Lu Yin is an Assistant Professor in the School of Computer Science and Electronic Engineering at the University of Surrey. He holds affiliations as a long-term visiting researcher at Eindhoven University of Technology (TU/e) and collaborator with the Visual Informatics Group (VITA) at the University of Texas at Austin. Previously, he served as a Postdoctoral Fellow at TU/e and worked as a research scientist intern at Google's New York City office. His work bridges academic and industrial research, focusing on AI Efficiency, AI for Science, and Large Language Models. His research emphasizes optimizing neural networks through sparsity techniques, including pruning strategies for LLMs and vision models. Notable contributions include the OWL method for LLM pruning and Lottery Pools for improving sparse network performance. Yin actively collaborates with institutions like TU/e, Google Research, and Intel Research, and has organized conferences such as CAPBS 2025 and CAI 2025 Workshops. Yin has secured significant grants, including a 10,000,000 NWO-funded grant for NVIDIA A100 GPU resources. He has delivered invited talks at prestigious institutions like Carnegie Mellon University and City University of Hong Kong. His work has been recognized with the Best Paper Award from LoG 2022.
Anirban Mondal is an Associate Professor and Director of Graduate Studies at Case Western Reserve University's Department of Mathematics, Applied Mathematics and Statistics, specializing in Bayesian Inference, Markov Chain Monte Carlo Methods, and Uncertainty Quantification. Holding a Ph.D. in Statistics from Texas A&M University, his research spans spatial statistics, inverse problems, and data mining applications across biomedical, materials science, and public health domains. Education: Ph.D. in Statistics, Texas A&M University His recent publications (2022-2024) demonstrate interdisciplinary applications including heart disease prediction via optimized machine learning, additive manufacturing defect analysis, and pandemic transmission modeling. While primarily focused on Bayesian frameworks and computational statistics, his work extends to geomechanics, remote sensing, and environmental risk assessment. Current research explores advanced sampling algorithms, functional data emulation, and multiscale hierarchical modeling for complex systems. Key trends include uncertainty quantification in machine learning systems (2024), Bayesian calibration methods (2023), and pandemic modeling (2022). His work balances methodological innovation with real-world applications in medical diagnostics, materials science, and climate science. Contact: anirban.mondal@case.edu
Dr. Judith Verstegen is an Assistant Professor in the Department of Human Geography and Spatial Planning at Utrecht University's Faculty of Geosciences. Her research focuses on geosimulation modeling and spatial optimization, with applications in urban planning, environmental vulnerability assessment, and policy analysis. She leads projects such as HEADS 4 Health (2023-2024), which integrates agent-based models into urban digital twins, and coordinates the GeoSIM research group. Her work emphasizes interdisciplinary collaboration, including projects analyzing linguistic diversity in South America and environmental threats to Amazonian indigenous lands. She is the Program Chair of the MSc Geographical Information Management and Applications (GIMA) program and serves as Editor-in-Chief of the Journal of Spatial Information Science. Notable contributions include methodologies for spatial optimization under uncertainty and agent-based modeling of pedestrian behavior in urban environments. Key research areas include applied data science, complex systems analysis, and the PtS - Transforming Cities initiative. She has advised PhD students on topics ranging from fire prevention optimization to indigenous land vulnerability. Her lab at the University of Münster previously focused on spatial modeling frameworks, and she collaborates internationally with institutions like Leiden University and the PBL Netherlands Environmental Assessment Agency. Recent projects highlight innovation in computational methods, such as Python-based open-source tools for land-use modeling (IMAGE-land) and immersive video experiments for behavioral studies. Her work bridges theoretical modeling with practical policy applications, addressing challenges in sustainable urban development and environmental conservation.
Dr Steve Maddock is a Senior Lecturer in Computer Graphics and Acting Head of the Visual Computing research group at the University of Sheffield's School of Computer Science. He holds a Class I Degree in Computer Science (University of Sheffield), a PGCE in Mathematics (11-18), and a PhD in computer graphics modeling and animation, all from the University of Sheffield. With over 30 years of experience in computer graphics software development, he has contributed to the computer games industry through a six-month secondment at Gremlin/Infogrames. His research focuses on facial modeling and animation, augmented/virtual/mixed reality applications, and sketch-based interfaces. Key areas include 3D computer graphics, real-time rendering, and human-robot collaboration systems. Maddock has led and co-led several grants, including projects on game software engineering, rail network surveillance, and heritage visualization using immersive technologies. He is a member of INSIGNEO, Sheffield Robotics, and the Cultural Industries Research Network. Publications highlight contributions to facial analysis for medical diagnostics, style transfer techniques for games, and safety zone visualization in robotics. His work integrates interdisciplinary approaches, combining computer science with fields like biology and robotics. Maddock's Visual Computing research group explores cutting-edge solutions in graphics, virtual environments, and computational tools for real-world applications.
Dr. Michael Shekelyan is a Lecturer (Assistant Professor) in Computer Science at Queen Mary University of London (QMUL), part of the School of Electronic Engineering and Computer Science. He holds a PhD in Computer Science from the Libera Università di Bolzano (2018) and a Diploma in Media Informatics from the University of Munich (2014). His research focuses on developing algorithms and data structures for managing large and sensitive datasets, with a particular emphasis on privacy-preserving techniques like differential privacy and federated learning. He has held postdoctoral roles at the University of Warwick and King's College London before joining QMUL in 2023. Research Interests: Privacy-preserving algorithms, differential privacy, federated learning, data management systems, randomized algorithms, and efficient query processing. His work bridges theoretical foundations with practical applications, aiming to enable secure data sharing while preserving individual privacy. Teaching: Leads undergraduate modules in Database Systems and Operating Systems at QMUL. His teaching emphasizes foundational concepts in computer science through rigorous coursework and practical projects. Grants and Funding: Currently supervises a PhD studentship titled 'Privacy-Preserving Algorithms: Unlocking Data Sharing for Medical Sciences & Machine Learning', funded by QMUL and open to UK home students. The role involves exploring privacy-preserving frameworks for collaborative data analysis. Professional Contributions: Serves as a reviewer for top-tier conferences (NeurIPS, ICML, SIGMOD, ICDE) and journals (IEEE TKDE, Data & Knowledge Engineering). Actively involved in conference organization, including NeurIPS Area Chair (2024) and ICDT Proceedings Chair (2024). Labs and Collaborations: Affiliated with the Centre for Fundamental Computer Science at QMUL, fostering interdisciplinary research in theoretical and applied computing. Engages with industry partners on privacy-enhancing technologies and data management solutions.
Dr. Frank Maurer is a Professor in the Department of Computer Science at the University of Calgary, specializing in Extended Reality (XR) and Immersive Analytics. His work focuses on serious applications of XR in healthcare, energy, and engineering sectors. He leads the SEER Group, advancing cross-reality prototyping tools and digital twin technologies. Maurer holds a Diplom and a Dr. rer. nat. in Computer Science from the University of Kaiserslautern (1989 and 1993). Research areas include XR prototyping , immersive analytics , UX design for 5G-XR , and healthcare applications like pre-surgical epilepsy evaluations. He collaborates on strategic initiatives such as Brain/Mental Health and Energy Innovations. Recent work explores hydrogen pipeline monitoring, seismic data analysis (Siera tool), and generative AI integration in XR. Publications emphasize technical challenges in 3D medical imaging, reservoir engineering workflows, and cross-reality application testing. Maurer teaches Research Methods in Computer Science and has pioneered tools like NiwViw (immersive analytics authoring) and SpyREST (API documentation). His lab develops solutions for multi-surface environments and emergency response systems.
Behrouz Far is a Professor at the University of Calgary’s Schulich School of Engineering, Department of Electrical and Software Engineering. He holds a PhD in Artificial Intelligence from Chiba University, Japan (1990) and degrees from the University of Teheran including a B.S. in Electrical Engineering (1983) and M.S. in Electrical Engineering (1986). His research focuses on AI applications in medical imaging, software engineering, transportation systems, and data mining. He has contributed to advancements in fundus image analysis, deep learning models for disease detection, and intelligent traffic management systems. Dr. Far has received notable awards such as the 2017 SSE Achievement Award and the AITF-AMA Tier-2 Chair in Smart Multimodal Transportation Systems (2013). His work bridges theoretical AI with practical healthcare solutions, including tools like LETTA for traffic management systems and methodologies for detecting ocular lesions using CNNs. He teaches courses on software testing, reliability engineering, and agent-based systems. His publications highlight contributions to medical diagnostics (e.g., choroidal nevi classification), transportation optimization (e.g., real-time traffic signal control), and machine learning explainability. Collaborative research includes projects on biopotentiostat biosensors for SARS-CoV-2 detection and data mining for cancer patient stratification.
Eric Mörth is a PhD Scientist in Multimodal Medical Visualization at the University of Bergen's Department of Informatics, collaborating with the Mohm Medical Imaging and Visualization Center (MMIV). His research focuses on innovative medical data visualization techniques, such as MuSIC and ICEVis, which enhance clinical decision-making. Currently on a research stay at Harvard University's VCG Group, he holds a Master's in Medical Informatics from the Medical University of Vienna and a degree in Biomedical Engineering from the Technical University of Vienna. His awards include the Best Paper Honorable Mention (VCBM2022) and Best Short Paper (VINCI2022). Mörth's work spans cancer imaging, scrollytelling narratives, and interactive visualization tools, supported by grants from Trond Mohn Stiftelse. He advises through Team Smit and has contributed to projects like RadEx and ParaGlyder, advancing medical data exploration and communication.
Jeppe Revall Frisvad is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), within the Visual Computing group. His work bridges computer graphics, material science, and applied optics, focusing on realistic rendering and material appearance modeling. Ph.D. in Computer Graphics, DTU Informatics (2008) M.Sc. in Engineering (Applied Mathematics), DTU (2004) Education in University Teaching, LearningLab DTU (2008–2009) His research centers on computing material appearance from physical and chemical properties, developing faster and more accurate physically based rendering methods. Key areas include light scattering, bidirectional reflectance distribution functions (BRDF), anisotropy, and translucency modeling. Applications span computer games, movies, digital prototyping, architectural visualization, and training simulators. His recent publications (2024–2025) reflect a strong trend in digitizing material appearance, especially for 3D printing and food science, using spectrophotometry and optical validation. The work combines computer graphics with interdisciplinary applications in food structure and biological imaging, emphasizing measurement, modeling, and simulation. Scientific Awards: Research travel prize from AEG Elektronfonden (2006) IGDA scholarship for GDCE 2005 DTU Ph.D. scholarship (2004) He actively supervises Ph.D. students and leads multiple research projects, including those on organ-on-chip imaging, cheese quality analysis, and optical modeling of teeth. He collaborates across disciplines and institutions, with external research stays at UC San Diego and the University of Otago. He is involved in projects integrating AI with 3D imaging and material digitization. Jeppe is a key member of the Visual Computing research environment at DTU, contributing to both fundamental rendering algorithms and practical applications in industry and science.
Dr. Dipanwita Thakur serves as Assistant Professor at the Department of Computer Engineering, Modeling, Electronics and Systems (DIMES) at the University of Calabria, Italy since July 2023. She is an active member of the European Cooperation in Science & Technology (COST Action CA22104) focusing on cybersecurity and serves in the IEEE Future Networks Working Group for Artificial Intelligence/Machine Learning. Previously, she held a 15-year Assistant Professor position at Banasthali University, Rajasthan, and has industry experience at TechMahindra and C-DAC. Education: Ph.D. in Smart Healthcare from West Bengal University of Technology, Kolkata M.Tech. in Software Engineering from Banasthali Vidyapith MCA from NIELIT, Government of India B.Sc. from University of Calcutta Her research pioneers Green Artificial Intelligence with emphasis on energy-efficient federated learning and smart healthcare applications. She develops privacy-preserving human activity recognition systems using multimodal data fusion, focusing on performance evaluation and environmental sustainability. Her work bridges theoretical machine learning with practical healthcare solutions, optimizing AI systems for reduced carbon footprint while maintaining clinical efficacy through hardware-algorithm co-design and quantization techniques. Recent publications reveal a strong trajectory toward sustainable AI, with increasing focus on energy-aware federated learning frameworks, multimodal medical segmentation, and non-IID data handling. Her work consistently addresses the critical balance between model accuracy, convergence speed, and energy consumption across edge devices, with growing emphasis on quantization techniques and hardware-algorithm co-design for real-world deployment. Scientific Awards: Elevated to IEEE Senior Member (2024) Dr. B.C. Roy Memorial Scholarship for outstanding 10th Board results (1992) Student Science Seminar Award by West Bengal Government (1990) Dr. Thakur actively mentors students as evidenced by her congratulations to advisee Farwa for paper acceptances. She serves as Associate Editor for Information Fusion (Elsevier) and IEEE Sensors Journal, and holds editorial roles at Scientific Reports. Her research is advanced through COST Action CA22104 and IEEE working groups, though specific grant details aren't listed in the source material. She has organized key workshops including Green-Aware AI 2024 and Green Federated Learning at IJCNN 2025. She leads research within the MONAI community on data quality and federated learning, and contributes to IEEE IoT and Future Networks initiatives. Her work with the COST Action CA22104 Behavioral Next Generation in Wireless Networks connects cybersecurity with sustainable AI development, while her Missouri S&T visiting scholar position focuses on energy optimization for federated learning systems.
James Nugent, MD, MPH, is an Instructor of Pediatrics in the Division of Pediatric Nephrology at Yale School of Medicine and an Investigator at Yale’s Clinical and Translational Research Accelerator (CTRA). He practices pediatric nephrology at Yale-New Haven Children’s Hospital and provides primary care at Fair Haven Community Health Center. Appointments: Pediatric Nephrology, General Pediatrics Organizations: Clinical and Translational Research Accelerator (CTRA), Janeway Society Dr. Nugent’s research is focused on pediatric hypertension, particularly improving the diagnosis and evaluation of high blood pressure in children. His work spans clinical epidemiology, health services research, and translational applications, with emphasis on home and ambulatory blood pressure monitoring, EHR-based phenotyping, and the epidemiology of pediatric hypertension. He has contributed to national efforts such as the SUPERHERO registry. His recent publications reflect a strong trend in understanding diagnostic accuracy, monitoring practices, and risk factors for hypertension in youth, especially in the context of obesity and preterm birth. Much of his research aims to bridge gaps between primary care and specialty nephrology in the management of pediatric hypertension. Board Certified: Pediatrics, Pediatric Nephrology Education: MD (Duke), MPH (UNC), Residency (Walter Reed), Fellowship (Yale) Dr. Nugent has served as an active-duty pediatrician in the U.S. Air Force and integrates clinical care with research to advance preventive strategies in pediatric kidney health. He collaborates frequently with researchers such as F. Perry Wilson and Jason Greenberg. While no formal advisees or awards are listed, his scholarly output and institutional roles indicate active mentorship and research leadership.
Lisa Gale Suter, M.D. is a Professor of Medicine in the Section of Rheumatology, Department of Internal Medicine at Yale University School of Medicine. She serves as Senior Director of Quality Measurement Programs at the Center for Outcomes Research & Evaluation (CORE), where she leads major federal contracts with the Centers for Medicare & Medicaid Services (CMS) on hospital and clinician performance measures. She is also co-chair of the Quality Measures Subcommittee of the American College of Rheumatology’s Quality of Care Committee, overseeing national measure development. Professor, Section of Rheumatology, Department of Internal Medicine, Yale School of Medicine Senior Director, Quality Measurement Program, Center for Outcomes Research & Evaluation (CORE) Co-Chair, Quality Measures Subcommittee, American College of Rheumatology Dr. Suter’s research centers on quality measurement, healthcare outcomes, and technology assessment in rheumatology and musculoskeletal diseases . She has led or consulted on over 33 hospital-level outcome measures, with many implemented in federal payment programs. Her work emphasizes patient-reported outcomes, risk adjustment, and performance evaluation in complex healthcare systems. Her recent publications reveal a strong trend in hospital quality metrics, data validation in Medicare Advantage, patient-reported outcomes in lupus, and infection screening in biologic therapy . She frequently collaborates with leading health services researchers at Yale, such as Harlan Krumholz and Arjun Venkatesh, and contributes to high-impact journals including JAMA Network Open , Health Services Research , and Arthritis Care & Research . Dr. Suter has received significant recognition for her contributions to arthritis research: Named One of Most Important OA Imaging Publications in 2009 (World Congress on Osteoarthritis) Named Top Ten Advance in Arthritis Research for 2009 (Arthritis Foundation) She continues to advise on national quality initiatives and lead interdisciplinary research teams focused on improving rheumatology care delivery and healthcare system performance. Her leadership in both academic medicine and national policy underscores her role as a key figure in advancing evidence-based, patient-centered care.
Johannes R. Kratz, MD, is an Associate Professor in the Department of Surgery at the University of California, San Francisco (UCSF), School of Medicine. He holds the Van Auken Endowed Chair in Thoracic Oncology and serves as Interim Chief of the Division of Thoracic Surgery. Kratz is also the Program Director of the Thoracic Surgery Residency Program, Director of Minimally Invasive and Robotic Thoracic Surgery, and Medical Director of Robotic Surgery at UCSF Health, highlighting his dual leadership in both academic and clinical domains. He earned his M.D. magna cum laude from Harvard Medical School, a Master of Arts in Philosophy from Stanford University, and completed his clinical training including a residency in General Surgery at Massachusetts General Hospital and a fellowship in Cardiothoracic Surgery at UCSF. His academic credentials are further strengthened by additional training in Diversity, Equity, and Inclusion from UCSF. Kratz’s research is centered on improving outcomes for patients with early-stage thoracic malignancies. His work focuses on developing and validating molecular prognostic assays, particularly a 14-gene signature for lung cancer, understanding tumor immunology, and advancing precision medicine through multi-omics and organoid models. He leads the Kratz Lab, which investigates the genetic and immunological drivers of lung, esophageal, and thymic cancers to discover novel therapeutic targets. His most recent publications, spanning 2023–2025, reveal a strong emphasis on neoadjuvant targeted therapies (especially osimertinib), immune microenvironment profiling using spatial and single-cell techniques, molecular risk stratification, and surgical outcomes research. His work bridges clinical trials, translational science, and surgical innovation, with a growing interest in the impact of the pandemic on surgical care and biomarker development for precision oncology. Scientific Awards and Honors: Van Auken Endowed Chair in Thoracic Oncology (2018) Michael DeBakey Research Scholarship, American Association for Thoracic Surgery (2019) Hellman Family Clinical-Translational Research Development Award (2019) UCSF Health Exceptional Physician Award (2017) Haile T. Debas Academy of Medical Educators Excellence in Teaching Award (2019, 2023) Cardiothoracic Surgery Faculty Teaching Award (2021) Soma Weiss Scholar, Harvard Medical School (2005) Howard Hughes Research Training Fellowship (2004) Kratz has been a Principal Investigator on multiple grants from the American Association of Thoracic Surgery, Bakar ImmunoX Foundation, Helen Diller Family Comprehensive Cancer Center, and UCSF. He actively mentors surgical trainees and contributes to medical education, reflected in his residency program leadership and teaching awards. He is also involved in clinical trials related to minimally invasive surgery and perioperative care. His lab, launched in 2018, is a hub for innovative research in early-stage lung cancer therapeutics.
Michelle Borkin is an Assistant Professor in the Khoury College of Computer Sciences at Northeastern University’s Boston campus, where she co-leads the Visualization @ Khoury Lab and co-directs the Northeastern Visualization Consortium (NUVis). She additionally serves as Affiliated Faculty with the NULab for Text, Maps, and Networks and with the Information Design & Data Visualization Program in the College of Arts, Media, and Design. Education PhD, Applied Physics, Harvard University School of Engineering and Applied Sciences (2014) MS, Applied Physics, Harvard University BS, Astronomy & Astrophysics and Physics, Harvard University Research Interests Borkin’s research integrates data visualization and human-computer interaction to create novel techniques that enable discovery across disciplines. Her work spans: Multidimensional brushing-and-linking methodologies 3D data visualization and selection techniques Tree and network visualization Visualization evaluation methodologies and perception/cognition theory Accessibility and visualization for social good Medical and astrophysical visualization applications Publication Trends Across more than 50 peer-reviewed papers, Borkin’s research exhibits three dominant threads: (1) foundational studies on visualization perception and memorability, (2) design and evaluation of novel interactive tools for complex data (medical, astronomical, political, and social media), and (3) methodological contributions such as the Design Study “Lite” Methodology that accelerate visualization pedagogy and community-engaged research. Awards & Honors CHI 2020 Best Paper Award IEEE VIS 2020 Best Poster Honorable Mention IEEE VIS 2018 Best Poster Award NSF Graduate Research Fellowship NDSEG Graduate Fellowship TED Fellow Advising & Grants Borkin currently advises five PhD students—Jane Adams, Mackenzie Creamer, Franc O, Aditeya Pandey, and Laura South—and has previously mentored Michail Schwab and Uzma Haque Syeda. Her research has been supported by NSF, NDSEG, and TED fellowships, as well as internal Northeastern awards. Labs & Teams Co-Lead, Visualization @ Khoury Lab Co-Director & Co-Founder, Northeastern Visualization Consortium (NUVis) Affiliated Faculty, NULab for Text, Maps, and Networks Affiliated Faculty, Information Design & Data Visualization Program, CAMD
Prof.dr. R. Arthur Bouwman is a Full Professor at the Electrical Engineering department of the Eindhoven University of Technology and affiliated with the Eindhoven MedTech Innovation Center . His work bridges biomedical engineering and clinical medicine , focusing on physiological monitoring , medical imaging , and biomarker validation for real-time patient care. Education : Not explicitly detailed in the text His research emphasizes non-invasive diagnostics and AI-driven health monitoring , including video-based cardiac arrhythmia detection , sweat-based renal function analysis , and Doppler ultrasound optimization . Recent work explores causal inference in observational studies and automated early warning systems in surgical wards. Key article trends highlight biomedical signal processing , medical device innovation , and integration of wearables in perioperative care . Collaborations span institutions like Catharina Hospital and research centers across cardiovascular and renal domains.
