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Daniela BotaView profile

Professor

• Neuro-Oncology
• Stem Cell Therapy
• Immunotherapy
• +7 more

Daniela Bota is the Vice Dean for Clinical Research at the University of California, Irvine (UCI) School of Medicine and a Professor in the Department of Neurology. She directs the UCI Alpha Clinic at the Sue and Bill Gross Stem Cell Research Center and co-leads UCI Health's Comprehensive Brain Tumor Program. Her academic roles are complemented by leadership in translational research, clinical trial oversight, and bridging laboratory innovations to patient care. Her research focuses on neuro-oncology, particularly advancing therapies for brain malignancies like glioblastoma. This includes pioneering stem-cell targeted treatments, immunotherapy (e.g., dendritic cell vaccines and checkpoint inhibitors), and medical devices such as tumor-treating fields (TTFields). She explores personalized medicine, overcoming chemotherapeutic resistance, and molecular mechanisms underlying tumor progression. Her work integrates genetic analysis, preclinical drug testing, and clinical trial design to address unmet needs in brain tumor treatment. Dr. Bota’s recent articles emphasize novel therapeutic strategies, including LonP1 and MAGMAS inhibitors, phase II/III trials for drugs like mirdametinib and marizomib, and investigations into chemotherapy-induced neurotoxicity. She also evaluates combination therapies involving natural marine products and cognitive training for cancer-related impairment. No scientific awards are explicitly mentioned in the provided text. Her contributions to translational research and clinical leadership are highlighted through her roles in managing large-scale trials, directing clinical programs, and training future researchers. She is affiliated with the Sue and Bill Gross Stem Cell Research Center and the Alpha Clinic, where she drives interdisciplinary efforts to integrate cutting-edge science with patient care.

Marcello CarioniView profile

Assistant Professor

• Inverse Problems
• Optimal Transport
• Regularization Techniques
• +3 more

Marcello Carioni is an Assistant Professor affiliated with the Digital Society Institute, TechMed Centre, and Mathematics of Imaging & AI. His research focuses on inverse problems, optimal transport, regularization techniques, and mathematical analysis with applications in machine learning and imaging. He has published extensively, including 35 research outputs since 2016, with a notable h-index of 23 and 228 citations. His work bridges theoretical mathematics and practical applications, addressing challenges in sparsity recovery, dynamic systems, and convex optimization. Recent contributions include advancements in optimal transport regularization, sparse optimization frameworks, and neural network representations in infinitely wide regimes. Collaborations span international teams, reflecting his interdisciplinary approach to mathematical challenges. Carioni has delivered invited talks on topics like dynamic inverse problems and optimal transport methods, showcasing his engagement with both academic and applied communities. His research emphasizes methodological innovation, with a focus on solving complex problems in imaging and data science through rigorous mathematical foundations.

• Additive Manufacturing
• Materials Science
• Aerospace Engineering
• +3 more

Prof. Alberto Boschetto is a Full Professor in the Department of Industrial and Information Engineering at Sapienza University of Rome. He specializes in advanced manufacturing technologies, with a focus on additive manufacturing processes and their applications in aerospace, materials science, and surface engineering. His research integrates computational modeling, material characterization, and digital image processing to optimize manufacturing workflows and improve material performance. Key research areas include 4D printing, laser powder bed fusion (LPBF), fused filament fabrication (FFF), and the development of corrosion-resistant coatings for aerospace components. He has contributed to projects on lightweight satellite structures and CubeSat fabrication, demonstrating expertise in both academic and industrial applications. His work also addresses surface finishing techniques like barrel finishing and CNC machining to enhance part quality. Prof. Boschetto has authored over 70 peer-reviewed articles, with recent publications emphasizing Industry 4.0 integration in manufacturing, defect detection via digital image analysis, and material property optimization. His research bridges theoretical advancements with practical implementations, particularly in aerospace and biomedical applications. No scientific awards or grants are explicitly listed in the provided texts. His advising record remains unspecified, though his extensive publication history suggests active mentorship of graduate students in materials engineering and manufacturing systems.

Yihai FangView profile

Senior Lecturer

• Construction Automation and Informatics
• Construction Robotics
• Digital Twin for Construction
• +3 more

Dr. Yihai Fang is a Senior Lecturer in Construction Engineering and Management at the Department of Civil and Environmental Engineering, Monash University. He holds a Ph.D. from Georgia Institute of Technology (2016) and a B.Sc. from Tongji University (2011). Previously, he served as a Postdoctoral Associate at the University of Florida (2016–2017). His research focuses on Construction Automation and Informatics, Construction Robotics, Digital Twin applications in built environments, and Construction Safety and Human Factors. Qualifications include advanced expertise in machine learning, sensing technologies, and Virtual/Augmented Reality (VR/AR). Key research projects include enhancing road work zone safety through smart sensing, collaborative robotics for structural assembly, and BIM-based cyber-physical systems for crane operations. He has led and contributed to multiple grants, including ARC-funded initiatives and industry partnerships. Dr. Fang’s recent work emphasizes predictive analytics, digital twin frameworks, and automation in construction. His publications span safety technologies, crane operation optimization, and remote inspection methods. He supervises a dynamic team of PhD candidates addressing topics like crane hazard exposure, modular assembly automation, and road roughness monitoring. Grants include leadership in the ARC Research Hub for Smart Transport Pavements and Building 4.0 CRC projects. His work aligns with UN Sustainable Development Goals related to infrastructure and sustainable cities.

• Data Assimilation
• Meteorology
• Numerical Weather Prediction
• +4 more

Javier Amezcua is a researcher specializing in data assimilation, numerical weather prediction, and atmospheric dynamics, affiliated with the University of Maryland. He holds a PhD in Atmospheric Sciences from the University of Maryland (2012), focusing on sequential data assimilation and numerical weather forecasting. His work bridges advanced statistical methods with meteorological modeling, particularly in ensemble Kalman filters, model error estimation, and tropical climate dynamics. Research interests include: Development of ensemble-based data assimilation techniques for improving weather and climate models Integration of observational data (e.g., infrasound, satellite) to enhance atmospheric wind field estimation Study of model error dynamics and their impact on forecast accuracy Applications in renewable energy, such as wind resource prediction and statistical-dynamical downscaling Notable contributions include advancements in ensemble transform filters, weak-constraint 4D ensemble variational methods, and the implicit equal-weights particle filter. Collaborations span institutions like the University of Reading, the Norwegian Meteorological Institute, and the European Centre for Medium-Range Weather Forecasts. His thesis explored sequential data assimilation methodologies, including the Ensemble Transform Kalman-Bucy Filter and the effects of time-stepping schemes in atmospheric models. Ongoing work emphasizes interdisciplinary applications of data assimilation in epidemiology and hydrology.

Graeme KellyView profile

Researcher

• Numerical Weather Prediction (NWP)
• Data Assimilation
• Satellite Meteorology
• +3 more

Graeme Kelly is a Researcher at the Department of Meteorology, University of Reading, and an Expert Scientist in Data Assimilation at the Met Office. His work focuses on advancing numerical weather prediction (NWP) through the use of satellite data, particularly in variational analysis and data assimilation systems. He has expertise in integrating SEVIRI and IASI radiance data into operational models, improving forecast accuracy, and optimizing observation systems. His career spans roles at ECMWF (1983–2007) and the Met Office (2007–present), where he contributed to projects like the ERA-40 reanalysis and the RTTOV radiative transfer model. His research interests include satellite data assimilation, cloudy radiance processing, and observation error characterization. Notable contributions involve enhancing the use of geostationary satellite observations (e.g., SEVIRI, IASI) in high-resolution models and developing infrastructure for cloudy radiance assimilation. He has also advised on reanalysis projects and satellite bias correction initiatives. Key technical achievements include improving the Met Office’s UKV model through 4D-Var techniques and advancing understanding of atmospheric motion vector (AMV) errors. His work bridges satellite remote sensing and operational NWP, emphasizing the value of targeted observations and interdisciplinary collaborations with space agencies.

Amos LawlessView profile

Professor

• Data Assimilation
• Numerical Weather Prediction
• Meteorology
• +4 more

Professor Amos Lawless is a leading academic in the Department of Meteorology at the University of Reading, specializing in data assimilation and inverse problems. His research focuses on advancing numerical methods for meteorological and oceanographic modeling, particularly in variational data assimilation, error covariance analysis, and coupled atmosphere-ocean systems. He holds a Professorship position and contributes to the development of algorithms for improving forecasting accuracy in numerical weather prediction (NWP) systems. His work integrates computational mathematics, environmental science, and applied statistics to address challenges in climate modeling and predictive systems. Key research themes include the conditioning of data assimilation problems, model bias correction, and the application of optimization techniques to enhance forecast precision. His publications span prestigious journals like the Quarterly Journal of the Royal Meteorological Society and SIAM Journal on Applied Dynamical Systems, reflecting his expertise in both theoretical and applied aspects of data assimilation. Collaborations involve institutions such as the Met Office and international research groups, aiming to bridge the gap between computational methods and real-world environmental modeling challenges. Recent work emphasizes hybrid data assimilation approaches, cross-domain error correlations in coupled systems, and the use of randomized preconditioning to tackle large-scale computational problems. His contributions are pivotal in advancing the mathematical foundations of environmental forecasting systems, ensuring robustness and efficiency in modern NWP frameworks.

• Electrocatalysis
• Defect Engineering
• Fuel Cells
• +4 more

Dr. Miquel Vega-Paredes is a Researcher at the Max Planck Institute for Sustainable Materials in Düsseldorf, Germany. He leads the Work Package 'MEA-Entwicklung. Projekt zur Aktivierung von Ultra-langen Lebensdauern (PAULL)' since October 2024. Previously, he completed his PhD in Material Science at RWTH Aachen (2021–2024) with distinction, focusing on improving electrocatalysts via advanced nanostructure characterization. His academic journey includes a Master’s degree in Advanced Chemistry (Universitat Autònoma de Barcelona, 2020–2021) and a Bachelor’s in Nanoscience and Nanotechnology (same institution, 2016–2020), both with distinction. His research interests center on electrocatalysis for fuel cells and water electrolyzers, defect engineering, and advanced microscopy techniques like (Scanning) Transmission Electron Microscopy (STEM), 4D-STEM, EELS, and tomography. He has conducted research stays at Lawrence Berkeley National Laboratory (2024) and the Max-Planck-Institut für Eisenforschung, exploring degradation mechanisms in fuel cell catalysts and materials. Dr. Vega-Paredes’ publications highlight advancements in catalyst stability, nanostructure characterization, and operando insights into degradation. His work bridges theoretical simulations (e.g., atomic cluster expansions) with experimental microscopy to optimize electrochemical systems. Awards include distinctions for his PhD and Bachelor’s degrees. His contributions to labs/teams include the Nanoanalytics and Interfaces department at his institute, focusing on sustainable materials for energy applications.

• Cardiology and Cardiovascular Disease
• Medical Biotechnology
• Molecular Biology
• +4 more

Per Eriksson is a Professor in the Department of Medicine, Solna at Karolinska Institutet. His research focuses on molecular and epidemiological studies of ascending aortic aneurysms, particularly within the Research Group of Hanna Björck. He holds a Docent title from Karolinska Institutet (1999). His work bridges cardiology, molecular biology, and cell biology, investigating mechanisms underlying aortic aneurysm development and cardiovascular disease progression. Research Interests: Dr. Eriksson’s research emphasizes molecular mechanisms of aortic aneurysm formation, cardiovascular risk prediction using carotid imaging (e.g., IMPROVE Study), and the role of lipoproteins, immune cells, and endothelial dysfunction in vascular pathology. He also explores genetic and proteomic factors in metabolic liver diseases and atherosclerosis. Recent Article Themes: His 15 most recent articles (2023–2025) highlight advanced techniques like machine learning for subclinical atherosclerosis endotyping, proteomic profiling of cardiovascular risk factors, and mechanistic studies on Kupffer cell roles in hepatic lipid metabolism. Key topics include statin effects on hepatic genes, endothelial regulatory pathways in elite athletes, and predictive models for aortic valve-related dilatation. Grants: His funding includes Swedish Research Council grants for thoracic aortic aneurysm research (2021–2023), the MIBAVA Leducq Transatlantic Network (2012), and Swedish Heart-Lung Foundation support for aortic valve disease studies. Industrialist Fredrik Lundberg’s donations have also supported cardiovascular research at KI. Labs/Teams: A key member of the Molecular and Epidemiological Studies group under Prof. Hanna Björck, focusing on translational research connecting genetic, proteomic, and clinical data to vascular disease mechanisms.

Laszlo A. JeniView profile

Assistant Professor

• Computer Vision
• Digital Humans
• Computational Behavior Science
• +6 more

Laszlo A. Jeni is an Assistant Research Professor at Carnegie Mellon University's Robotics Institute, leading the Computational Behavior (CUBE) Lab. His research focuses on computer vision, digital humans, and computational behavior science, with applications in healthcare, affective computing, and assistive technologies. He develops methods to model human behavior using multi-modal sensors, including facial, body, and physiological data. Current research emphasizes human motion synthesis, clinical movement analysis, and 3D scene reconstruction. Key research topics include action recognition for clinical applications, generative models for 4D scene synthesis, and video-based physiological estimation. Jeni supervises a team of PhD and master's students in the CUBE Lab, advancing interdisciplinary projects at the intersection of AI and behavioral science. His work has led to innovations in non-contact health monitoring and virtual avatar control systems. Notable contributions include frameworks for sim-to-real transfer in human mesh recovery, diffusion-based camera alignment, and video transformers optimized for efficiency. Jeni's lab actively participates in challenges like the V4V (Vision for Vitals) initiative and benchmarks for 3D facial alignment, maintaining a strong presence in both academic and applied computer vision communities.

Dongyeon KimView profile

Researcher

• Augmented Reality (AR)/Virtual Reality (VR)
• Computational Optics
• 3D/4D Displays
• +3 more

Dongyeon Kim is a Researcher at the University of Cambridge's Department of Computer Science and Technology, collaborating with Prof. Rafal Mantiuk. She holds a Ph.D. in Electrical and Computer Engineering from Seoul National University (SNU), where she contributed to the Optical Engineering and Quantum Electronics Lab under Prof. Byoungho Lee. Her work focuses on bridging computational optics with visual perception, particularly in next-generation AR/VR displays, holographic systems, and 3D display technologies. Education: Ph.D., Electrical and Computer Engineering, Seoul National University (2023) Bachelor's Degree, Electrical and Computer Engineering, Seoul National University (20XX) Research Interests: Augmented/Virtual Reality (AR/VR) display systems Holographic display optimization and human perception modeling Computational optics for 3D/4D displays Visual perception evaluation and quality metrics Integration of machine learning in display technology Awards & Recognition: Sejong Science Fellowship Grant (Korean Government) Best Paper Honorable Mention at SIGGRAPH Asia 2024 Grants & Funding: Sejong Science Fellowship for postdoctoral research Lab & Team: Collaborates with Prof. Mantiuk's research group at the University of Cambridge, focusing on perceptual evaluation of AR displays and dataset development (e.g., AR-DAVID).

Daniel CremersView profile

Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +10 more

Prof. Daniel Cremers is a Professor of Informatics and Mathematics at the Technical University of Munich (TUM), leading the Chair of Computer Vision & Artificial Intelligence within the TUM School of Computation, Information and Technology. His work spans computer vision, machine learning, and mathematical image analysis, with contributions to autonomous systems, SLAM, and self-driving cars. Affiliations: Chair of Computer Vision & AI, TUM School of Computation, TUM. Education: Bachelor's in Mathematics and Physics (University of Heidelberg), Master's in Theoretical Physics (University of Heidelberg), PhD in Computer Science (University of Mannheim). Research Interests: Focuses on computer vision fundamentals, deep learning, variational methods, and real-world applications like autonomous systems. Key areas include 3D reconstruction, SLAM, and geometric processing. Recent Contributions: Over 12 papers accepted to CVPR 2025, including work on probabilistic bundle adjustment and dynamic scene reconstruction. Notable achievements include the ECCV 2024 Koenderink Award for LSD SLAM and the Leibniz Prize (2016). Awards: Leibniz Prize, ERC grants (Starting, Consolidator, Advanced), multiple best paper awards. Teaching: Offers courses like Computer Vision II: Multiple View Geometry and Introduction to Deep Learning. Supervises practical courses on deep learning methods and autonomous systems. Labs/Teams: Leads the Computer Vision Group, actively organizing conferences (e.g., GCPR/VMV 2024) and contributing to open datasets like DeepScenario. Engages in industry through startups and advisory roles.

• 3D Computer Vision
• Computer Graphics
• Machine Learning
• +5 more

Dimitris Tzionas is an Assistant Professor for 3D Computer Vision at the University of Amsterdam (UvA), leading research in the intersection of Computer Vision, Computer Graphics, and Machine Learning. His work focuses on modeling human-object interactions, 3D/4D perception, and synthesis for applications in Ambient Intelligence, Virtual Assistants, and the Metaverse. He holds an ERC Starting Grant (€1.5M) and ELLIS Amsterdam affiliations. Previously, he was a Research Scientist at Max Planck Institute for Intelligent Systems (MPI-IS), contributing to projects like the GRAB dataset and MANO/SMPL body models. He earned his PhD from the University of Bonn on hand-object interaction, advised by Jürgen Gall, and completed his MSc at Aristotle University of Thessaloniki in Electrical & Computer Engineering. His research has produced impactful datasets (e.g., GRAB, InterCap, DAMON) and methods like InteractVLM and PICO, advancing human-centric AI. He actively serves on CVPR/ICCV/ECCV committees and teaches 'Introduction to Computer Vision' at UvA. Notable awards include NVIDIA grants, Google Research Gifts, and multiple Outstanding Reviewer recognitions. His lab collaborates widely, advising over 10 PhD/MSc students and postdocs, with projects funded by ERC, ELLIS, and industry partners like NVIDIA and Google. Key themes include 3D reconstruction, interaction modeling, and generative AI for realistic avatar creation.

Skylar TibbitsView profile

Associate Professor

• Self-Assembly Technologies
• Programmable Materials
• 4D Printing
• +4 more

Skylar Tibbits is an Associate Professor of Design Research at MIT's Department of Architecture, where he leads undergraduate programs and co-directs the Self-Assembly Lab. His roles include teaching design studios and coordinating MIT's undergraduate Design programs. He is also an Assistant Director for Education at the Morningside Academy for Design. His work focuses on programmable materials and self-assembly technologies for manufacturing, construction, and product innovation. Education: Bachelor of Architecture (Professional Degree) from Philadelphia University with a minor in experimental computation Master of Science in Design Computation from MIT Master of Science in Computer Science from MIT, advised by Patrick Winston, Terry Knight, Erik Demaine, and Neil Gershenfeld Research Interests: Self-assembly systems and programmable material technologies 4D printing and shape-changing materials Robotics, smart manufacturing, and architectural applications Material agency and adaptive systems His lab explores innovations like reversible textile transformation, active haptic knits, and gel-supported 3D printing. Key Awards: R&D Magazine's 2015 Innovator of the Year National Geographic Emerging Explorer (2015) WIRED Fellow (2014) Fast Company Innovation by Design Award (2013) Advising & Labs: Director of MIT's undergraduate Design programs Founder and co-director of the Self-Assembly Lab Collaborates with industry leaders in architecture, robotics, and materials science Future Work: Expanding applications of programmable matter in construction, healthcare, and wearable technologies.

C. Alberto FigueroaView profile

Professor

• Cardiovascular Biomechanics
• Computational Fluid Dynamics (CFD)
• Medical Imaging Analysis
• +5 more

C. Alberto Figueroa is the Edward B. Diethrich Professor of Surgery and Professor in the Department of Biomedical Engineering at the University of Michigan. His primary affiliation is with the College of Engineering, where he leads the Computational Vascular Biomechanics Lab. He holds a Ph.D. in Mechanical Engineering from Stanford University (2006). Research interests focus on computational modeling of vascular systems, including hemodynamics, biomechanics, and medical imaging analysis. His work integrates machine learning, computational fluid dynamics (CFD), and multi-scale modeling to study diseases such as aortic aneurysms, pulmonary hypertension, and cerebrovascular disorders. Key areas include vascular deformation mapping, surgical intervention planning, and AI-driven diagnostics for coronary and cerebrovascular diseases. Recent publications emphasize predictive models for aneurysm growth, hemodynamic effects in carotid stenosis, and AI-based 3D reconstruction from medical imaging. His lab develops tools like AngioNet for vessel segmentation and CRIMSON software for cardiovascular modeling. Collaborative projects address clinical challenges in endovascular repair, myocardial perfusion, and hypertension pathophysiology. Dr. Figueroa’s research spans academia and clinical practice, with applications in surgical decision-making, device design, and precision medicine. His work bridges engineering and medicine, aiming to translate computational insights into patient-centered solutions.