Find Academic Supervisors

• Medical Imaging
• Deep Learning
• Computer Vision
• +3 more

Dr. Thomas Pinetz is a researcher at the University of Bonn's Institute for Applied Mathematics, affiliated with the Effland Group. His work focuses on advancing medical imaging through deep learning, particularly in reducing contrast agent doses in MRI and enhancing image reconstruction techniques. He holds a PhD from Graz University of Technology, where his research centered on inverse problems in imaging using learning-based approaches and Wasserstein distances. Prior to his current position, he collaborated with Prof. Alexander Effland in Graz, extending this research to medical applications. His expertise spans computer vision, machine learning, and medical imaging, addressing challenges like signal amplification in brain MRI and synthetic data generation for radiology. His recent publications emphasize ethical concerns in AI generalizability, contrast agent reduction, and clinical applications such as stroke detection via CT-angiography. He actively contributes to open-source tools and collaborates across disciplines, bridging mathematics and life sciences. Office: N 2.002, Endenicher Allee 60, Bonn. Contact: tpinetz@uni-bonn.de.

• Abdominal Oncologic Imaging
• Genitourinary Imaging
• Radiomics
• +5 more

Vinay Anant Duddalwar is a Clinical Professor of Radiology, Urology, and Biomedical Engineering at the Keck School of Medicine, University of Southern California. He also serves as Medical Director for Imaging Services at the USC Cancer Center. Education: MD from Nagpur, Radiology residencies in Pune (India) and Aberdeen (UK), Abdominal Imaging Fellowship at University of British Columbia (Vancouver) Current Affiliations: Keck School of Medicine (USC), USC Radiomics Lab (radiomicslab.usc.edu) Dr. Duddalwar specializes in abdominal oncologic imaging , particularly genitourinary imaging , with a focus on radiomics, quantitative imaging, and multiomic evaluation of neoplasms. His lab develops AI-driven decision support systems for cancer imaging and explores radio-genomics , angiogenesis evaluation , and immune correlates in tumors. Key research themes include: Radiomics for cancer characterization Artificial Intelligence in imaging analysis Multiomic biomarker development Treatment response monitoring Explainable AI frameworks Imaging standardization The USC Radiomics Lab he leads is funded by federal, foundation, and industry grants, with active projects in: Multi-parametric imaging of prostate cancer Texture analysis in renal tumors Deep learning applications Biomarker discovery Imaging-pharmacologic correlations Quality improvement initiatives He actively collaborates with national/international radiology societies and serves as GU section editor for Clinical Radiology . His work appears in journals like J Urol , Eur Radiol , and Sci Rep .

Toshiya HachisukaView profile

Associate Professor

• Computer Graphics
• Light Transport Simulation
• Computational Statistics
• +3 more

Toshiya Hachisuka is an Associate Professor in the Department of Computer Science at the University of Waterloo, part of the School of Computer Science. His research focuses on combining applied mathematics, computer science, and physics to address challenges in visual simulation, particularly in computer graphics, light transport, and computational statistics. He holds a Ph.D. from the University of California, San Diego (2011) and a B.Eng. from the University of Tokyo (2006). Research Interests: Hachisuka’s work spans light transport simulation , Monte Carlo rendering , numerical analysis , and fluid dynamics . He develops algorithms for efficient rendering and simulation, including methods like segment-based light transport and quantum ray marching. His techniques often integrate mathematical rigor with computational efficiency. Publications: His recent work explores advancements in Monte Carlo methods, fluid simulation, and quantum computing applications in rendering. Notable themes include denoising techniques , boundary element methods , and gradient-domain rendering . Awards: No scientific awards explicitly mentioned in the text. Advising & Grants: No specific students or grants listed, but his extensive publication record suggests active research collaboration. Labs/Teams: Involved in computer graphics research groups at the University of Waterloo, contributing to cutting-edge rendering and simulation projects.

Maria AnticoView profile

Research Fellow

• Medical Imaging
• Robotics
• Biomedical Engineering
• +3 more

Dr. Maria Antico is a Research Fellow in the Biomechanics and Spine Research Group (BSRG) and Queensland Unit for Advanced Shoulder Research (QUASR), and a Research Engineer in the School of Electrical Engineering and Robotics at Queensland University of Technology. Her work focuses on advanced tissue recognition using deep learning, robotic automation of medical imaging, and AI-driven surgical guidance systems. She holds a BEng in Engineering Sciences from the University of Rome Tor Vergata (2014) and an MEng in Biomechanical Engineering from Delft University of Technology (2016), collaborating with Philips Research. Her PhD (QUT, 2024) explored automatic image analysis for autonomous robots in minimally invasive surgery. Her research interests span medical imaging, robotics, and AI applications in surgery. Her articles highlight advancements in lung ultrasound diagnostics, spinal growth modelling, and real-time 3D ultrasound reconstruction. Notably, her work bridges robotics and imaging for precision medicine, with contributions to automated MRI synthesis and robotic guidance systems. Her research has been applied to clinical contexts, such as prostate radiotherapy planning and knee arthroscopy. Collaborations include Philips Research, emphasizing translational research.

Fred ParkView profile

Assistant Professor

• Mathematical Image Processing
• Computer Vision
• Numerical Partial Differential Equations
• +3 more

Fred Park serves as Assistant Professor of Mathematics in the Department of Mathematics & Computer Science at Whittier College. His research bridges mathematical theory with practical applications in image processing and computer vision, maintaining active collaborations across computational disciplines. Education: Ph.D. in Mathematics, University of California, Los Angeles M.A. in Mathematics, University of California, Los Angeles Professor Park's research focuses on mathematical image processing with emphasis on total variation methods, blind deconvolution, and shape modeling. His work spans computer vision applications including 3D surface reconstruction, texture extraction, and medical image analysis. Current projects explore non-convex optimization for deep learning compression and scientific computing implementations for real-world problem solving. His teaching philosophy integrates real-world mathematical problems to inspire undergraduate learning. Recent publications demonstrate strong trends in image segmentation frameworks combining anisotropic-isotropic total variation, network slimming for CNN efficiency, and medical imaging applications such as COVID-19 lung scan clustering. His work consistently bridges theoretical mathematics with computational implementation. Professor Park actively seeks to incorporate research into undergraduate education, developing methodologies to motivate learning through practical applications. His teaching experience spans undergraduate lower and upper division courses at UCLA, UC Irvine, and University of Michigan during postdoctoral positions. Research facilities include computational resources at Whittier College's Science & Learning Center (SLC 218), supporting work in image processing, shape modeling, and 3D reconstruction from point cloud data.

Joseph P. HavlicekView profile

Professor

• Signal Processing
• Image Processing
• Video Processing
• +7 more

Dr. Joseph P. Havlicek is the Gerald Tuma Presidential Professor in the Gallogly College of Engineering at the University of Oklahoma, where he has served since 1997. He directs the OU Center for Intelligent Transportation Systems and is a full member of the OU Institute for Biomedical Engineering, Science, and Technology. His extensive career spans both academic and professional realms, with significant contributions to signal processing, image analysis, and transportation systems. His educational background includes: BS in Electrical Engineering from Virginia Tech (1986) MS in Electrical Engineering from Virginia Tech (1988) PhD in Electrical and Computer Engineering from the University of Texas at Austin (1996) Dr. Havlicek's research spans multiple domains including signal, image, and video processing; modulation domain signal processing; target tracking; medical imaging; and intelligent transportation systems. His work bridges theoretical signal processing with practical applications in healthcare and transportation. He has pioneered techniques in AM-FM signal and image modeling, uncertainty measures, and developed innovative approaches for medical image analysis and infrared target tracking. Analysis of his recent publications reveals a strong interdisciplinary trend, with significant work at the intersection of signal processing and medical imaging, particularly in bone marrow assessment and cancer treatment monitoring using PET/CT imaging. His computer vision research focuses on infrared object detection and tracking, with recent work on dual-band infrared systems improving small object detection. The publications also demonstrate his continued theoretical contributions to signal processing, particularly in modulation domain techniques and entropy-based uncertainty measures. His scientific achievements have been recognized with numerous awards: University of Oklahoma Outstanding Faculty Advisor Award (2006) University of Oklahoma College of Engineering Brandon H. Griffith Faculty Award (2003) University of Oklahoma IEEE Favorite Instructor Award (1998, 2000) Department of the Navy Award of Merit for Group Achievement (1990) University of Texas Engineering Foundation Award for Exemplary Engineering Teaching (1992) Dr. Havlicek has been exceptionally successful in securing research funding, serving as PI or co-PI on more than 100 externally funded grants and contracts totaling over $25 million. His professional service includes editorial positions with IEEE Transactions on Image Processing and IEEE Transactions on Industrial Informatics, as well as leadership roles in major conferences including ICIP and ICASSP. He has chaired both graduate and undergraduate studies committees in the ECE department. He leads the OU Center for Intelligent Transportation Systems, which develops advanced technologies for traffic management and traveler information systems. His biomedical engineering work through the OU Institute focuses on medical image analysis, particularly for cancer treatment monitoring and bone marrow assessment. His research teams regularly collaborate with medical institutions to translate signal processing techniques into clinical applications.

Mathieu Alemany OliverView profile

Associate Professor

• Marketing
• Consumer Behavior
• Marketing Ethics
• +3 more

Mathieu Alemany Oliver is an Associate Professor in the Department of Marketing at Toulouse Business School. His research focuses on consumer behavior, brand management, and marketing ethics with an emphasis on topics such as conspiracy theory impacts, childlikeness in consumption, and brand-age perception. He explores intersections between narrative structures, digital consumption patterns, and sociocultural dynamics through a multidisciplinary lens. Key research areas include analyzing how conspiracy narratives shape consumer-brand relationships, investigating the role of brand foundation dates in consumer attitudes, and studying fan communities' roles in service recovery. His work integrates qualitative comparative analysis (fsQCA), narratology, and ethical frameworks to address modern marketing challenges. Notable contributions include studies on sharing economy bypass practices, materialism reinterpretations through cultural lenses, and the application of new materialism theories. His recent publications highlight evolving consumer behaviors in contexts like connected running technologies and manga market dynamics. Mathieu has published extensively in peer-reviewed journals and presented at academic conferences. His research often bridges cultural studies, psychology, and business practices, addressing contemporary issues such as environmental anxiety and the ethical implications of big data in marketing.

• Hybrid Variational/DL methods under topological constraints
• Hybrid Variational/DL methods under geometric constraints
• PDE Analysis and numerical simulation
• +5 more

Carole Le Guyader is an Associate Professor in the Mathematical and Software Engineering Department at the National Institute of Applied Sciences of Rouen (INSA Rouen), France. She has been actively contributing to the fields of image processing, mathematical analysis, and computational methods since joining INSA Rouen in 2009, after previously serving as an Assistant Professor at INSA Rennes from 2005 to 2009. Her academic leadership extends to various administrative roles including Head of Mathematics teaching at the STPI INSA Rouen department since 2019 and Director of the Mathematical Engineering Department since 2013. Dr. Le Guyader earned her Habilitation to Supervise Research on November 16, 2012, at Rouen University with the thesis titled "Contribution to the mathematical analysis of images: segmentation, registration and decomposition." Her habilitation jury included distinguished mathematicians such as Grégoire ALLAIRE from Ecole Polytechnique and Gabriel PEYRE from University Paris-Dauphine. Her primary research interests focus on the development and application of variational methods and deep learning techniques for image processing, with particular emphasis on incorporating topological and geometric constraints. She investigates mathematical models for image segmentation, registration, and decomposition, applying these techniques to biomedical imaging, geophysics, and other scientific domains. Her work bridges theoretical mathematics with practical applications in medical diagnostics and industrial settings. Le Guyader's publication record demonstrates a consistent focus on advancing image processing methodologies, particularly in the integration of traditional variational approaches with modern deep learning techniques. Her recent work shows increasing emphasis on medical applications, especially in radiotherapy planning and MRI reconstruction, where geometric and topological constraints play critical roles in ensuring clinically meaningful results. Dr. Le Guyader has successfully supervised multiple PhD students throughout her career, demonstrating her commitment to mentoring the next generation of researchers. Her collaborative approach is evident in her extensive network of academic and industrial partnerships across Europe and the United States. She has secured and managed numerous research projects including the LMI project "Mathematical Imaging and Digital Analysis" at CRIHAN since 2013, and the wind field modeling project with LABEX AMIES since 2014. Her leadership extends to coordinating INSA teams at the GENCI/CRIANN Hackathon since 2017 and participating in regional and European research initiatives.

DEBROUX NoémieView profile

Assistant Professor

• Image Processing
• Numerical Analysis
• Topology Optimization
• +7 more

DEBROUX Noémie is a French academic specializing in image processing and numerical analysis. She earned her Ph.D. in 2018 from INSA Rouen, supported by the M2NUM project co-financed by the European Union and Normandie Regional Council. Currently, she is a Maître de Conférences (Assistant Professor) at Clermont Auvergne University since 2019. Her research focuses on variational models, nonlocal methods, and topology-preserving segmentation for shape analysis and biomedical imaging applications. Ph.D. in Mathematics (2018), INSA Rouen MCF (Assistant Professor) at Clermont Auvergne University (2019–present) Research partnerships with University of Cambridge, UC Los Angeles, and Monash University Her work spans three key areas: variational models for image segmentation and registration, nonlocal characterization of total variation and shape descriptors, and topology optimization for 3D printing. She integrates mathematical rigor with practical applications in biomedical imaging, materials science, and computational geometry. Her publications demonstrate expertise in joint segmentation/registration frameworks (2016–2020), nonlocal topology preservation (2017), and crack recovery in bituminous surfacing (2018). Collaborations include Dassault Systèmes (2015) and CEREMA (2015–2016). She has presented at major conferences: SIAM Imaging Science (2016), Applied Inverse Problems (2017), and ISMRM (2019). Scientific Awards: Bourse Newton (2017) for Cambridge research L’Oréal-UNESCO for Women in Science nominee (2017) Prix de la Vocation Scientifique et Technique des Filles (2017) She has supervised engineering students' projects and taught mathematical analysis, variational methods, and machine learning (2016–2018). As an elected member of INSA Rouen's Scientific Council (2015–2016), she contributed to academic governance.

Dovilė BarauskaitėView profile

Research Fellow

• Consumer Behavior
• Health-Related Decision Making
• Compensatory Consumption
• +3 more

Dr. Dovilė Barauskaitė is a Senior Researcher at ISM University of Management and Economics. She holds an MSc in Marketing (Cum Laude) from Vilnius University and has five years of industry experience in market research. Her research focuses on consumer behavior, particularly the psychological mechanisms behind compensatory consumption, health-related decisions, and the impact of aversive states like stress or disease threats. She has conducted research visits at Columbia University, University of Groningen, and Tokyo University, and has been awarded doctoral scholarships from the Research Council of Lithuania (2018) and the Association for Consumer Research (2017). Her work explores how marketing tools can promote healthier choices, including studies on nature’s influence on body image, functional food consumption as self-medication, and stress-induced nostalgic purchasing. She has led projects such as the RCL World Class R&D Project (€596,951) on nudges for positive health behaviors, and the Lithuanian-Japan Cooperation Project (€79,915) on functional food preferences. Her research has been presented at major conferences like the Association for Consumer Research and Society for Consumer Psychology. Key contributions include uncovering how stress and life history strategies shape hedonic consumption, the role of nostalgia in coping with threats, and the reward value of natural environments. Her interdisciplinary approach bridges consumer psychology, health behavior, and economic modeling of production networks.

Juan FengView profile

Lecturer

• Nation Branding
• Place Branding
• Sports Communication
• +3 more

Dr. Juan Feng is a Lecturer in Public Relations at RMIT University’s School of Media & Communication. She holds a PhD in Media and Communication from RMIT and a Master’s in Communication Studies from Huazhong University of Science and Technology, China. With over a decade of experience, she specializes in teaching advertising, public relations, and digital media, emphasizing hands-on, project-based learning. Her research focuses on nation branding, place branding, sports communication, and public relations. Notably, she explores how digital media and social platforms influence brand authenticity and stakeholder engagement in national and cultural narratives. She has published in high-impact journals like Public Relations Review and European Journal of Cultural Studies , and presented at conferences such as the International Communication Association (ICA) and IAMCR. Juan’s work bridges academic and applied contexts, addressing topics like China’s international broadcasting strategy, sports diplomacy, and the role of influencers in shaping national identity. Though no formal research grants are listed, her contributions reflect significant scholarly impact in media and cultural studies.

Matthew M MarsView profile

Professor

• Community Development
• Entrepreneurship
• Marketing
• +7 more

Matthew M. Mars is a Professor and Interim Head at the University of Arizona, specializing in interdisciplinary research on community innovation and market narratives. He holds a PhD from the Center for the Study of Higher Education at the University of Arizona, an MEd in Counseling and Human Relations from Northern Arizona University, and a BA in Sociology and Anthropology from Utica College of Syracuse University. His research focuses on visual narratives in local consumption spaces (e.g., coffee shops, breweries), entrepreneurial community development, and higher education. He has been recognized with teaching awards including the Dorrance Scholarship Program Professor of Excellence in Teaching (2017–present), USDA/APLU Excellence in College and University Teaching Award (2020), and APLU Innovative Teaching Award (2018). Current teaching roles include courses such as PAH 200 (Introduction to Applied Humanities), PAH 391 (Preceptorship), and PAH 420 (Innovation and the Human Condition). He serves as Co-Editor of Advances in the Study of Entrepreneurship, Innovation and Economic Growth and Associate Editor of Community Development . Mars explores how local businesses and spaces shape community identity and economic resilience. His work bridges sociology, marketing, and environmental science to address challenges in sustainability, eco-entrepreneurship, and urban development.

• Statistical Shape Analysis
• Medical Imaging and Brain Structure Analysis
• Machine Learning
• +4 more

Anuj Srivastava is a Professor in the Department of Statistics at Florida State University. His research focuses on statistical shape analysis, computational geometry, and their applications in medical imaging, computer vision, and machine learning. He develops novel methods for analyzing complex shapes, trajectories, and functional data, with particular emphasis on elastic geometry and manifold-based techniques. Key research themes include shape analysis of 3D objects, brain subcortical structures, and tree-like anatomical networks. His work integrates statistical theory with computational tools to address challenges in biomedical imaging, motion tracking, and data-driven modeling. Notable contributions include frameworks for longitudinal elastic shape analysis (LESA), graph-based mobility modeling, and Bayesian emulation of human motion. Recent projects involve NSF-funded collaborations on stochastic shape processes, pandemic simulation (RAW-ALPS), and statistical analysis of chromosome conformations. His methods are applied to diverse domains such as neuroimaging, cell morphology analysis, and electrical load profiling. The research emphasizes interdisciplinary applications with clinical, environmental, and engineering relevance.

Shishir K. ShahView profile

Professor

• Computer Vision
• Machine Learning
• Statistical Image Analysis
• +5 more

Shishir K. Shah serves as Professor and Chair of the Department of Computer Science within the College of Natural Sciences and Mathematics at the University of Houston. His leadership extends to university-wide committees including Chair of the Undergraduate Committee of the Faculty Senate. Education: B.S. in Mechanical Engineering, The University of Texas at Austin M.S. & Ph.D. in Electrical and Computer Engineering, The University of Texas at Austin Professor Shah's research centers on computer vision fundamentals , specializing in machine learning and statistical methods for image/data analysis. His work addresses critical challenges in human behavior analysis, person re-identification under occlusion and clothing changes, video analytics, biometrics, and microscope image analysis. The Quantitative Imaging Laboratory he directs develops solutions for real-world surveillance, educational technology, and medical imaging applications. His recent publications reveal strong focus trends in occlusion-robust person re-identification (addressing both clothing changes and visual obstructions) and educational video analytics . These works demonstrate innovative approaches using graph attention networks, cross-modality learning, and transformer architectures to solve persistent challenges in visual recognition. Scientific Awards: John C. Butler Teaching Excellence Award (2011) Department of Computer Science Academic Excellence Award (2010, 2016) Student Vuong Dustin Nguyen received Dan E. Wells Outstanding Dissertation Award Professor Shah actively mentors graduate students, with documented success in dissertation supervision. His research program includes significant grant activity supporting the Quantitative Imaging Laboratory. He serves as Associate Editor for Image and Vision Computing, IEEE Journal of Translational Engineering on Health and Medicine, and Pattern Recognition, demonstrating strong community engagement. The Quantitative Imaging Laboratory operates at the intersection of theoretical computer vision research and practical applications, with projects spanning educational technology (Videopoints lecture video portal), surveillance systems, and biomedical imaging. Current work emphasizes robustness to real-world challenges like clothing changes, occlusions, and viewpoint variations in visual recognition systems.

Anupam SinghView profile

Lecturer

• GenAI
• Machine Learning
• Text Mining
• +5 more

Dr. Anupam Singh is a Lecturer in Marketing Analytics and Data Science at the Adam Smith Business School, University of Glasgow. He previously held positions at the University of Stirling and Nicolaus Copernicus University, Poland. He earned his Ph.D. from National Institute of Technology (NIT), Bhopal, India, and holds prestigious research fellowships from UGC and IIT Delhi. His research focuses on AI-driven decision-making in digital environments, algorithmic consumption patterns, and sustainability analytics. He applies machine learning and big data techniques to study consumer behavior, environmental consciousness, and pro-social consumption. Education: Ph.D., National Institute of Technology (NIT), Bhopal, India Senior Research Fellowship (SRF), UGC, India (2015–2017) Junior Research Fellowship (NET-JRF), UGC, India (2013–2015) His research spans GenAI, text mining, and algorithmic influence on consumer choices. He explores how algorithms shape preferences in digital markets and investigates barriers to sustainable consumption through predictive modeling. He integrates methodologies from computer science, marketing, and mathematics to address interdisciplinary challenges. Dr. Singh teaches Marketing Analytics, Python programming, and AI & Machine Learning. He actively reviews for journals like PLOS ONE, Journal of Big Data, and Nature Climate Change. He is a Fellow of the UK Higher Education Academy and member of The Asia Computational Intelligence Society. Awards: Teacher of the Year 2024 (Runner-up), University of Stirling Faculty Research Fellowship, IIT Delhi (2020) He supervises Ph.D. research at the intersection of technology, data science, and business applications. His work emphasizes responsible AI and data-driven strategies to drive sustainable consumption and brand equity in dynamic markets.