Find Academic Supervisors

• Geomatics
• Computer Vision
• Artificial Intelligence
• +7 more

Maarten Vergauwen is a Professor at KU Leuven's Faculty of Engineering Technology, where he serves as head of Geomatics and campus chair for Science, Engineering and Technology Group across Ghent and Aalst Campuses. His institutional roles include head of the Division Digital and Sustainable Civil Engineering, Subdivision Geomatics Ghent, and contact person for Geomatics. His research focuses on the intersection of geomatics, computer vision, and artificial intelligence with applications in heritage documentation, 3D modeling, and infrastructure analysis. Key research thrusts include BIM automation, digital twin development for sewer systems, UAV-assisted bridge inspections, and AI-driven cultural heritage preservation. His work leverages advanced techniques in point cloud processing, transformer networks, and semantic segmentation to solve complex spatial problems. Vergauwen actively supervises graduate students on topics ranging from SCAN-to-BIM automation to digital epigraphic documentation of archaeological sites. His research is supported by numerous grants including CHAI: Cracking built Heritage with AI, Deep Sewer, Foundation Models for EO Big Data, and UAV-assisted bridge inspections projects. Member of East and West Flanders Workplace Safety Committee Member of Bureau ADS and Board of Faculty of Engineering Technology Observer of KU Leuven Student Services Council, Brugge He teaches courses including Geomatics 1 & 2, Fotogrammetrie, GIS, Topografie, and 3D-modelleren across KU Leuven's engineering programs.

• Computer Vision
• 3D Mesh Quality Assessment
• Deep Learning
• +3 more

Ilyass Abouelaziz is a Research Fellow at CESI Reims Campus, where he is affiliated with the LINEACT research team. His academic affiliation is with CESI, an engineering institution in France, where he focuses on computer science and engineering disciplines. Dr. Abouelaziz completed his PhD at the Faculty of Sciences of Rabat (2014-2020) with research on reference-free evaluation of 3D mesh quality using deep learning and visual saliency. He also earned a Research Master's Degree in Computer Science and Telecommunications from the same institution (2012-2014), with focus on Computer Vision and Visual Saliency, and a Fundamental License in Computer Science, Electronics, and Automation (2009-2012). His research interests span multiple areas at the intersection of computer vision and artificial intelligence, with particular expertise in 3D mesh quality assessment, point cloud processing, deep learning applications, and computer vision. His work combines theoretical foundations with practical applications in digital modeling and visual quality evaluation. His publication record demonstrates consistent output in top-tier journals and conferences, with a clear research trajectory focused on blind and no-reference quality assessment methods for 3D meshes. His work increasingly incorporates deep learning approaches, particularly convolutional neural networks, while maintaining connections to traditional computer vision techniques and geometric features. Dr. Abouelaziz serves as a reviewer for prestigious publications including Optics Express and IEEE International Conference on Image Processing, and has participated in various scientific committees for international conferences in signal processing and telecommunications. He currently supervises doctoral student Hafsa Benallal on research related to point cloud segmentation and cleaning for digital model creation. His teaching activities encompass algorithms, web development, programming (C, JAVA, Python), Artificial Intelligence, and Image processing for engineering students at levels ranging from 2nd to 5th year.

Mohammed SbihiView profile

Research Professor

• Operations Research
• Mixed Integer Optimization
• Digital Optimization
• +4 more

Mohammed Sbihi is a Research Professor at the Ecole Nationale de l'Aviation Civile (ENAC), affiliated with the OPTIM team within the MORO axis. He serves as Head of the Operational Research Masters program and focuses on mixed integer optimization, digital optimization, and robustness of machine learning algorithms for air traffic management applications. PhD in Mathematics from Université de Franche-Comté (2005) HDR (Habilitation) in Mathematics of Optimization and Operational Research His research integrates mathematical optimization and machine learning to address air traffic control challenges, including trajectory prediction, runway scheduling, and uncertainty management. Recent work explores robust SVM in Banach spaces and Lipschitz constant estimation for ReLU networks. Key publication trends span 2005–2025, covering air traffic optimization (2005–2025), robust machine learning (2022–2024), and industrial machining optimization (2016–2021). Collaborative projects involve co-authors like Daniel Delahaye, Marcel Mongeau, and Ji Ma.

Maximilian SchifferView profile

Associate Professor

• Operations Research
• Management Science
• Machine Learning
• +7 more

Prof. Dr. Maximilian Schiffer is an Associate Professor at the Professorship of Business Analytics & Intelligent Systems , TUM School of Management , Technical University of Munich. He holds a Ph.D. in Operations Research from RWTH Aachen University (2017) and has previously served as a visiting postdoctoral scholar at Stanford University and a postdoctoral scholar at RWTH Aachen. Education: Ph.D. in Operations Research, RWTH Aachen University (2017) Maximilian’s research focuses on developing operations research and prescriptive analytics methods to address societal mobility and transportation challenges, including electric vehicles , autonomous mobility-on-demand systems , and multi-modal transportation networks . His interdisciplinary work bridges robotics , computer science , and transport engineering . Recent publications highlight advancements in autonomous fleet control , electric vehicle charging infrastructure , and collusion detection in dynamic pricing . Scientific Awards: INFORMS TSL Dissertation Prize GOR Doctoral Dissertation Prize Best Paper Award, IEEE Conference on Intelligent Transportation Systems Maximilian is an associate member of the GERAD research group and serves on the editorial board of OR Spectrum and the TSL Dissertation Prize committee . His lab, the Professorship of Business Analytics & Intelligent Systems , trains students in machine learning, optimization, and future mobility systems.

Andreas Paul Eberhard KloecknerView profile

Associate Professor

• Scientific Computing
• Parallel Computing
• Numerical Techniques
• +7 more

Andreas Paul Eberhard Kloeckner is an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), where he has been serving since 2013 (promoted to Associate Professor in 2019). He also holds an affiliate faculty appointment in the Department of Electrical and Computer Engineering since 2016. His academic journey includes a PhD in Applied Mathematics from Brown University (2010), an MSc from Brown University (2006), and a Diplom in Applied Mathematics from Universität Karlsruhe (2005). Prior to joining UIUC, he was a Courant Instructor at the Courant Institute of Mathematical Sciences at New York University. Dr. Kloeckner's research focuses on high-order accurate integral equation methods, fast algorithms for elliptic boundary value problems, and code transformation for high-performance scientific computing. His work bridges mathematical theory with practical implementation, with particular emphasis on GPU computing and parallel architectures. He has made significant contributions to the development of open-source scientific software, most notably PyCUDA and PyOpenCL, which have become widely used tools in the scientific computing community. His publication record shows a consistent focus on advancing numerical methods for scientific computing, with recent work emphasizing code generation techniques, fast integral equation solvers, and optimization of algorithms for modern hardware architectures. The trajectory of his publications reveals a progression from foundational work on GPU-based discontinuous Galerkin methods toward increasingly sophisticated approaches for integral equation methods and automatic code generation. Among his notable recognitions is the 2017 National Science Foundation CAREER Award, which supports his work on general-purpose, high-order integral equation methods for computer simulation in engineering. His research has been published in prestigious venues including SIAM Journal on Scientific Computing and has influenced both academic research and practical applications in scientific computing. Dr. Kloeckner has advised numerous graduate students through completion of their PhD and MS degrees, with alumni moving to positions at Apple, NVIDIA, Rice University, and other leading institutions. His research group maintains an active portfolio of open-source software projects that advance the state of scientific computing infrastructure.

Adrian EvansView profile

Professor

• Biometrics
• 3D Face Recognition
• Colour Image Processing
• +7 more

Prof. Adrian Evans serves as Deputy Head of Department in the Department of Electronic & Electrical Engineering at the University of Bath, where he leads research within the Electronics Materials, Circuits & Systems Research Unit (EMaCS) and The Foundry: Centre for Digital, Manufacturing & Design. His academic profile demonstrates active engagement in doctoral supervision and cutting-edge research across multiple domains of image processing and biometrics. His primary research focuses on biometrics—particularly 3D face recognition techniques resilient to facial expressions—and advanced colour/multispectral image processing. He has pioneered methods for colour edge detection, nonlinear filtering, and scale-space sieve-based segmentation. His motion estimation work specializes in analyzing non-rigid bodies like clouds and glaciers, while his nonlinear image processing research centers on mathematical morphological sieves and granulometric texture analysis. Recent publication trends (2021-2025) reveal a strategic shift toward computer vision applications in transportation (multi-camera vehicle tracking systems) and healthcare (non-contact cardiorespiratory monitoring). His work increasingly integrates radar technology with computer vision for vital sign detection while maintaining foundational contributions to mathematical morphology and image enhancement techniques. No major scientific awards are documented in the provided materials. His supervisory record includes 11 doctoral students, with current openness to new PhD candidates. Research funding spans UK government and industry collaborations including EPSRC projects like High Speed 4K Video Transmission (2016-2018) and multiple KTP partnerships with Seiche Measurements Limited and Navtech Radar Limited. He operates within Bath's EMaCS research unit and The Foundry center, leveraging these interdisciplinary environments for digital manufacturing and design innovation. His work directly supports UN Sustainable Development Goals through applications in smart transportation systems and healthcare technology development.

Chandrama SarkerView profile

Researcher

• Deep Learning
• Image Analysis
• Remote Sensing Applications
• +6 more

Dr. Chandrama Sarker is an Experimental Scientist at CSIRO Environment , specializing in advanced applications of deep learning, remote sensing, and GIS for environmental monitoring. Her work focuses on flood mapping, semantic segmentation, and spatio-contextual image analysis using satellite data and neural networks. Field: Remote Sensing, Artificial Intelligence, Environmental Science Key Tools: Convolutional Neural Networks, ICESAT-2 Altimetry, Landsat Multispectral Data Research Highlights Pioneering flood mapping techniques using CNNs and spatio-contextual pixel information (2019) Refining satellite altimetry data for river elevation accuracy (2024) Integrating Bayesian estimation with SVM for flood probability modeling (2016) Scientific Awards Australian Postgraduate Award for PhD research DSARC Fellowship (61,920 AU$) ITC/CBP Euro 330/month award for GIS/RS training

Xiaojie QiuView profile

Assistant Professor

• single-cell genomics
• spatial genomics
• machine learning
• +6 more

Dr. Xiaojie Qiu serves as an Assistant Professor at Stanford University with dual appointments in the Department of Genetics and Department of Computer Science, as well as the BASE program. He holds additional memberships in Bio-X, the Cardiovascular Institute, the Maternal & Child Health Research Institute (MCHRI), and the Wu Tsai Neurosciences Institute, reflecting his interdisciplinary approach to biomedical research. His educational journey includes: Postdoctoral fellowship at Whitehead Institute & MIT (2023) in single cell and spatial genomics; systems biology PhD from University of Washington (2018) in Molecular & cellular biology; genome sciences Masters from East China Normal University (2012) in Bioinformatics and systems biology BS from Changchun University of Technology (2008) in Bioengineer Dr. Qiu's research integrates single-cell and spatial genomics with machine learning to unravel the complexities of heart evolution, development, and disease. His lab employs a full experimental-computational cycle, beginning with novel genomics approaches to measure single-cell states over time and space, then developing interpretable machine learning models to understand underlying gene regulatory networks. These computational models enable predictions of congenital heart diseases, aiming to identify when and where disease begins in the heart to develop preventive therapies. His scientific excellence has been recognized with several prestigious awards: NIH Director's New Innovator Award (2024) Arc Ignite Award (2023) Pathway to Independence Awards (K99/R00) (2023) Dr. Qiu mentors a diverse group of researchers including Tony Zeng (Doctoral Dissertation Reader), Sijie Chen, Naoki Konno, Nianping Liu, and Weize Xu (Postdoctoral Faculty Sponsors), and Peter Pao-Huang (Doctoral Dissertation Co-Advisor). His research program receives substantial support from the National Human Genome Research Institute, Chan Zuckerberg Institute, Impetus longevity grant, and Arc institute. The publication trends reveal his expertise spans computational biology, single-cell analysis, and cardiac development, with increasing focus on spatial transcriptomics and predictive modeling. The Qiu Lab has pioneered several influential computational tools including Monocle 2/3 (for pseudotemporal trajectory analysis), Dynamo (for RNA velocity vector field reconstruction), and Spateo (for multi-dimensional spatiotemporal modeling). Currently, the lab is developing the Aristotle ecosystem, an end-to-end predictive software framework that aims to bridge the gap between big data from single-cell and spatial genomics and quantitative predictive modeling of mammalian cell fate transitions.

• Mathematics
• Applied Mathematics
• Graph Theory
• +5 more

Dr. Marius Buliga is a Professor of Mathematics and Director of Applied Mathematics at the University of Pittsburgh's Division of Physical and Computational Sciences. He contributes to both academic research and educational innovation, focusing on graph theory and mathematical software integration in undergraduate teaching. Ph.D. in Mathematics (2002) M.S. in Information Science (2002) M.S. in Computer Science (1995) B.S. in Computer Science (1994) His research spans graph theory , computational modeling for educational tools (Mathematica/Java applets), and interdisciplinary work in biomedical informatics and systems medicine . Recent publications analyze inflammatory responses in trauma/sepsis, postural control in Parkinson's disease , and combinatorial graph designs , demonstrating a multidisciplinary approach combining mathematics with clinical and computational applications. Dr. Buliga teaches undergraduate mathematics courses including Calculus, Linear Algebra, and Numerical Analysis. His work integrates algorithmic development with biomedical modeling , reflecting collaborations across disciplines.

Animesh MandalView profile

Associate Professor

• Near Surface Geophysics
• Geophysical Data Enhancement
• Subsurface Modeling
• +7 more

Animesh Mandal serves as an Associate Professor in the Department of Earth Sciences at Indian Institute of Technology Kanpur (IITK) , where he has been a faculty member since April 2015. His academic journey includes a B.Sc. in Physics from University of Calcutta (2005), M.Sc. in Physics from IIT Delhi (2007), and Ph.D. in Geophysics from IIT Kharagpur (2013). Prior to joining IITK, he worked as a Project Scientist at the National Geophysical Research Institute (NGRI). Research Interests : Dr. Mandal specializes in near-surface geophysical studies , geophysical data enhancement , and subsurface modeling . His work focuses on integrated geophysical approaches to understand shallow crustal configuration and delineate natural resources, utilizing gravimeter, magnetometer, electrical, and EM equipment. He has pioneered research in machine learning-assisted interpretation of geophysical data, particularly in seismic impedance inversion, reservoir characterization, and geothermal system analysis. His recent publications demonstrate expertise in applying deep learning techniques to solve complex geophysical problems. Research Trends : Analysis of his recent publications reveals a strong focus on geothermal energy systems , particularly non-volcanic hot springs in the Eastern Ghats Mobile Belt. His work combines gravity-magnetic studies with advanced computational methods to understand crustal configuration and thermal structure. Another major trend involves machine learning applications in seismic data processing, where he develops novel deep learning architectures for impedance inversion and reservoir characterization. His research bridges traditional geophysical methods with cutting-edge AI techniques, creating innovative approaches to subsurface imaging. Scientific Awards : Supervised students who received Prime Minister's Research Fellowship (PMRF) Advisees awarded FARE Fellowship, ONGC-IGU Best Poster Presentation Award, and EAGE PACE grants Students received FULL sponsorship to attend major international conferences like IMAGE 2024 Mentored students who secured postdoctoral positions at prestigious institutions including KAUST and University College Dublin Academic Leadership : Dr. Mandal has successfully supervised multiple Ph.D. and M.Tech. students to completion, with recent graduates securing positions at institutions like University College Dublin, Queen's University, and industry roles at companies including Verisk Analytics and ONGC. His research group maintains active collaborations with international institutions and regularly presents at major geoscience conferences including EAGE, SEG, and AOGS. The group has secured competitive research funding that supports field studies, laboratory work, and student training in advanced geophysical techniques.

• Computational Linguistics
• Natural Language Processing
• Terminology Management
• +3 more

Jody Foo is a Lecturer at the Department of Computer and Information Science, Linköping University. They are affiliated with the Natural Language Processing Lab (NLPLAB) and the Human-Centered Systems (HCS) research group. Their work bridges computational methods with terminology management. Education: Licenciate degree in Computational Linguistics; Magistersexamen (~MSc) in Cognitive Science. Research Focus Jody’s research centers on computational terminology management , involving: Monolingual and bilingual automatic term extraction Term clustering and semantic mirroring Terminology harmonization for standardization Integration of machine learning and language models Applications in patent analysis and SMT (Statistical Machine Translation) Publication Trends Their publications (2003–2014) span computational linguistics, NLP, and terminology engineering. Notable areas include SVM-based term detection, semantic mirroring for patent terminology, and corpus augmentation for machine translation. Collaborative works with Merkel, Andersson, and others highlight interdisciplinary approaches. Contact Email: jody.foo@liu.se | Office: Room E 3F:482, Linköping

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +5 more

Oliver Mortensen is a PhD Fellow (Research Fellow) at the Machine Learning Section , Department of Computer Science (DIKU) , University of Copenhagen , Denmark. He is affiliated with the university’s Faculty of Science and participates in the cross-faculty SCIENCE AI Centre , a strategic initiative to advance artificial intelligence research and applications. Research Interests Mortensen’s research lies at the intersection of machine learning , quantum computing , and neuro-symbolic AI . His work spans both theoretical foundations—such as entropic risk optimization in reinforcement learning and Riemannian generative models—and highly applied domains including medical AI, recommender-system fairness, and brain-computer interfaces. A recurring theme is trustworthy AI , where he investigates explainability, fairness, and sustainability across large language models and clinical decision-support systems. Scientific Contributions & Trends Across more than 60 peer-reviewed contributions (2024-2025), Mortensen demonstrates a clear trajectory toward hybrid quantum-classical algorithms , energy-efficient AI , and human-centric evaluation . His publications integrate rigorous theoretical guarantees with empirical validation on real-world data from electronic health records, satellite imagery, and conversational corpora. Collaborations & Resources He carries out his doctoral research under the supervision of Professor Yevgeny Seldin within DIKU’s vibrant Machine Learning Section. The group offers access to a dedicated high-performance compute cluster, the SCIENCE AI Centre ’s GPU/TPU pools, and interdisciplinary ties to life-science, geoscience, and humanities researchers across the university.

Xiaohui XieView profile

Professor

• AI/Machine Learning
• Neural Networks
• Deep Learning
• +3 more

Xiaohui Xie is a Professor in the Department of Computer Science at the Bren School of Information and Computer Sciences, University of California, Irvine. He joined UC Irvine in 2007 after completing his PhD at MIT and postdoctoral training at the Broad Institute of MIT and Harvard University. His research focuses on AI/machine learning, neural networks, deep learning, and genomics. PhD in Computer Science, MIT Postdoctoral training at Broad Institute Research interests span AI, neural networks, deep learning, and genomics, with applications in medical imaging, computational biology, and computer vision. Recent publications highlight advancements in 3D convolutional networks for nodule detection, medical image analysis, and hybrid models for DNA sequence function quantification. Scientific awards and honors are not explicitly mentioned in the provided text. His teaching includes courses like CS273P (Machine Learning and Data Mining), CS175 (Project in AI), and foundational classes in computational linear algebra and optimization, reflecting his interdisciplinary expertise.

Daniel CooleyView profile

Professor

• Sustainable Agriculture
• Plant Disease Management
• Integrated Pest Management (IPM)
• +3 more

Daniel Cooley is a Professor at the University of Massachusetts Amherst, affiliated with the Stockbridge School of Agriculture and the School of Earth and Sustainability. His work focuses on sustainable disease management in fruit crops, particularly through modeling, climate adaptation, and cyber technology applications. A.B., 1974, Harvard College M.S., 1978, University of Vermont Ph.D., 1986, University of Massachusetts Amherst Dr. Cooley's research spans integrated pest management (IPM) for fruit crops, with emphasis on apple scab, fire blight, and plum curculio control. His work combines Climate change adaptation strategies Computer vision for disease monitoring Fungicide resistance management Biological control methods Recent publications highlight decision support systems, organic agriculture integration, and electrophysiological pest responses. Scientific contributions include Comparative genomics of plant pathogens Disease forecasting models Attract-and-kill techniques Fungicide stewardship protocols though no specific awards are listed in the provided text.

Will JonesView profile

Lecturer

• AI in Healthcare
• Medical Diagnostics
• Evidence Generation for AI Technologies
• +3 more

Dr Will Jones is a Lecturer and Director of Research at the Data Science AI and Modelling Centre (DAIM) within the Faculty of Science and Engineering at the University of Hull. He holds an honorary NHS researcher role in Medical Oncology at Hull University Teaching Hospitals. His expertise lies in developing and evaluating AI medical diagnostics, focusing on bridging technical innovation with clinical needs. Key roles include leading the £11M NIHR Centre for Addiction and Mental Health Research and co-investigating a £600K EPSRC project optimizing diagnostic trial designs using Bayesian methods. Research interests span AI ethics, clinical trial design for diagnostics, and qualitative studies on medical technologies. His work emphasizes translating AI into practical clinical tools, including recent studies on population drift detection in ML models and patient self-testing for kidney function. He supervises PhD and MSc students in AI healthcare ethics and predictive analytics for neurological conditions. Notable contributions include over 20 medical test evaluation projects, securing £20M in research funding. Current grants include AI-wearable laboratory equipment (£520K) and a collaboration with IIT-Madras on responsible AI. His work integrates IoT-enabled health monitoring, cost-effectiveness analysis, and clinician perspectives on diagnostic adoption barriers. Major publications address critical issues like cholesterol point-of-care test reliability, molecular breast imaging patient experiences, and asthma symptom-air quality linkages via smart sensors. Jones actively participates in interdisciplinary teams across academia and NHS, driving innovation in diagnostics while maintaining rigorous evidence standards.