Find Academic Supervisors

Guillermo GallegoView profile

Professor

• Robotics
• Computer Vision
• Event-based Vision
• +5 more

Guillermo Gallego is a Professor of Robotic Interactive Perception at the Faculty of Electrical Engineering and Computer Science , Technische Universität Berlin , holding the Einstein Center Digital Future (ECDF) Professorship since 2019. His research bridges robotics , computer vision , and applied mathematics , focusing on optimization methods for interdisciplinary imaging and control problems. Education : PhD in Electrical and Computer Engineering (Georgia Tech, 2011), MS in Mathematics (Georgia Tech, 2009), MS in Electrical Engineering (Georgia Tech, 2007), MS in Mathematical Engineering (Universidad Complutense de Madrid, 2005). Gallego's work explores event-based vision to enhance robot perception through low-latency sensing and real-time 3D reconstruction . He previously held postdoctoral positions at the Institute of Neuroinformatics (University of Zurich/ETH Zurich) and Technical University of Madrid (Marie Curie Experienced Researcher). His interdisciplinary projects span applications in ocean remote sensing , autonomous driving , and space exploration . Key scientific awards include the Fulbright Fellowship (2005-2010) and Marie Curie Experienced Researcher (2011-2014). His recent publications focus on event camera algorithms for optical flow , SLAM , and noise estimation , reflecting his leadership in event-based vision research. Collaborations include institutions like University of Zurich , Georgia Tech , and University of Pennsylvania . Research Grants : Funded through ECDF and Marie Curie programs. Labs : Affiliated with the Einstein Center Digital Future and Institute of Neuroinformatics (Zurich/ETH Zurich).

• Perceptually guided graphics
• Virtual characters and crowds
• Virtual reality
• +4 more

Cathy Ennis is an Assistant Professor in the Department of Computer Science at Maynooth University, Ireland, specializing in perceptually guided graphics and virtual reality. She is actively involved in research and teaching, with affiliations to the ADAPT Centre and D-REAL SFI Centre for Research Training. Education: BEng in Electronic Engineering, Maynooth University MSc in Cognitive Science, University College Dublin PhD in Computer Graphics, Trinity College Dublin PG Dip in Higher Education Teaching and Learning, DIT Research Interests: Dr. Ennis's research centers on creating realistic virtual humans and crowds, exploring multisensory perception in VR, and applying these technologies in serious games and interactive systems. Her work integrates AI, HCI, and immersive technologies to enhance user engagement and learning. Her recent publications reflect a strong focus on virtual character realism, VR-based education, and machine learning applications in animation and interaction. Notable themes include speech-animation realism, cultural VR experiences, and reinforcement learning for character control. Scientific Awards: Best Paper Award, IEEE VR 2022 Teaching and Supervision: Dr. Ennis currently teaches CS401 (Machine Learning and Neural Networks) and CS261 (Multimedia Technology). She is actively seeking PhD students and collaborators interested in VR, games, and virtual characters, particularly using machine learning for gesture generation and engagement. Labs and Teams: She is a Funded Investigator with the ADAPT Centre and D-REAL SFI Centre for Research Training, contributing to interdisciplinary research in AI, HCI, and immersive technologies.

Leif KobbeltView profile

Professor

• Computer Graphics
• Geometry Processing
• Interactive Visualization
• +3 more

Leif Kobbelt serves as a University Professor at RWTH Aachen University, leading the Computer Graphics Group within the Department of Computer Science (Informatik 8). His research focuses on advancing geometry processing, interactive visualization, and computer graphics through innovative algorithmic solutions and interdisciplinary collaborations. Professor Kobbelt's research program centers on geometry acquisition and processing, with significant contributions to mesh generation, surface reconstruction, and neural rendering techniques. His work bridges theoretical geometry with practical applications in computer vision, photo-realistic image synthesis, and multimedia data transmission, often involving collaborations with industry partners and international research teams funded by DFG and EU sources. Recent publications (2023-2025) reveal a strategic integration of deep learning with traditional geometry processing, particularly in Gaussian splatting for real-time rendering, NeRF-based 4D content generation, and robust mesh Boolean operations. His group maintains leadership in quad mesh optimization and surface mapping while expanding into immersive visualization techniques for complex data analysis. The group has earned recognition through prestigious awards: Günter Enderle Best Paper Award at Eurographics 2023 Best Paper Award (1st place) at Symposium on Geometry Processing 2022 Honorable Mention for Best Paper at ACM Symposium on Virtual Reality Software and Technology Funding from Deutsche Forschungsgemeinschaft and European Union programs supports the group's research infrastructure and international collaborations. The team actively supervises graduate theses while developing open-source software tools that translate theoretical advances into practical industry applications, particularly in digital fabrication and immersive visualization systems. The Computer Graphics Group operates as a central hub for visual computing research at RWTH Aachen, maintaining strong ties with both academic institutions and technology companies. Their recent work on virtual reality educational tools and high-fidelity 3D reconstruction systems demonstrates commitment to knowledge transfer and real-world impact beyond traditional publication venues.

Alison LangmeadView profile

Associate Clinical Professor

• Digital Humanities
• Digital Art History
• Visual Culture Studies
• +3 more

Alison Langmead serves as Clinical Associate Professor and Director of the Visual Media Workshop in the Department of Art History and Architecture at the University of Pittsburgh's Dietrich School of Arts and Sciences, with a joint appointment as Associate Professor in the School of Computing and Information. Her work bridges digital humanities, visual culture studies, and information management history through mindful technological application. Education: PhD, Columbia University MLIS, University of California at Los Angeles Langmead's research centers on the intersection of digital methods and visual/material culture, examining historical information practices alongside contemporary computational approaches. She pioneers sustainable digital scholarship frameworks while critically analyzing AI's role in art historical connoisseurship and cultural preservation. Her work emphasizes intentional technology use that respects scholarly integrity and humanistic values. Recent publications reveal consistent focus on collaborative digital art history methodologies, computational modeling of visual data, and sustainable infrastructure development. Key trends include network analysis applications, role-based collaboration frameworks, and ethical considerations in AI-driven art scholarship, demonstrating evolution from technical implementation toward systemic sustainability and human-centered design. Scientific Awards: Award for Excellence in Graduate Mentoring, Dietrich School of Arts and Sciences, University of Pittsburgh, May 2023 Langmead actively mentors graduate students through the DHRX initiative while securing significant research funding. Her current grants include 'Teaching Art History with AI' (PI, 2023-present), 'InfoEco Cookbook' (2021-2022), and 'The Socio-Technical Sustainability Roadmap' (PI, 2015-2018, ongoing dissemination), reflecting her commitment to practical digital scholarship frameworks. As Director of the Visual Media Workshop, Langmead cultivates interdisciplinary collaboration across campus, creating spaces where faculty and students develop innovative digital research tools while critically examining technology's role in scholarly practice and cultural preservation.

Kobus BarnardView profile

Professor

• Computer Vision
• Machine Learning
• Scientific Applications
• +3 more

Kobus Barnard is a Professor in the Department of Computer Science at the University of Arizona, with his office located in GS 708. His research bridges computer vision, machine learning, and interdisciplinary scientific applications across diverse domains. Education: Ph.D. from Simon Fraser University (1999) His research interests focus on extracting meaningful insights from complex data through computer vision and probabilistic modeling. Key areas include machine learning for environmental monitoring (flood detection, plant disease analysis), social dynamics (interpersonal coordination, emotional coregulation), astronomy (transient classification), and multimodal learning (visual-linguistic integration). His work consistently applies deep learning to real-world problems requiring high-resolution data interpretation. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Environmental applications using satellite imagery for flood mapping and agricultural monitoring, (2) Cognitive modeling of human teams and emotional dynamics through probabilistic frameworks, and (3) Astronomical data analysis leveraging host galaxy properties for transient classification. These threads demonstrate his commitment to solving practical scientific challenges through computational innovation. While scientific awards aren't documented in available sources, his leadership in projects like FloodPlanet and ToMCAT indicates significant contributions to data infrastructure. His advising and grant activities remain unreported in the source material, though his extensive interdisciplinary collaborations suggest substantial mentorship impact. Barnard's work operates at the intersection of multiple scientific communities, evidenced by applications spanning neuroscience, agriculture, astronomy, and social science. His current focus on high-resolution data fusion and multimodal modeling positions him at the forefront of real-world AI deployment.

Subrahmanya Sastry ChallaView profile

Professor

• Wavelets
• Sparse Optimization Theory
• Inverse Problems
• +7 more

Professor Subrahmanya Sastry Challa is affiliated with the Department of Mathematics at Indian Institute of Technology Hyderabad. His academic journey includes a PhD from IIT Kanpur under Prof. P. C. Das, an M.Sc(Tech) from JNT University, and a B.Sc from Hindu College, Machilipatnam. Research Focus: He specializes in Wavelets and Sparse Optimization Theory Frame Theory and Data-driven Learning Methods Applications in Medical Imaging and Signal Processing His recent work explores sparsity-driven optimization techniques with applications in tomography, ECG signal recovery, and machine learning algorithms. Publications & Collaborations: He has contributed to advancements in compressive sensing, inverse problems, and numerical linear algebra through collaborations with researchers like Dr. Phanindra Jampana and Dr. Praveen Pradhan. Key journals include IEEE Transactions on Signal Processing , Inverse Problems , and Neurocomputing . Teaching: Courses taught include Wavelets & Applications, Compressive Sensing, Numerical Linear Algebra, and Mathematics Behind Machine Learning, emphasizing both theoretical and applied aspects. Administrative Roles: Served as Associate HoD/HoD (2010-2014), Chief Vigilance Officer (2015-2019), and participated in policy-drafting committees during IIT Hyderabad's formative years.

Stuart GibsonView profile

Senior Lecturer

• Digital Image Processing
• Machine Learning
• Forensic Science
• +5 more

Dr. Stuart Gibson is a Senior Lecturer in Physics and Astronomy at the School of Physics and Astronomy, University of Kent. He is the co-inventor of the EFIT-V facial composite system, widely adopted by UK police constabularies and international agencies. His academic contributions span interdisciplinary research bridging forensic science, computational methods, and machine learning. Research Interests: Forensic applications of digital image processing Machine learning in natural sciences Facial composites for criminal investigations Medical image analysis Computer vision with security applications Teaching: Stuart teaches numerical and computational methods, mathematical techniques for physical sciences, and digital forensics. His pedagogical focus integrates theoretical frameworks with practical forensic and computational tools. Publications & Collaborations: Over his career, Dr. Gibson has published extensively in journals such as Pattern Recognition Letters , ACS Nano , and Utilities Policy . His work includes innovations in evolutionary algorithms, facial composite systems, and applications of machine learning to muon spectroscopy and Raman spectroscopy.

Kristen DonnellView profile

Associate Professor

• Microwave and millimeter-wave nondestructive testing
• Thermography
• Frequency selective surfaces
• +7 more

Kristen Donnell is an Associate Professor in the Department of Electrical and Computer Engineering at Missouri University of Science and Technology (Missouri S&T) . She also serves as the Interim Director of the Center for Infrastructure Engineering Studies (CIES) and Director of the Microwave Sensing (mSense) Laboratory . She is recognized as the Woodard Associate Professor of Excellence . Education: Ph.D. in Electrical Engineering, Missouri University of Science and Technology (2010) M.S.E.E., University of Missouri-Rolla (2003) B.S.E.E., Colorado State University (2001) Research Interests: Dr. Donnell's research lies at the intersection of microwave engineering and nondestructive evaluation. Her work focuses on microwave and millimeter-wave nondestructive testing , active microwave thermography , frequency selective surfaces (FSS) , and materials characterization . She has developed novel techniques for structural health monitoring of infrastructure, particularly in carbon fiber reinforced composites and cement-based materials . Her lab explores embedded sensing , 3D-printed microwave structures , and real-time defect detection using advanced microwave imaging. Scientific Awards & Honors: Woodard Associate Professor of Excellence Senior Member of IEEE Member, IEEE Instrumentation and Measurement Society Administrative Committee Laboratory & Leadership: She leads the Microwave Sensing (mSense) Laboratory at Missouri S&T, which focuses on cutting-edge research in microwave sensing and imaging. Under her leadership, the lab has contributed significantly to nondestructive evaluation techniques using microwave and millimeter-wave technologies. She also oversees the Center for Infrastructure Engineering Studies (CIES) as interim director, guiding interdisciplinary research in infrastructure health monitoring. Professional Affiliations: IEEE Instrumentation and Measurement Society – Administrative Committee member since 2007 IEEE – Senior Member

• Image Analysis
• Artificial Intelligence
• Deep Learning
• +5 more

Joakim Lindblad is a Professor at the Department of Information Technology, Uppsala University , and holds affiliated roles as Senior Research Associate at the Mathematical Institute of the Serbian Academy of Sciences and Arts, and Head of Research at Topgolf Sweden AB. With over two decades of expertise in image analysis and machine learning , his work bridges computational methods with biomedical applications. Key affiliations: Uppsala University, Serbian Academy of Sciences, Topgolf Sweden Specializations: Deep Learning, Multimodal Image Registration, Quantitative Microscopy His research focuses on reliable image processing frameworks that integrate intensity and spatial information , particularly for biomedical applications . Recent publications highlight innovations in autofluorescence-based cancer detection , self-supervised one-class learning for sparse instance identification, and rotation-equivariant CNNs for robust analysis of cytology images. Recent article trends demonstrate expertise in multimodal image analysis (2024: 3 papers), oral cancer detection (2025: 2 papers), and multiscale biomedical imaging . His 2025 work on the Uppsala Storytelling Dataset introduces novel frameworks for multimodal dataset creation in AI research. While no scientific awards are explicitly mentioned, his extensive publication record (2000-2025) across top venues like Pattern Recognition , PLOS ONE , and IEEE Transactions indicates significant academic impact. His methodological contributions span stochastic distance transforms , fuzzy set defuzzification , and multimodal image registration techniques. Collaborative work with researchers like Nataša Sladoje and interdisciplinary teams has produced innovations in automated cytology analysis , TEM image enhancement , and AI-driven medical diagnostics . His 2021-2022 projects introduced contrastive learning approaches for multimodal image registration and explainable AI frameworks for infant engagement analysis.

• Cardiovascular Imaging
• Translational Research
• Molecular Imaging
• +9 more

Dr. Albert J. Sinusas is a Professor of Medicine (Cardiology) , Radiology & Biomedical Imaging , and Biomedical Engineering at Yale University . He serves as Director of the Yale Translational Research Imaging Center (Y-TRIC) and Advanced Cardiovascular Imaging at Yale New Haven Hospital. Education: BS from Rensselaer Polytechnic Institute (1979), MD from University of Vermont (1983), Internal Medicine training at University of Oklahoma (1986), Cardiology/Nuclear Cardiology at University of Virginia (1989) Dr. Sinusas specializes in non-invasive cardiovascular imaging with expertise in PET/CT, SPECT/CT, echocardiography, and MR imaging . His research focuses on molecular imaging of myocardial injury , angiogenesis , post-infarction remodeling , and deep learning applications in cardiac diagnostics. He has pioneered multimodality imaging approaches for cardiovascular pathophysiology assessment. Recent publications highlight his work in AI-driven cardiac imaging , novel PET tracers , and medical robotics . His team's 15 most recent articles (2024-2025) span topics from ARDS diagnostics to cardiovascular risk stratification using CT and PET technologies. Scientific Awards: SNMMI Hermann Blumgart Award (2008) Best Doctor in America (2001-2002, 2005-2015) M.A. Privatim from Yale (2006) Robert Wilkinson Lectureship (2014) Interurban Clinical Club membership (2017) As Principal Investigator on multiple NIH grants, Dr. Sinusas directs the NHLBI-funded T32 training program in multimodality cardiovascular imaging. His lab (Y-TRIC) houses state-of-the-art imaging resources including hybrid SPECT/CT , microCT , and 3D ultrasound systems for translational research from animal models to clinical applications.

Geoff DullerView profile

Professor

• Luminescence Dating
• Quaternary Environmental Change
• Geomorphological Processes
• +3 more

Professor Geoff Duller has been a faculty member at Aberystwyth University's Department of Geography & Earth Sciences since 1995, holding a Personal Chair since 2005. He studied Geography at St Catherine's College Oxford (BA 1987), completed his PhD in 1992 at Aberystwyth on luminescence dating of Quaternary sediments in New Zealand, and established the Aberystwyth Luminescence Research Laboratory in 2000. His research focuses on luminescence dating method development and application, particularly for Quaternary environmental change studies. Key contributions include creating single-aliquot dating methods and leading the NERC-funded BRITICE-Chrono project for ice sheet deglaciation chronology. He serves as Associate Editor of Radiation Measurements and editor of Journal of Quaternary Science , with editorial board membership at Ancient TL . His article portfolio demonstrates expertise in: Luminescence signal analysis (2025: IRPL/IRSL in feldspars; 2024: opercula thermoluminescence) Quaternary chronology (2025: Thar desert floodouts; 2024: Tibetan glaciofluvial sediments) Methodological innovation (2025: EMCCD crosstalk reduction; 2024: pIRIR protocol optimization) Scientific recognition includes the Bigsby Medal (2012). Current supervision includes PhD students Robyn Pinder, Svenja Riedesel, and Nina Ataee. He co-directs the Aberystwyth Luminescence Research Laboratory and manages specialized equipment including Risø luminescence readers.

• Transmission Electron Microscopy (TEM) Method Development
• Charge Particle Optics and Scattering Theory
• Magnetic Nanotextures
• +3 more

Dr. Axel Lubk is a Group Leader at the Institute for Solid State Research (IFW Dresden) , specializing in advanced electron microscopy techniques for materials science. His research spans four key areas: (1) TEM method development (high-resolution imaging, tomography, holography, and in-situ techniques), (2) charge particle optics and scattering theory , (3) magnetic nanotextures (domain walls, skyrmions), and (4) plasmonics (mode hybridization in heterogeneous structures and semiconductor heterostructures). Dr. Lubk’s work focuses on three-dimensional magnetic texture analysis using electron holography and tomography, particularly in systems like skyrmion tubes , FeGe , and Cr2O3 thin films . He has pioneered techniques for vector-field electron tomography and phase retrieval under varying boundary conditions, advancing nanoscale magnetic imaging. His recent studies include plasmonic properties in AgAu nanosphere chains , thermoelectric multilayer systems , and topological insulators like NiRh2Sb and TaTMTe4 . Dr. Lubk has published extensively in high-impact journals such as Nature Communications and Advanced Materials , with a focus on TEM instrumentation and quantitative analysis . He frequently presents at international conferences like the International Microscopy Congress and European School of Magnetism , emphasizing applications in spintronics , quantum materials , and nanostructured systems . His contributions to holographic vector-field electron tomography and machine learning for spectrum-image data have set new standards in electron microscopy.

Bopaya BidandaView profile

Professor

• Manufacturing Systems
• Group Technology
• Reverse Engineering
• +11 more

Bopaya Bidanda serves as the Ernest E. Roth Professor and Chairman of the Department of Industrial Engineering at the University of Pittsburgh, where he has led since joining the faculty in 1987 after completing his PhD at Penn State. His institutional impact includes founding the Manufacturing Assistance Center, Automated Data Collection Laboratory, and Swanson Center for New Product Innovation. His research expertise spans manufacturing systems with concentrated focus on Group Technology, Reverse Engineering, Cellular Manufacturing, Lean Manufacturing, and Human Issues in Manufacturing. This work bridges advanced computational methods with industrial applications to solve real-world production challenges. Publication trends from 2000-2002 reveal strong emphasis on human capital integration in cellular systems, reverse engineering applications in biomedical contexts, and virtual laboratory development for manufacturing education. Key thematic threads connect workforce skills assessment with lean transformation and advanced computing techniques for production optimization. Scientific recognition includes: Fellow of the Institute of Industrial Engineers His grant portfolio demonstrates exceptional funding diversity, securing support from NSF, Department of Commerce, W.M. Keck Foundation, and industry partners like FedEx Ground for initiatives in manufacturing modernization and workforce development. He actively mentors doctoral candidates through flexible international programs and industry-university collaborations. Leadership extends to operational hubs including the Manufacturing Assistance Center which provides direct technical support to regional manufacturers, and the Swanson Center driving product innovation through interdisciplinary industry partnerships.

Markus ZimmermannView profile

Researcher

• Medical Imaging
• Quantitative MRI
• Neuroscience
• +3 more

Dr. Markus Zimmermann is a researcher at the Institute of Neuroscience and Medicine (INM-4: Physics of Medical Imaging) at the Research Center Jülich. His work focuses on advancing quantitative MRI techniques, particularly in water content mapping, multiparametric imaging, and ultrahigh-field MRI applications. He contributes to developing methods for eddy current characterization, multi-exponential relaxometry, and rapid whole-brain protocols. His research addresses neurological and medical imaging challenges, including cerebral pathologies and neurobiological implications. Key areas of expertise include MRI parameter estimation, medical imaging algorithms, and the integration of advanced imaging techniques for clinical and neuroscience applications. His projects often involve collaborations to validate methodologies using in vivo/ex vivo experiments and super-resolution reconstruction. Dr. Zimmermann’s work aims to enhance diagnostic precision and understanding of brain physiology through innovative MRI technologies.

Russell DetwilerView profile

Associate Professor

• Fluid Flow in Porous Media
• Subsurface Contaminant Transport
• CO2 Sequestration
• +4 more

Russell Detwiler is an Associate Professor in the Department of Civil and Environmental Engineering at the Samueli School of Engineering, University of California, Irvine. He holds a Ph.D. and B.S. in Civil Engineering from the University of Colorado and University of Vermont, respectively. As Associate Director of the UCI WEX Center, his work combines laboratory experimentation with computational modeling to study subsurface fluid dynamics. Education: Ph.D., Civil Engineering, University of Colorado B.S., Civil Engineering, University of Vermont Research Focus: Detwiler's research examines fluid flow processes in porous and fractured media, with emphasis on multiphase flow, subsurface property alteration, and environmental applications. Key areas include contaminant remediation, CO2 sequestration, geothermal energy, and nuclear waste management. Publication Trends: His recent work (2022-2024) emphasizes subsurface PFAS behavior, coastal aquifer modeling, and fracture clogging dynamics. Earlier research (2019-2021) focused on mineral precipitation effects and kinetic modeling for contaminant removal. Common sub-fields span fracture sealing mechanisms, particle retention, and reactive transport modeling. Laboratory: Leads the Subsurface Processes Visualization Lab, developing parallel computational models to scale laboratory observations to field applications. Research integrates high-resolution imaging with numerical simulation for subsurface system analysis.