Professor Carlo Harvey is a creative technologist at the School of Digital Arts (SODA), Manchester Metropolitan University. His interdisciplinary research merges games , machine learning , virtual production , and cultural heritage reinterpretation . He leads industry collaborations with entities like Jaguar Land Rover and Epic Games, focusing on AI-driven interactive audio, real-time visualization, and accessibility solutions. Award-winning projects : TIGA, Innovate UK, and Epic Games MegaGrant for Accession Industry partnerships : Automotive sector, cultural institutions His research spans human-computer interaction , multisensory virtual environments , and acoustic-visual cross-modal perception . Recent publications address robotic simulations, motion alignment, and haptic feedback systems. Scientific recognition : TIGA Award, Innovate UK Funding, Epic Games MegaGrant Advocacy : Digital inclusion, creative collaboration, social impact of technology
Dr. Chandranath Adak is an Assistant Professor at the Department of Computer Science and Engineering, Indian Institute of Technology Patna (IIT Patna), and concurrently serves as a Visiting Fellow at the School of Computer Science, University of Technology Sydney (UTS), Australia. He holds a Ph.D. in Analytics from UTS (2019) and previously served as an Assistant Professor at Indian Institute of Information Technology Lucknow (IIITL) and the Centre for Data Science at JIS Institute of Advanced Studies, Kolkata. Education: Ph.D. (Analytics), University of Technology Sydney (2019) M.Tech., Computer Science and Engineering, University of Kalyani (2014) B.Tech., Computer Science and Engineering, West Bengal University of Technology (2012) Research Interests: His work spans Computer Vision, Deep Learning, Reinforcement Learning, Document Image Analysis, and AI-driven solutions for healthcare, forensics, and industrial automation. He has pioneered methods in biomarker detection using electrochemical sensors combined with ML models, handwriting analysis for educational and forensic applications, and anomaly detection in industrial systems. His research bridges theoretical advances with real-world applications, such as medical diagnostics and quality control systems. Publications: His recent work includes innovations in biosensor-based medical diagnostics, handwriting evaluation systems, and transformer networks for historical document analysis. These contributions reflect a focus on interdisciplinary applications of AI across healthcare, cultural heritage preservation, and industrial automation. Awards: Start-up Research Grant, SERB, India (2022) Dr. Kalam Doctoral Scholarship, UTS (2018) IEEE CIS Graduate Student Research Grant (2017) Senior Member, IEEE (2024) Teaching & Supervision: Taught courses at UTS including 'Introduction to Data Analytics' and supervised research in machine learning and computer vision. His mentorship emphasizes hands-on experience with AI tools and real-world problem-solving. Labs & Teams: Engaged in collaborative projects at UTS's CIBCI Centre and Griffith University's IIIS, focusing on computational intelligence and sensor-driven AI systems.
Farnoush Banaei-Kashani is an Associate Professor (Tenured) in the Department of Computer Science and Engineering at the University of Colorado Denver. She also holds an Adjunct Associate Professor position in the Department of Mathematical and Statistical Sciences. As the founder and director of the Big Data Management and Mining Lab (BDLab), she leads multiple GAANN Fellowship Programs, including BDSE (Big Data Science and Engineering), DDC (Data-Driven Cybersecurity), and II (Infrastructure Informatics). She directs the 'Data Science in Biomedicine' MS Track and focuses on data-driven decision systems (DDSs), integrating machine learning and big data analytics into healthcare, energy, transportation, and environmental applications. Education: Details not explicitly provided in the text. Her research spans data management cycles for DDSs, addressing challenges like big data volume, velocity, and variety. Key projects include iWatch (crime surveillance), POCM (mobility monitoring), and GeoSIM (urban texture documentation). She teaches courses such as Machine Learning Systems, Big Data Science, and Data Mining. Publications highlight advancements in sea ice classification, federated learning, proteomic networks, and privacy-preserving AI. Her work is funded by NSF, NIH, DOT, and industry partners like Google and IBM. She has advised numerous students and contributes to academic leadership as editor, conference chair (ACM SIGSPATIAL 2018/2019), and program committee member for venues like SIGMOD and KDD.
Charless Fowlkes is a Professor in the Department of Computer Science at the University of California, Irvine (UCI). His research focuses on computational vision, spanning human visual system understanding, machine vision systems, and applications in biomedical informatics and forensic science. He holds a Ph.D. from UC Berkeley (2005). His work integrates techniques from computer vision, AI, and applied mathematics to address challenges in automated biological data analysis, morphology, and spatial gene expression. Key research areas include forensic science (e.g., shoeprint matching via 3D reconstruction), biomedical applications (e.g., heart function mapping and pollen classification), and AI-driven systems for scene understanding. Recent projects include a $20M forensic science center funded by the National Institute of Justice. His publications emphasize geometric reasoning, 3D reconstruction, and adaptive learning algorithms. Notable contributions include developing algorithms for 3D human pose estimation with scene constraints, automated pollen identification via CNNs, and frameworks for cross-domain forensic analysis. His work bridges theoretical computer vision with real-world applications in forensics, healthcare, and environmental science.
Professor Jerome Liang is a distinguished faculty member at Stony Brook University's Renaissance School of Medicine, holding professorships in Radiology, Biomedical Engineering, Electrical and Computer Engineering, and Computer Science. He serves as Co-Director of Radiology Research and has established himself as a leading expert in medical imaging reconstruction techniques. Dr. Liang's educational background includes a Ph.D. in Physics from City University of New York, postdoctoral training at Duke University, and fellowship at Albert Einstein College of Medicine. His undergraduate degree in Modern Physics was obtained from Lanzhou University in China. His primary research interests focus on advanced medical imaging techniques, particularly low-dose computed tomography image reconstruction, quantitative SPECT reconstruction, high-resolution PET imaging, tissue segmentation from multi-spectral images, computer-aided diagnosis systems, and virtual colonoscopy development. His work bridges engineering principles with clinical applications to improve diagnostic imaging capabilities while reducing radiation exposure. Analysis of his recent publications reveals a strong focus on machine learning applications in medical imaging, particularly in polyp classification, dual-energy CT spectral analysis, and virtual endoscopy. His research consistently aims to enhance diagnostic accuracy while optimizing radiation dose and improving visualization techniques for various medical conditions. 1981 China-US Physics Examination and Application Program (CUSPEA) Winner (Top 25 among 250,000 candidates) 1990 NIH First Investigator Award 1996 American Heart Association Established Investigator Award 1996 Radiological Society of North America Certificate of Merit Award 2002 SUNY Chancellor's Entrepreneur Award 2007 IEEE Society Fellow 2011-2013 SBU, BNL and CSHL Certificates of Excellence in Research and Invention 2013 Stony Brook School of Medicine Award for Excellence in Translational Research Dr. Liang has secured significant research funding including NIH/NCI R01 grants for "Advanced Virtual Colonoscopy for Early Cancer Screening" and "Radiogenomics of Colorectal Polyps." He currently leads active protocols including IRB 93995-MODCR005 focused on integrating virtual and optical colonoscopies with pathological analysis. His laboratory (IRIS - Imaging Research and Informatics) continues to advance medical imaging technology while mentoring the next generation of researchers in this critical field.
Dr. Andy Nguyen is a Senior Lecturer in the School of Engineering at the University of Southern Queensland. He holds a PhD from Queensland University of Technology (QUT), an MEng from the National University of Civil Engineering (NUCE), and a BEng from NUCE. His research focuses on structural health monitoring, integrating machine learning and deep learning techniques to assess infrastructure integrity. Key areas include damage detection in bridges, pavements, and buildings, as well as sustainable construction materials like bamboo. Nguyen leads projects such as the 'Next Generation Living Laboratory for Engineering Education and Engagement,' emphasizing real-world applications of technology in civil infrastructure. His work spans crack detection algorithms, finite element model updating, and vibration-based structural analysis. He collaborates on AI-driven solutions for autonomous vehicle object detection and smart maintenance planning. Nguyen’s contributions include over 50 peer-reviewed publications and active supervision of postgraduate research in composite materials and transport infrastructure. His research outputs highlight advancements in computational mechanics, sensor technologies, and data-driven methods for infrastructure resilience. Nguyen’s expertise bridges civil engineering challenges with cutting-edge machine learning, advancing both theoretical and applied solutions for sustainable and safe structures.
Novi Quadrianto is a Professor of Machine Learning at the School of Engineering and Informatics, University of Sussex, where he joined as a Lecturer in February 2014. He is currently a Principal Investigator on three active EU grants: BayesianGDPR (ERC), TANGO (EU Horizon RIA), and Act.AI (ERC Proof of Concept). He also holds an Adjunct Professor position in Data Science at Monash University, Indonesia, and serves as Strategic Lab co-Leader of the BCAM Severo Ochoa Strategic Lab on Trustworthy Machine Learning in Bilbao, Spain. His educational background includes a PhD in Machine Learning from the Australian National University (2012) and a BEng in Electrical and Electronics Engineering from Nanyang Technological University, Singapore. During his PhD, he conducted research at multiple international institutions including HIIT-Finland, Yahoo! Research-US, University of Alberta-Canada, Fraunhofer IAIS-Germany, and IST Austria. From 2012-2014, he was a Newton International Fellow of the Royal Society at the University of Cambridge. Professor Quadrianto directs the Predictive Analytics Lab (PAL) since 2017, which focuses on "Responsible AI" research developing AI models that embed fairness, accountability, transparency, and trustworthiness. His research spans algorithmic fairness, federated learning, and computer vision, with applications in sustainable development, healthcare, and finance. His work has been funded by prestigious organizations including the European Research Council, EPSRC, and HM Treasury. His publications reveal a strong focus on addressing challenges in AI fairness, robustness, and privacy, particularly in dynamic environments and heterogeneous data settings. Recent work explores performative prediction, diversity-driven learning, and efficient vision transformer inference, demonstrating his leadership in cutting-edge machine learning research. European Research Council ERC Proof of Concept Grant (2023) Guarantor Researcher for BCAM Severo Ochoa Excellence Accreditation (2023) European Lab for Learning and Intelligent Systems (ELLIS) Scholar/Fellow (2020) European Research Council ERC Starting Grant (2019) Newton International Fellowship (2012) Microsoft Research Asia Fellowship (2009) Professor Quadrianto currently supervises six PhD students and five postdoctoral researchers. He has served as Action Editor for Transactions on Machine Learning Research since 2022 and as Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence since 2016. He has also been an Area Chair for major conferences including NeurIPS, ICML, and AAAI. His PAL laboratory hosts a team of 15 members focused on inter-disciplinary AI research with domain experts across various sectors. The PAL Lab operates three innovation strands: AI for Sustainable Development (supporting UN SDGs), AI for Healthcare (transforming health outcomes), and AI for Finance (personalized loan decision-making). The lab also leads initiatives in Diversity & Inclusion in AI and offers Pro-Bono Office Hours to organizations seeking guidance on machine learning aspects.
Curtis Lee Baker is a Professor in the Department of Ophthalmology & Visual Sciences at McGill University's Faculty of Medicine, with an associate appointment in the Department of Biomedical Engineering. His research focuses on understanding human visual perception through neural mechanisms relevant to real-world visual processing. His laboratory investigates how early visual processing detects complex cues like contrast, texture, and motion to establish figure-ground relationships and depth perception. Key research areas include: Neural mechanisms of second-order vision Texture and motion processing Figure-ground segregation Depth perception from motion parallax Computational modeling of visual cortex Dr. Baker employs diverse methodologies including single-unit electrophysiology, optical imaging, human psychophysics, and machine learning. His recent publications (2022-2014) demonstrate consistent focus on neural processing of visual boundaries, texture perception, and motion-based depth cues, with increasing integration of computational approaches like convolutional neural networks. His work bridges neuroscience, engineering, and computational modeling to understand fundamental visual processing mechanisms. Current students include Ana Ramirez Hernandez, Jinani Sooriyaarachchi, and Ethan Pirso, with several alumni having completed graduate work in neuroscience, physiology, and biomedical engineering. The lab actively recruits students with quantitative backgrounds for projects involving signal processing, machine learning, and neurophysiological data analysis.
Alan H. Barr is a Professor of Computer Science at the California Institute of Technology (Caltech), affiliated with the Division of Engineering and Applied Science and the Computation & Neural Systems (CNS) department. He is a founding member of the Caltech Computer Graphics Group and a leader in developing mathematically rigorous methods for computer graphics and predictive modeling. His research focuses on enhancing computational modeling accuracy through approaches like interval analysis and constraint-based systems. Notable contributions include deformable models, quaternion interpolation, and cellular simulation frameworks. He has advised over 20 graduate students, many of whom became industry leaders at Pixar, Microsoft Research, and academic institutions like NYU and Brown University. Awards include the ACM SIGGRAPH Achievement Award (1988) and ACM Fellow (1995). Research Interests: Predictive modeling with error bounds Scientific visualization and MRI data analysis Biophysical systems simulation (e.g., cellular organelles) Self-assembling robotic structures for space colonization Mathematically robust computer graphics techniques Key Collaborations: Caltech Biological Imaging Center (Beckman Institute) JPL (Jet Propulsion Laboratory) New computational substrates research (quantum/DNA computing) Recent Work: Expanding into computational biology, medical imaging optimization, and high-confidence systems for managing complex computational interactions. Active in interdisciplinary projects across Caltech divisions.
Adrien Depeursinge is a Professor at HES-SO Valais-Wallis - Haute Ecole de Gestion, affiliated with the School of Economics and Services and the Management Information Systems department. His research focuses on radiomics, personalized medicine via image-based analysis, and clinical workflow optimization. He leads the development of the QuantImage platform, a physician-centered web-based tool for radiomics research, and contributes to radiomics standardization efforts through initiatives like the Image Biomarker Standardization Initiative (IBSI). His work emphasizes machine learning applications in healthcare, including tumor segmentation, biomarker extraction, and improving diagnostic accuracy through computational models. Key research themes include: 1) Radiomics – developing quantitative imaging features for cancer diagnosis/prognosis; 2) Medical Imaging Analysis – advancing texture-based models, multi-modal fusion, and automated lesion detection; 3) Physician-AI Collaboration – designing user-centric tools for clinical integration. His contributions span neuro-oncology (brain metastases), head-and-neck cancer, and multiple sclerosis imaging. Publications emphasize methodological advancements (e.g., kernel optimization in CNNs, steerable detectors) and clinical validation (e.g., reproducibility of radiomics features across imaging protocols). He collaborates with institutions like the University Hospital of Lausanne (CHUV) and international teams on projects like the HECKTOR challenge for PET/CT tumor segmentation. His work bridges technical innovation with clinical impact, aiming to translate radiomics into actionable clinical tools. QuantImage v2, his flagship tool, enables no-code development of machine learning models using clinical imaging data. Research also includes phantom-based validation of radiomics features and addressing challenges in feature stability across imaging modalities. Current projects explore improving contour quality for radiomics studies and optimizing AI explainability in medical decision-making.
Tommy Löfstedt is an Associate Professor at Umeå University , affiliated with the Department of Computing Science and the Department of Mathematics and Mathematical Statistics. His research focuses on machine learning , computer vision , and medical image analysis , with applications in life sciences, radiation therapy, and biomedical imaging. He leads multiple research projects, including AI-driven delineation in radiation therapy, quantitative MRI for radiotherapy, and machine learning for plant nutrient uptake. Current research emphasizes structured regularization methods to improve model interpretability and robustness. Key applications include medical image segmentation , Alzheimer's classification , and uncertainty estimation in MRI . Recent publications highlight his work on morphological regularization , adversarial attack mitigation , and multi-task learning in medical imaging contexts. His projects span 2022–2026 with funding for pediatric oncology automation and gynecological cancer staging. Affiliated with both computing and mathematical departments, he bridges algorithm development with applied mathematical frameworks in medical and life science domains.
Stephen Brooks is a Professor in the Faculty of Computer Science at Dalhousie University, actively contributing to research and education in computer graphics, visualization, and human-computer interaction. He is affiliated with the Human-Computer Interaction, Visualization & Graphics research cluster and currently supervises multiple graduate students on diverse projects. PhD in Computer Science, University of Cambridge (2004) MSc in Computer Science, University of British Columbia (2000) BSc, Brock University (1998) His research focuses on computer graphics and visualization, particularly non-photorealistic rendering, image editing, 3D geospatial systems, ocean visualization, and real-time rendering of natural phenomena. He has also worked in sound synthesis and motion editing. His recent publications show a strong emphasis on visual analytics, network flow visualization, and ocean science applications. His work spans interdisciplinary domains including environmental science, genomics, cybersecurity, and digital art. He has developed visualization tools for ocean science under a major CFREF-funded initiative and created novel methods for rendering stained glass, mixed media art, rivers, and ocean surfaces. His research integrates perception, automation, and user interaction to enhance visual analysis. Notable scientific contributions include work on tone mapping optimization, uncertainty visualization using chromatic aberration, semantic object clouds, and hybrid 2D/3D GIS. His publications appear in top venues such as IEEE TVCG, ACM Transactions, and SIGGRAPH. NSERC Discovery Grants Canada First Research Excellence Fund (CFREF) NSERC CREATE Mitacs Accelerate and Globalink CFI New Opportunities Grant Cyber Security Research and Development Grant He has supervised numerous PhD, Master’s, and undergraduate students in areas including ocean visualization, tone mapping, network security, VR, and geospatial analytics. He teaches courses in Game Design, Visualization, Computer Animation, and Network Computing, emphasizing project-based and interdisciplinary learning. He leads research in visual analytics for network data (FloVis), ocean science, and mixed reality collaboration. His lab develops interactive systems for data exploration in domains ranging from marine biology to cybersecurity. Future work includes expanding ocean-first climate visualization and enhancing mixed presence collaboration in immersive environments.
Bjoern Menze is a Professor and Rudolf Mößbauer Tenure Track Chair at the Technical University of Munich (TUM), leading the Image-based Biomedical Modeling Group within the Munich School of Bioengineering. His research focuses on medical image computing, tumor growth modeling, and computational physiology, with applications in clinical neuroimaging and personalized radiotherapy design. He holds a Ph.D. in Computer Science from Heidelberg University and has held positions at ETH Zurich, INRIA Sophia Antipolis, MIT, and Harvard Medical School. His academic journey includes a postdoc at MIT’s CSAIL and Harvard Medical School, followed by roles at ETH Zurich and INRIA. His work bridges biomedical imaging with machine learning, emphasizing model-driven analysis of physiological processes. He has been a visiting professor at Maastricht University and contributes to initiatives like the Center for Translational Cancer Research at TUM. Key research areas include tumor growth modeling, quantitative imaging biomarkers, and integrating mathematical models with clinical data. His awards include the MICCAI Young Scientist Award (2014), Leopoldina Fellowship (2009), and DFG Research Fellowship (2008). He advises on medical AI, leads interdisciplinary projects, and publishes extensively in top journals like Nature Neuroscience and IEEE Transactions on Medical Imaging. His lab’s work spans applications such as glioblastoma radiotherapy optimization, whole-body bone lesion detection, and neural connectivity imaging. Collaborations include institutions like Harvard, MIT, and ETH Zurich. He emphasizes translating computational methods into clinical practice for personalized healthcare solutions.
Dr. Andy Nguyen is a Senior Lecturer in Structural Engineering at the University of Southern Queensland, within the School of Engineering. He is an active researcher and educator, specializing in the Structural Health Monitoring (SHM) of critical civil infrastructure such as bridges, buildings, and transport tunnels. Bachelor of Engineering (BEng), NUCE, 1999 Master of Engineering (MEng), NUCE, 2003 Doctor of Philosophy (PhD), Queensland University of Technology (QUT), 2014 Dr. Nguyen's research is at the forefront of integrating advanced technologies into civil engineering. His primary focus is on developing and deploying sophisticated SHM systems that utilize sensors, data analytics, and machine learning to provide real-time insights into the structural integrity of ageing infrastructure. His work aims to enable proactive maintenance, extend the lifespan of structures, and enhance public safety. He has successfully implemented monitoring systems on major bridges and high-rise buildings in Queensland and New South Wales, with systems capable of even detecting distant earthquake events. His research interests span Structural Health Monitoring, Machine Learning for Engineering, Damage Detection, Finite Element Model Updating, Sustainable Building Materials like bamboo, and the application of AI for automated condition assessment of transport infrastructure. The analysis of his recent publications reveals a strong and consistent research trajectory centered on the application of data-driven and AI methods to solve practical problems in civil infrastructure. His work frequently combines signal processing techniques (like Stockwell Transform) with deep learning models for tasks such as crack detection in concrete and pavement. He also conducts significant research on model updating for complex structures like cable-stayed and arch bridges, using vibration data and optimization algorithms. The integration of machine learning for overload classification and the development of cost-effective, automated monitoring systems are key trends in his recent output. Advanced Queensland Fellow (2024-2027) Dr. Nguyen is actively involved in research supervision and collaboration. He is currently supervising several postgraduate students on projects related to AI-powered condition assessment, bamboo as a sustainable building material, and railway track design. He receives research funding from the Queensland Government through his Advanced Queensland Fellowship. His research has direct practical applications, as evidenced by his public engagement, such as writing for The Conversation on safeguarding ageing bridges, and his work with the Australian Network of Structural Health Monitoring. Dr. Nguyen's work embodies the development of a next-generation 'Living' Laboratory for engineering education, where research, teaching, and real-world infrastructure monitoring are integrated. His current projects involve creating smart, automated fault detection systems and advancing 'digital twin'-based monitoring platforms for infrastructure.
Ali Gooya is a Senior Lecturer (Associate Professor) in Machine Learning at the School of Computing Science, University of Glasgow, UK. His research focuses on probabilistic deep learning applied to medical imaging, particularly in cardiology and oncology, emphasizing semi/unsupervised methods due to sparse expert annotations. He holds a PhD in medical image analysis from the University of Tokyo (2007) and has held academic positions at the University of Leeds and Sheffield before joining Glasgow in 2022. Affiliations: Senior Lecturer in Machine Learning, University of Glasgow (2022–present) Lecturer in Computing, University of Leeds (2018–2022) Lecturer in Computing, University of Sheffield (2016–2018) Postdoctoral Researcher, University of Pennsylvania (2008–2011) Research Interests: Deep learning for medical imaging, probabilistic modeling, cardiac and cancer imaging, computational anatomy, and marker discovery. Key applications include motion analysis, segmentation, and predictive modeling in healthcare. Key Achievements: Won prestigious fellowships including Allen Touring Institute (2022), JSPS Short-Term (2020), Marie-Curie IIF (2014), and JSPS-PDRA (2008). Pioneered Bayesian deep learning frameworks for cardiac motion assessment and generative models in medical imaging. Grants & Supervision: EPSRC Impact Acceleration Award (PI) EPSRC New Investigator Grant (EP/S012796/1) Actively supervising PhD students in areas like Bayesian deep atlases for cardiac motion analysis. Labs & Teams: Leads research in medical AI within the School of Computing Science, collaborating on projects integrating imaging and patient metadata for clinical decision support.
