Find Academic Supervisors

• Artificial Intelligence in Healthcare
• Cardiovascular Disease
• Cancer Biology
• +3 more

Dhruva Biswas is an Associate Research Scientist at the Yale School of Medicine and a Clinician-Data Scientist at the Cardiovascular Data Science (CarDS) Lab. His research focuses on integrating AI-driven insights into clinical practice, particularly in cardiovascular care and cancer biomarker development. He holds MD and PhD degrees from University College London (2022), with a PhD thesis on molecular portraits of cancer evolution at the UCL Cancer Institute and Francis Crick Institute. Education: MA in Neuroscience (University of Cambridge, 2015), MB/PhD in Medicine and Molecular Cancer Biology (University College London, 2022), and Academic Foundation training in Cardiology (King’s College London). Research Interests: AI in healthcare diagnostics and personalized medicine Development of molecular and digital biomarkers for high-risk patient identification Cancer evolution and clonal heterogeneity Health equity in cardiovascular interventions Notable Articles: Focus on AI-driven biomarker validation (e.g., ORACLE for lung cancer prognosis), cardiovascular AI applications, and genomic analyses of cancer progression. His work has been published in Nature Medicine , Nature Cancer , and European Heart Journal Digital Health . Awards: Recognized as a Francis Crick Institute Translation Fellow (2021), Sir David Cooksey Translation Prize (2020), and featured in Nature Medicine as ‘at the forefront of medical research’. Advocacy & Mentorship: Supervised MSc students achieving distinction grades, involved in South London structural heart equity initiatives, and promotes patient/public involvement in research. Labs: Cardiovascular Data Science (CarDS) Lab at Yale School of Medicine.

Keith SmithView profile

Lecturer

• Biomedical Network Analysis
• Neuroimaging Data Analysis
• Machine Learning in Neuroscience
• +4 more

Keith Smith is a Lecturer in the Department of Computer and Information Sciences at the University of Strathclyde, where he leads the Digital Health & Wellness Research Group (DHaWG). Previously, he held positions as a lecturer at Nottingham Trent University and as a UKRI Innovations Fellow in Medical Informatics at the University of Edinburgh. He earned his PhD in biomedical signal processing from the University of Edinburgh's IDCOM lab. His research focuses on computational tools for analyzing biomedical networks, including neuroimaging data from structural and functional brain networks, protein interaction networks, and metabolic networks. Key areas include hierarchical complexity of brain networks, machine learning applications for neuroimaging, and theoretical foundations of network geometry and complexity. He collaborates widely with clinical and academic partners to address challenges in neurological and mental health conditions like Alzheimer’s, depression, and schizophrenia. His work contributes to UN Sustainable Development Goals, particularly improving health and well-being through advanced data-driven methodologies. He currently oversees PhD projects exploring brain networks, deep learning for disease prediction, and evolutionary protein biology. Notable achievements include the UKRI fellowship and impactful interdisciplinary collaborations. His talks and presentations, such as on statistical complexity of brain networks, highlight his commitment to advancing theoretical and applied network science.

• Cardiovascular Physiology
• Vascular Biology
• Autonomic Neuroscience
• +5 more

Professor Craig Daly holds the position of Professor of Physiology in the School of Cardiovascular & Metabolic Health at the University of Glasgow. He has been affiliated with the university since 1980, progressing through roles such as research technician, research fellow, and lecturer before achieving his current academic rank. His research focuses on vascular physiology, autonomic nervous system regulation of blood vessels, and the integration of advanced imaging and VR technologies into medical education and research. Education: Completed MSc and PhD in Medicine through the University of Glasgow's Faculty of Medicine. Awarded a Senior Fellowship of the Higher Education Academy in 2017. Research Interests: Specializes in vascular structure/function, adrenergic receptor distribution, and the application of confocal microscopy, 3D imaging, and virtual reality for biomedical visualization and education. Recent work includes developing mixed-reality tools for medical training and VR-based anatomical models. Publications: Over 150 peer-reviewed articles since 1985, emphasizing vascular biology, pharmacology, and innovative teaching methodologies. Recent trends include VR/AR applications in education, molecular dynamics of receptors, and biomedical visualization techniques. Awards: Recognized for contributions to education with the 2017 Higher Education Academy Senior Fellowship. Labs/Teams: Active in research groups such as Cellular Signalling & Mechanisms and Vascular Structure & Function. Collaborates on projects involving medical animation, drug-receptor interactions, and technology-driven learning solutions.

• Computer Architecture
• Microelectronics
• Nanoscale Technology
• +9 more

Dr. Paul Beckett is an Honorary Associate Professor in the School of Engineering at RMIT University. His research focuses on cutting-edge areas such as computer architecture, nanoscale microelectronics, digital logic design, and photonics. He specializes in low-power circuit design, FPGA-based systems, and sensor technologies with applications in healthcare and environmental monitoring. Beckett's teaching interests include embedded systems, VLSI design, and hardware description languages (VHDL). His research spans disciplines such as electronics, communications engineering, and distributed computing, with notable contributions to null convention logic (NCL) circuits, plasmonic sensors, and spectral imaging systems. He has collaborated on innovative projects like the TIARA apnea monitoring system and wearable health technologies. His recent work emphasizes interdisciplinary applications, including healthcare technology and biomimetic optical systems. Notable publications include advancements in gas sensing (H2S detection), THz spectrum analysis, and machine learning optimization for embedded systems. Beckett actively supervises research projects in asynchronous computing architectures and nanotechnology.

• Artificial Intelligence
• Medical Imaging
• Structural Engineering
• +5 more

Dr. Azadeh Alavi is a Lecturer at RMIT University's School of Computing Technologies in Melbourne, Australia. Her research focuses on interdisciplinary applications of artificial intelligence, particularly in medical imaging, structural engineering, quantum computing, and sustainability. She supervises PhD and master's projects in areas such as AI-driven medical image analysis, digital twin technology for infrastructure monitoring, and psychedelics-inspired therapeutics design. Her work spans diverse domains including healthcare analytics (e.g., diabetic foot ulcer classification, dementia prediction), civil infrastructure monitoring (e.g., bridge integrity assessment, real-time structural health systems), and theoretical AI (e.g., analyzing AI systems against consciousness definitions). Key contributions include developing PRIMITI for miRNA-mRNA interaction prediction and SellaMorph-Net for sella turcica segmentation in cephalometric imaging. Dr. Alavi’s recent publications highlight advancements in multimodal fusion learning for medical imaging, quantum-classical hybrid models for structural monitoring, and AI applications in fashion sustainability. She is an active collaborator in international research networks and maintains an ORCID profile (0000-0002-9565-217X). Her supervision portfolio includes projects on AI ethics, digital twin implementations, and health data mining. She teaches advanced computing modules and advises students on cutting-edge AI methodologies for real-world challenges across engineering and healthcare sectors.

Wai Lok WooView profile

Professor

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +7 more

Prof Wai Lok Woo is a Chair in Machine Learning at Northumbria University's Computer and Information Sciences Department. He serves as Faculty Postgraduate Research Director (Engineering and Environment) and Head of the Artificial Intelligence and Digital Technology Research Cluster. With a PhD in Statistics and Machine Learning from Newcastle University, he has over 450 publications and holds IET and IEEE Fellowships. Education PhD in Statistics and Machine Learning, Newcastle University, UK MSc in Communications Engineering, Newcastle University, UK BEng (Hons) in Electrical and Electronic Engineering, Newcastle University, UK Research Interests His work focuses on machine learning foundations, signal/image processing, and smart sensing for applications like non-destructive testing, energy systems, and mental health monitoring. Core themes include: AI & Machine Learning: Deep neural networks, ethical AI, reinforcement learning Data Science: Latent variable analysis, blind signal separation Sustainable Development: Smart grids, precision farming, disaster resilience Awards Fellow of IET and IEEE IEE Prize British Commonwealth Scholarship Grants & Projects Leads the EU-funded CO VER project (€6M) on UAV-based emergency response systems. Active in UK/EU grant evaluations and has supervised 8 PhD students. Labs & Teams Directs the AI & Digital Technology Research Cluster, collaborating globally on UN SDGs like affordable energy and climate action.

• Neuroimaging
• Mental Disorders
• Bipolar Disorder
• +4 more

Fei Wang is an Adjunct Associate Professor in the Department of Psychiatry at Yale School of Medicine. Her research focuses on identifying neural circuitry abnormalities using multimodal neuroimaging and investigating genetic influences on mental disorders such as schizophrenia, bipolar disorder, and major depressive disorder. She develops novel imaging techniques to characterize structural and functional connectivity in cortico-limbic circuits, integrating genetics to understand disease mechanisms. Research Interests: Multimodal neuroimaging techniques (DTI, fMRI) Genetic contributions to brain circuitry abnormalities Early diagnosis of mood disorders in adolescents Machine learning applications in mental health diagnostics Key Contributions: Identified white matter abnormalities in cingulum across psychiatric disorders Linked neuregulin 1 gene variants to frontotemporal white matter deficits in schizophrenia Pioneered acoustic feature fusion networks for depression detection Awards: Young Investigator Grant, Brain & Behavior Research Foundation (2012) Collaborations: Active collaborations with researchers like Hilary Blumberg, Joel Gelernter, and Xingguang Luo on translational neuroscience projects.

• Embedded Systems
• Digital Signal Processing
• Analog Electronics
• +3 more

Dr. Joan Carletta is a Professor and Associate Dean at the University of Akron's College of Engineering and Polymer Science. She has been affiliated with the Department of Electrical and Computer Engineering since 1999 and currently serves in the Dean's Office. Her research focuses on embedded systems, digital signal processing, analog electronics, and structural health monitoring through digital image correlation. Education: Ph.D. in Computer Engineering, Case Western Reserve University (1995) B.S. in Electrical Engineering, State University of New York at Buffalo (1988) Research Interests: Her work spans embedded real-time systems, fixed-point mathematics for signal processing, and civil engineering structural health monitoring. Notable contributions include tone-mapping algorithms for high dynamic range images and collaborations with industry and academic partners. Awards: Received the Moody Award (2009) as part of a team recognized by ASME's Fluids Engineering Division. Grants & Advising: Secured funding from the National Science Foundation and industry. Advised students including S. Murali, X. Xiu, and A. Jagtiani on impactful projects.

Thinh NguyenView profile

Professor

• Stochastic Processes
• Signal Processing
• Video Coding
• +3 more

Thinh Nguyen is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University . His research spans theories and applications of stochastic processes, with focus areas in signal processing, video coding, networking, communication systems, and quantum information theory. He co-authored multiple award-winning papers and received the NSF CAREER Award (2009) and Engelbrecht Young Faculty Award (2007) . Education : Ph.D. in Electrical Engineering and Computer Science from UC Berkeley (2003), B.S. in Computer Engineering from University of Washington (1995) Research Focus : Information representation, processing, and transmission with applications in multimedia streaming optimization Key Contributions : Network coding theories for efficient multimedia transmission His recent publications address topics in machine learning optimization, fluid dynamics in medical systems, and microfabrication techniques. Outside academia, he maintains strong musical interests as a classical violinist and emphasizes family values. Scientific Awards NSF CAREER Award (2009) Engelbrecht Young Faculty Award (2007) Currently advises graduate students in information theory and signal processing while leading research at the intersection of electrical engineering and computer science.

• Nanoparticle Analysis
• Inductively Coupled Plasma Mass Spectrometry (ICP-MS/TOFMS)
• Environmental Chemistry
• +4 more

Alexander Gundlach-Graham is an Assistant Professor at Iowa State University, specializing in advanced mass spectrometry techniques and nanoparticle analysis. His work focuses on developing novel methodologies using inductively coupled plasma time-of-flight mass spectrometry (ICP-TOFMS) for characterizing engineered nanoparticles, environmental pollutants, and geological materials. He has pioneered techniques such as single-particle ICP-TOFMS for multi-element fingerprinting and online microdroplet calibration, enabling high-precision quantification of nanoparticles in complex matrices. His research bridges environmental science, materials science, and analytical chemistry, with applications in forensic analysis, water quality monitoring, and semiconductor material characterization. Dr. Gundlach-Graham’s educational background includes expertise in geochemistry and analytical instrumentation. He has contributed to instrument development, including digital quadrupole systems and capillary electrophoresis-ICP-MS interfaces. His work frequently integrates machine learning for data analysis and classification of nanoparticle populations. His research portfolio includes over 50 peer-reviewed articles, with recent emphases on wildfire-induced nanoparticle emissions, 3D printer aerosol toxicity, and nanomaterial fate in wastewater systems. He collaborates with environmental agencies and industrial partners to advance real-world analytical solutions.

• Energy Conversion
• Electrical Machinery
• Statistics and Probability
• +4 more

Dr. Andrew Allison is a Lecturer in the School of Electrical and Mechanical Engineering at the University of Adelaide, within the Faculty of Sciences, Engineering and Technology. His primary roles include teaching and research in areas such as Energy Conversion, Electrical Machinery, and Applied Mathematics. Research Interests: 1. Energy Conversion and Electrical Power : Focuses on transformers, rotating machines, solar panels, batteries, and switching electronics. 2. Electrical Machinery Modeling : Applies variational techniques and mathematical frameworks (Euler, Lagrange, Hamilton) to analyze electromagnetic fields and energy storage. 3. Statistics and Probability : Uses stochastic processes and statistical models for parameter estimation in electrical systems. 4. Biomedical Signal Processing : Develops methods to identify signals in noisy data for clinical applications. 5. Mathematical Innovations : Applies advanced techniques like geometric algebra and complex optimization to solve engineering problems. Publications Trends: Recent work emphasizes renewable energy systems (e.g., grid optimization), tumor vascular modeling, and cybersecurity in key distribution. His articles span interdisciplinary fields such as biomedical engineering, economics, and signal processing, reflecting his broad expertise in mathematical and engineering applications. Supervision and Grants: Eligible to supervise Masters and PhD students in his research areas. Collaborates on projects requiring expertise in electrical engineering, statistics, and computational methods.

• Photonics
• Optical Sensors
• Fiber Optic Sensing
• +4 more

Dr. Frederic Surre is a Lecturer in Autonomous Systems & Connectivity at the University of Glasgow. His academic journey includes a PhD in Electronic Engineering (Computational Electromagnetics) from Toulouse INP, France, followed by postdoctoral research at Queen Mary University of London, Trinity College Dublin, and Dublin City University. He has held visiting roles at institutions like the University of British Columbia (2017) and has been an invited professor at INPT (France) and Universidad San Francisco de Quito (Ecuador). His research focuses on photonic integrated sensors for vibration/gas detection, optical fiber sensors for structural health monitoring (strain, humidity, pH), sapphire-based systems for extreme environments, and IoT-enabled digital instrumentation. Recent work includes advancements in SiN-based refractometers and biomedical diagnostic tools. Grants include leadership in the €415k PICSONDE project (ANR-funded) and collaborations like the Learning with Wearables initiative (£10k). He teaches courses in Digital Circuit Design, Dynamics & Control, and Wireless/Optical Transmission Systems. Professional memberships include Senior IEEE and AAAS. He contributes to IEEE standards committees and has advised on elderly monitoring devices via consultancy projects like Acticheck.

Jean-Christophe Olivo-MarinView profile

Professor

• Bioimage Analysis
• Computational Biology
• Machine Learning
• +13 more

Jean-Christophe Olivo-Marin is the Head of the Biological Image Analysis Unit and Director of the Carnot Pasteur Institute for Microbes and Health at the Institut Pasteur in Paris, France. He previously served as Director of the Department of Cell Biology and Infection (2010–2014) and continues to lead impactful research at the intersection of biology, computation, and imaging. His research focuses on bioimage informatics , developing computational methods in Machine learning and deep learning for image analysis Bayesian tracking and optical flow algorithms Statistical modeling of spatial patterns in biological systems Mathematical imaging and computational cell biophysics Digital pathology and cancer microenvironment analysis His work enables rigorous quantitative analysis of complex biological phenomena such as cell motility, host-pathogen interactions, and neural dynamics. His recent publications (2024–2025) reflect a strong trend toward AI integration in bioimaging , with advances in deep learning-based segmentation (e.g., Deep ContourFlow), large-scale image annotation (SAMJ), and frameworks for evaluating neuron tracking. There is also a focus on spatial analysis in disease contexts , particularly in cancer and neurodevelopment, combining deep learning with spatial statistics. He has been awarded research funding for projects including Next-generation Structured Illumination Microscopy (SIM) Machine learning for cancer detection using FFOCT Compressive sensing in biological imaging Statistical analysis of spatial coupling in bioimaging (SODA) Olivo-Marin actively mentors students and postdoctoral researchers and contributes to open science through the development and maintenance of Icy , a widely used open-source bioimage analysis platform. He regularly participates in international conferences such as QBI, ICPR, and NEUBIAS, and leads advanced training courses, including the upcoming Advanced Bioimage Analysis with Artificial Intelligence (AI) course at Institut Pasteur in 2026. He leads a dynamic research unit with PhD students, postdocs, and engineers working on cutting-edge image analysis challenges. The team fosters collaboration across disciplines and institutions, promoting open science and community-driven software development.

• Hardware Design
• FPGA Programming
• Parallel Computing
• +5 more

Satnam Singh is a Professor at Newcastle University's School of Electrical and Electronic Engineering, UK. With a research career spanning over three decades from 1989 to present, Singh has established himself as a leading expert in hardware design, FPGA programming, and parallel computing systems. His research interests focus on hardware-software co-design , reconfigurable computing , and functional programming applications for hardware design. Singh has pioneered work in using functional languages like Haskell for hardware description and verification, particularly through his contributions to the Lava hardware description language. His work bridges theoretical computer science with practical hardware implementation challenges. Analysis of his publication history reveals a clear evolution from early work on formal verification and FPGA design in the 1990s, through substantial contributions to parallel programming models in the 2000s, to more recent applications of machine learning techniques in diverse domains including cheminformatics and sensory systems. His 2022-2025 publications demonstrate continued innovation in specialized processor programming, AI applications for olfactory systems, and health hazard classification using deep learning. Singh has maintained extensive collaborations throughout his career, notably with Krishna R. Pattipati (15 joint publications), Anuradha Kodali (8 publications), and David J. Greaves (5 publications), reflecting his ability to bridge theoretical computer science with practical engineering applications. His work spans multiple prestigious venues including FPGA, FCCM, ICFP, and IEEE Transactions on Systems, Man, and Cybernetics, demonstrating both theoretical depth and practical impact across computer architecture, programming languages, and applied machine learning domains.

• International Market Research
• Renewable Energy from Algal Biomass
• Digitalization in Healthcare
• +4 more

Prof. Ute Marx is a Professor of Medical Technology at Hochschule Pforzheim, Germany. She holds a habilitation (2003) and PhD (1996) in Natural Sciences from Universität Bayreuth. Her research focuses on renewable energy from algal biomass, digital healthcare innovation, and market research in medical technology. Previously, she served as Senior Research Officer at the University of Queensland (2004-2008), and held academic roles at Universität Bayreuth including Privatdozentin (2003-2015). Education: Diplom-Naturwissenschaftler (1993), Dr. rer. nat. (1996), Habilitation (2003) Professional Memberships: Active in biofuels research, medical imaging AI, and hospital technology modernization Her awards include the Bavarian Habilitation Fellowship (2003) and recognition from the Chemical Industry Fund (1996) for academic excellence. She has co-authored over 30 peer-reviewed publications across biomedical engineering, metabolic disorders, and renewable energy systems.