Find Academic Supervisors

• Computer Graphics
• Computer Vision
• Video Processing
• +4 more

Prof. Dr. Thorsten Thormählen is a University Professor for Computer Graphics and Multimedia Programming in the Department of Mathematics and Computer Science at Philipps University Marburg, Germany, a position he has held since October 2012. Prior to this, he served as Substitute Professor for Interactive Graphics Systems at the University of Tübingen (2011-2012) and led the independent research group "Image-based 3D Scene Analysis" at the Max Planck Center for Visual Computing and Communication (2007-2012). His academic background includes a PhD (Dr.-Ing.) from the University of Hannover (2000-2005), where he also worked as a full-time scientific assistant, and a Diploma in Electrical Engineering from the University of Duisburg (1994-1999). Prof. Thormählen's research centers on Visual Computing , spanning Computer Graphics , Computer Vision , Video Processing , and Human-Computer Interaction . His work pioneers tools for 3D reconstruction (e.g., Multi-View Photometric Stereo), video manipulation (e.g., MovieReshape), and multimedia processing (e.g., GSN Composer), with groundbreaking contributions to accessibility through haptic and auditory interfaces for blind 3D modelers. His lab develops practical systems that bridge theoretical computer vision with real-world applications. Analysis of his 15 most recent publications reveals a consistent focus on photometric stereo techniques for 3D reconstruction, video processing innovations, and accessibility-driven interfaces. His work increasingly emphasizes inclusive design, particularly for visually impaired users, while maintaining technical rigor in camera calibration, surface modeling, and multi-sensor fusion. At the Max Planck Center, he established and led the "Image-based 3D Scene Analysis" research group, which advanced methods for reconstructing 3D environments from video sequences. His current work at Marburg continues this trajectory, integrating computer vision, graphics, and human-centered design to solve complex problems in digital content creation and accessibility.

• Statistical Signal and Image Analysis
• Biomedical Applications
• Environmental Statistics
• +3 more

Armin Schwartzman is a Professor at the Halıcıoğlu Data Science Institute and Department of Biostatistics at the University of California, San Diego (UCSD). He holds a PhD in Statistics from Stanford University, an MSc in Electrical Engineering from the California Institute of Technology, and a BSc in Electrical Engineering from Technion-Israel Institute of Technology. His research focuses on statistical methods for signal and image analysis with applications in biomedical and environmental domains, including spatial inference, high-dimensional data analysis, and random field theory. Education : PhD in Statistics, Stanford University, 2006 MSc in Electrical Engineering, California Institute of Technology, 1996 BSc in Electrical Engineering, Technion-Israel Institute of Technology, 1995 Research Interests : Dr. Schwartzman develops statistical methodologies for analyzing complex data, particularly in neuroimaging, climate science, and genomics. Key areas include spatial inference for image analysis, multiple testing in high-dimensional data, and applications of random field theory. His work bridges theoretical statistics and practical challenges in biomedical and environmental research. Grants & Funding : NIH R01EB026859 (2019–2023): Spatial inference methods for image analysis NIH R21EB013795 (2012–2015): Voxelwise analysis of imaging response in neuro-oncology NIH R01CA157528 (2012–2019): Multiple testing methods for random fields Advising : Current advisees include Sam Davenport, Anubhav Singh-Sachan, and others. Former students have transitioned to roles at institutions like Moderna, University of Florida, and Cruiser. Labs & Collaborations : His research group works on projects such as: Random field theory for noise modeling in images Spatial inference in neuroimaging and climate data Machine learning for medical imaging (e.g., glaucoma diagnosis) Mountain glacier monitoring via satellite imagery

Susanne WenzelView profile

Researcher

• Pattern recognition
• Image interpretation
• Machine Learning
• +6 more

Dr. Susanne Wenzel is a postdoctoral researcher and teaching assistant at the Photogrammetry group (IGG - Institute of Geodesy and Geoinformation) at the University of Bonn, and a scientific coordinator at Forschungszentrum Jülich since February 2018. Her academic journey began with studies in Geodesy at the Technical University of Berlin and University of Bonn, following professional training as a surveying technician at the Berlin Senate of Urban Development. Her research focuses on pattern recognition and image interpretation, particularly applying machine learning and deep learning techniques to photogrammetry and remote sensing problems. Wenzel's work prominently features Markov Marked Point Processes and the analysis of symmetries and repeated structures in images, with applications ranging from facade interpretation to oceanographic analysis. Her interdisciplinary approach bridges computer vision, geospatial analysis, and machine learning. The analysis of her 15 most recent publications reveals a strong trend toward applying advanced machine learning techniques, particularly deep learning and self-taught learning approaches, to photogrammetric and remote sensing problems. Her research spans multiple domains including urban modeling (facade interpretation), environmental monitoring (ocean eddies, sea level anomalies), and forensic applications (latent trace detection), demonstrating remarkable versatility while maintaining a core focus on image interpretation methodologies. Faculty Teaching Award 2014 Faculty Award for the best student in 2007 in Geodesy and Geoinformation Turbo-Preis 2007 of Society for Geodesy, Geoinformation and Land Management (DVW) Dr. Wenzel has supervised numerous Master's and Bachelor's students on diverse topics including neural network applications for ocean eddy tracking, hyperspectral imaging for latent trace detection, and deep learning for remote sensing image classification. Her teaching portfolio includes Photogrammetrie I and II courses since 2009, and she managed the development of the Geodetic Engineering Master's program at IGG from 2015-2018. Her research has been supported through positions at both the University of Bonn and Forschungszentrum Jülich, where she contributes to interdisciplinary projects bridging geospatial analysis and machine learning.

Konstantinos BarmpasView profile

Researcher

• Brain-Computer Interfaces (BCIs)
• Deep Learning
• Neurosciences
• +3 more

Konstantinos Barmpas is a Postdoctoral Research Associate at Imperial College London's Department of Computing within the Faculty of Engineering. His research focuses on developing Generative Foundation Models for biosignals in Brain-Computer Interfaces (BCIs), emphasizing causal reasoning and deep learning applications. He holds a PhD in Computer Science (AI) from Imperial College London and an MEng from Imperial College London/ETH Zurich. Education : PhD in Computer Science (Imperial College London, 2020–2024) MEng in Electrical & Electronic Engineering (Imperial College London, 2016–2020) Master Year Abroad at ETH Zurich (2019–2020) His research bridges deep learning with BCIs, addressing topics like differentiable signal processing, causal modeling, and ethical challenges in GenAI-powered neural interfaces. He leads projects like MyBrainCommands and MyBrainStates , approved by Imperial's Research Governance team. His work has been featured in outlets like Sky News, BBC, and The Times. Barmpas contributes extensively to academia: he co-organized the London Geometry and Machine Learning (LOGML) Summer School, led the Imperial Computing Conference (ICC), and served on Imperial's Equity, Diversity, and Culture Committee. He reviews for top AI journals/conferences (e.g., IEEE Transactions, NeurIPS workshops) and mentors students through initiatives like Microsoft Student Ambassador programs. He is a Fellow of the Higher Education Academy (FHEA) and an ELLIS Society member. His innovations include the CogiGame EEG-VR platform for stroke rehabilitation and causal frameworks for brainwave modeling. Over 15 peer-reviewed publications span topics like EEG signal processing, neural network geometry, and BCI ethics. Patents include classification of brain activity signals (GB2605270).

Diana Chua HalikiasView profile

Research Fellow

• Numerical Analysis
• Scientific Machine Learning
• Randomized Linear Algebra
• +4 more

Diana Chua Halikias is a Ph.D. Candidate in Mathematics at Cornell University, with a focus on numerical analysis and scientific machine learning. Starting in January 2026, she will join New York University as a Courant Instructor in the Department of Mathematics, collaborating with Chris Musco. Her current role as a Simons-Berkeley Research Fellow underscores her expertise in computational mathematics and machine learning. Education: B.S. in Mathematics from Yale University (2020) Ph.D. in Mathematics from Cornell University (2025) Her research bridges randomized linear algebra, matrix theory, and operator learning, with applications in scientific computing and machine learning. Recent work includes hierarchical matrix approximation and data-efficient PDE learning. She has held internships at the Flatiron Institute and Lawrence Berkeley National Laboratory. Scientific Awards: Simons-Berkeley Research Fellow Graduate Student Funding and Awards at Cornell Halikias actively mentors undergraduate researchers, including directing projects in machine learning and spectral graph theory. She also contributes to outreach through the Cornell REU program and local math circles in Ithaca.

Georgios AngelisView profile

Research Fellow

• Medical Imaging
• Biomedical Engineering
• PET/SPECT Image Reconstruction
• +3 more

Dr Georgios Angelis is a National Imaging Facility Fellow at the University of Sydney, specializing in PET/SPECT image reconstruction and pharmacokinetic modeling. His research focuses on developing advanced imaging techniques for neuroscience and oncology applications, including motion correction algorithms, parametric image analysis, and deep learning approaches. He holds a PhD in Biomedical Engineering from the University of Manchester and has held postdoctoral roles at the University of Sydney's Brain and Mind Centre and the Northern Sydney Cancer Centre. His work bridges imaging physics and clinical practice, with contributions to open-field PET systems and tumor tracking for radiotherapy. Key research areas include: medical imaging algorithms, neurochemical PET analysis, machine learning in imaging, and translational imaging tools for freely moving subjects. Recent work emphasizes integration of MRI and PET data for improved spatial-temporal resolution, and development of Bayesian methods for neurotransmitter quantification. His projects frequently involve collaborations in oncology and neuroscience, with applications to both preclinical and clinical settings.

• Knowledge Graph Construction & Curation
• Neural-Symbolic Reasoning
• Large Language Models (LLMs)
• +4 more

Dr. Jiaoyan Chen is a Lecturer (teaching & research) in the Department of Computer Science at The University of Manchester, transitioning to Senior Lecturer (Associate Professor) in July 2025. Previously, she was a Senior Researcher at the University of Oxford (2017-2022) and held visiting roles at Zurich University and Heidelberg University. She holds a PhD and BEng in Computer Science from Zhejiang University. Her research focuses on neural-symbolic systems, integrating knowledge graphs/ontologies with large language models and machine learning. Key areas include knowledge graph construction/curation, ontology embeddings, and data-intensive systems. She leads EPSRC-funded projects like 'OntoEm' (427.9K GBP) and collaborates on initiatives such as the Manchester-Melbourne-Toronto AI for Child Protection project. Teaching responsibilities include units like 'Data Engineering Technologies' and 'Advanced Topics in Knowledge Representation.' She actively supervises 9 PhD/postdoc researchers across topics like knowledge-aware AI and semantic search. Professional services include associate editor roles at Transactions on Graph Data and Knowledge and membership in the EPSRC Peer Review College. Her software contributions include OWL2Vec* (ontology embedding framework) and DeepOnto (ontology engineering toolkit). Recent achievements include ACL 2025 acceptances and an IEEE TKDE survey paper on ontology embeddings.

Angela VioliView profile

Professor

• Bio-Nanotechnology
• Biomedical Computation and Modeling
• Nanoparticle-Biological Interactions
• +4 more

Angela Violi is the Arthur F. Thunau Professor of Biomedical Engineering at the University of Michigan, with affiliations in Mechanical Engineering, Electrical Engineering and Computer Science, and Chemical Engineering. Her research focuses on computational methods to study nanoparticle interactions with biological systems, leveraging molecular simulations and machine learning. The Violi Lab develops interdisciplinary approaches to address challenges in nanoscience, including nanoparticle-membrane interactions, drug delivery, and environmental applications. Key research areas include bio-nanotechnology, biomembranes, and biomedical computation. Recent work emphasizes geometric deep learning for nonthermal plasma modeling and ensemble methods for feature selection. The lab's innovations aim to reduce computational costs while enhancing predictive accuracy in complex systems. Publications highlight advancements in nanoparticle growth prediction, machine learning-driven reaction network analysis, and domain-agnostic interaction modeling. Collaborative efforts span engineering, biology, and computer science, reflecting a commitment to translational research with societal impact. Angela Violi actively contributes to diversity initiatives in academia, as seen in her participation in DEI lectures and mentorship programs. Labs/Teams: The Violi Lab is a leader in nanobiotechnology, combining experimental and computational methods. Current projects focus on predictive models for nanoparticle behavior, sustainable materials design, and biomedical applications of AI.

Hanna KurniawatiView profile

Professor

• Robotics
• Decision-Making Under Uncertainty
• Motion Planning
• +4 more

Hanna Kurniawati is a Professor at the ANU School of Computing and holds the SmartSat CRC Professorial Chair for System Autonomy, Intelligence, and Decision-Making. She leads the ANU Node and Planning & Control theme of the ARIAM Hub, and founded the Robot Decision Making group. Her research focuses on scalable algorithms for decision-making under uncertainty, particularly in robotics and autonomous systems. She has received prestigious awards, including the RSS 2021 Test of Time Award and multiple best-paper recognitions. She is an active conference organizer and editorial board member for journals like IEEE Transactions on Robotics. Education: BSc Computer Science, University of Indonesia PhD Computer Science (Robot Motion Planning), National University of Singapore (2008) Research Interests: Planning under uncertainty and POMDP frameworks Robot motion planning and computational geometry Integration of planning and reinforcement learning Autonomous system assurance and safety Service and Leadership: Former President of the Australian Robotics and Automation Association (2019–2020) Keynote speaker at ICRA 2025, IROS 2018, and ICAPS 2025 Program Co-Chair for ICRA 2022 and Editor roles for IEEE TRO and RA-L Teaching: Advanced Topics in AI (COMP4620/8620) Advanced Topics in Machine Learning (COMP4680/8650) Algorithms (COMP3600/6466)

Noomane Ben KhalifaView profile

Professor

• Sustainable Manufacturing
• Metal Forming Technologies
• Additive Manufacturing
• +4 more

Prof. Dr.-Ing. Noomane Ben Khalifa is a Professor of Manufacturing – Innovative Manufacturing and Head of the Institute for Production Technology and Systems (IPTS) at Leuphana University. He concurrently leads the Institute for Material and Process Design at Helmholtz Center Hereon. His work focuses on sustainable manufacturing processes, particularly extrusion, sheet metal forming, and additive manufacturing, emphasizing eco-friendly production across material value chains. Education: Awarded a Doctorate in Engineering from TU Dortmund University (2012) with distinction for research on helical profile extrusion. Holds a Mechanical Engineering degree from TU Dortmund (2005), specializing in production engineering and forming technology. Research Interests: Sustainable manufacturing processes; material recycling (e.g., aluminum chips); lightweight material development; composite extrusion; die design optimization; and process simulation. His work bridges advanced manufacturing with environmental impact reduction, leveraging AI and machine learning for material-property modeling. Key Article Trends: Recent studies emphasize machine learning applications in materials science, die design for magnesium alloys, and sustainable recycling processes. His work spans from fundamental material behavior analysis to industrial process optimization. 2025: Focus on AI-driven texture-property modeling in Mg alloys and dieless wire drawing improvements 2024: LCA of aluminum recycling and deep-drawing process reliability Awards: 2009 ISPF Award for extrusion research; 2014 ZukunftErfindenNRW for innovative extrusion press design; invited keynote speaker at ICIT & MPT 2014. Advising & Grants: Oversees OPTUM-MAGNA (magnesium nanocomposites), FERNAPRO (sustainable manufacturing tech), and projects like TrICo (innovation cooperation). Collaborates with industry on tool design and eco-processes. Labs & Teams: Leads IPTS and co-leads Helmholtz material design institute. Supervises interdisciplinary teams focusing on compound casting, incremental forming, and smart manufacturing systems.

Jens HegerView profile

Professor

• Manufacturing Engineering
• Production Technology
• Simulation
• +5 more

Jens Heger is a Professor of Engineering specializing in Modeling and Simulation of Technical Systems and Processes at Leuphana University Lüneburg, Germany. He is affiliated with the Institute for Production Technology and Systems (IPTS) and the Research Center for Digital Transformation. His academic career spans over a decade of research and teaching in production engineering, manufacturing systems, and simulation technologies. Dr. Heger completed his Master of Science in Computer Science with a minor in Business Studies at the University of Paderborn (1999-2006). He then earned his doctorate degree at the University of Bremen in the Department of Production Engineering. His dissertation focused on "Dynamic selection of rules for sequence planning in workshop and flexible flow production" under the supervision of Prof. Scholz-Reiter and Prof. Jürgen Branke from the University of Warwick, UK. Professor Heger's research interests center around the intersection of manufacturing engineering, simulation, and artificial intelligence. His work demonstrates a strong focus on applying advanced computational methods to solve complex production challenges. Key research areas include: Manufacturing process optimization through simulation and modeling Application of machine learning and reinforcement learning in production scheduling Development of intelligent control systems for manufacturing processes Energy efficiency and sustainability in production systems Quality control and prediction in sheet metal forming processes Integration of AI technologies in traditional manufacturing environments Analysis of Professor Heger's recent publications reveals a clear trajectory toward increasingly sophisticated applications of artificial intelligence in manufacturing. His work has evolved from traditional simulation and optimization techniques toward integrating deep learning, reinforcement learning, and computer vision technologies into production systems. A significant portion of his research focuses on dynamic scheduling and optimization problems, particularly using reinforcement learning approaches to address complex manufacturing challenges. His publications also demonstrate a growing interest in sustainable manufacturing practices, energy efficiency, and quality control applications through AI technologies. Professor Heger has contributed significantly to the field through numerous publications in manufacturing engineering, production systems, and AI applications. His work spans both theoretical advancements in scheduling algorithms and practical implementations in industrial settings, particularly focusing on small and medium enterprises.

Yann DisserView profile

Professor

• Combinatorial Optimization
• Online Algorithms
• Graph Exploration
• +6 more

Yann Disser is a Professor (W2) at the Department of Mathematics, Technische Universität Darmstadt. His research focuses on combinatorial optimization, online algorithms, and graph theory. He holds a PhD in Theoretical Computer Science from ETH Zurich (2011) and has held academic positions at TU Berlin and ETH Zurich. Disser has led numerous DFG-funded research projects, including work on incremental maximization and potential-based flow networks. His teaching includes courses on discrete optimization, algorithmic discrete mathematics, and online optimization. He supervises a research group with members such as Júlia Baligács and Annette Lutz, and has advised multiple PhD students including David Weckbecker. His recent work explores competitive analysis, algorithmic lower bounds, and graph exploration. Education: PhD in Theoretical Computer Science (ETH Zurich, 2011), MSc in Physics (TU Darmstadt), Diplom in Computer Science (TU Darmstadt). Research Interests: Combinatorial Optimization, Online Algorithms, Graph Exploration, Computational Complexity, Incremental Algorithms, Approximation Algorithms. Grants: DFG TRR 154 (2022–2026), DFG Individual Grants (2019–2023), EU EFRE Project (2016–2019). Labs: Deep Learning Lab (TU Darmstadt), Algorithms Lab (ETH Zurich).

Alexander TereninView profile

Research Professor

• Machine Learning
• Artificial Intelligence
• Decision Theory
• +7 more

Alexander Terenin is an Assistant Research Professor at Cornell University , specializing in machine learning and artificial intelligence. His work focuses on decision-making under uncertainty, Bayesian optimization, and Gaussian processes, particularly in non-Euclidean spaces. He has contributed to geometric learning, scalable Gaussian process methods, and applications in robotics, plasma science, and legal AI. His research integrates theoretical foundations with practical algorithms, emphasizing principles like the Gittins Index for optimal decision-making. Notable projects include the GeometricKernels software package for manifold learning and the Cambridge Law Corpus for legal AI. His work bridges statistics, geometry, and computer science to address challenges in autonomous systems, energy optimization, and data-driven decision-making. Recent Talks and Contributions: An Adversarial Analysis of Thompson Sampling (INFORMS Applied Probability Society 2025) Cost-aware Bayesian Optimization (NeurIPS 2024) Stochastic Poisson Surface Reconstruction (ICML 2025) Key Research Themes: Bayesian Optimization for multi-objective problems (e.g., plasma-driven energy systems) Geometric Gaussian Processes for robotics and 3D modeling Statistical guarantees for Gaussian processes on manifolds Grants and Collaborations: His work involves interdisciplinary projects with institutions like Carnegie Mellon University, ETH Zürich, and the University of Cambridge, reflecting a global network in AI and statistical learning.

Ali GolbafView profile

Lecturer

• Artificial Intelligence
• Clinical Management of Cancers
• Medical Imaging
• +6 more

Ali Golbaf is an Associate Lecturer at the School of Engineering, Computing and Mathematics within the Faculty of Science and Engineering at the University of Plymouth. He is currently pursuing a PhD in Computer Science, funded by UK Research and Innovation (UKRI). Prior to this, he earned a BSc in Mechanical Engineering from Shahrood University of Technology (2014) and an MSc in Mechanical Engineering from the University of Guilan (2017), both in Iran. His research focuses on leveraging Artificial Intelligence to develop non-invasive, objective biomarkers for clinical management of cancers, particularly brain tumors. His interdisciplinary work bridges mechanical engineering principles—such as finite element analysis, fluid-structure interaction, and scaling laws—with AI-driven healthcare solutions. Notable contributions include studies on turbulent boundary layers, dynamic systems scaling, and material compaction processes. Ali’s publications span topics ranging from AI applications in oncology to mechanical engineering fundamentals, reflecting his dual expertise. He has actively collaborated on experimental and numerical investigations across diverse fields, including impact mechanics, powder metallurgy, and thermal dynamics. His PhD research aims to integrate machine learning with clinical diagnostics, addressing unmet needs in precision oncology. While no scientific awards are explicitly listed, his work contributes to the UN Sustainable Development Goals (SDGs) related to health and innovation. Ali is funded by UKRI for his doctoral studies and currently has no listed advisees or grants.

• Agro Food Robotics
• Precision Farming
• Optimal Control
• +3 more

Eldert van Henten is a full professor of Biosystems Engineering at Wageningen University & Research. His research focuses on agro food robotics, precision farming, and optimal control systems in agriculture. He earned his MSc (1987) and PhD (1994) in Agricultural and Environmental Sciences from Wageningen University. He previously led the Farm Technology Group until 2021 and currently leads projects like FlexCRAFT (Cognitive Robots for Flexible Agro Food Technology) and Trimbot2020, developing robotics and automation for agricultural applications. His work emphasizes translating technology to practical environments (TRL 5-6), with contributions to robotic harvesting, precision weed control, and greenhouse climate modeling. Education: MSc (cum laude, 1987), PhD (1994) in Agricultural Sciences at Wageningen University. Key Projects: FlexCRAFT: Generic robotics principles for food processing, packing, and greenhouse operations. Trimbot2020: Outdoor garden trimming robot with 3D path planning. PoultryBot: Autonomous egg collection in poultry farming. Editorial Roles: Member of editorial boards for Biosystems Engineering, Computers and Electronics in Agriculture, and others. Research Themes: Agro food robotics (harvesting, vision systems, machine learning). Optimal control of agricultural processes (greenhouse climate, irrigation). Generic robotics frameworks for agri-food applications. Awards: Not explicitly listed, but recognized via research impact (h-index 31–47 across platforms). His teaching includes courses on greenhouse technology, field robotics, and thesis supervision. He collaborates with industry through user groups and consortia to ensure practical technology transfer.