جست‌وجوی استاد و پژوهشگر

• DNA Repair
• Drug Repurposing
• Melanoma
• +۴ مورد دیگر

Associate Professor Nikola Bowden , co-Director of the University of Newcastle Centre for Drug Repurposing and Medicines Research, is a leading molecular biologist specializing in DNA repair pathways and drug repurposing for cancer , particularly melanoma and ovarian cancer. Affiliated with the School of Medicine and Public Health, she aims to deliver personalized diagnosis and treatment by leveraging existing therapies in unconventional ways. Education: PhD in Medical Genetics (University of Newcastle), BSc (Hons) in Molecular Biology. Her research focuses on DNA repair mechanisms , epigenetic regulation , and chemotherapy resistance , with a breakthrough linking dysfunctional DNA repair pathways to melanoma's resistance to treatment. By combining epigenetic drugs with chemotherapy, she has pioneered clinical trials for immunotherapy-resistant melanoma , achieving long-term survival in patients with no other options. Recent publications highlight her work on DDR gene expression in glioblastoma and ovarian cancer, exploring how ATR inhibitors , PARP modulators , and epigenetic therapies can overcome resistance. Her studies also investigate UV-light-induced DNA damage and immune checkpoint biomarkers to improve patient outcomes. Scientific Awards : Australian Financial Review Emerging Leader in Higher Education Finalist (2019) Cancer Institute NSW Research Fellow of the Year Finalist (2018) Best Oral Presentation Awards (2016, 2015) Young Tall Poppy Science Award (2015) Bowden leads collaborations with institutions like HMRI and AACR, and her translational research bridges bench-to-bedside strategies to combat aggressive cancers. She advocates for merit-based clinical trial design and systematic drug repurposing frameworks , emphasizing urgency in treating patients with short prognoses.

Boris Bordeauxمشاهده پروفایل

پژوهشگر

• Geometric Modeling
• Fractal Structures
• Lightweight Structures
• +۲ مورد دیگر

Boris Bordeaux is a PhD researcher at the University of Burgundy , affiliated with the Geometric Modeling team within the LIB Laboratory . His research focuses on the design of lightweight structures and computational geometric modeling. Research Work: Boris has contributed to the development of fractal structures with locally controlled lacunarity, presented at the International Conference in Central Europe on Computer Graphics (WSCG 2024). This work intersects geometric modeling and structural optimization. Teachings : He delivers 54 hours of practical work in Year 1 Computer Science courses. Team Affiliation : Geometric Modeling team, LIB Laboratory, University of Burgundy

Ren, Leiمشاهده پروفایل

استاد

• Image-guided Radiation Therapy
• Artificial Intelligence in Radiotherapy
• Medical Imaging Physics
• +۶ مورد دیگر

Lei Ren, PhD is a Professor and Associate Chief of Physics Research in the Department of Radiation Oncology at the University of Maryland School of Medicine. Previously, he held faculty positions at Duke University Medical Center from 2011-2021, progressing from Assistant Professor to Professor and Director of Physics Research. A clinical medical physicist certified by the American Board of Radiology (ABR), Dr. Ren leads a research program focused on advancing precision radiation therapy through innovative imaging and AI technologies. Dr. Ren's research centers on image-guided radiation therapy (IGRT) and AI applications in radiotherapy. His seminal contributions include developing digital tomosynthesis (DTS) for fast target localization, creating the limited-angle intrafraction verification (LIVE) system, and pioneering cone-beam CT (CBCT) scatter correction methods. Current work focuses on deep learning for deformable image registration, virtual MRI/CBCT generation, XMAN phantom development, and proton dose verification using prompt gamma imaging. His research integrates biomechanical modeling with AI to improve soft tissue contrast and motion management. His 15 most recent publications (2023-2025) reveal a strong trend toward clinical translation of AI in radiation oncology, with emphasis on interpretable AI for clinical decision making, radiomics for cancer prognostics, and novel imaging techniques for proton therapy verification. Approximately 70% of recent work involves deep learning applications, while 30% focuses on advanced imaging physics for motion management and dose verification. Award highlights include: Fellow of the American Association of Physicists in Medicine (AAPM), 2020 Thomas Gorrie Clinical Leadership Impact Award, Duke University, 2019 Top 5 team in AAPM Grand Challenge for 4D-CBCT reconstruction, 2018 Multiple Excellence in Research/Mentorship awards from Duke Medical Physics Program Distinguished Graduate Award from Tsinghua University, 2003 Dr. Ren actively mentors postdoctoral fellows and has secured substantial NIH funding as Principal Investigator for projects including 3D prompt gamma imaging (R01CA279013), XMAN phantoms (R01EB032680), and hybrid virtual-MRI/CBCT systems (R01EB028324). His lab collaborates with industry partners including Varian Medical Systems and ScandiDos AB on clinical implementation of imaging technologies. He serves on NIH study sections and as Deputy Editor for Medical Physics journal. His research group operates within the Radiation Oncology Department's imaging physics division, developing clinical tools for liver SBRT, proton therapy verification, and AI-driven treatment planning. Current projects include FLASH proton therapy dosimetry and virtual clinical trials using XMAN phantoms.

PAOLO CASTELLINIمشاهده پروفایل

دانشیار

• Mechanical Measurements
• Vibration Analysis
• Hyperspectral Imaging
• +۴ مورد دیگر

Paolo Castellini is an Associate Professor at the Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche (UNIVPM). His research focuses on mechanical measurements, vibration analysis, and advanced imaging techniques. He teaches courses in the College of Engineering and has published extensively on applications of hyperspectral imaging, 3D scanning, and neural networks in industrial and biomedical contexts. Office hours: Monday-Friday, 08:30-13:00 Email: p.castellini@univpm.it Location: via Brecce Bianche, 12, Ancona Recent research trends include vibration decoupling metastructures, microplastic detection in biological samples, and UAV-based photogrammetric analysis of olive trees. His publications emphasize non-destructive testing, acoustic beamforming, and uncertainty evaluation in measurement systems. Notable projects involve mmWave radar displacement analysis, cardiac simulators for prosthetic valves, and light-controlled polymer films for mechanical motion.

Raja Shaikhمشاهده پروفایل

استادیار

• Vascular Anomalies
• Pediatric Interventional Oncology
• Minimally Invasive Radiology
• +۲ مورد دیگر

Raja Shaikh, MBBS, DNB, MD, serves as an Attending Physician at the Vascular Anomalies Center and Director of Pediatric Interventional Oncology at the Division of Vascular and Interventional Radiology. He is an Assistant Professor of Radiology at Harvard Medical School, specializing in minimally invasive, image-guided treatments for pediatric patients. Education Medical School: Jawaharlal Nehru Medical College (1999) Internship: KLE Hospital/Jawaharlal Nehru Medical College (2000) Residency: Jawaharlal Nehru Medical College (2005) Fellowship: Body MRI at University of Arkansas Medical Sciences (2010) Fellowship: Vascular and Interventional Radiology at Beth Israel Deaconess Medical Center (2011) Fellowship: Pediatric Interventional Radiology at Boston Children's Hospital (2012) Research Focus Dr. Shaikh's research concentrates on vascular anomalies and interventional oncology, including: Endovascular approaches for arteriovenous malformations Minimally invasive treatments for aneurysmal bone cysts Cryoablation applications in vascular anomalies Pediatric interventional oncology for relapsed solid tumors Scientific Contributions His publications span: Pediatric lymphatic interventions MRI-based vascular anomaly analysis Cryoablation techniques Post-operative lymphatic leak management Image-guided tumor biopsies and treatments Contact Information Phone: 617-355-6286 Fax: 617-730-0541 Email: Raja.Shaikh@childrens.harvard.edu NPI: 1477888204

Yan Xiaمشاهده پروفایل

پژوهشگر

• 3D Vision
• Localization and Navigation
• Robotics
• +۲ مورد دیگر

Yan Xia is a Senior Researcher at the Computer Vision Group of the Technical University of Munich (TUM) and a Research Scientist at the Munich Center for Machine Learning. She collaborates with Prof. Daniel Cremers and previously completed her PhD at TUM under supervision of Prof. Uwe Stilla and Prof. Daniel Cremers, with a visiting period at the Visual Geometry Group of the University of Oxford under Dr. João Henriques.

• Machine Perception
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Dr. Vasileios Balntas is a leading researcher in machine perception, currently serving as a Research Science Manager at Reality Labs Research. He holds an Honorary Research Associate position at the Department of Electrical and Electronic Engineering , Imperial College London, and previously led research at Scape Technologies and conducted post-doctoral work at the University of Oxford. His research spans computer vision , machine learning , and artificial intelligence , focusing on camera pose estimation 3D scene reconstruction image retrieval local feature descriptors egocentric vision systems privacy-preserving AI The 15 most recent publications highlight his work in neural rendering, adversarial learning, and structured language models for visual perception. Key trends include egocentric multi-modal AI 3D localization descriptor security autoregressive scene modeling infilling techniques second-order loss functions At Imperial College London, he contributes to the Faculty of Engineering 's research in electrical engineering and collaborates across Reality Labs Research and Scape Technologies on cutting-edge machine perception projects.

René Vidalمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Biomedical Data Science
• +۳ مورد دیگر

René Vidal is the Rachleff & Penn Integrates Knowledge (PIK) University Professor at the University of Pennsylvania, with appointments in the Departments of Electrical and Systems Engineering, Radiology, Computer and Information Science, and Statistics and Data Science. He also serves as Director of the Center for Innovation in Data Engineering and Science (IDEAS) and the NSF-Simons Collaboration on the Mathematical Foundations of Deep Learning (THEORINET). A dual faculty member at Johns Hopkins University in Biomedical Engineering, Computer Science, and other departments, Vidal is an Amazon Scholar and Affiliated Chief Scientist at NORCE. PhD, Electrical Engineering and Computer Sciences, University of California, Berkeley (2003) M.S., Electrical Engineering, University of California, Berkeley (2000) B.S. (valedictorian), Electrical Engineering, Pontificia Universidad Catolica de Chile (1997) His research spans the mathematical foundations of deep learning, focusing on non-convex optimization , learning dynamics , and overparametrization . Key contributions include Sparse Subspace Clustering , Kernel GPCA , and Low-Rank Matrix Factorization , with applications in autism diagnosis , robotic surgery , and cardiac imaging . Recent work explores continual learning , adversarial robustness , and trustworthy AI in biomedical contexts. His 15 most recent publications highlight advances in medical imaging , language models , and robust computer vision , spanning topics from chest X-ray analysis to motor imitation tasks in autism . Articles like Geometric Analysis of Nonlinear Manifold Clustering underscore his theoretical contributions, while works on KDA: Knowledge-Distilled Attacker and Conformal Information Pursuit address practical AI safety and interpretability. Scientific accolades include: 2022 ACM Fellow 2021 IEEE McCluskey Technical Achievement Award 2017 Jean D’Alembert Fellowship 2012 J.K. Aggarwal Prize 2009 Sloan Research Fellow 2005 NSF CAREER Award His lab mentors 11 current PhD students across Johns Hopkins and University of Pennsylvania , with alumni contributing to institutions like Meta , Amazon , and GE Research . Vidal’s interdisciplinary work bridges mathematics , engineering , and healthcare , supported by grants from the DoD , NSF , and ONR .

Jürgen Eckertمشاهده پروفایل

استاد

• Materials Science
• Physics
• Materials Physics
• +۷ مورد دیگر

Jürgen Eckert is a Professor and Director at the Erich Schmid Institute of Materials Science of the OAW and holds the Chair of Materials Physics at the University of Leoben . He is a Corresponding Member of the Division of Mathematics and Natural Sciences in Austria since 2017. His research spans Materials Science , Condensed Matter Physics , and Metallurgy , focusing on metallic glasses , high-entropy alloys , microstructure engineering , and additive manufacturing . He has pioneered work on crystallization kinetics , phase transformations , and mechanical property optimization . Recent publications (2024-2025) highlight advances in 3D-printed titanium alloys , nitrogen-stabilized nanocrystalline alloys , and multi-stage heterostructures , reflecting his interdisciplinary approach combining experimental physics , computational modeling , and materials engineering . THERMEC 2023 Distinguished Award Gottfried Wilhelm Leibniz-Preis (2009) ERC-Advanced Grant (2013) DGM-Preis der Deutschen Gesellschaft für Materialkunde (2014) Dr. honoris causa, Slovak Technical University (2018) He leads the ERC Project INTELHYB and collaborates with institutions in India , Germany , and Europe , advancing heterogeneous materials design and sustainable metallurgical solutions .

• Robotics
• Evolutionary Robotics
• Artificial Evolution
• +۶ مورد دیگر

Leni Le Goff is a Lecturer at the School of Computing, Engineering and the Built Environment , Edinburgh Napier University. Her research bridges robotics , evolutionary computation , and environmental sustainability . Key research interests include: Evolutionary design of robot morphologies and controllers Integration of individual and cultural learning in artificial evolution Application of autonomous systems for terrestrial biodiversity monitoring Optimization frameworks for robotic policy search Manufacturability constraints in morpho-evolution Transdisciplinary collaboration between ecologists and roboticists Recent publications highlight her work on: Developing generalized early stopping criteria for policy search Mapping fitness landscapes in morpho-evolution Assessing robotic solutions for ecological surveying Overcoming hierarchical optimization challenges in coevolution Creating biodegradable and deployable robotic systems Evaluating manufacturability trade-offs in evolved robot designs Active in the Autonomous Robot Evolution (ARE) project, she collaborates with leading researchers like Prof Emma Hart and Prof Karen Diele. Her work receives funding from EPSRC and the Carnegie Trust , focusing on real-world deployment challenges and computational efficiency improvements.

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Simone Bianco is an Associate Professor at the Department of Informatics, Systems and Communication (DISCo) of the University of Milano-Bicocca, Italy. His academic and research contributions span computer vision, artificial intelligence, machine learning, and optimization algorithms applied to multimodal and multimedia systems. His educational background includes a PhD in Computer Science (2010) and BSc/MSc degrees in Mathematics (2003/2006), both from the University of Milano-Bicocca. Bianco’s research focuses on color constancy, deep learning for video restoration, neural architecture search, and computational color imaging, with a strong emphasis on practical applications like biometric recognition, medical imaging, and environmental monitoring. The 15 most recent articles (2025–2020) highlight trends in computer vision, including uncertainty estimation in color constancy, portable material appearance modeling, temporal consistency in low-light videos, and advanced deep learning architectures for image and video processing. His work often integrates photogrammetry, sensor technology, and multimodal data analysis. Scientific accolades include recognition on Stanford University’s World Ranking Scientists List for achievements in artificial intelligence and image processing. Bianco also serves as R&D Manager for the University of Milano-Bicocca spin-off Imaging and Vision Solutions and contributes to international conferences and workshops.

• Autonomous Driving
• Computer Vision
• Robotics
• +۳ مورد دیگر

Markus Enzweiler serves as Professor of Computer Science and Autonomous Systems at Esslingen University of Applied Sciences within the Department of Computer Science and Engineering. He concurrently holds the leadership position of Director at the Institute for Intelligent Systems, where he oversees research initiatives focused on intelligent systems development for real-world autonomous applications. His research program centers on computer vision for autonomous systems , with specialized expertise in visual-inertial SLAM, collective perception, and neural rendering techniques. Key investigation areas include environmental robustness across agricultural and urban settings, real-time processing constraints for embedded systems, sensor fusion methodologies (particularly camera-radar integration), and the application of generative models for perception enhancement. His work consistently addresses practical implementation challenges such as computational efficiency and sensor calibration in unstructured environments. Analysis of his 2023-2025 publications reveals three dominant research trajectories: (1) Advancement of lightweight perception systems through stixel-based representations and neural rendering; (2) Development of infrastructure-supported collective perception frameworks with datasets like CoopScenes and OPNV; and (3) Rigorous benchmarking of SLAM components in domain-specific contexts including agricultural robotics and multi-season navigation. His recent systematic review on LLM-based vulnerability detection also demonstrates expanding interest in software security for autonomous systems. As Director of the Institute for Intelligent Systems, Prof. Enzweiler leads a research ecosystem focused on translating theoretical advances into practical autonomous vehicle technologies. His team develops specialized datasets (Rover, OPNV) and software stacks for smart city environments, emphasizing the integration of novel perception approaches with vehicle dynamics modeling and real-time operational constraints.

Jana Fortesمشاهده پروفایل

مدرس

• Signal Processing
• Localization Technology
• Tracking Systems
• +۳ مورد دیگر

Jana Fortes is a Lecturer at the Faculty of Electrical Engineering and Informatics, Technical University of Košice, Slovakia. She teaches Applied Statistics, Cryptography, and Mathematics courses, including Numerical Mathematics and Probability and Statistics. Her research focuses on signal processing for localization and tracking systems using Ultra-Wideband (UWB) radar and sensor networks. Teaching: Applied Statistics, Cryptography, Mathematics I/II, Numerical Mathematics, Probability and Statistics, Mathematics Tutoring Research: UWB radar signal processing for person localization and tracking, software development (MATLAB, LabVIEW), cluster analysis for movement detection. Publications: Recent work includes studies on 2D/3D localization algorithms, UWB sensor system implementations, and mathematical analyses of graph crossing numbers. Contact: Email: jana.fortes@tuke.sk , Room N32 615, Phone: +421 55 602 3252.

Albert Zelenikaمشاهده پروفایل

پژوهشگر

• Materials Physics
• Dislocations in Ceramics
• Dark Field X-ray Microscopy (DFXM)
• +۳ مورد دیگر

Albert Zelenika is a postdoctoral researcher at the Karlsruhe Institute of Technology (KIT), affiliated with the Mechanics of Materials 1 (WM1) group within the Institute of Applied Materials. His research focuses on dislocation dynamics, materials physics, and advanced X-ray microscopy techniques. Education: BSc in Physics, University of Trieste (2014-2018) MSc in Physics, University of Trieste (2018-2021) PhD in Physics, Technical University of Denmark (2021-2024) Zelenika specializes in the application of Dark Field X-ray Microscopy (DFXM) and X-ray diffraction microscopy to study structural evolution during plastic deformation in metals and ceramics. His work reveals critical insights into dislocation patterning, cell formation, and strain dynamics in crystalline materials. Recent research trends highlight Zelenika's contributions to understanding dislocation boundaries , geometrically necessary boundaries (GNBs) , and self-organization of dislocations using in-situ and 4D X-ray imaging. His studies span applications in aluminum , ferritic alloys , and borophene characterization. Laboratory & Collaborations : Zelenika works in Xufei Fang's Lab at KIT and collaborates with researchers at the Technical University of Denmark and European Synchrotron Radiation Facility . His work involves interdisciplinary teams utilizing synchrotron radiation and advanced diffraction techniques.

Donald R. Sheehyمشاهده پروفایل

دانشیار

• Geometric Algorithms
• Topological Data Analysis
• Computational Geometry
• +۴ مورد دیگر

Donald R. Sheehy is an Associate Professor of Computer Science in the College of Engineering at North Carolina State University. His research focuses on the intersection of geometric algorithms and topological data analysis, with significant contributions to computational geometry and persistent homology. Sheehy's research interests span geometric algorithms, topological data analysis, computational geometry, persistent homology, metric spaces, Voronoi diagrams, and Delaunay triangulations. His work bridges theoretical computer science with practical applications in data analysis, where he develops algorithms that extract meaningful topological information from complex datasets. His research has particular relevance for understanding the structure of high-dimensional data through geometric and topological lenses. Analysis of his recent publications reveals a strong focus on developing efficient algorithms for topological data analysis. His work on sparse filtrations, greedy permutations, and metric properties of persistence diagrams has advanced the field by providing computationally tractable methods for analyzing large datasets. Sheehy frequently explores how geometric structures like Voronoi diagrams and Delaunay triangulations can be adapted to topological contexts, creating bridges between classical computational geometry and modern data analysis techniques. Sheehy actively collaborates with researchers across multiple institutions, as evidenced by his extensive publication record in top venues like SOCG (Symposium on Computational Geometry) and SODA (Symposium on Discrete Algorithms). His work demonstrates a consistent trajectory of advancing both theoretical foundations and practical applications of geometric and topological methods in computer science.