Thomas Berger is a Professor at the University of Hohenheim , affiliated with the Faculty of Agricultural Sciences and leading the Department of Economics of Land Use . He also contributes to the Computational Science Hub and Hohenheim Tropics initiatives. Focus Areas: Climate change adaptation, land-use modeling, biodiversity-productivity trade-offs, agent-based simulation, and machine learning in agricultural systems. Key Projects: Simulation frameworks for smallholder resilience in Ethiopia, bioeconomic modeling in the Amazon, and hybrid intelligence applications in European agricultural policy. Recent Publications: 2025 study on climate change effects on insecticide reduction in Germany, 2024 work on reconciling biodiversity with productivity via hybrid models, and 2023 methodological contributions to surrogate modeling and seasonal forecast integration. Research Trends: Interdisciplinary integration of climate science, agricultural economics, and computational modeling, with increasing emphasis on AI-assisted decision support systems and sustainability policy validation. Teaching & Outreach: Offers Agricultural Economics seminars and Hohenheim Tropics discussions, requiring advance email registration for office hours.
Professor David Taubman is a distinguished academic serving as Professor and Deputy Head of School (Research) at the School of Electrical Engineering and Telecommunications (EE&T) at the University of New South Wales (UNSW) in Sydney, Australia. He is also co-director of Kakadu Software Pty. Ltd. and its affiliates Kakadu R&D and Kakadu GPU. With a career spanning over three decades, Professor Taubman has made significant contributions to the field of image and video compression, most notably as the author of the EBCOT coding algorithm adopted in the JPEG2000 international standard. Professor Taubman earned his B.Sc. in Mathematics and Computer Science (1986) and B.E. (Medal) in Electrical Engineering (1988) from the University of Sydney, followed by an M.Sc. (1992) and Ph.D. (1994) in Electrical Engineering from the University of California at Berkeley. His professional journey includes engineering work at the Electricity Commission of N.S.W. (1988-1990), research positions at Hewlett-Packard Laboratories in Palo Alto (1994-1998), and an academic career at UNSW where he progressed from Senior Lecturer (1998-2003) to Associate Professor (2004-2009) and finally to Professor (2009-present). He has held various leadership roles including Head of the EE&T Telecommunications Research Group (2003-2014), Head of the EE&T Signal Processing Research Group (2014-present), Director of Research for the School of EE&T (2011-2016), and Deputy Head of School (Research) since 2017. Professor Taubman's research interests center on image and video compression, with particular expertise in JPEG2000 standards and implementations. His work spans signal processing, wavelet transforms, scalable video coding, motion modeling, and multimedia systems. He has pioneered numerous compression algorithms and frameworks, including the EBCOT coding algorithm that became central to the JPEG2000 standard. His recent research focuses on efficient motion modeling with cuboidal partitioning, learned lifting-based transform structures, and high-throughput implementations of JPEG2000 for video applications. His work bridges theoretical foundations with practical implementations, as evidenced by the commercially successful Kakadu Software tools that have garnered around 500 commercial licensees. Analysis of Professor Taubman's recent publications reveals a consistent focus on advancing compression technologies with particular emphasis on scalability, efficiency, and adaptability. His work spans traditional image compression (JPEG2000 extensions), video coding (cuboid-based partitioning for UHD/360-degree video), and emerging applications (nanopore sequencing data compression). A notable trend is the integration of machine learning techniques with traditional compression frameworks, as seen in his work on learned lifting-based transform structures. His research maintains strong connections to real-world applications across diverse domains including medical imaging, astronomical data processing, and genomic sequencing. IEEE Fellow Engineers Australia Fellow (by invitation) Professor Taubman has served as Associate Editor for the IEEE Transactions on Image Processing for two four-year appointments (2003-2005 and 2010-2013). He has been actively involved in numerous research grants focused on image and video compression technologies, particularly those related to the JPEG2000 standard and its extensions. His work has received significant industry support, reflected in his consultancy with various U.S., Japanese, and Australian corporations. He has also contributed to international standards development as a member of Standards Australia Technical Committee MS-065 (mirroring ISO TC42 on Digital Photography) and as a constitutional member of Standards Australia Technical Committee IT-029 (Coded Representation of Picture, Audio and Multimedia/Hypermedia Information). Professor Taubman co-directs Kakadu Software Pty. Ltd. and its research affiliates Kakadu R&D and Kakadu GPU, which have developed the commercially successful Kakadu Software tools for JPEG2000. His research group at UNSW focuses on advanced image and video compression techniques, with particular expertise in wavelet-based methods, scalable coding, and motion modeling. The group maintains strong industry connections and has contributed significantly to the development and standardization of image compression technologies worldwide.
Dustin Scheinost is an Associate Professor at Yale School of Medicine, affiliated with the Department of Radiology & Biomedical Imaging, Yale Child Study Center, Department of Statistics, and Yale Biomedical Imaging Institute. His research focuses on connectomics , machine learning , and neuroinformatics through the Multi-modal Imaging, Neuroinformatics, & Data Science (MINDS) Lab. Radiology & Biomedical Imaging (Primary) Child Study Center (Secondary) Statistics (Secondary) Wu Tsai Institute Yale Stress Center Research Interests include developing novel statistical and machine learning methods for functional connectivity in big neuroscience data, leading the BioImage Suite Web (BISWeb) platform, and advancing early life neuroimaging through the Fetal, Infant, Toddler Neuroimaging Group (FIT’NG). His work is supported by grants from NIMH, NIAA, NIDA, and NHLBI. Selected Scientific Contributions span functional connectivity in laterality preferences, anti-racist AI governance in psychiatry, self-citation trends in neuroscience, and predictive modeling of mood disorders. He collaborates extensively with Todd Constable and others on multimodal neuroimaging studies.
Anup Basu is a Professor in the Department of Computing Science at the University of Alberta. His research focuses on computer graphics, computer vision, and multimedia communications. He holds an B.S. in Math & Statistics from the Indian Statistical Institute (1980), an M.E. in Computer Science from the Indian Statistical Institute (1983), and a Ph.D. in Computing Science from the University of Maryland (1990). His work emphasizes Quality of Service (QoS) in multimedia delivery for e-commerce and telelearning, adaptive bandwidth monitoring, and 3D visualization tools. He pioneered foveated image compression and stereo visualization techniques, contributing to MPEG-4 coding standards. He leads major initiatives like the ASRA/TelePhotogenics/IBM 3D Medical Imaging project ($2M+ funding) and developed patented SHR Stereo/3D scanning technologies. Awards include the American Neurological Association Fellowship. He has held leadership roles as General Chair for IEEE International Conferences on SMC (2017), Multimedia & Expo (2013), and SMC (2014). His research integrates interdisciplinary collaborations across universities and industry partners, leveraging advanced equipment like the CAVE system for immersive visualization.
Truong Q. Nguyen is a Professor in the Electrical and Computer Engineering (ECE) Department at the University of California San Diego (UCSD), affiliated with the Jacobs School of Engineering. He holds positions at the Center for Wireless Communications and the California Institute for Telecommunications and Information Technology. His research focuses on image/video processing, wavelets, 3D video technology, and applications in healthcare and robotics. He has authored influential textbooks like Wavelets & Filter Banks and pioneered low-power video processing algorithms for mobile devices. Nguyen earned his B.S., M.S., and Ph.D. in Electrical Engineering from the California Institute of Technology (1985–1989). He held roles at MIT Lincoln Laboratory and Boston University before joining UCSD in 1998. His honors include the IEEE Signal Processing Paper Award (1992), NSF Career Award (1995), IEEE Fellow (2005), and UCSD’s Distinguished Teaching Award (2019). His research interests span 3D video processing, machine learning for health monitoring, and biomedical imaging. Notable contributions include wavelet-based compression techniques and AI-driven medical image analysis. He leads the UCSD Video Processing Lab, exploring computer vision, robotics, and generative AI applications. Nguyen is committed to educational innovation, co-creating programs like the Hands-on Curriculum, Summer Research Internship Program (SRIP), and Project-in-a-Box (PIB) for K-12 students. Nguyen’s work bridges academia and industry, with patents in wavelet design and signal analysis. Recent projects include NSF-funded initiatives to develop inclusive engineering curricula and collaborate on graduate pathways programs through the Inclusive Engineering Consortium (IEC).
Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at University of Maryland, College Park, with a joint appointment in the Institute of Advanced Computer Studies (UMIACS). Previously, he served as an Assistant Professor at the same institution from August 2018 to June 2024, and spent one year as a Visiting Research Scientist at Google Research from September 2017 to August 2018. His educational background includes: PhD in Robotics and Artificial Intelligence from Carnegie Mellon University (2017), advised by Abhinav Gupta, with thesis titled 'Discovering and Leveraging Visual Structure for Large-scale Recognition' MS in Artificial Intelligence from Carnegie Mellon University (2011), supervised by Alyosha Efros and Martial Hebert BTech in Computer Science and Engineering from Jaypee Institute of Information Technology (2010) Professor Shrivastava's research focuses on computer vision and machine learning, with particular expertise in object detection, image recognition, and neural representations. His work bridges theoretical advances with practical applications, exploring how visual systems can discover and leverage structure in large-scale recognition problems. He has made significant contributions to understanding the role of supervision in vision transformers, developing novel approaches for object-state composition recognition, and creating efficient neural representations for videos and 3D scenes. His research often addresses fundamental challenges in visual recognition, including handling novelty in open-world environments and improving the efficiency of visual systems. An analysis of his recent publications reveals a strong emphasis on neural representations, particularly for dynamic content like videos and 3D scenes. His work demonstrates increasing sophistication in handling open-world vision problems, with research spanning object discovery, localization, and representation learning. The publications show a clear progression toward more efficient and scalable models, with recent work focusing on model compression, sparse representations, and addressing the challenges of working with limited annotations. His scientific contributions have been recognized with several prestigious awards: Best Paper Award (Applications) at IEEE Winter Conference on Applications of Computer Vision (2020) Microsoft Research PhD Fellowship (2014-2016) Best Student Paper Award at IEEE Winter Conference on Applications of Computer Vision (2014) Outstanding Reviewer Award at IEEE CVPR (2015) Professor Shrivastava has successfully mentored numerous graduate students, many of whom have become prominent researchers in computer vision. His Amazon Research Awards (2020 and 2023) have supported innovative projects including 'The pursuit of knowledge: discovering and localizing new concepts using dual memory' and 'Audio-conditioned Diffusion Models for Generating Lip-synchronized Videos.' He has served as Area Chair for major conferences including ICCV, CVPR, and AAAI, demonstrating his leadership in the computer vision community. His research has attracted significant funding from both academic and industry sources, supporting his exploration of fundamental questions in visual recognition and representation learning.
Dr. Joanna Deaton Bertram is an Assistant Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University’s Pratt School of Engineering. She concurrently holds an Assistant Professor appointment in Surgery, underscoring her interdisciplinary commitment to advancing medical robotics. Dr. Bertram leads a research laboratory devoted to the design, modeling, and control of robotic systems for surgical and interventional applications, working closely with Duke’s clinical and engineering communities. Education Ph.D. in Robotics, Georgia Institute of Technology, 2024 M.S. in Mechanical Engineering, Georgia Institute of Technology, 2024 B.S. in Biomedical Engineering, Georgia Institute of Technology, 2018 Research Interests Dr. Bertram’s research program is centered on medical robotics , with particular emphasis on continuum robotics and image-guided interventions . Her work integrates novel mechanical design with advanced control algorithms and smart materials to create robotic systems capable of navigating complex anatomical pathways. A hallmark of her approach is the incorporation of real-time fiber-optic shape and force sensing (using Fiber Bragg Grating technology) to provide surgeons with unprecedented feedback during procedures. Application domains include steerable needles for brachytherapy , robotic guidewires for endovascular surgery , and pediatric neuroendoscopy . Publication Themes Across more than fifteen peer-reviewed articles, Dr. Bertram has systematically advanced the state of the art in surgical robotics , fiber-optic sensing , and robotic system modeling . Her 2024 tutorial on Nitinol and Tungsten tendon attachment techniques provides practical guidance for building highly articulated continuum robots, while her 2023 series on the COAST guidewire robot demonstrates model-based design and simultaneous shape/force sensing for large-deflection medical devices. Earlier work explored 3D-printed patient-specific robotic tools and carbon-nanotube flexible sensors, illustrating a trajectory from fundamental sensor research to full robotic system integration. Scientific Recognition & Collaboration Although no major external awards are explicitly listed, Dr. Bertram’s publications in top-tier venues such as IEEE Robotics and Automation Letters , IEEE Transactions on Medical Robotics and Bionics , and IEEE/ASME Transactions on Mechatronics attest to strong peer recognition. She actively invites motivated graduate students, post-docs, and research staff to join her lab, fostering an open and interdisciplinary environment. Advising & Grants Dr. Bertram’s lab is presently recruiting trainees at all levels. While specific funded grants are not enumerated, her dual departmental appointments and extensive publication record suggest active federal or foundation support. Prospective students and collaborators are encouraged to contact her directly at joanna.d.bertram@duke.edu . Laboratory & Teams Dr. Bertram directs a laboratory within Duke University’s Pratt School of Engineering that collaborates closely with clinicians in the School of Medicine. The group focuses on rapid prototyping of medical devices, in-vitro and ex-vivo validation, and translation of robotic technologies to the operating room.
Mehrdad Salehi is a researcher at the Chair of Computer Science Applications in Medicine at the Technical University of Munich (TUM) . His work focuses on the intersection of computer science and medical imaging, with expertise in ultrasound technology, deep learning, and surgical navigation systems. Key research areas include sonification of medical data, 3D ultrasound reconstruction, and machine learning-based segmentation. He has contributed to innovative projects like PRO-TIP calibration phantoms and ColibriDoc autonomous docking systems. His publications highlight trends in acoustic feedback mechanisms, neural radiance fields for medical imaging, and real-time image analysis. He can be reached at mehrdad.salehi@tum.de .
James C. Gee is a Professor of Radiologic Science in Radiology at the University of Pennsylvania's Perelman School of Medicine. He serves as Director of the Penn Image Computing and Science Laboratory and Co-Director of the Translational Biomedical Imaging Center , with affiliations in Bioengineering and Applied Mathematics graduate groups. His research focuses on biomedical image analysis, specialization in segmentation, registration, and morphometry applied to neurodegenerative diseases and multi-organ systems. Education : B.S. in Computer Science/Electrical Engineering (University of Washington, 1987), Ph.D. in Computer and Information Science (University of Pennsylvania, 1996) Research : Quantitative medical imaging methods, brain connectomics, neurodegeneration mapping, and translational imaging technologies Publications : 15+ recent works on AI-driven image analysis for Alzheimer's disease, cardiac amyloidosis, and radiomics applications Leadership : Directs MSE-DS Online Degree Program, co-chairs Radiology DCOAP Committee, and founded RISE (Radiology Initiative to Support Inclusive Excellence) His laboratory develops advanced computational tools like ITK-SNAP for biomedical imaging, with applications in both in vivo clinical imaging and ex vivo histology . The work spans cross-disciplinary collaborations in computer science, neuroscience, and clinical medicine.
Cheung Ngai-Man is an Associate Professor and Associate Head of Pillar (Education) at Singapore University of Technology and Design (SUTD), part of the Information Systems Technology and Design (ISTD) pillar. He holds a Ph.D. in Electrical Engineering from the University of Southern California (2008) and has held research positions at Stanford University, Texas Instruments, IBM, and others. His research focuses on image and signal processing, computer vision, machine learning, and artificial intelligence. Education: Ph.D., Electrical Engineering, University of Southern California (2008); Postdoctoral research at Stanford University (2009–2011). Research Interests: Develops algorithms for multimedia data processing, explores interdisciplinary applications of signal processing and AI, and addresses challenges in computer vision and generative models. Recent work includes fairness in generative models, few-shot image generation, and adversarial robustness. Publications: Over 100+ peer-reviewed papers in top venues (CVPR, NeurIPS, IEEE TIP, TPAMI) focusing on computer vision, generative models, and AI security. Notable 2023 work includes studies on label-only model inversion attacks and fairness metrics in generative systems. Awards: Best Paper Finalist (CVPR 2019), SAIL Award Finalist (WAIC 2019), Outstanding Associate Editor (IEEE T-MM), Croucher Foundation Fellowship. Students: Supervised postdocs (Hossein Nejati, Fang Lu), research assistants (Mohammad Rostami), and visiting students (Ma Rui). Labs/Teams: Leads research groups in AI, computer vision, and multimedia systems at SUTD. Has spun off AI initiatives for wound care and contributed to Singapore’s National AI Strategy.
Xieyuanli Chen is an Associate Professor at the National University of Defense Technology (NUDT), China. He holds a Dr.-Ing. (summa cum laude) from the University of Bonn (2022), a Master's in Robotics from NUDT (2017), and a Bachelor's in Electrical Engineering from Hunan University (2015). His research focuses on robot learning, perception, and navigation, with an emphasis on LiDAR-based SLAM, autonomous systems, and semantic perception. Education: PhD: University of Bonn, 2018-2022 (supervised by Prof. Cyrill Stachniss) Master's: NUDT, 2015-2017 Bachelor's: Hunan University, 2011-2015 Research interests include robotics, autonomous systems, computer vision, and LiDAR perception. He has authored over 90 papers in top venues like TRO, RSS, ICRA, and CVPR. He serves as an Associate Editor for IEEE RA-L, ICRA, and IROS, and is a member of the RoboCup Rescue Robot League Technical Committee. Awards include the RSS Pioneer Award (2021), Best-in-Class RoboCup awards, and recognition as a World’s Top 2% Scientist (2024). His work spans LiDAR localization, moving object segmentation, and efficient semantic mapping. He advises students in robotics and autonomous systems. Labs/Teams: Active in the PRBonn group (University of Bonn) and leads research at NUDT on LiDAR-based perception systems.
Dr. Oscar Meruvia-Pastor is a faculty member in the Department of Computer Science at Memorial University of Newfoundland, within the Faculty of Science. He holds a B.Sc. from ITESM-Monterrey, Mexico, an M.Sc. from the University of Alberta, and a Ph.D. from Otto-von-Guericke Universität Magdeburg, Germany. His research focuses on interactive 3D graphics, non-photorealistic rendering, and biomedical visualization, with applications in telepresence systems, augmented reality (AR), and virtual reality (VR). He has developed tools like OMARC for respiratory condition training and GeNET for gene co-expression network analysis. Dr. Meruvia-Pastor has supervised numerous graduate students and contributed to over 50 publications. His work includes evaluating stereo correspondence methods in AR, robot arm manipulation via depth sensors, and smartphone integration in immersive VR. He has been recognized with awards such as the Best HCI Poster at Graphics Interface 2014 and a semi-finalist poster at SIGGRAPH 2015. He teaches courses in computer science, including computer graphics, multimedia development, and introductory science modules. His research lab focuses on 3D telepresence, medical visualization, and human-centered VR/AR solutions. His academic contributions span software tools for medical imaging analysis, interactive visualization systems, and educational technologies. He actively collaborates with health professionals to advance telemedicine and remote procedural training through AR platforms. His work bridges computer graphics with real-world applications in healthcare, education, and environmental advocacy.
Herman Bruyninckx is a Part-Time Full Professor at Eindhoven University of Technology (TU/e) in the Mechanical Engineering department, specifically within the Control Systems Technology group and EAISI High Tech Systems initiative. He also serves as a professor (Hoogleraar) at KU Leuven in Belgium. Academic focus on robotics, control systems, and multi-agent coordination Active research in model predictive control , semantic mapping , and dynamic constraint algorithms Recent publications address industrial automation , agro-food robotics , and haptic technology Research Highlights : Developed hybrid decision-making frameworks for multi-agent navigation Innovated swing-free control methods for robotic pick-and-place operations Formulated constrained dynamics algorithms with LQR-Gauss principle integration Created ExoTen-Glove for haptic feedback in virtual environments Collaborative Projects : Coordinated with researchers like René van de Molengraft , Elena Torta , and Koen de Vos Contributed to NWO/TTW FlexCRAFT project for cognitive robotics in agro-food technology
Yang Zhang is a Visiting Assistant Professor in the Department of Mathematics at the University of California, Irvine (UCI), working under Prof. Katya Krupchyk. His research focuses on inverse problems in imaging sciences, nonlinear hyperbolic equations, and medical imaging applications. He previously held a postdoctoral position at the University of Washington, Seattle, under Prof. Gunther Uhlmann. His work integrates microlocal analysis and partial differential equations to address challenges in wave propagation, nonlinear acoustics, and elasticity. Education & Career: PhD from Purdue University (advisor: Prof. Plamen Stefanov) Postdoc: University of Washington, Seattle (2020–2024) Research Interests: Dr. Zhang's work spans inverse problems for nonlinear hyperbolic equations, acoustic imaging, and integral transforms in medical contexts. He develops novel methodologies using multi-fold linearization, wave interactions, and advanced calculus techniques. His studies on Rayleigh and Stoneley waves in elasticity further demonstrate his expertise in microlocal analysis. Key Contributions: His research bridges theoretical mathematics and applied imaging, with notable publications on inverse scattering, damping effects in wave equations, and Compton camera imaging. He is an active member of the Inverse Problems International Association (IPIA). Grants & Collaborations: Collaborations with prominent figures like Prof. Gunther Uhlmann and Prof. Katya Krupchyk highlight his network in inverse problems. His work often involves both analytical and computational approaches, with applications in medical diagnostics and geophysics.
Indrani Bhattacharya, PhD, is an Assistant Professor in the Department of Biomedical Data Science and the Center for Precision Health and Artificial Intelligence (CPHAI) at Dartmouth College's Geisel School of Medicine. Her research focuses on developing human-centered AI systems for healthcare, particularly in multimodal medical imaging and behavioral health analytics. She holds a BS in Electrical Engineering from Jadavpur University (India), and MS/PhD from Rensselaer Polytechnic Institute (USA). Postdoctoral training at Stanford University's Department of Radiology further specialized her in biomedical imaging informatics. Research interests include: Integrating imaging and non-imaging data for precision medicine AI-driven prostate cancer detection/classification Multimodal behavior estimation for doctor-patient interactions Privacy-preserving sensor systems for group interaction analysis Her work bridges computer vision, medicine, and social science, with recent breakthroughs in MRI-ultrasound fusion AI outperforming radiologist interpretations in multi-center studies. Active in AI ethics and translational research, she leads teams developing clinical decision support tools for oncology and behavioral health. Key career milestones include: Postdoctoral scholar at Stanford University School of Medicine (2016-2021) Academic research staff at Stanford Radiology (2021-2022) Founding member of Dartmouth CPHAI precision health initiatives Labs/Teams: Leads the Biomedical AI for Healthcare group at Dartmouth, collaborating with Stanford and industry partners on AI-driven diagnostic systems.
