Find Academic Supervisors

Danny ThomasView profile

Professor

• Pediatric Emergency Medicine
• Concussion Management
• Brain Injury
• +3 more

Danny Thomas, MD, MPH, is a Professor of Pediatrics at the Medical College of Wisconsin (MCW) and serves as Research Director of the Section of Pediatric Emergency Medicine. He holds leadership roles including Chair of the MCW IRB Pediatric Minimal Risk Committee and Medical Staff President-Elect. His primary affiliation is at Children's Wisconsin, Emergency/Trauma in Milwaukee, WI. Dr. Thomas completed his MD and MPH degrees at Northwestern University (2002), followed by pediatric residency and pediatric emergency medicine fellowship at Children's Hospital of Pittsburgh (2005-2008). His research focuses on improving concussion identification and management in acute care settings. Key interests include objective concussion severity assessment, recovery factors post-concussion, and challenges to traditional rest-based recovery protocols. Notable work includes a landmark randomized trial on post-concussion rest, widely cited in national media for redefining activity guidelines. Current projects explore accelerated recovery methods, risk reduction for recurrent injuries, and post-concussive syndrome prediction. Publications highlight interdisciplinary approaches in pediatric emergency care, including biomarker research, cannabis exposure trends, and mental health service optimization. His work bridges clinical practice with public health policy, emphasizing evidence-based practices in acute care environments.

• Neuroscience
• Visual Cortex
• Neural Circuits
• +4 more

Kristina Nielsen is an Associate Professor of Neuroscience at Johns Hopkins University School of Medicine and a researcher at the Zanvyl Krieger Mind/Brain Institute. She investigates the function, development, and plasticity of higher-level visual cortex circuits. PhD, Max Planck Institute for Biological Cybernetics (Germany) Postdoctoral work at Salk Institute (2006-2012) with Ed Callaway and Rich Krauzlis Her research focuses on: Structure-function relationships in higher visual cortex Neural mechanisms of object recognition Developmental plasticity of visual circuits Two-photon microscopy applications in primates Viral vector-based circuit analysis Key article trends include primate visual cortex organization, motion and shape integration, and cortical development mechanisms. She has contributed to journals like Nature , Neuron , and Journal of Neuroscience . Scientific awards: CNRS 2025 bronze and silver medals Current lab members include graduate researchers Dallas Khamiss, Emmanuel Osikpa, and Brandon Nanfito. Her team has received ANR funding for projects like 'Earlier stages of development of the motion pathway' and 'Encoding of 2D and 3D shape in primate V4.'

Susanne WenzelView profile

Research Fellow

• Artificial Intelligence
• Remote Sensing
• Machine Learning
• +3 more

Dr. Susanne Wenzel is a Research Fellow at the Structural and Functional Organisation of the Brain (INM-1) within the Forschungszentrum Jülich GmbH. She serves as the Scientific Coordinator for the Helmholtz AI Local Unit at INM-1 and the Jülich Supercomputing Centre (JSC), fostering collaboration with the Helmholtz-wide AI platform. Additionally, she leads the Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL), a partnership with McGill University’s Montreal Neurological Institute and the Mila Quebec AI Institute. Her work focuses on advancing AI and high-performance computing for neuroimaging, particularly in developing 3D brain models through interdisciplinary research with CIFAR and MILA. Her research expertise spans artificial intelligence, remote sensing, and image analysis, with a strong emphasis on machine learning applications in environmental science, neuroscience, and urban scene understanding. She coordinates projects that integrate cutting-edge technologies to address complex challenges in climate modeling, neuroimaging, and spatial data analysis. Dr. Wenzel’s contributions include pioneering work in neural networks for ocean eddy tracking, sea level anomaly prediction, and facade image interpretation. Her projects emphasize interdisciplinary collaboration, combining computational methods with domain-specific knowledge across multiple institutions globally. Her current initiatives reinforce the utilization of AI and supercomputing resources to enhance scientific discovery, particularly in neuroscience through HIBALL. This collaboration aims to leverage advanced analytics for detailed brain model development, reflecting her commitment to bridging computational innovation with real-world applications.

Alexandre MendesView profile

Senior Lecturer

• Machine Learning
• Optimization
• Deep Learning
• +6 more

Dr. Alexandre Mendes is a Senior Lecturer at the University of Newcastle, affiliated with the School of Information and Physical Sciences and the Department of Computing and Information Technology. His research focuses on machine learning, optimization, and deep learning, with applications in robotics, energy systems, and transport logistics. Education: PhD in Electrical Engineering, Universidade Estadual de Campinas, Brazil MSc in Electrical Engineering, Universidade Estadual de Campinas, Brazil Bachelor in Applied Mathematics, Universidade Estadual de Campinas, Brazil Graduate Certificate in the Practice of Tertiary Teaching, University of Newcastle Research Interests: Mendes specializes in applying machine learning to real-world problems such as optimizing railway logistics, predicting solar energy output, and improving robotic systems. His work bridges theory and practice, collaborating with industries like CSIRO and Hunter Valley Coal Chain. Grants and Funding: He has secured over AU$1.5M in grants from ARC, CSIRO, and industry partners. Key projects include coal train scheduling optimization and solar energy prediction using deep learning. Awards: Work Integrated Learning Staff Member of the Year (2020) Best Student Paper Award (2012) Pro Vice-Chancellor's Research Excellence Award (2011) Labs and Teams: He leads the NUbots robotics team, focusing on humanoid robots and autonomous systems. His research group collaborates on projects like the Sydney Metro and Hunter Region rail network optimization.

Marcus HolzingerView profile

Professor

• Space Policy
• Space Situational Awareness
• Information Theory
• +4 more

Dr. Marcus Holzinger is the Joseph T. Negler Professor and Hatfield Endowed Professor in Space Policy & Law at the University of Colorado Boulder's Department of Aerospace Engineering Sciences. He leads the Colorado Center for Astrodynamics Research (CCAR) and the VADeR Laboratory, focusing on advancing space exploration through interdisciplinary research in space policy, autonomy, and space situational awareness. His work emphasizes collaboration and sustainable practices in space. Education: PhD in Aerospace Engineering Sciences, University of Colorado Boulder (2011) MS in Aeronautics & Astronautics Engineering, University of Washington (2005) BS in Aeronautics & Astronautics Engineering, University of Washington (2003) Research Interests: Space Policy & Law Space Domain Awareness Autonomy & Decision Support Systems Cislunar Space Dynamics Information Theory & Estimation Space Traffic Management Recent Research Trends: Holzinger’s work prioritizes cislunar space exploration and decentralized decision-making for space systems, with applications in orbital debris mitigation, sensor tasking, and spacecraft autonomy. His publications frequently address challenges in initial orbit determination and spacecraft maneuver detection . Awards: American Astronautical Society Fellow (2022) AIAA Associate Fellow (2018) AFOSR Young Investigator Award (2017) Advising & Labs: Holzinger mentors students in the STARLIT and VADeR Observatories. Notable advisees include Sam Fedeler (2023 PhD) and Katie Melbourne (PhD student). His labs develop technologies like astrographic mapping and sensor networks for space surveillance. Labs/Teams: CCAR and the VADeR Lab collaborate on projects funded by NASA, AFRL, and industry partners, emphasizing hardware-in-the-loop testing and space policy frameworks.

• Computer Architecture
• Hardware Acceleration
• Deep Learning
• +6 more

Hyuk-Jae Lee is a prominent researcher in computer architecture and hardware acceleration for deep learning systems, with an extensive publication record spanning over two decades. His work primarily focuses on hardware implementations for video processing, memory systems, and neural network acceleration. Through numerous collaborations with researchers at Korean institutions (particularly with Hyun Kim, Chae-Eun Rhee, and Xuan Truong Nguyen), Lee has established himself as a leading figure in circuit design for AI applications. Lee's research interests center around computer architecture, hardware acceleration, deep learning systems, video coding and compression, memory systems, and image processing. His work demonstrates a consistent focus on bridging the gap between theoretical algorithms and practical hardware implementations, with particular emphasis on optimizing performance and efficiency for real-world applications. His recent work shows a strong shift toward accelerating large language models and transformer-based architectures, reflecting current trends in AI hardware. Analysis of Lee's recent publications (2023-2025) reveals a clear research trajectory toward solving memory bandwidth and computational efficiency challenges in modern AI systems. His work spans the spectrum from low-level circuit design to high-level system architecture, with particular strength in memory systems optimization and hardware acceleration for neural networks. The consistent publication record in top-tier IEEE journals demonstrates sustained research productivity and impact in the field. Throughout his career, Lee has collaborated extensively with a core group of researchers, suggesting stable research teams and laboratories focused on hardware acceleration. His publications in IEEE Transactions on Circuits and Systems, IEEE Transactions on Computers, and IEEE Transactions on Video Technology indicate recognition by multiple relevant academic communities.

Cathleen HoenigerView profile

Professor

• Cultural Heritage Preservation
• Art Restoration and Conservation
• Italian Renaissance Art
• +3 more

Cathleen Hoeniger is a Professor in the Department of Art History and Art Conservation at Queen's University, Canada. She holds a Ph.D. in Art and Archaeology from Princeton University, with academic affiliations in the Faculty of Arts and Science. Her research focuses on cultural heritage preservation, particularly the impact of military conflicts, natural disasters, and restoration practices on artistic works. She teaches courses on war's impact on art, cultural heritage preservation strategies, and Italian Renaissance art. Her research emphasizes the destructive effects of war on Italian art, including Napoleonic campaigns and WWII, and the evolution of restoration ethics. Notable works include The Afterlife of Raphael’s Paintings (2011) and recent studies comparing heritage protection strategies in WWII Italy and modern Ukraine. Dr. Hoeniger's interdisciplinary work bridges art history with science, examining historical medical illustrations and the intersection of conservation practices with political contexts. She actively contributes to global discussions on safeguarding cultural heritage during conflicts through academic publications and conferences.

• Artificial Intelligence
• Internet of Things
• Computer Vision
• +4 more

Miloš Dobrojević serves as a Professor at Singidunum University's Faculty of Informatics and Computing in Belgrade, Serbia. His academic foundation includes doctoral, postgraduate, and bachelor studies at the University of Belgrade's Faculty of Mechanical Engineering. His research spans Artificial Intelligence , Internet of Things , and Computer Vision with emphasis on practical applications. Key focus areas include metaheuristic optimization of neural networks for tasks like crop yield prediction, medical diagnostics, and cybersecurity. Recent work demonstrates strong industry relevance in precision agriculture, drone detection systems, and sustainable homestead automation. Analysis of his 15 most recent publications reveals a consistent pattern of applying modified metaheuristics to enhance deep learning models across diverse domains. His work bridges theoretical AI advancements with real-world implementations in agriculture, healthcare, and energy systems. Scientific Contributions: Author of 3 textbooks including 'Veb informacioni sistemi' (2024) and 'Veb programiranje' (2021) Published 40+ journal/conference papers between 2003-2025 Active contributor to Springer book chapters on AI applications Dobrojević's work demonstrates strong practical orientation with projects in Serbian renewable energy transition, flood prevention systems, and municipal e-government solutions. His recent collaborations with researchers like N. Bacanin and M. Zivkovic show consistent output in high-impact journals. Current projects focus on generative AI for medical data and computer vision for agricultural optimization. His laboratory work centers on IoT-based monitoring systems for environmental applications, particularly water management in mountainous regions. Ongoing research explores drone detection networks and waste classification systems using advanced computer vision techniques.

Daniel GöhringView profile

Professor

• Autonomous Vehicles
• Mobile Robotics
• Computer Vision
• +6 more

Prof. Daniel Göhring is a professor in the Department of Computer Science at the Free University of Berlin, leading the Autonomous Cars Lab and part of the Dahlem Center for Machine Learning and Robotics. His research emphasizes robotic perception, object tracking, and real-time planning under computational constraints, with a focus on autonomous vehicles and cooperative systems. Education: Bachelor's/Master's in Robotics (exact program unspecified) PhD in Computer Science at Humboldt University Berlin Postdoctoral Research at International Computer Science Institute (ICSI), Berkeley, CA Research Interests: Daniel's work integrates machine learning and sensor technologies like LiDAR and cameras to address challenges in autonomous driving. Key areas include SLAM algorithms, trajectory prediction, cooperative perception, and real-time systems. He explores how limited sensor data and computational resources can be optimized for dynamic traffic environments. Grants and Projects: Leader of the Autonomous Cars Lab Involved in EU-funded projects such as H2020 HIVEOPOLIS and KIS-M (AI-based mobility systems) Past projects include CRTX (recycling optimization), Open.Make (open hardware), RoboFish (biological swarm analysis), and SAFARI Awards: Best Poster Award at IAAS Workshop 2024 Best Paper Award at ICAIR-CACRE 2019 Teaching: He has taught courses such as Image Processing, Robotics, and Advanced Robotics. Recent semesters include modules on self-supervised learning, autonomous vehicle research, and continuous learning software projects. Labs and Teams: Daniel heads the Autonomous Cars Lab and collaborates with the BioRobotics Lab, focusing on interdisciplinary projects like 'Robots Communicating with Fish' and 'Open Hardware for FAIR Robotics.'

Houra MahmoudzadehView profile

Associate Professor

• Operations Research
• Healthcare Management
• Radiation Therapy Optimization
• +3 more

Houra Mahmoudzadeh is an Associate Professor at the University of Waterloo, affiliated with groups such as Data Analytics, Applied Operations Research, and Healthcare. Her research focuses on optimization techniques applied to healthcare systems, particularly radiation therapy planning, robust decision-making under uncertainty, and pandemic-era staff scheduling. She specializes in developing advanced algorithms for IMRT treatment planning and addressing challenges in healthcare operations through mathematical modeling. Her work integrates robust optimization frameworks with clinical applications, emphasizing practical solutions for radiation therapy delivery, resource allocation in healthcare, and multi-objective decision-making. Key contributions include methodologies for inverse optimization in medical contexts, Pareto robust optimization strategies, and pandemic-specific staff scheduling models. Recent publications highlight innovations in dose-based constraint generation, light Pareto robust optimization, and effective budget of uncertainty formulations. Her research bridges theoretical operations research with real-world healthcare challenges, aiming to improve treatment efficacy and operational efficiency in medical settings.

Scott HuffmanView profile

Professor

• Analytical Chemistry
• Forensic Chemistry
• Physical Chemistry
• +8 more

Scott Huffman is a Professor in the Department of Chemistry and Physics at Western Carolina University (WCU), part of the College of Arts and Sciences. His research focuses on non-destructive analytical methods for cultural/historical objects using Raman and infrared spectroscopy, paper-based microfluidic sensors, and advanced numerical analysis. Collaborations include projects with WCU's Mountain Heritage Center and Fine Art Museum. His teaching interests span analytical chemistry, forensic chemistry, physical chemistry, and data science. Education: PhD from University of Rhode Island; MS and BS from University of North Carolina Wilmington. Research emphasizes chemometrics, vibrational spectroscopy for liquid structure analysis, and sensor development. Laboratory activities include developing spectral reference libraries, statistically significant material identification systems, and nondestructive Raman spectroscopic imaging techniques. Lab management resources include standardized log sheets, research paper templates, and protocols for handling sensitive samples. Current projects involve extracting minor component information from spectroscopic data and applying advanced algorithms to industrial/real-world datasets.

• Virtual Reality (VR)
• Augmented Reality (AR)
• Mixed Reality Training
• +7 more

Christian Schönauer is a PostDoc Researcher at TU Wien's Department of Virtual and Augmented Reality. His work focuses on advanced AR/VR applications in emergency response training, construction quality assurance, and healthcare rehabilitation. He leads the BIMCheck project (2021–2024), developing smart BIM-real building comparison systems. Notable research includes immersive training systems for first responders, wireless VR platforms like ImmersiveDeck, and AR frameworks for distributed collaboration. Education: PhD in Computer Science (2015, TU Wien) and MSc in Technical Mathematics. His projects have been funded by FFG (Austrian Research Promotion Agency), OEAD, and EU Commission grants. Research highlights include: Development of photogrammetry-based autonomous robots for 3D reconstruction Integration of BIM models with AR for construction site inspections VR training systems for CBRN defense and fire brigade operations Human-robot interaction studies in hazardous environments He has supervised 12 diploma theses on topics ranging from laser tracking systems to motion capture rehabilitation tools. Active in international conferences like IEEE VR and ISMAR, his work bridges academic research with practical industry applications in construction safety and emergency preparedness.

• Computer Vision
• Stereo Matching
• 3D Reconstruction
• +3 more

Michael Bleyer is a Researcher in the Department of Computer Vision at the Technische Universität Wien (TU Wien), affiliated with the Faculty of Informatics. His work focuses on advanced imaging technologies, particularly in stereo matching, sensor design, and applications in augmented/mixed reality. He has contributed to projects funded by the Vienna Science and Technology Fund (WWTF), Austrian Science Fund (FWF), and the Federal Ministry of Transport, Innovation, and Technology (bm:vit). Education: Diplom-Ingenieur (Dipl.-Ing.) from TU Wien (2002), followed by a Dr.techn. (PhD) thesis on 'Segmentation-based stereo and motion with occlusions' (2006). He has supervised four students, including Armin Haßlacher (2012), Gregor Braun (2011), Roman Gross (2009), and Christian Rhemann (2005). Research Interests: Bleyer’s work bridges theoretical computer vision and practical sensor engineering. Recent trends emphasize SPAD-based imaging systems for low-light environments and head-mounted displays, addressing challenges like dark current compensation and temporal filtering. Earlier contributions include global stereo matching algorithms, optical flow estimation, and 3D scene reconstruction. Grants and Advising: Projects include Temporal-Consistent Stereo Matting (2009–2015, WWTF) Energy Functions for Global Stereo Matching (2007–2012, FWF) Video Engine Design Methodology (2006–2015, bm:vit) His advising spans topics like color in stereo matching and image filtering optimization.

John K. TsotsosView profile

Research Professor

• Computer Vision
• Visual Attention
• Active Visual Search
• +5 more

John K. Tsotsos is a Distinguished Research Professor at York University's Lassonde School of Engineering, holding positions in both the Department of Electrical Engineering & Computer Science and the Centre for Vision Research (CVR). His work bridges computer science, cognitive science, and neuroscience with a focus on visual attention and active vision systems. Dr. Tsotsos's research interests center on visual attention mechanisms, active visual search, and visuospatial reasoning. His work explores how humans and machines process visual information, with applications in autonomous driving, robotics, and human-computer interaction. He investigates the computational principles underlying visual attention, comparing biological systems with artificial implementations. His research has significant implications for developing more human-like computer vision systems that can effectively navigate and interpret complex visual environments. Analysis of his recent publications reveals a strong focus on active vision systems where observers dynamically control their viewpoints during visual search tasks. His work spans both theoretical foundations of visual attention and practical applications in autonomous vehicles. A significant portion of his recent research addresses driver attention modeling, gaze prediction, and the challenges of real-world visual processing where traditional computer vision approaches often fail. His work consistently bridges cognitive theory with practical engineering applications. Dr. Tsotsos has made substantial contributions to the field of computational vision through his theoretical work on attentional mechanisms and their implementation in artificial systems. His research has influenced both academic understanding of visual processing and practical applications in autonomous systems and human-machine interfaces. As a faculty member at York University, Dr. Tsotsos contributes to the vibrant research ecosystem of the Centre for Vision Research, where interdisciplinary teams work on cutting-edge problems in visual perception, cognitive modeling, and machine vision. His work exemplifies the integration of cognitive science principles with advanced computational techniques to solve complex visual processing challenges.

• Geovisualization
• Geographic Information Systems
• Uncertainty Visualization
• +5 more

Jochen Schiewe is a Researcher specializing in geovisualization, GIS, and spatial uncertainty analysis, with a career spanning over two decades. His work bridges cartography, urban planning, and computational methods. Key Affiliations: University of Hanover (PhD), ISPRS community, EuroVA workshop Research Focus: Geovisualization techniques, uncertainty quantification, smart cities, and historical map analysis His 2024 publication introduces agent-based approaches for preserving spatial patterns in urban data. Earlier works examine sound maps for data representation, land cover change analysis, and interactive GIS for public participation. Schiewe's scholarly contributions emphasize human-computer interaction and cognitive aspects of mapping . He has served as editor for special issues on smart city solutions and collaborated with leading experts in GIS and visualization.