Find Academic Supervisors

• Neuroscience
• Physics of Complex Systems
• Computational Neuroscience
• +5 more

Prof. Viola Priesemann is a physicist and neuroscientist at the Max Planck Institute for Dynamics and Self-organization and University of Göttingen (W3 Professorship, 20% position). Her research spans self-organization in adaptive networks across neural and societal scales, with applications to neural criticality , information processing , and COVID-19 pandemic dynamics . She leads an independent Max Planck Research Group since 2017 and has held affiliations with Caltech , MPI Brain Research , and Bernstein Center Göttingen . Her research combines statistical physics , information theory , and non-equilibrium systems to study: Self-organized criticality in brain networks Subsampling theory for large-scale systems Neural basis of curiosity and learning Test-trace-isolation strategies for epidemics Information spread in social networks Design of energy-efficient AI architectures Key scientific awards include: Communitas Award (Max Planck Society, 2021) Science award of Lower-Saxony (2021) Medaille für naturwissenschaftliche Publizistik (DPG, 2021) Young Scientist Award (DPG, 2024) Her work on cross-scale information flow has influenced both neuroscience and public health policy , including advisory roles in the German government's COVID-19 expert panel .

• Geometric Data Processing
• Computational Geometry
• Optimal Transport
• +7 more

Justin Solomon is an Associate Professor in the Department of Electrical Engineering & Computer Science at Massachusetts Institute of Technology, where he serves as Principal Investigator of the Geometric Data Processing Group. He maintains dual affiliations with the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT Center for Computational Science and Engineering (CCSE), reflecting his interdisciplinary research bridging theoretical mathematics with practical applications in graphics and machine learning. His research interests center around geometric data processing, computational geometry, and optimal transport theory, with significant contributions to computer graphics, machine learning, and computer vision. Solomon's work spans fundamental mathematical theory to practical implementations, particularly in shape analysis, 3D reconstruction, and geometric deep learning. His research demonstrates consistent innovation in developing algorithms that bridge discrete and continuous geometry with applications in graphics, vision, and AI. The publication trends reveal Solomon's evolving research trajectory from foundational work in geometry processing toward increasing integration with modern machine learning techniques. His recent work shows strong emphasis on diffusion models, geometric deep learning, and applications of optimal transport in AI, with significant contributions to SIGGRAPH, NeurIPS, and ICML proceedings. The research demonstrates both mathematical rigor and practical impact, with applications spanning character animation, 3D reconstruction, and generative AI. Amazon Research Award (2017) for Large-Scale Geometrically-Structured Sampling Amazon Research Award (2023) for Lightweight Algorithms for Generative AI Ben Wegbreit Prize for Best Undergraduate Honors Thesis Firestone Medal for Excellence in Undergraduate Research Boothe Prize for Excellence in Writing 2nd place, SGP best paper awards (2010) Solomon has secured substantial research funding through awards like the Amazon Research Awards and maintains active collaborations across academia and industry. His group has produced numerous influential publications with students and collaborators, contributing significantly to both theoretical foundations and practical implementations in geometric data analysis. His textbook "Numerical Algorithms" demonstrates his commitment to education alongside research. As Principal Investigator of the Geometric Data Processing Group, Solomon leads a research team focused on developing mathematical foundations for analyzing and processing geometric data. The group maintains strong connections with both theoretical mathematics and practical applications, working at the intersection of computer graphics, machine learning, and computational geometry. Their work has significant implications for fields ranging from computer animation to medical imaging and scientific computing.

• Computer Vision
• Robotics
• 3D Vision
• +3 more

Dr. Diego Viejo Hernando is a Professor in the Department of Computer Science and Artificial Intelligence at the Higher Polytechnic School II, University of Alicante. He has been a faculty member since 2004, progressing from assistant professor to full professor in 2019. He is affiliated with the Institute of Computer Research and the Robotics, Vision and Intelligent Technologies (RoViT) research group. His academic qualifications: PhD in Computer Engineering and Computing (University of Alicante, 2008) Computer Engineering Degree (Alicante Polytechnic School, 2002) Dr. Viejo's primary research area is Computer Vision, specifically developing 3D vision systems for mobile robots to operate in real environments. His contributions include advancements in robot localization, navigation, and object manipulation. Recently, he has focused on Social Robotics for assistive applications, aiming to help disabled individuals through robotic assistance. He has led regional and local research projects and participated in national projects. His international experience includes a research residency at the Australian Centre for Field Robotics (University of Sydney) with Dr. Eduardo Nebot.

Stefanie RedemannView profile

Associate Professor

• Cell and Developmental Biology
• Molecular Biology
• Biophysics
• +3 more

Stefanie Redemann is an Associate Professor at the University of Virginia , affiliated with the Department of Molecular Physiology and Biological Physics in the School of Medicine . She holds a Diploma in Biology from Technische Universität Darmstadt and a PhD in Cell Biology from the Max Planck Institute for Cell Biology and Genetics, with postdoctoral training at Technische Universität Dresden. Education: Diploma in Biology (Technische Universität Darmstadt), PhD in Cell Biology (Max Planck Institute), Postdoc in Cell Biology/Biophysics (Technische Universität Dresden) Her research focuses on chromosome segregation and aneuploidy in meiosis and mitosis , using advanced imaging techniques like 3D electron tomography and CRISPR tools in models such as tissue culture cells, mouse oocytes, and C. elegans embryos. She investigates molecular and mechanical principles of spindle assembly to address errors linked to cancer progression and reproductive health issues. Recent publications highlight work on microtubule dynamics, chromokinesin function, and tubulin regulation in meiosis. Awards include being named a Cell Scientist to Watch (2021). Her lab at the University of Virginia integrates interdisciplinary approaches to unravel mechanisms of cell division.

• Proteomics
• Metabolomics
• Plant-Microbe Interactions
• +3 more

Prof. Dr. Karsten Niehaus serves as Head of the Proteome and Metabolome Research Group at the Center for Biotechnology (CeBiTec) and Faculty of Biology, University of Bielefeld. His research focuses on proteomics and metabolomics applications in plant-microbe interactions, bacterial stress responses, and disease model systems. His laboratory employs advanced mass spectrometry imaging and cell phenotyping technologies to investigate molecular responses in crops like sugar beet and grapevines under abiotic stress conditions, as well as in cancer models where differentiation therapy impacts tumor malignancy. The group also explores microbial biotechnology through Xanthomonas campestris studies on xanthan production and stress adaptation. Selected publications highlight innovations in 3D microfluidics for biomarker detection and bioinformatics platforms like MetHoS for metabolomics data analysis. His work appears in journals covering Frontiers in Plant Science , Scientific Reports , and Journal of Experimental Botany . Contact: kniehaus@cebitec.uni-bielefeld.de | Office: UHG W7-117

Julien MoreauView profile

Lecturer

• Computer Vision
• Robotics
• Autonomous Vehicles
• +4 more

Julien Moreau is a Lecturer at the University of Technology of Compiègne (UTC), affiliated with the Heudiasyc-SyRI laboratory (UMR CNRS 7253). His research focuses on computer vision, robotics, and autonomous vehicle systems, particularly in sensor calibration and 3D modeling. His work includes the PFSeq dataset, a photorealistic fisheye sequence for evaluating self-calibration and stereo matching, developed during his PhD thesis. He has contributed to advancements in camera-LiDAR fusion and urban localization algorithms. Recent achievements include a paper accepted for oral presentation at BMVC 2023 titled PseudoCal: Towards Initialisation-Free Deep Learning-Based Camera-LiDAR Self-Calibration , co-authored with Mathieu Cocheteux and Franck Davoine. Julien participates in academic collaborations, including organizing interdisciplinary events like the trans-GdR ISIS and Robotics day on 'Event camera applied to robotics'. His current research is supported by the ANR MEDDTL PREDIT CAPLOC project.

• DNA Repair
• Drug Repurposing
• Melanoma
• +4 more

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 BordeauxView profile

Researcher

• Geometric Modeling
• Fractal Structures
• Lightweight Structures
• +2 more

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, LeiView profile

Professor

• Image-guided Radiation Therapy
• Artificial Intelligence in Radiotherapy
• Medical Imaging Physics
• +6 more

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 CASTELLINIView profile

Associate Professor

• Mechanical Measurements
• Vibration Analysis
• Hyperspectral Imaging
• +4 more

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 ShaikhView profile

Assistant Professor

• Vascular Anomalies
• Pediatric Interventional Oncology
• Minimally Invasive Radiology
• +2 more

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 XiaView profile

Researcher

• 3D Vision
• Localization and Navigation
• Robotics
• +2 more

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
• +4 more

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 VidalView profile

Professor

• Machine Learning
• Computer Vision
• Biomedical Data Science
• +3 more

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 EckertView profile

Professor

• Materials Science
• Physics
• Materials Physics
• +7 more

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 .