Find Academic Supervisors

• Machine-learning-aided Antenna/Circuit Design
• Data-Driven Education
• Learner Engagement Analytics
• +3 more

Dr. Chao Shu is a Lecturer (Assistant Professor) in the School of Electronic Engineering and Computer Science at Queen Mary University of London (QMUL). He holds a PhD in Electronic Engineering from QMUL (2021) and has prior industry experience as an R&D software engineer and division manager. His research focuses on machine-learning-aided antenna/circuit design, data-driven education, and AI-enabled personalized learning. He has authored/co-authored over 20 journal/conference papers, a book chapter, and 13 patents. He currently co-leads the Data Analytics Scholarship working group in his school and teaches modules such as Data Design, Digital Circuit Design, and Control Theory. Research & Awards : Dr. Shu secured a £960,000 grant (2023–2025) for a wireless power transmission project. His work has earned a Best Conference Paper Award at IEEE TALE 2023. He reviews for journals like IEEE Transactions on Antennas and Propagation and serves on conference committees, including IEEE EDUCON 2023. Teaching : He organizes modules covering data analytics, digital circuits, communication systems, and microprocessor design. His courses emphasize practical skills in Python, embedded systems, and control theory.

Jeremy GordonView profile

Associate Professor

• Medical Physics
• Magnetic Resonance Imaging (MRI)
• Metabolic Imaging
• +3 more

Dr. Jeremy Gordon is an Associate Professor in the Department of Radiology & Biomedical Imaging at the University of California, San Francisco (UCSF). He holds a PhD in Medical Physics from the University of Wisconsin-Madison (2013) and a BS in Physics & Astronomy from the University of Georgia (2008). Prior to his faculty role, he worked as a postdoctoral scholar (2013–2016) and Senior Bioengineer (2016–2020) at UCSF. His research focuses on developing novel hyperpolarized MRI techniques for metabolic imaging, particularly in pancreatic cancer, multiple sclerosis, Alzheimer’s Disease, and brain perfusion studies. These methods leverage hyperpolarized 13C and x-nuclei to assess metabolic pathways and treatment responses. Key applications include non-invasive metabolic biomarker identification and improved clinical care for cancer and neurological disorders. Major awards include the Distinguished Reviewer Award (2018), RSNA ITARSc Program recognition (2017), and multiple merit awards from the International Society for Magnetic Resonance in Medicine (2013). He is affiliated with the Advanced Imaging Technologies Resource Group and the Hyperpolarized MRI Technology Resource Center at UCSF. Recent work emphasizes multi-center clinical trials, data standardization (DICOM integration), and open-source tools for X-nuclear imaging. His lab collaborates widely, with studies involving metabolic imaging of the brain, abdomen, and heart under various physiological conditions (e.g., glucose challenges).

Philip R.O. PayneView profile

Professor

• Artificial Intelligence in Healthcare
• Biomedical Informatics
• Clinical Decision Support Systems
• +7 more

Philip R.O. Payne holds multiple prestigious roles at Washington University in St. Louis and the BJC Health System. As the Janet and Bernard Becker Professor , he leads the Institute for Informatics, Data Science and Biostatistics (I2DB) . He also serves as Vice Chancellor for Biomedical Informatics and Data Science at WashU Medicine and Chief Health AI Officer for BJC Health System and WashU Medicine. Additionally, he is the founding director of the Center for Health AI , a joint initiative between the two institutions. His academic appointments include professorships in General Internal Medicine and Computer Science and Engineering . Dr. Payne’s research focuses on four key areas: AI-driven biomolecular and clinical phenotype analysis , interventional applications of EHRs and clinical decision support systems , human factors and workflow optimization in healthcare IT , and data sharing and analytics platforms for healthcare systems and research initiatives . His work emphasizes leveraging advanced technologies to enhance healthcare outcomes and operational efficiency, such as developing the M3NetFlow AI model for multi-omic data analysis and the Cumulus federated EHR learning system. In terms of awards, he is an elected fellow of the American College of Medical Informatics (ACMI) American Medical Informatics Association (AMIA) American Institute for Medical and Biological Engineering (AIMBE) International Academy of Health Sciences Informatics (IAHSI) He currently serves as president-elect of AMIA, the leading professional organization in Biomedical and Health Informatics. Dr. Payne’s advising and grants narrative involves leading a research group with over 300 publications and fostering interdisciplinary collaboration. While no formal advisees or grant details are explicitly listed, his entrepreneurial ventures include founding digital health companies and advising health/life sciences organizations and venture capital firms. He emphasizes translating academic innovations into real-world solutions. He is affiliated with key labs/teams like the Institute for Informatics (I2DB) , Center for Health AI , and the Collaborative Resource for Intensive care Translational science, Informatics, Comprehensive Analytics, and Learning (CRITICAL) . These teams focus on AI-driven healthcare solutions, data integration, and clinical informatics advancements.

• Wireless Communication
• Network Optimization
• Machine Learning
• +3 more

Athanasios Gkelias is a Research Fellow in the Department of Electrical and Electronic Engineering at Imperial College London's Faculty of Engineering. His research focuses on advanced wireless communication systems, network optimization, machine learning applications, and quantum computing methodologies. He contributes to interdisciplinary projects involving IoT coalitions, adversarial signal detection, and cognitive behavior analysis. Key research areas include: (1) Network resource management in Software-Defined Networks (SDN), (2) Localization systems for indoor environments, (3) 3D reconstruction using photometric stereo and origami-based modeling, and (4) distributed optimization frameworks for ad-hoc networks. His recent work explores quantum approaches to combinatorial optimization and self-supervised learning methods. Publications span 20+ years with notable contributions in network coding, vehicular ad-hoc networks (VANETs), and cross-layer design for wireless mesh networks. His work emphasizes practical implementations through frameworks like iVisher for caller ID spoofing detection and cooperative MAC protocols for multi-antenna systems. No scientific awards are listed in the provided information. His research has been applied in healthcare technology through emotion understanding systems for Alzheimer’s patients and in battlefield communication through IoBT coalitions. Active participation in Imperial College's Engineering faculty reflects his commitment to advancing telecommunications and network science.

• Data-efficient deep learning
• Adversarial attacks
• Deep learning interpretations
• +4 more

Anshul Thakur is a Departmental Lecturer in Clinical Machine Learning at the University of Oxford's Institute of Biomedical Engineering. His research focuses on advancing data-efficient deep learning techniques, adversarial attacks, and interpretable AI frameworks for healthcare applications. He holds a PhD from IIT Mandi (2020), where his thesis explored audio signal analysis using dynamic kernels and deep learning. Education: PhD in Computing & Electrical Engineering, Indian Institute of Technology Mandi (2020) Research concentrated on bioacoustic signal pattern analysis and ML frameworks for acoustic classification. Research Interests: His work emphasizes clinical AI applications, including federated learning for medical data, multimodal diagnosis systems, and mitigating class imbalance in healthcare datasets. He develops interpretable models for medical practitioners and explores ethical AI deployment in clinical settings. Recent Trends in Publications: Recent work addresses federated learning optimization, multimodal clinical diagnosis, and early disease prediction using biomarker patterns. His studies highlight innovations in EHR analysis, privacy-preserving techniques, and cross-domain medical model adaptation. Labs & Teams: Active in the Institute of Biomedical Engineering, collaborating on projects like the RapiD_AI framework for pandemic preparedness and Continuous Patient State Attention Models for irregular EHR data analysis.

• Image and Video Processing
• Multimedia Analysis
• Computer Vision
• +5 more

Prof. Tan Yap Peng is a Professor and Chair of the School of Electrical & Electronic Engineering at Nanyang Technological University (NTU), Singapore. He holds the President's Chair in Electrical and Electronic Engineering and serves as Associate Vice President (Lifelong Learning – Postgraduate Programmes by Coursework). His research focuses on multimedia analysis, computer vision, machine learning, and data analytics. He earned his B.S. from National Taiwan University and M.A./Ph.D. from Princeton University. He has led major initiatives including the INFINITUS Infocomm Research Centre and contributed to IEEE technical committees. His over 200 publications span image/video processing, neural network robustness, and cross-modal systems. Awards include IEEE Fellow status. Education: B.S. Electrical Engineering (NTU), M.A./Ph.D. (Princeton) Research interests emphasize interactive digital media, content-based analysis, and AI-driven solutions for visual and signal processing. His work addresses challenges in adversarial attacks, video generation, and low-light image enhancement. He has held editorial roles at IEEE Transactions and EURASIP journals. Conference leadership includes chairs for ICME and ICIP. His contributions bridge academia and industry through collaborative research networks.

Sebastian MichelmannView profile

Assistant Professor

• Cognitive Neuroscience
• Memory Processes
• Neurophysiology
• +7 more

Sebastian Michelmann is an Assistant Professor of Psychology at New York University's College of Arts and Science. His research focuses on understanding how temporal dynamic content is neurally represented and processed in episodic memory, with particular emphasis on fast-timescale memory retrieval mechanisms and their interaction with event knowledge. He employs electrophysiological methods (EEG/MEG), computational modeling, and behavioral experiments to study memory formation under naturalistic conditions. Education: PhD in Psychology from the University of Birmingham (2018), thesis awarded the Glushko Dissertation Prize (2020) Master's (Diploma) in Psychology from Julius-Maximilians-Universitaet Wuerzburg (2014) Postdoctoral research at Princeton University (2018–2023) under Professors Ken Norman and Uri Hasson Research interests include neural oscillations, event segmentation, and the role of generalized event knowledge in memory retrieval. His work bridges cognitive neuroscience with computational approaches, exploring how the brain encodes, stores, and retrieves temporally precise information. Key research directions involve studying how memory processes are embedded in continuous experience and developing tools to analyze neural data (e.g., intracranial EEG re-referencing). Recent studies explore event boundary detection using large language models and investigate the neural correlates of natural language comprehension through ECoG datasets. Scientific Awards: Glushko Dissertation Prize (2020) Laboratory activities focus on future projects combining electrophysiology with computational modeling to understand interactive dynamics of episodic memory. Collaborative efforts include developing semi-automatic alignment tools and cloud-based neuroimaging frameworks.

• Artificial Intelligence
• Machine Vision
• Distributed Computing
• +11 more

Dr. Dan Ionescu is a Professor at the University of Ottawa's School of Electrical Engineering and Computer Science, Department of Electrical and Computer Engineering. He has been affiliated with the university since 1985, contributing extensively to teaching and research. His career includes visiting professorships at École Nationale Supérieure de Télécommunications (Paris) and Universitat Politècnica de Catalunya (Barcelona). He founded the Machine Intelligence Research Laboratory (1988) and the Network Computing and Control Research Laboratory (NCCT, 1999), which he currently directs. His research spans Artificial Intelligence, Machine Vision, Distributed Computing, Network Control, and Formal Methods. Notable contributions include methodologies in Expert Systems, Image Processing, Temporal Logic, and Network Management. His work has received industrial and governmental grants from CITO, Nortel, NSERC, and others. Recent research focuses on Web-based collaborative platforms (e.g., UC-IC, Watch-Together), Autonomic Computing, and AI applications in gesture control and 3D IR camera systems. Dr. Ionescu’s technical leadership includes pioneering the first distributed network management platform with industry partners and designing the NCIT*net 2 network architecture. His current interests include AI-driven solutions for cybersecurity, medical imaging, and IoT-enabled disaster response systems. He has advised numerous projects in AI ethics, quantum computing, and real-time control systems. His research trends emphasize interdisciplinary innovation, combining AI with healthcare (e.g., MRI analysis, deepfake detection), cybersecurity (GAN-based intrusion detection), and IoT (Salv AIoT platform). Collaborations with IBM CAS and Diatem Networks highlight his industry-academia integration. His legacy includes over 40 years of impactful contributions to computing and engineering education.

Roberto BresinView profile

Professor

• Expressive Music Performance
• Emotion in Sound and Music
• Sound in Human-Robot Interaction
• +3 more

Roberto Bresin is a Professor of Media Technology at KTH Royal Institute of Technology's Division of Media Technology and Interaction Design (MID), part of the School of Electrical Engineering and Computer Science (EECS). He leads the Sound and Music Computing group and serves as Director of Studies for the Media Technology program and Director of NAVET, a center for art, technology, and design research. His work bridges music, robotics, and human interaction. Research focuses include expressive music performance, emotion in sound, robot sound design, and sonification. He holds a PhD in Music Acoustics from KTH (2000) and has extensive experience in sound engineering and interactive systems. Key projects involve designing auditory feedback for robots and exploring light's psychological impacts. His publications span robotics, sonification, and human-centered technology. Recent articles address robot sound aesthetics, daylight effects on behavior, and sonification of computer processes. Bresin collaborates internationally, contributing to EU initiatives like SAME and Nordic SMC networks. His work emphasizes cross-disciplinary innovation, with applications in education, healthcare, and environmental science.

Richard SaveryView profile

Research Fellow

• Artificial Intelligence
• Robotics
• Human-Robot Interaction
• +3 more

Richard Savery is an Honorary Research Fellow at Macquarie University's Faculty of Arts, affiliated with the Performance and Expertise Research Centre and the Centre for Applied Artificial Intelligence. His work bridges artificial intelligence, robotics, and music, focusing on creative interactions and trust-building through robotic musicianship. He holds a PhD in Music Technology from Georgia Institute of Technology (2021) and a Master's from UC Irvine. Key roles include developing the NSF-funded Keirzo rapping/drumming robot and publishing the edited book Sound and Robotics (2023). Research interests include emotional musical prosody, human-robot collaboration, and robotic performance. Notable projects include Shimon the singing robot and IVF sonification. Awards include a GANG Award (2015) for game audio. Active in interdisciplinary collaborations, he has composed music for films, ads, and video games, including Gunman Taco Truck and Gathering Sky . His work spans academic grants ($800k NSF-NRI grant), industry partnerships, and public installations like the Un[contained] Arts Festival's beehive exhibit. Over 50 publications highlight his contributions to robotics, music tech, and AI ethics.

• Visual Media Compression
• Machine Learning
• End-to-End Optimization
• +5 more

Jona Ballé is an Associate Professor in the Department of Electrical and Computer Engineering at New York University Tandon School of Engineering. His research focuses on visual media compression, leveraging machine learning and end-to-end optimization to advance compression techniques for traditional and emerging modalities (e.g., AR, plenoptic imaging). He holds a PhD in signal processing from RWTH Aachen University (2012), followed by postdoctoral work at NYU’s Center for Neural Science and a Research Scientist role at Google (2017–2024). His contributions include foundational work on JPEG AI and leadership in conferences such as CLIC and DCC. Education: PhD in Signal Processing, RWTH Aachen University (2012) Master’s in Signal Processing, RWTH Aachen University (2007) Research: Ballé bridges machine learning and perceptual science to improve compression efficiency. Key areas include perceptual metrics, end-to-end optimization, and distributed coding. His work on Wasserstein distortion and Fourier basis models reflects cutting-edge advancements in compression theory. Impact: As co-organizer of the Challenge on Learned Image Compression (CLIC) and program committee member of the Data Compression Conference (DCC), he drives community progress. His industry collaborations and leadership in the JPEG AI standard highlight his translational research.

Paschalis GkoupidenisView profile

Associate Professor

• Neuromorphic Engineering
• Bioelectronics
• Materials Science
• +5 more

Paschalis Gkoupidenis is an Associate Professor at North Carolina State University (NC State), holding a joint appointment in the Department of Electrical and Computer Engineering and the Department of Physics. He is a pioneer in organic neuromorphic electronics, focusing on emulating and interfacing biological systems with advanced electronic devices. His work bridges materials science, neuroscience, and robotics, aiming to develop energy-efficient bioelectronic interfaces and distributed intelligent circuits. Education: BSc in Physics (University of Ioannina, 2005), MSc in Microelectronics (University of Athens, 2007), and PhD in Materials Science (National Centre of Scientific Research, Demokritos, 2014). Postdoctoral research included work at the Department of Bioelectronics (EMSE, France) and the Max Planck Institute for Polymer Research (Germany), where he led a group in molecular electronics. Research interests span neuromorphic computing, biohybrid robotics, and organic materials for adaptive systems. His group develops devices emulating neural processes, enabling applications in sensors, soft robotics, and biocomputing. Notable contributions include organic artificial neurons and bio-inspired learning algorithms featured in Nature Communications and Science Advances . Awards and recognition include frequent media coverage (Scientific American, Max Planck Society Newsroom) and invited talks at MRS and SPIE conferences. He co-leads NC State’s Organic and Carbon Electronics Cluster (ORaCEL) and participates in the Chancellor’s Faculty Excellence Program. Grants and collaborations focus on interdisciplinary projects merging electronics with biology. His lab’s innovations include bio-inspired multimodal learning systems and energy-efficient bioelectronic circuits on biodegradable substrates.

Yaoping HuView profile

Professor

• Brain-Machine Interfaces
• Multi-sensory User Interaction in XR
• Immersive Analytics
• +5 more

Yaoping Hu is a Professor in the Department of Electrical and Software Engineering at the Schulich School of Engineering, University of Calgary, and an Associate Member of the Hotchkiss Brain Institute. He holds a Ph.D. in Robotics and Neuroscience from the University of Western Ontario. His research focuses on Human-Computer Interaction within Virtual Environments, particularly in Brain-Machine Interfaces, multisensory user interaction in XR, and immersive analytics. He teaches courses such as ENSF 619.36 (Brain-Machine Interface Systems), ENSF 519.51 (Brain-Machine Interfaces), ENEL 602 (Virtual Environments), and ENSF 545 (Introduction to VR). Research interests include modeling neural mechanisms for visuomotor processes, cognitive-ergonomic interaction design, and applications in surgical planning, data visualization, and human-machine collaboration. He leads the Visualization and Interaction Laboratory, collaborating with institutions like the neuroArm Project, Fluid Dynamics Group, and international partners in France. Past and current students include Stanley Tarng, Lida Ghaemi Dizaji, and Aida Erfanian. His work spans projects like neuroSim (a surgical simulator) and BioVista (biofilm visualization tools). Collaborations with industry partners such as Nova Chemicals and Calgary Scientific Inc. support applied research in complex data analysis and virtual reality systems.

Markus H. FlierlView profile

Associate Professor

• Visual Computing and Communication
• Machine Learning
• Information Theory
• +3 more

Markus H. Flierl is an Associate Professor at the School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology in Stockholm. He holds a PhD in Engineering from Friedrich Alexander University (2003). His career includes senior research roles at École Polytechnique Fédérale de Lausanne (2003-2005), leadership of the Max Planck Center for Visual Computing and Communication (2005-2008), and a Visiting Assistant Professorship at Stanford University (2000-2002). He has served as Program Director of the EIT Digital Master School’s 'Visual Computing and Communication' program and as Associate Editor for the IEEE Transactions on Circuits and Systems for Video Technology. Education: PhD in Engineering, Friedrich Alexander University, 2003 Studies at Friedrich Alexander University (1999-2002), EPF Lausanne (2003-2005), and Stanford University (2005-2008) Research Interests: Focused on visual computing, machine learning, and information theory. Key areas include distributed coding of dynamic scenes, multiview video coding, motion-compensated transforms, and applications in video compression and multimedia systems. His work explores efficient signal processing techniques for video and 3D visual search, with recent contributions in drone-based environmental monitoring and adversarial training methods. Awards: SPIE VCIP Young Investigator Award (2007) Teaching & Grants: Teaches courses on image/video processing, information theory, and multimedia systems. His research is supported by Ericsson, the Swedish Research Council, the European Commission, and KTH’s Digitalization Platform. He advises PhD and master’s students in visual computing and communication. Labs & Teams: Leads the Visual Computing and Communication research group at KTH, collaborating with industry partners like Ericsson and Qamcom. Former members include researchers now at Tobii AB and the University of Iceland.

Ganeshsree SelvachandranView profile

Professor

• Artificial Intelligence
• Fuzzy Mathematics
• Deep Learning
• +12 more

Ganeshsree Selvachandran is a Professor at the School of Business, Monash University Malaysia (MUM), where she conducts cutting-edge research in artificial intelligence, fuzzy mathematics, deep learning, and computational intelligence. She is actively involved in PhD supervision and leads significant research projects in biomedical imaging and forecasting. PhD in Pure Mathematics, Universiti Kebangsaan Malaysia (UKM), 2013 MSc in Pure Mathematics, Universiti Kebangsaan Malaysia (UKM), 2010 BSc (Hons) in Mathematics, Universiti Kebangsaan Malaysia (UKM), 2009 Her research focuses on fuzzy mathematics , deep learning , computational intelligence , and hybrid AI models , with applications in biomedical imaging (e.g., lung cancer detection), financial and environmental forecasting, and emotion recognition. She develops advanced fuzzy logic systems integrated with neural networks for real-world diagnostic solutions. The recent publications highlight a strong trend in applying deep convolutional neural networks and fuzzy logic to medical diagnostics, particularly in lung cancer and diabetic retinopathy detection. Her work increasingly combines generative models and hybrid AI for data augmentation and improved accuracy in healthcare applications. She also contributes comprehensive reviews in AI subfields, establishing foundational knowledge in reinforcement learning and medical AI. Scientific Awards and Recognitions: Ranked No. 6 globally in fuzzy sets/systems research by Scopus (2019) Member, Special Interest Group on Blue Economy, Malaysian Academy of Sciences (2020) Contributor to the Position Paper on Blue Economy (2022) Recipient of multiple national and international research awards Dr. Selvachandran has secured over MYR880,000 in research funding, including four Fundamental Research Grant Scheme (FRGS) grants, an industry grant from MTSFB, and a professorial chair grant from PNB. She mentors PhD students and collaborates on interdisciplinary projects. She serves as an editor for top journals like Engineering Applications of Artificial Intelligence and Computers and Electrical Engineering , and has reviewed over 150 papers for Web of Science/Scopus-indexed journals. She is involved in active research projects such as Advancing Lung Cancer Detection through Deep Fuzzy Convolutional Neural Networks and Sustainability Impact: Case Studies of UNGCMYB Member Companies . She also holds an adjunct professorship at Symbiosis International University (2023–2025), contributing to international academic collaboration.