Find Academic Supervisors

Cynthia H. McColloughView profile

Professor

• Computed Tomography (CT) Imaging Technology
• Radiation Dose Management
• Material Composition Evaluation via CT
• +3 more

Cynthia H. McCollough, Ph.D., is a Professor of Biomedical Engineering and Medical Physics at Mayo Clinic, where she serves as a Consultant in the Department of Radiology. She leads the CT Clinical Innovation Center, focusing on advancing CT imaging technology and clinical applications. Her work emphasizes radiation dose reduction, quantitative material composition analysis (e.g., kidney stones, gout), and optimizing CT protocols for diagnostic accuracy while minimizing patient risk. Dr. McCollough holds leadership roles in national and international organizations, including the NIH Biomedical Imaging Technology Study Section and the American Association of Physicists in Medicine (AAPM). She has received numerous awards, including the Distinguished Investigator Award from Mayo Clinic and the William D. Coolidge Gold Medal from AAPM. Her research spans photon-counting CT, dual-energy CT, and translational imaging solutions for vascular and abdominal diseases. Dr. McCollough’s academic contributions include over 600 peer-reviewed publications and leadership in grant-funded projects such as the National Institute of Biomedical Imaging and Bioengineering initiatives. Education: PhD in Medical Physics from the University of Wisconsin-Madison, MS in Medical Physics from the University of Wisconsin-Madison, BS in Physics from Hope College. Research Highlights: Her lab develops tools for radiation dose estimation, non-invasive material characterization via CT, and optimizing CT parameters using human and model observers. Recent work includes improving kidney stone analysis, bone mineral density measurements using photon-counting CT, and reducing radiation exposure in pediatric imaging. Grants & Funding: Principal investigator on multiple NIH grants, including projects on coronary atherosclerosis characterization, urinary stone composition analysis, and photon-counting CT for vascular disease detection. Awards: Over 100 awards, including the Distinguished Mayo Clinic Investigator Award (2020), Honorary Doctorate from Linkoping University (2020), and AAPM’s highest honor, the William D. Coolidge Gold Medal (2024). Labs & Teams: Directs the CT Clinical Innovation Center and the X-Ray Imaging Research Core at Mayo Clinic, collaborating with multidisciplinary teams across radiology, biomedical engineering, and physics.

• Urban Water Management
• Nature-Based Solutions
• Circular Economy
• +7 more

Bernhard Pucher is a Senior Scientist at the Institute of Sanitary Engineering, Industrial Water Management and Water Pollution Control (SIG), within the Department of Landscape, Water and Infrastructure at the University of Natural Resources and Life Sciences, Vienna (BOKU). He holds a doctorate in technical natural sciences and is actively leading and participating in multiple research projects focused on urban water management and nature-based solutions. His research interests lie at the intersection of urban sustainability and environmental engineering, with a strong focus on urban water management , circular economy , nature-based solutions , treatment wetlands , vertical greening systems , and greywater treatment . His work emphasizes process-based modeling of water and solute transport, particularly using the HYDRUS software, to optimize the design and performance of decentralized water treatment systems. The recent articles highlight a consistent trend in developing and evaluating nature-based technologies for sustainable urban water cycles. Key themes include the performance assessment of vertical and horizontal flow wetlands, numerical modeling for design optimization, the multifunctionality of green walls for cooling and water reuse, and the integration of blue-green infrastructure into circular city frameworks. His research bridges technical modeling with practical implementation, aiming to quantify resource recovery and improve urban resilience. Board member of the IWA national committee Austria (2023) Member of the Management Committee, IWA Specialist Group on 'Wetland Systems for Water Pollution Control' (2019) Active reviewer for journals including Chemical Engineering Journal , Science of the Total Environment , and Water Science and Technology Served on scientific advisory boards for major conferences like WETPOL and IWA ecoSTP He has supervised numerous master’s theses on topics such as greywater use for vertical greening, treatment performance of living walls, and modeling of rainwater use. His projects are funded by diverse sources including the European Commission (Horizon), FFG, national companies, and private foundations, demonstrating strong grant acquisition capability. He is also involved in knowledge transfer through public reports, media contributions, and participation in policy-relevant seminars. Bernhard Pucher is a key member of a research team that includes collaborators like Günter Langergraber, Ulrike Pitha, and Isabella Zluwa. His work is often conducted within interdisciplinary projects such as 'resilientRAIN', 'UrBan hEat islands REsilience', and the EU COST Action 'Circular City Re.Solution', reflecting a collaborative approach to addressing complex urban environmental challenges.

• Crystal Plasticity
• Materials Science
• Computational Materials Engineering
• +5 more

Martin Diehl is a computational materials scientist affiliated with KU Leuven (Departments of Computer Science and Materials Engineering) and the Max-Planck-Institut für Eisenforschung GmbH in Germany. His work focuses on crystal plasticity simulations, computational materials engineering, and multi-physics modeling of metallic systems. Research interests include: Crystal plasticity finite element method (CPFEM) and spectral solvers Microstructure evolution and damage mechanics Machine learning applications in materials design Development of the DAMASK simulation toolkit Multi-phase steel alloys and heterogeneous deformation Integrated computational materials engineering (ICME) Key trends in his publications since 2021 highlight advancements in: Multi-physics DAMASK framework for coupled chemo-mechanical and thermal simulations AI-driven inverse design of steel microstructures Damage modeling in dual-phase steels Collaborative software development for materials science Experimental-simulation integration for stress-strain partitioning High-resolution spectral methods for finite strain analysis He actively collaborates with institutions like Harbin Institute of Technology, University of Oxford, and research groups across Europe and Asia.

Arash KhatamianfarView profile

Lecturer

• Control Engineering
• Robotics and Mechatronics
• Dynamic Systems Modelling and Control
• +3 more

Dr. Arash Khatamianfar is a Lecturer in the School of Electrical Engineering and Telecommunications at the University of New South Wales (UNSW), specializing in Control Engineering and Robotics. With a strong background in both academia and industry, he has made significant contributions to engineering education and control systems research. His educational background includes a B.Sc. in Electrical Engineering (Electronics major) from Iran (2005), an M.Sc. in Electrical Engineering (Control Engineering and Robotics major) from Iran (2008), and a Ph.D. in Electrical Engineering with focus on Control Systems and Robotics from UNSW (2015). Dr. Khatamianfar's research spans two primary domains: educational technologies in engineering education and advanced control systems. In engineering education, he has focused on developing effective online laboratory practices, managing hands-on labs during the pandemic, and comparing online versus in-person teamwork. In control engineering, his work centers on overhead crane systems, model predictive control, and applications in renewable energy systems. His publications reveal a clear trajectory from theoretical control methods to practical industrial applications, with recent work emphasizing educational technologies alongside continued contributions to control theory. Best Lab Demonstrator Award in the School of Electrical Engineering and Telecommunications at UNSW (2014) Nominated for Best Lab Demonstrator Award at UNSW (2015) Nominated for Best Lecturer Award in the Faculty of Engineering at UNSW (2018) Dr. Khatamianfar has demonstrated significant commitment to teaching excellence, earning the first-ever Best Lab Demonstrator Award in his school based on student satisfaction. His industry experience includes work at Buildings Alive Pty. Ltd. as a Systems and R&D Engineer, where he developed methods for improving energy consumption in commercial buildings, and professional training in SIEMENS PLC systems. He has been active in the Systems and Control group at UNSW, particularly in running teaching laboratories and collaborating in research laboratories. His work environment includes the Systems and Control Research laboratories at UNSW, where he has supervised undergraduate thesis students and contributed to developing advanced control methodologies with practical industrial applications.

• Medical Imaging
• Artificial Intelligence
• Nuclear Medicine
• +5 more

Priv. Doz. Dr. Kuangyu Shi is an Associate Professor and Senior Lecturer at the Chair for Computer-aided Medical Procedures, School of Computation, Information and Technology (CIT), Technical University of Munich (TUM). He also serves as Chief Medical Physicist and Head of the Lab for Artificial Intelligence & Translational Theranostics at the Department of Nuclear Medicine, Inselspital, University of Bern. His research focuses on translational molecular imaging computing, AI in nuclear medicine, digital twin technology, and computational biology for theranostics. Dr. Shi holds a Ph.D. from the Max-Planck Institute for Computer Science (2008) and an Habilitation from TU Munich (2018). He teaches courses including Introduction to Artificial Intelligence in Medical Imaging , Clinical Decision Support , and Advanced Medical Imaging . He is actively involved in professional organizations like the European Association of Nuclear Medicine (EANM) and the International Commission on Radiological Protection (ICRP), and serves on editorial boards for journals such as Eur J Nucl Med Mol Imaging . His research projects include the DHM (German Heart Center Munich) lab, NARVIS Lab, and computational surgineering. He supervises student projects in AI-driven treatment planning, low-dose imaging, and early diagnosis of neurodegenerative diseases. His work bridges clinical needs with advanced computational methods, emphasizing AI and medical imaging innovations.

Yann PontyView profile

Researcher

• RNA Folding
• RNA Design
• RNA Evolution
• +9 more

Yann Ponty is a tenured CNRS Researcher at the Computer Science Department (LIX) of École Polytechnique (Institut Polytechnique de Paris, France). He leads the AMIBio team and serves as Deputy Director of LIX. His work focuses on developing bioinformatics methods at the intersection of computer science, mathematics, and molecular biology, particularly for RNA structure prediction, design, and evolution. He holds leadership roles in the ISCB Board of Directors (2025-2027) and the HDR referent for the IDIA department (CS&Interactions) at IP Paris. Research Interests: RNA folding/design/evolution, RNA-RNA/RNA-protein interactions, random generation, enumerative combinatorics, discrete algorithms, parameterized complexity, RNA visualization Key Contributions: Developed algorithms for RNA inverse folding, pseudoknot modeling, and dynamic programming optimization Collaborations: Partnerships with institutions like Simon Fraser University, Boston College, and Université Paris-Saclay His recent publications (15 most recent) span RNA structure prediction, pseudoknot partition functions, linear-time inverse folding algorithms, and parameterized sampling techniques. The work emphasizes dynamic programming, combinatorial approaches, and integration of experimental data for improving RNA modeling. Scientific awards include election to the ISCB Board of Directors (2025-2027) and leadership roles in academic networks like GdR BIM. He actively contributes to software development (VARNA, RNANR, SPARCS, IncaRNAtion, RNARedPrint) and serves as Associate Editor for Bioinformatics (OUP). Teaching engagements include graduate-level courses in combinatorial optimization, RNA bioinformatics, and algorithms at Université Paris-Saclay and École Polytechnique.

• Computer Animation
• Geometry Processing
• Fluid Dynamics
• +2 more

Chris Wojtan is a Professor at the Institute of Science and Technology Austria (ISTA) , leading the Visual Computing Group . His research focuses on geometric and numerical algorithms for computer animation and geometry processing , particularly in simulating solid and fluid dynamics , controlling physics simulations, and computing with 3D shapes. Research Interests : Computer Animation, Geometry Processing, Fluid Dynamics, Solid Mechanics, 3D Shape Processing. Notable Awards : ERC Consolidator Grant (2022) SIGGRAPH Significant New Researcher Award (2016) Eurographics Young Researcher Award (2015) ERC Starting Grant (2014) Education : PhD in Computer Science from Georgia Institute of Technology (2010). Funding : Recipient of NSF Graduate Research Fellowship (2005-2008), and principal investigator for ERC grants.

• Signal Processing
• Electronics
• Biomedical Engineering
• +5 more

Marek Miśkowicz serves as a Professor at the Department of Metrology and Electronics within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His primary institutional contact is miskow@agh.edu.pl, with physical location in building B-1, room 212. His research spans signal processing, biomedical engineering, and electronics, specializing in event-based sampling methodologies, time-to-digital conversion techniques, and reconstruction of bandlimited signals from nonuniform samples. Key contributions include QRS detection algorithms for ECG monitoring, POCS-based reconstruction frameworks, and event-driven control systems for industrial IoT applications. His work emphasizes resource efficiency in embedded systems and mobile health monitoring through approximate computing and adaptive sampling strategies. Recent publications (2022-2025) demonstrate consistent focus on signal reconstruction from irregular samples, with growing emphasis on spiking neural networks for event classification and industrial IoT optimization. Biomedical applications (particularly ECG analysis) and industrial control systems represent dominant application domains, while methodological innovations center on iterative reconstruction algorithms and temporal accuracy evaluation in noisy environments. No scientific awards were referenced in the source materials. No information regarding student advising or research grants was available in the provided documentation. The source texts contained no details about laboratory facilities, research teams, or collaborative groups associated with Professor Miśkowicz.

Vanessa MagnanimoView profile

Professor

• Discrete Element Method (DEM)
• Soil Micromechanics
• Granular Materials
• +4 more

Vanessa Magnanimo is a Full Professor at the MESA+ Institute, University of Twente, specializing in the micromechanics of granular materials and clays using advanced Discrete Element Method (DEM) simulations . Her research bridges micro- and macro-scale behavior of soils, focusing on computational modeling , small-strain stiffness , and material stabilization . She has led 107 research outputs and 2 datasets , including studies on wet granular systems , bio-cemented soils , and vegetated soil mechanics . Recent work includes 2025 publications on coarse-grained DEM and mixing indices in industrial processes. Notable activities include keynote talks at international events (2018–2020), visiting researcher appointments at institutions like 3SR Laboratory (2017) and Politecnico di Bari (2020), and editorial contributions. Her research spans geotechnical engineering , powder technology , and multi-scale modeling , with applications in soil stabilization , industrial powder handling , and eco-mechanical systems .

• Cybersecurity
• Privacy
• Industrial Networks
• +7 more

Martin Henze is a tenure-track Assistant Professor at RWTH Aachen University's Department of Computer Science, where he leads the Security and Privacy in Industrial Cooperation (SPICe) research group. Additionally, he co-leads the Secure Production & Energy Networks research group at the Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIE in Bonn, Germany. His work bridges academic research with practical industrial security applications, focusing on critical infrastructure protection. Dr. Henze's research interests center on technical security and privacy aspects of industrial networks and data sharing, with special emphasis on energy and production sectors. His work spans industrial intrusion detection, 5G security for industrial applications, IoT security in constrained environments, and blockchain security. He develops practical security solutions that balance protection needs with the resource constraints and operational requirements of industrial systems, particularly focusing on making security both effective and comprehensible for operators. His recent publications demonstrate a strong focus on industrial security challenges, with particular emphasis on intrusion detection systems that maintain operator control, TLS optimization for resource-constrained industrial IoT, 5G security for production systems, and novel approaches to securing legacy industrial protocols. His work consistently addresses the tension between security requirements and operational constraints in industrial settings. Nachwuchsförderpreis Verbraucherforschung NRW Borchers-Plakette ICT Young Researcher Award Dr. Henze actively contributes to the academic community through service on numerous prestigious program committees including ACM CCS, IEEE S&P, NDSS, and USENIX Security. His teaching portfolio includes graduate courses on Industrial Data Security, Industrial Network Security, and specialized seminars on 5G/6G Security and IoT Security. His research is highly collaborative, frequently involving partnerships across institutions and with industry to address real-world security challenges in critical infrastructure. He heads the SPICe research group at RWTH Aachen, which focuses on developing practical security and privacy solutions for industrial cooperation scenarios. The group's work emphasizes creating security mechanisms that are not only technically sound but also comprehensible and usable by industrial operators, recognizing that the human element is critical in maintaining security in complex industrial environments.

Kevin S. LaBarView profile

Professor

• Cognitive Neuroscience
• Emotional Memory
• Fear Conditioning
• +8 more

Kevin S. LaBar is Professor of Psychology and Neuroscience and Professor in Psychiatry and Behavioral Sciences at Duke University's Trinity College of Arts & Sciences. He serves as Associate Director of the Center for Cognitive Neuroscience and maintains affiliations with the Duke Initiative for Science & Society, the Center for Brain Imaging and Analysis, and the Center for Cognitive Neuroscience. His academic journey began with a B.A. from Lafayette College in 1990, followed by a Ph.D. from New York University in 1996. Dr. LaBar's research focuses on understanding how emotional events modulate cognitive processes in the human brain. His laboratory aims to identify brain regions that encode the emotional properties of sensory stimuli and demonstrate how these regions interact with neural systems supporting social cognition, executive control, and learning and memory. His integrative approach utilizes psychophysiological monitoring, functional magnetic resonance imaging (fMRI), machine learning, and behavioral studies in both healthy adults and psychiatric patients. His work spans multiple domains including fear conditioning, emotional memory, emotion regulation, and the neural basis of emotional experience. His recent publications reveal a strong emphasis on emotional memory mechanisms, emotion regulation strategies across the lifespan, neural correlates of anxiety and fear, and developing neuroscience-informed interventions for emotional dysregulation. His research increasingly incorporates advanced neuroimaging techniques, computational approaches, and translational applications for clinical populations. Fellow, Association for Psychological Science (2010) Young Investigator Award, Cognitive Neuroscience Society (2005) CAREER Award, National Science Foundation (2003) Ralph E. Powe Junior Faculty Enhancement Award, Oak Ridge Associated Universities (2001) Young Investigator Award, National Alliance for Research on Schizophrenia and Depression (2000) Scholar of the Year Award, Lafayette College Alumni Association (1990) Dr. LaBar has secured substantial research funding, including multiple NIH grants and VA awards, with projects spanning from basic emotion research to clinical applications for conditions like PTSD, depression, and misophonia. His laboratory has trained numerous graduate students and postdoctoral fellows who have gone on to successful careers in academia and research. His work bridges cognitive neuroscience with clinical applications, particularly in developing neurostimulation-enhanced behavioral interventions for emotion dysregulation.

• Biostatistics
• Environmental Dose-Response Modeling
• Quantitative Risk Assessment
• +7 more

Paul J. Catalano is a Senior Lecturer in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. He is affiliated with the Dana-Farber Cancer Institute, where he serves as a statistician for clinical trials and collaborates on cancer research. His research focuses on biostatistical methods for analyzing multiple outcomes and repeated measures in environmental dose-response modeling, quantitative risk assessment, and neurotoxicity studies. He also develops software for implementing modeling algorithms and investigates experimental design in low-dose risk estimation. Recent publications highlight collaborations in immunotherapy for urothelial carcinoma radiation therapy for brain metastases pediatric neuroblastoma treatment AI-driven MRI segmentation . Dr. Catalano's work extends to clinical trials in colorectal, genito-urinary, and breast cancer, with emphasis on prognostic factor identification and translational research.

• Civil Engineering
• Materials Science
• Concrete Technology
• +3 more

Teemu Ojala is a Postdoctoral Researcher in Civil Engineering at Aalto University, specializing in concrete materials and mechanics. He works within the Mineral Based Materials and Mechanics research group, focusing on automated workability control and compositional property measurement in concrete production. Research Interests His work bridges Civil Engineering and Materials Science, emphasizing: Concrete durability and segregation monitoring Digital image analysis for microstructure characterization Smart sensing techniques (AC impedance, stereovision) Optimization of air content and compaction Key Contributions He has advanced automated quality control methods in concrete mixing through: Developing stereovision-based workability estimation Investigating freeze-thaw resistance mechanisms Pioneering AC impedance spectroscopy for segregation detection

• Bio-inspired Sensors
• MEMS
• Neuromorphic Engineering
• +3 more

Claudia Lenk serves as Full Professor at Ulm University since 2024, leading the Biomedical Sensor Systems and Microsystems research group. Her work focuses on developing bio-inspired acoustic sensors to enhance human and machine hearing capabilities, particularly for speech processing in noisy environments through MEMS-based adaptive technologies. Education: Technical Physics, TU Ilmenau PhD in Biophysics (specializing in numerical/chemical modeling of atrial fibrillation mechanisms) Postdoctoral research developing MEMS-based artificial hair cells for hearing enhancement Her research integrates bio-inspired engineering with acoustic sensor design to create noise-robust systems that mimic biological hearing mechanisms. Key innovations include tunable MEMS resonators, neuromorphic auditory processing, and integrated signal pre-processing for hearing aids and robotics. This interdisciplinary approach bridges microsystems engineering, neuroscience, and auditory perception to address limitations in current speech processing technologies. Analysis of her 15 most recent publications (2020-2025) reveals a consistent trajectory toward adaptive neuromorphic acoustic systems. Dominant themes include resonance frequency control, dynamic range expansion through nonlinear dynamics, and bio-inspired feature extraction for low signal-to-noise ratio environments. These advancements target practical implementations in energy-constrained devices like hearing aids and autonomous systems. Scientific Awards: No awards documented in source materials Advising and Grants: Source materials contain no information regarding student supervision, research grants, or collaborative funding initiatives. Labs and Teams: She directs Ulm University's Biomedical Sensor Systems and Microsystems group, which develops cutting-edge sensor technologies for hearing applications, robotics, and speech processing systems through MEMS fabrication and neuromorphic computing approaches.

• Biostatistics
• Statistical Analysis
• Applied Statistics
• +5 more

Professor Ufuk Karadavut serves at Karabük University Faculty of Medicine, Department of Basic Medical Sciences, where he holds the position of Professor and currently serves as Vice Dean. Previously, he was Dean of the Faculty of Medicine from 2021-2022 and has held multiple leadership roles including Head of Department since 2020. With an extensive publication record of 493 works and significant contributions to both medical and agricultural statistics, his career spans multiple Turkish universities including Ahi Evran University and Bingöl University. PhD, Selçuk University, Institute of Science (2001-2005) Bachelor's Degree, Ankara University, Faculty of Agriculture, Department of Animal Science (1987-1991) Professor Karadavut's research spans biostatistics, medical informatics, and agricultural statistics, with particular expertise in statistical experimental design. His work bridges healthcare applications (medical informatics, clinical data analysis) with agricultural science (crop breeding, animal husbandry). He has developed statistical methodologies applicable across diverse fields, from analyzing palliative care outcomes to optimizing chickpea breeding programs for drought tolerance. His research demonstrates a consistent pattern of applying advanced statistical techniques to solve practical problems in both medical and agricultural contexts. Analysis of his recent publications reveals a strong focus on applying statistical methodologies to address contemporary challenges. In medical informatics, he examines healthcare quality, medical education, and pandemic impacts. In agricultural science, his work centers on crop improvement (particularly chickpeas and other legumes) and animal breeding. His statistical approaches include multivariate analysis, QTL mapping, growth modeling, and machine learning techniques, demonstrating methodological versatility across disciplinary boundaries. While specific awards aren't detailed in the available information, his h-index of 11 (UNIS) and 8 (WoS), along with 583 total citations, indicate significant scholarly impact. His research has attracted funding from major Turkish research bodies including TÜBİTAK and university-supported projects. Professor Karadavut has supervised 13 theses across multiple institutions and taught diverse courses ranging from medical bioinformatics to agricultural statistics. His research portfolio includes 31 projects, with recent work focusing on climate change adaptation in livestock, medical informatics applications, and advanced statistical methodologies for agricultural research. He has collaborated extensively with researchers across multiple Turkish institutions, demonstrating strong interdisciplinary networking capabilities.