Find Academic Supervisors

Matthew PalmView profile

Assistant Professor

• Transportation Planning
• Urban Mobility Equity
• Shift Work Commuting
• +3 more

Matthew Palm is an Assistant Professor in the Department of City and Regional Planning at the University of North Carolina at Chapel Hill, holding the Cities Pardue Fellowship. His research focuses on transportation equity, shift work commuting challenges, and mobility barriers affecting essential workers in 24-hour economies. He examines how transportation systems impact social participation, healthcare access, and economic opportunities while advocating for equitable planning solutions. Recent work explores post-pandemic transit trends, ride-sharing equity, and the intersection of transportation policy with marginalized communities. Palm collaborates with community organizations to co-create evidence-based solutions for mobility challenges. He currently holds a $1.09 billion grant reflecting North Carolina’s renewed interest in rail infrastructure. His research agenda includes three core areas: 1) analyzing how shift workers’ nighttime commuting affects social exclusion and family care logistics, 2) studying transportation affordability’s role in economic participation, and 3) evaluating transportation infrastructure’s dual potential to improve lives while mitigating negative externalities like pollution. He emphasizes participatory approaches to planning and has published extensively on pandemic-era transit usage shifts, accessibility metrics, and housing-transit linkages.

• Medical Physics
• Artificial Intelligence in Medicine
• Radiation Therapy Treatment Planning
• +7 more

Dan Nguyen, Ph.D., is a faculty member in the Department of Radiation Oncology at UT Southwestern Medical Center, where he is part of the Division of Medical Physics and Engineering. He is a founding member of the Medical Artificial Intelligence and Automation (MAIA) Laboratory, collaborating closely with Dr. Steve Jiang to advance AI applications in radiotherapy. His work focuses on deep learning for treatment planning, dose prediction, auto-segmentation, and adaptive radiotherapy. Ph.D. in Biomedical Physics, University of California, Los Angeles (UCLA), 2017 Mentor: Dr. Ke Sheng Faculty appointment at UT Southwestern since 2017 Dr. Nguyen’s research is centered on applying artificial intelligence to solve critical challenges in radiation oncology. His primary interests include deep learning-based dose prediction, auto-segmentation of anatomical structures, optimization of treatment plans, and real-time adaptive radiotherapy. He has pioneered work in direct aperture optimization, 4π radiotherapy, and uncertainty quantification in AI models. His research bridges the gap between AI innovation and clinical implementation, with a focus on improving plan quality, reducing planning time, and enhancing accessibility for less experienced clinicians. The most recent publications (2023–2025) demonstrate a consistent trend in developing fast, accurate, and robust deep learning models for radiotherapy. Key themes include dose prediction with transfer and meta-learning, adaptive segmentation using test-time optimization, uncertainty assessment in AI predictions, and mathematical modeling of radiotherapy-immunotherapy synergy. These works span high-impact journals in medical physics, AI, and oncology, reflecting interdisciplinary innovation. While no specific scientific awards are listed, Dr. Nguyen’s leadership in the MAIA Lab and extensive publication record in top-tier journals indicate significant recognition in the field of medical physics and AI in medicine. Dr. Nguyen has co-authored numerous studies involving mentoring and collaborative research, particularly with trainees and junior faculty in the MAIA Lab. His work is supported by institutional and likely federal funding, given the scale and scope of AI deployment studies. He has contributed to large-scale collaborative efforts such as OpenKBP-Opt, involving international teams evaluating knowledge-based planning pipelines. The MAIA Laboratory is a multi-investigator research group focused on innovating, developing, and applying artificial intelligence technologies to empower clinicians—especially those with less experience or limited resources—for improved patient care. The lab’s work spans machine learning, deep learning, reinforcement learning, and mathematical modeling in radiation oncology.

Hirokatsu KataokaView profile

Researcher

• Computer Vision
• Deep Learning
• Artificial Intelligence
• +5 more

Hirokatsu Kataoka serves as Chief Senior Researcher at the National Institute of Advanced Industrial Science and Technology (AIST) in Japan, with multiple academic affiliations including Academic Visitor at the Visual Geometry Group (VGG) at University of Oxford, Visiting Associate Professor at Keio University, and Adjunct Associate Professor at Tokyo Denki University. He is Principal Investigator of both cvpaper.challenge and LIMIT.Lab, and serves as Research Advisor for SB Intuitions. Dr. Kataoka earned his Ph.D. in Engineering from Keio University (April 2011 - March 2014), where he received the Fujiwara Prize in 2014 as valedictorian equivalent. His research primarily focuses on innovative pre-training methodologies that eliminate dependency on natural image datasets, with his Formula-Driven Supervised Learning (FDSL) framework being particularly influential in the field. Kataoka's research interests center around representation learning with limited data resources, including zero-shot, unsupervised, and synthetic learning approaches. His work explores how visual/multimodal models can be effectively trained with minimal real-world data, addressing critical ethical concerns related to large-scale datasets. He has pioneered methods using fractal geometry, mathematical formulas, and procedural generation to create effective pre-training frameworks that rival traditional ImageNet-based approaches. His publication record shows a clear trajectory toward solving the challenges of learning with limited resources, with recent work expanding FDSL to audio processing, microfossil analysis, and visible-to-infrared translation. His papers consistently address the core challenge of building robust visual recognition systems without relying on massive annotated datasets, with increasing focus on practical applications across diverse domains. Scientific Awards & Recognition ACCV 2020 Best Paper Honorable Mention Award for 'Pre-training without Natural Images' AIST Best Paper Award (2019, 2022) BMVC 2023 Best Industry Paper Finalist Featured in MIT Technology Review His 3D ResNets paper ranks among the top 0.5% most-cited CVPR papers over a five-year period Dr. Kataoka actively advises numerous researchers across multiple institutions, with his research team comprising Ph.D. and Master's students from various universities. He has served as Area Chair for CVPR 2024 and 2025, will serve as IEEE TPAMI Associate Editor beginning in 2025, and organizes the LIMIT Workshop series at major computer vision conferences. His LIMIT.Lab, established in June 2025, serves as a collaboration hub focused on building multimodal AI models under constrained resources including compute, data, and labels.

Marc PollefeysView profile

Professor

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

Marc Pollefeys is a Full Professor of Computer Science at ETH Zurich and Director of the Microsoft Mixed Reality and AI Zurich Lab. He has held roles such as Visiting Professor at Stanford University (2007) and Assistant/Associate Professor at UNC-Chapel Hill (2002–2009). His research focuses on 3D computer vision, robotics, machine learning, and augmented reality. Education: PhD in Computer Science from KU Leuven (1999), followed by postdoctoral research there until 2002. He transitioned to academic roles at UNC-Chapel Hill before joining ETH Zurich in 2007. Research interests include 3D reconstruction, visual localization, SLAM, and applications in archaeology, urban modeling, and robotics. Notable projects include real-time 3D scanning, city-scale reconstruction, and autonomous vision-based drones. Key awards include ACM Fellow (2022), IEEE Fellow (2012), and ERC Starting Grant (2008). He advises numerous PhD students and collaborates with institutions like Google and Microsoft. Labs and teams: Leads the Computer Vision and Geometry (CVG) lab at ETH Zurich and directs the Microsoft Mixed Reality and AI Lab. His work bridges academia and industry, focusing on perception for mixed reality and autonomous systems.

• Machine Learning
• Explainable AI (XAI)
• Deep Learning
• +8 more

Haoyi Xiong is an active academic researcher in artificial intelligence, machine learning, and data science, with extensive publications in top-tier journals and conferences including IEEE TPAMI, NeurIPS, ICML, KDD, and AAAI. His work spans explainable AI, graph neural networks, diffusion models, remote sensing, and large language models. Research Interests: Explainable AI (XAI) and model interpretability Graph Neural Networks and contrastive learning Diffusion models and generative AI Medical and remote sensing image analysis Large language models and autonomous agents Learning to rank and web search His recent publications (2023–2025) show a strong trend toward self-supervised learning , model robustness , and integration of LLMs with structured data and knowledge graphs . He frequently collaborates with researchers from major tech and academic institutions. Scientific Awards: No explicit awards mentioned in the provided text. Advising and Grants: While no direct mention of students or grants, his role as a senior author on numerous papers suggests he advises graduate students and likely leads funded research projects in machine learning and AI. His work on frameworks like COLTR , GS2P , and MUSCLE indicates leadership in developing scalable AI systems. Labs and Teams: Though not explicitly stated, his frequent collaboration with Jiang Bian, Dejing Dou, and Dawei Yin suggests affiliation with a well-established AI research lab or industry-academia partnership focused on data mining, intelligent systems, and large-scale learning.

• Tomography
• Computational Imaging
• Computer Science
• +5 more

Joost Batenburg is a Professor at Leiden Institute of Advanced Computer Science (LIACS) , with a chair in Imaging and Visualization . He is affiliated with the Centrum Wiskunde & Informatica (CWI) and serves as Program Director for the interdisciplinary Society, Artificial Intelligence and Life Sciences (SAILS) initiative. His research focuses on tomographic image processing and reconstruction , where he has published over 80 journal articles and 60 conference papers. Current projects include Universal Three-dimensiOnal Passport for process Individualization in Agriculture (UTOPIA) and Center for Optimal, Real-Time Machine Studies of the Explosive Universe (CORTEX) , both funded by NWO grants. He leads the FleX-Ray Lab , a custom CT system integrated with advanced data processing algorithms. His research spans discrete tomography , real-time imaging pipelines , and AI-enhanced reconstruction methods , with applications in industrial inspection, agricultural analysis, and cultural heritage conservation. Recent articles demonstrate novel approaches to: Single-shot dynamic object tomography using level-set methods and motion modeling X-ray scattering quantification for defect detection in real-time systems Cross-modal image registration between CT scans and physical photographs Auto-differentiation in CT workflows combining classical and machine learning algorithms Scientific Awards: Dutch Award for ICT Research (2018) C.J. Kok Prize (2007) Philips Mathematics Prize (2006) He has supervised numerous PhD candidates including Mary Go, Eani Lachmansingh, and Zhichao Zhong, while maintaining editorial roles at IEEE Transactions on Computational Imaging and Journal of Mathematical Imaging and Vision . His work bridges theoretical mathematics with practical applications in agriculture, industry, and art conservation.

François PomerleauView profile

Professor

• Robotics
• 3D Reconstruction
• Autonomous Navigation
• +6 more

François Pomerleau is a full-time Professor at the Department of Computer Science and Software Engineering at Université Laval since 2017. His research focuses on 3D environment reconstruction , autonomous navigation , search-and-rescue robotics , and scientific methodology in robotics . He has held postdoctoral fellowships at the University of Toronto and Université Laval, with technology transfer experience at Alstom Inspection Robotics and Robotiq. Ph.D. in Mechanical Engineering (2013) from ETH Zurich M.Sc. in Electrical Engineering (2009) and B.Ing. in Computer Engineering (2006) from Université de Sherbrooke His research integrates robotics , computer science , and environmental monitoring , with a focus on point cloud registration , Lidar-based SLAM , and trajectory planning for unstructured environments. Recent work includes UAV-assisted terrain awareness , exposure time emulation for vision algorithms , and multi-season datasets for autonomous navigation . François’s recent publications emphasize 3D mapping , SLAM robustness , and environmental adaptation across forestry, subarctic, and alpine domains. His team develops tools for autonomous vehicles , search-and-rescue , and industry 4.0 . Scientific awards include Best Robotic Vision Paper Awards at CRV 2016 and 2020, a Best Paper Award at the ICRA 2024 Workshop, and recognition as a Distal Fellow of the NSERC Canadian Robotics Network (NCRN). He collaborates with industry partners like Robotiq and serves as Associate Editor for IEEE Robotics and Automation Letters , Frontiers in Robotics and AI , and IROS , while contributing to international program committees for robotics conferences.

Sushil PrasadView profile

Professor

• Data Intensive Computing
• Energy-Efficient Deep Learning
• Parallel Algorithms
• +3 more

Sushil Prasad is a Professor of Computer Science at the University of Texas at San Antonio (UTSA), affiliated with the College of Sciences. His research focuses on data-intensive computing, energy-efficient deep learning models, parallel algorithms, and high-performance software systems. He holds a Ph.D. from the University of Central Florida, an M.S. from Washington State University, and a B.Tech. from the Indian Institute of Technology, Kharagpur. His work emphasizes integrating parallel and distributed computing into early computer science curricula. Key research interests include geospatial data analysis using ICESat-2 and Sentinel-2 imagery, edge device-optimized neural networks, and scalable polygon processing algorithms. He has contributed to frameworks like MPI-GIS and Crayons for high-performance geospatial computing. His educational initiatives include NSF-funded curriculum modernization efforts in parallel computing education. Recent work trends show a focus on climate science applications (e.g., polar sea ice classification), energy-efficient AI, and GPU/OpenMP parallelization. He has organized workshops like EduHPC and EduPar to advance HPC education strategies. Notable recognition includes the TCPP Outstanding Service Award (2012). His projects span cloud-based GIS systems, distributed ML training, and big spatial data processing. Collaborations include NSF-funded research on colocation mining, trajectory analysis, and curriculum development for undergraduate HPC education.

• Heterogeneous Serverless Computing
• Deep Learning
• Real-time Autonomous Driving Systems
• +3 more

Jianfeng Gu is a Ph.D. Candidate and researcher at the Technical University of Munich (TUM), affiliated with the Department of Computer Science and specifically the Chair of Computer Architecture and Parallel Systems led by Prof. Martin Schulz. He maintains an active research profile with numerous publications and contributes to the academic community through teaching seminars on Cloud Computing. His academic path began with a Bachelor of Software Engineering from Sun Yat-sen University in China (2014-2018), followed by a Master of Engineering from the same institution (2018-2020). Since April 2021, he has been pursuing his Ph.D. at TUM, advancing research in computing systems and architectures. Gu's research focuses on Heterogeneous Serverless Computing for Deep Learning applications, specializing in GPU, FPGA, and NPU technologies within serverless environments. His work addresses critical challenges in resource allocation, auto-scaling, and performance optimization for serverless inference systems. Additionally, he investigates Real-time Autonomous Driving Systems , developing advanced perception techniques through sensor fusion (particularly stereo-LiDAR fusion) for high-precision depth sensing and object detection in autonomous vehicles. His interdisciplinary approach bridges hardware acceleration, cloud infrastructure, and AI applications. His publication trajectory shows a progression from foundational computer vision and autonomous driving research (2018-2020) toward increasingly sophisticated work on serverless computing and federated learning (2021-2025). Recent publications focus on efficient resource sharing in heterogeneous serverless environments, with particular attention to GPU and FPGA allocation strategies that maintain service level objectives while optimizing costs. His work demonstrates strong technical depth across multiple computing domains. Best Paper Award at IEEE/ACM DATE 2021 15+ publications with 185+ citations Research featured in top venues for computer architecture and cloud computing As a Ph.D. researcher, Gu teaches seminars on Cloud Computing (IN2107) and contributes to multiple research projects at TUM's Chair of Computer Architecture and Parallel Systems. His work is supported by the department's research infrastructure and collaborations with faculty including Prof. Martin Schulz and Prof. Michael Gerndt. Gu works within TUM's advanced computing research environment, contributing to projects related to high-performance computing, serverless architectures, and autonomous systems. His research group maintains specialized hardware and software infrastructure for evaluating modern HPC architectures and accelerators, including FPGA clusters and GPU resources for deep learning research.

Mu-Han LinView profile

Professor

• Radiation Oncology
• Medical Physics
• Adaptive Radiation Therapy
• +3 more

Mu-Han Lin, Ph.D., is a Professor in the Department of Radiation Oncology at UT Southwestern Medical Center, with leadership roles including Senior Director of Clinical Physics and lead physicist for head and neck radiation oncology. Her academic profile spans Medical Physics , Adaptive Radiation Therapy , and Artificial Intelligence (AI) in Radiotherapy . Education: Master’s and Ph.D. in Medical Physics from National Tsing Hua University, Taiwan Training: Medical Physics Residency at Fox Chase Cancer Center, Philadelphia Dr. Lin’s research focuses on online adaptive radiation therapy (oART) systems, particularly cone beam CT-guided workflows for gastric MALT lymphoma and head/neck cancers. She pioneered techniques for dose prediction , plan quality assurance , and deep learning integration to address anatomical variations during treatment. Her work demonstrates reduced PTV margins (0.5-0.7 cm) while maintaining target coverage and minimizing organ-at-risk (OAR) doses through auto-contouring and synthetic CT optimization . Recent publications highlight her leadership in adaptive treatment planning (15+ articles from 2024-2025) across journals like Medical Physics , Practical Radiation Oncology , and Radiotherapy and Oncology . Key contributions include: X-Ray Guided Ethos Platform workflows Plan Quality Review Checklists HyperSight CBCT for image quality enhancement Inter-patient adaptive strategies for spine SAbR AI-driven segmentation and dose adaptation She actively contributes to clinical education as an instructor in UT Southwestern’s Medical Physics Certificate Program and Biomedical Engineering Graduate Program , and chairs symposiums on X-ray Guided Adaptive RT . Dr. Lin serves on editorial boards for Therapeutic Radiation and Oncology and Medical Physics , and collaborates on grants with Varian Medical Systems and other industry partners.

Sydnee CaldwellView profile

Assistant Professor

• Labor Economics
• Personnel Economics
• Wage-setting
• +5 more

Sydnee Caldwell is an Assistant Professor of Labor Economics holding a joint appointment between UC Berkeley's Haas School of Business (Economic Analysis and Policy group) and Department of Economics. She completed her PhD at MIT in 2019 and is currently on leave at SIEPR (Stanford Institute for Economic Policy Research) for the 2025-2026 academic year. Her research focuses on the relationship between workers' outside options and their wages, as well as the role of firms in the labor market, with particular emphasis on gender wage gaps and bargaining dynamics. Dr. Caldwell's research primarily investigates labor market structures, wage-setting mechanisms, and inequality. Her work leverages administrative data combined with experimental, quasi-experimental, and survey methods to examine how workers and firms interact in wage negotiations. She has made significant contributions to understanding how monopsony power affects wage determination, how outside options influence bargaining positions, and why gender wage gaps persist in different labor market contexts. Her recent work has shown that women earn approximately 5% less than men at employers that negotiate wages separately with each newly hired worker, with the gender wage gap being 3-6 percentage points higher at firms that bargain individually with workers. Analysis of Dr. Caldwell's publication record reveals a strong focus on labor market power dynamics, particularly monopsony and bargaining. Her research spans theoretical frameworks and empirical applications, with increasing attention to gender disparities in recent years. She frequently employs innovative data collection methods and combines administrative datasets with experimental approaches. Her work has important policy implications for minimum wage policies, non-compete agreements, and interventions to reduce gender wage gaps. The trajectory of her research shows a progression from general labor market structures to more specific examinations of gender and inequality within those structures. Early Career Research Awards program funding (Upjohn Institute) Active participant in NBER Gender meetings, including the Spring 2025 meeting themed 'Women in the Digital World' Dr. Caldwell serves as an advisor to PhD students in economics and labor-related fields at UC Berkeley. Her research has been supported by various funding sources including the Upjohn Institute's Early Career Research Awards program. She frequently collaborates with researchers like Ingrid Haegele and Jörg Heining on gender wage gap studies, and has presented her work at major economics conferences including the NBER Gender meetings and American Economic Association sessions. Her research has practical implications for labor policy, wage regulation, and gender equity initiatives. Dr. Caldwell is actively involved in the Opportunity Lab at UC Berkeley and collaborates with researchers at institutions including the Upjohn Institute. Her current research projects examine how digital platforms are changing labor market dynamics, particularly in relation to gender disparities. She is also engaged in international comparative work, having studied wage bargaining in the German context. Her interdisciplinary approach bridges economics with insights from sociology and gender studies to provide a more comprehensive understanding of labor market phenomena.

David ToalView profile

Associate Professor

• Design optimisation & automation
• Machine learning & deep learning
• Computer aided design
• +3 more

Dr. David Toal is an Associate Professor at the University of Southampton, specializing in the application of machine learning techniques to aerospace system design optimization. His research focuses on automated geometry creation, prediction of simulation outputs, and fundamental machine learning advancements. He is affiliated with the Computational Engineering and Design Group and the Institute for Life Sciences. His teaching interests include engineering design methods, optimization, reliability, and CAD integration. He supervises multiple PhD students in areas such as aerodynamic geometry generation and structural design automation. Dr. Toal has led projects funded by the European Union and EPSRC, including E-Break (FP7) and equipment grants for advanced computational tools. His work emphasizes multidisciplinary collaboration, leveraging CAD systems and deep learning for applications in advanced aerial mobility and turbine optimization. Recent publications highlight advancements in Kriging models, adversarial auto-encoders, and semantic segmentation for engineering design. Dr. Toal's research bridges computational methods with practical aerospace challenges, aiming to accelerate design processes through data-driven and AI-enhanced approaches.

James ShacklefordView profile

Associate Professor

• Medical Image Processing
• High Performance Computing
• Embedded Systems
• +2 more

James Shackleford serves as Associate Professor and Interim Associate Dean for Enrollment Management and Graduate Education in the Department of Electrical and Computer Engineering at Drexel University. His research bridges medical image processing, high performance computing, and emerging neuromorphic architectures with significant contributions to radiation therapy applications. Education: PhD in Electrical Engineering, Drexel University, 2011 MS in Electrical Engineering, Drexel University BS in Electrical Engineering, Drexel University Research Focus: Professor Shackleford's work centers on GPU-accelerated medical image registration (forming the core of the open-source Plastimatch software), real-time tumor motion management for radiation therapy, and digital spiking neuromorphic systems . His research integrates computer vision, machine learning, and embedded systems to solve clinical imaging challenges. Publication Trends: Recent work (2020-2024) reveals dual research trajectories: (1) advancing deformable image registration through CycleGAN-based domain adaptation for CT auto-segmentation in radiation oncology, and (2) pioneering neuromorphic computing with configurable hardware architectures, dataflow-based compilers, and resource-aware neural network mapping. These streams converge on high-performance solutions for medical imaging and efficient neural processing.

• Generative Programming
• Model-Based Development
• Software System Families
• +7 more

Krzysztof Czarnecki is a Professor at the University of Waterloo's Department of Electrical and Computer Engineering, with a cross-appointment to the School of Computer Science. He serves as leader of the Waterloo Intelligent Systems Engineering Lab and holds the title of University Research Chair. His research focuses on generative software development, model-driven engineering, and autonomous systems, particularly in automotive cybersecurity and perception safety. Education: Doctorate in Computer Science, Technical University of Ilmenau (1999) Master of Science in Computer Science, Technical University of Ilmenau (1995) Bachelor of Science in Computer Science, California State University (1994) Research Interests: Dr. Czarnecki's work spans generative programming, software product lines, and safety-critical AI for autonomous vehicles. Recent projects address robust perception systems, uncertainty quantification in neural networks, and strategic driving behavior modeling. He co-authored Generative Programming (Addison-Wesley, 2000), a foundational text in the field. Publications Trends: Recent work emphasizes multimodal AI integration (e.g., LEO-MINI), 3D object detection improvements (OV-SCAN), and safety assurance frameworks for autonomous systems. His research bridges theoretical software engineering with applied robotics challenges. Awards: Premier’s Research Excellence Award (2004) British Computing Society’s Upper Canada Award (2008) University Research Chair, University of Waterloo (2023) Teaching & Leadership: Teaches courses like ECE 495 (Autonomous Vehicles) and ECE 651 (Software Engineering Foundations). Oversees WatCAR initiatives and collaborates on industry projects through the NSERC Bank of Nova Scotia Industrial Research Chair (previous). Labs & Teams: Directs the Waterloo Intelligent Systems Engineering Lab, focusing on AI-driven solutions for autonomous systems and safety-critical software. Active in cross-disciplinary collaborations with automotive and robotics partners.

• Signal and Image Processing
• Machine Learning
• Computer Science
• +6 more

Yannick Berthoumieu is a Professor at the Université de Bordeaux , affiliated with the IMS Bordeaux laboratory. He leads the MOTIVE team under the Signal and Image Processing research group. His work spans Signal and Image Processing , Machine Learning , and Remote Sensing , with a focus on SAR image analysis, generative models, and geometric learning.