ready XR-WebApps. Awards include Best Paper at VISAPP 2020 and Honorable Mention at IEEE VR 2024.

• Solar Physics
• Space Weather
• Magnetic Fields
• +6 more

Haimin Wang is a Professor in the Department of Physics at New Jersey Institute of Technology (NJIT). He holds dual roles as Director of the Institute of Space Weather Sciences and Distinguished Professor at the Ctr for Solar-Terrestrial Research. His research focuses on solar dynamics, space weather, and solar magnetic field studies. He leads multiple NSF-funded projects exploring solar eruptions, magnetic flux ropes, and AI-driven space weather forecasting. Roles: Director (Institute of Space Weather Sciences), Distinguished Professor (Physics) Key Affiliations: NJIT, Big Bear Solar Observatory (BBSO), Daniel K. Inouye Solar Telescope (DKIST) Notable Grants: 33 federal grants since 2001, including NSF-funded projects on AI for space weather and high-resolution solar observations Research Interests: Solar flares, coronal mass ejections, magnetic flux ropes, solar wind dynamics, and machine learning applications in heliophysics. His work combines observational data from instruments like SDO/HMI and GST with advanced computational models to understand explosive solar phenomena. Recent studies include predicting halo CMEs using transformer models and analyzing small-scale magnetic structures in the solar wind. Collaborations: International partnerships in solar physics, including joint projects with researchers in magnetic field analysis, solar eruption dynamics, and space environment forecasting. Key Projects: CyberTraining AI initiative, DKIST Critical Science Program, SHINE solar flare studies Labs/Teams: Institute of Space Weather Sciences, NJIT Space Weather Lab

• Cyber Security
• Digital Forensics
• IoT Security
• +5 more

Madhu Khurana is a Lecturer in the Department of Digital Forensics and Cyber Security at the University of South Wales, affiliated with the Faculty of Computing, Engineering and Science. Her research focuses on cybersecurity, IoT security, AI-driven threat detection, and educational technology. Key areas include malware analysis, blockchain in IoT ecosystems, and network security challenges in encrypted environments. Her work spans technical innovation and interdisciplinary applications, such as using deep learning for deepfake detection and exploring gamification in cybersecurity education. She has conducted studies on simulation-based learning methodologies and the impact of digital marketing strategies on management practices. Notable projects include autonomic cloudlet management systems and partial confirmatory factor analysis in green marketing. Publications highlight her contributions to understanding IoT security risks, evaluating threat modeling approaches, and analyzing the societal impact of media and technology. Her research bridges theoretical frameworks with practical solutions for modern cybersecurity challenges.

István SárándiView profile

Researcher

• 3D Human Pose Estimation
• Computer Vision
• Machine Learning
• +4 more

István Sárándi is a postdoctoral researcher in the Real Virtual Humans group led by Prof. Gerard Pons-Moll at the University of Tübingen, Germany. He holds a PhD in Computer Vision from RWTH Aachen University (2023) and a master’s degree in Computer Science from RWTH Aachen (2015), with earlier studies at the Budapest University of Technology and Economics (2011). His research focuses on robust and efficient 3D human pose estimation, including work on truncation-robust methods (MeTRAbs), synthetic data generation (STAGE), and cross-dataset learning. He has won 3D pose estimation competitions at ECCV 2018 and 2020 and received Outstanding Reviewer Awards at CVPR 2021 and 2022. His PhD was funded by the Bosch Research Foundation. Education: PhD: RWTH Aachen University (2023) MSc: RWTH Aachen (2015) BEng: Budapest University of Technology and Economics (2011) Key Achievements: 1st place in ECCV 2018 and 2020 3D pose challenges Outstanding Reviewer Awards (CVPR 2021/2022) Developed MeTRAbs, STAGE, and other influential methods Publications emphasize advancing 3D human understanding through robust learning techniques, synthetic data, and multi-task frameworks. Supervised theses include work on temporal modeling of multi-person interactions and pose-conditioned human image synthesis. Active in teaching, including deep learning and computer vision courses at RWTH Aachen.