Find Academic Supervisors

• Embedded Machine Learning
• High-Performance Computing
• Computer Architecture
• +6 more

Holger Fröning is a full professor at Heidelberg University’s Institute of Computer Engineering (ZITI), where he leads the Hardware and Artificial Intelligence (HAWAII) Lab. His research focuses on embedded machine learning , high-performance computing , and hardware-software co-design , with emphasis on resource efficiency, power optimization, and emerging architectures like analog , photonic , and resistive memory systems. He has held leadership roles including Managing Director of ZITI (2023–present) and Dean of Studies for Computer Science (2019–2022) , and has collaborated with institutions such as NVIDIA Research, Chinese Academy of Sciences, and Graz University of Technology. Research Trends : His recent publications explore Bayesian neural networks , green machine learning , analog computing noise mitigation , and GPU/FPGA optimization . Articles highlight photonic computing for AI , memory-efficient training , and hardware-aware DNN compression . Scientific Awards : 2025 HiPEAC Paper Award (Nature Computational Science) 2014 Google Faculty Research Award Multiple Best Paper Awards (IPDPS, ICPP, ECML-PKDD workshops) Leadership & Service : Organized workshops (WEML, ITEM, F4HD), chaired tracks at EuroPar and ISC, and served on program committees for ICPR, ECAI, and FPL. Education & Affiliations : PhD and MSc from University of Mannheim (2007/2001). Sponsors include DFG, FWF, FFG, NVIDIA, SAP, and XILINX.

Matthias FeurerView profile

Professor

• Automated Machine Learning
• Hyperparameter Optimization
• Bayesian Optimization
• +5 more

Matthias Feurer is a Thomas Bayes Fellow and interim professor at the Chair of Statistical Learning and Data Science, funded by the Munich Center for Machine Learning (MCML) at Ludwig Maximilian University of Munich. He is a member of the Department of Statistics at LMU Munich, working under Prof. Dr. Bernd Bischl. His academic background includes: PhD in Computer Science from Albert-Ludwigs-Universität Freiburg, supervised by Prof. Dr. Frank Hutter M.Sc. in Computer Science from the University of Freiburg B.Sc. in Computer Science and Media from the Media University Stuttgart Feurer's research focuses on simplifying machine learning usage through Automated Machine Learning (AutoML). His work encompasses hyperparameter optimization, meta-learning, and model selection, with increasing emphasis on multi-objective AutoML that considers factors beyond predictive performance such as interpretability, deployability, and fairness. He actively develops open-source tools to advance the field. His recent publications demonstrate a strong trajectory in practical AutoML systems, with growing attention to tabular machine learning, foundation models integration, and addressing real-world constraints in optimization. His work consistently bridges theoretical advances with practical implementations through several widely-used open-source projects. Notable achievements include: 1st place in the warmstarting-friendly leaderboard of the BBO NeurIPS challenge Winner of the 2nd AutoML challenge Winner of the kdnuggets blog contest on AutoML Feurer is actively mentoring and teaching, having advertised PhD positions focused on AutoML, optimization, and benchmarking. He co-founded the Open Machine Learning Foundation supporting OpenML.org. His upcoming move to TU Dortmund as an assistant professor in AutoML and Optimization signals continued growth in his academic career while maintaining his research focus on making machine learning more accessible and rigorous.

• Self-organized criticality
• Neuronal network dynamics
• Excitation/Inhibition balance
• +5 more

Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .

Stefan LutherView profile

Professor

• Biomedical Engineering
• Cardiac Physiology
• Nonlinear Dynamics
• +3 more

Prof. Dr. Stefan Luther is a Max Planck Research Group leader (W2, tenured since 2013) at the Max Planck Institute for Dynamics and Self-Organization, Göttingen, and an Honorarprofessor at the Faculty of Physics, University of Göttingen. He holds adjunct roles as Adjunct Associate Professor at Cornell University (2009–2012) and Northeastern University (2016–2018), and serves as DZHK-Professor at the Institute of Pharmacology and Toxicology, University Medical Center Göttingen. His research focuses on nonlinear spatiotemporal dynamics in excitable biological media, particularly cardiac arrhythmias. He pioneered 4D imaging of heart function and developed algorithms for optogenetic and electrical control of arrhythmias. Translational efforts span basic research to preclinical and clinical studies. Education includes a Diplom in Physics (1997) and PhD (2000) from Georg-August-University, Göttingen. Postdoctoral training followed at the University of Twente (2001–2004) and Cornell University’s LASSP (2004–2006). His lab, the Biomedical Physics group, explores electromechanical coupling in cardiac systems and develops novel therapeutic approaches. Collaborations include work on computational modeling, uncertainty quantification in dynamical systems, and fluid dynamics of multiphase flows.

Agnes DesolneuxView profile

Professor

• Image Analysis
• Statistical Methods
• Computer Vision
• +7 more

Agnes Desolneux is a CNRS Research Director at the Borelli Centre (formerly CMLA) and a Professor attached to the Mathematics Department at ENS Paris-Saclay. Education: PhD in Applied Mathematics (2000) from ENS Cachan Habilitation in Applied Mathematics (2010) from Université Paris Descartes Her research focuses on image analysis via statistical methods , particularly a contrario approaches, image restoration, texture synthesis, Determinantal Point Processes (DPP), optimal transport, Gaussian mixtures, geometry of random field excursions, shot-noise models, and mathematical modeling of visual perception through Gestalt theory. The articles extracted reflect her expertise in applied mathematics and computer vision , with recent works (2025-2020) on optimal transport algorithms, DPP applications, multiscale texture analysis, and stochastic modeling in medical imaging. Keywords span machine learning, probability theory, medical imaging, and computer vision . She has no listed scientific awards but has authored influential works including the book From Gestalt Theory to Image Analysis: A Probabilistic Approach (Springer, 2008) and Pattern Theory: the stochastic analysis of real-world signals (AK Peters, 2010).

• Machine Learning
• Deep Learning
• Signal Processing
• +9 more

Prof. Dr.-Ing. Ahmad Osman is a Professor at the Saarland University of Applied Sciences (htw saar), specializing in Test Technologies and Test Methods within the Faculty of Engineering. He also holds an Adjunct Professor position at Laval University in Quebec, Canada, in the Department of Electrical Engineering and Computer Science. His research focuses on Artificial Intelligence applications in Signal and Image Processing for Non-destructive Testing (NDT) , with extensive work on Deep Learning , 3D Ultrasound Tomography , and Sensor Data Fusion in industrial contexts. Engineering Artificial Intelligence Signal Processing Image Processing Non-destructive Testing Quality Control Augmented Reality Osman leads the AutomaTiQ research group and serves as Head of the Algorithms/Signal and Data Processing Department at Fraunhofer IZFP . His recent publications (2017–2022) emphasize Deep Learning for defect detection in CFRP , Terahertz Imaging for artwork diagnostics, and Acoustic Sensors for agricultural quality control. He has organized international conferences on Structural Health Monitoring and contributed to Springer books on NDT technologies. His projects include ComforTex-AI (2024) and development of 3D positioners for ultrasound measurements. Collaborations span institutions in Germany, Canada, Italy, and Brazil, with advisory roles in the German Society for NDT and technical committees for conferences in Montreal and Egypt.

Jay PujaraView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Knowledge Graph Construction
• +5 more

Dr. Jay Pujara is a Research Associate Professor of Computer Science at the University of Southern California (USC) and Director of the Center on Knowledge Graphs. He is also a Principal Scientist at the Information Sciences Institute (ISI) and leads research teams in data science and AI. Ph.D., University of Maryland, College Park (2016) M.S. and B.S. in Computer Science, Carnegie Mellon University Research Interests include artificial intelligence, probabilistic models, knowledge graph construction, statistical relational learning, NLP, and streaming inference. His work focuses on scalable algorithms for big data and uncertainty modeling in dynamic environments. Recent Publications highlight advancements in knowledge graphs, LLM reasoning, and table understanding. Notable topics include non-verbal abstract reasoning , faithful conversational datasets , and KGQA re-ranking . Scientific Awards : SWSA Ten-Year Award (2023), Outstanding Paper (IUI 2019), Top Reviewer (NeurIPS 2018), Best Paper (SRL Workshop 2016) Advising & Grants : Mentored 12+ graduate students, including Ph.D. advisees on topics like causal modeling and neuro-symbolic tasks. Secured NSF funding for table understanding in paleoclimate studies.

• Gravitational wave detection
• Gravitational wave data analysis
• Early universe cosmology
• +6 more

Bruce Allen is the Director of the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) in Hannover, Germany, where he also heads the Observational Relativity and Cosmology department. He holds dual academic appointments as Honorary Professor of Physics at Leibniz Universität Hannover and Adjunct Professor of Physics at the University of Wisconsin-Milwaukee, USA. His career spans over three decades in gravitational physics research, with a leadership role in the LIGO Scientific Collaboration from 1997 to 2018. Dr. Allen's research focuses on gravitational wave detection and data analysis, early universe cosmology, de Sitter space, curved-space quantum field theory, cosmic strings, inflationary models of the early universe, and gravitational radiation emission by cosmic strings. His work extends to large-scale cluster computing and public distributed computing projects like Einstein@Home, which has led to significant discoveries in gravitational wave astronomy. His recent publications demonstrate expertise in pulsar timing arrays, Hellings-Downs correlation analysis, and optimization of computational methods for gravitational wave detection. Allen's scientific contributions have been recognized with numerous prestigious awards including the Richard A. Isaacson Award (2020), the Bruno Rossi Prize (2017), the Princess of Asturias Award (2017), and the Special Breakthrough Prize (2016), all shared with the LIGO team for groundbreaking gravitational wave discoveries. He is also an Elected Fellow of both the American Physical Society and the Institute of Physics, UK. As a research leader, Allen has secured approximately $10 million in research funding from the National Science Foundation (1987-2018) and has mentored numerous students and researchers in gravitational physics. His work on Einstein@Home has engaged the public in scientific discovery through distributed computing, leading to several important astrophysical findings including gamma-ray pulsar discoveries.

• Wireless Communications
• Optical Communications
• Radar Signal Processing
• +6 more

Saleh A. Alshebeili is a Professor in the Department of Electrical Engineering at King Saud University's College of Engineering, Riyadh, Saudi Arabia. With over 139 publications spanning from 1991 to 2024, his research demonstrates significant contributions across multiple engineering disciplines. His academic profile shows consistent collaboration with Saudi research institutions and international partners, particularly in communications and signal processing fields. Dr. Alshebeili's research interests span wireless communications, optical networks, radar systems, and biomedical signal processing. His work bridges theoretical signal processing with practical applications in 5G/6G communications, IoT security systems, and healthcare monitoring. The interdisciplinary nature of his research connects electrical engineering with computer science, particularly through machine learning applications for signal analysis and system optimization. His publications demonstrate expertise in both traditional signal processing techniques and emerging AI-driven approaches to engineering problems. Analysis of his recent publications (2021-2024) reveals a strong focus on next-generation communication technologies including 6G systems, optical wavelength conversion, and OAM-SDM communication. Simultaneously, he maintains active research in biomedical applications, particularly EEG signal processing for seizure detection and biometric authentication using physiological signals. His work consistently appears in top IEEE journals including IEEE Access, IEEE Transactions on Wireless Communications, and IEEE Journal of Biomedical and Health Informatics, reflecting the high quality and relevance of his research. Dr. Alshebeili has established extensive collaborations with researchers across King Saud University, particularly with Fathi E. Abd El-Samie (29 co-authored papers), Turky N. Alotaiby (22 papers), and Amr Ragheb (21 papers). These long-term collaborations suggest leadership in research groups focusing on communications systems and biomedical signal processing. His work spans theoretical development, simulation, and experimental validation, as evidenced by publications with 'Experimental Investigation' and 'Experimental Demonstration' in their titles.

Heinz KoepplView profile

Professor

• Self-Organizing Systems
• Systems Biology
• Control Theory
• +9 more

Prof. Heinz Koeppl is a Professor in the Department of Electrical Engineering and Information Technology at TU Darmstadt. His research focuses on self-organizing systems, systems biology, and control theory, with applications in synthetic biology, robotics, and stochastic processes. He explores interdisciplinary topics such as genetic circuit design, UAV swarm dynamics, and machine learning-driven modeling of biochemical systems. Key research areas include the development of deep learning frameworks for kinetic modeling, Bayesian optimization for riboswitch design, and mean field control theory for sparse networks. His work bridges theoretical foundations with practical engineering solutions, addressing challenges in molecular communication, gene regulation, and robotic swarm coordination. Publications from 2023–2025 highlight advancements in bio-inspired algorithms, swarm intelligence, and computational biology. Notable contributions include studies on RNA-based circuits, active matter dynamics, and optimization strategies for large-scale systems. His research emphasizes interdisciplinary collaboration, leveraging tools from electrical engineering, mathematics, and life sciences. No scientific awards are explicitly listed in the provided text. Advising and grants details are not available. Prof. Koeppl’s lab focuses on integrating systems biology approaches with engineering principles to solve complex problems in healthcare, environmental sustainability, and technological innovation.

Hans-Georg HerzogView profile

Professor

• Electrical Machines
• Electromechanical Drives
• Energy Conversion Technology
• +7 more

Prof. Dr.-Ing. Hans-Georg Herzog is a Professor of Energy Conversion Technology at the Technical University of Munich (TUM), School of Engineering and Design. He has headed the Energy Conversion Technology group at TUM since 2002 and is a Senior Member of IEEE and member of VDE and VDI professional organizations. His research focuses on energy-efficient electromechanical drives and related technologies critical for modern electric and hybrid vehicles. Prof. Herzog's research interests encompass energy-efficient electromechanical drives, with key expertise in design and optimization of hybrid-electric and battery-electric powertrains, automated design methods for electromechanical actuators, energy and power management systems, and analysis of loss mechanisms in soft magnetic materials. His work bridges fundamental electromagnetic theory with practical automotive applications, particularly in fault-tolerant systems and reliability engineering for electric propulsion. His recent publication trends show a strong focus on vehicular power systems, with particular emphasis on electronic fuses, fault diagnosis in multiphase machines, wireless power transfer, and reliability analysis of electric aircraft propulsion systems. The research spans from fundamental electromagnetic modeling to practical automotive applications, with increasing attention to autonomous driving power requirements and next-generation vehicle electrical architectures. Prize for Good Teaching of the Free State of Bavaria (2010) Prof. Herzog leads a substantial research team including doctoral candidates and postdoctoral researchers who contribute to his extensive publication record. His research group collaborates with automotive industry partners on various grants focused on electric vehicle technology, power system reliability, and advanced electromagnetic systems. The team regularly develops novel methodologies for machine design, fault tolerance analysis, and power system optimization. The research is conducted within TUM's Energy Technology Workshop with specialized facilities for electrical machine testing, power electronics development, and automotive power system simulation. The team maintains strong connections with industry partners in the automotive and aerospace sectors, facilitating technology transfer from academic research to practical applications.

Martin BurgerView profile

Professor

• Inverse Problems
• Applied Mathematics
• Mathematical Modelling
• +15 more

Prof. Dr. Martin Burger is a leading scientist at DESY and a Full Professor in the Department of Mathematics at Universität Hamburg, where he leads the Computational Imaging Group. His research bridges applied mathematics, imaging sciences, and machine learning, with a focus on inverse problems, mathematical modeling, and partial differential equations. He has held professorial positions at Universität Münster and FAU Erlangen-Nürnberg prior to his current dual appointment. Full Professor, Universität Hamburg (2023–present) Leading Scientist, DESY, Hamburg (2023–present) Full Professor, FAU Erlangen-Nürnberg (2018–2023) Full Professor, Universität Münster (2006–2018) His research interests include inverse problems, variational regularization, optimal transport, kinetic models, and mathematical modeling in biology and social sciences. He has made significant contributions to imaging reconstruction, sparse neural networks, and the analysis of transformer architectures. His work often integrates theoretical analysis with computational methods, influencing both pure and applied mathematics. The most recent articles reflect a strong trend toward interdisciplinary applications, combining deep learning with PDE-based modeling, analyzing social and biological systems via kinetic and mean-field models, and advancing mathematical imaging through graph-based and optimal transport methods. His publications span high-impact venues in applied mathematics and computational science. Calderon Prize, Inverse Problems International Association (IPIA) ERC Consolidator Grant (2014) Invited speaker at ECM (2021), ICM (2022), and ICIAM (2023) Editor-in-Chief, European Journal of Applied Mathematics (since 2017) Prof. Burger has supervised numerous PhD students and postdoctoral researchers, many of whom appear as co-authors in his publications. His research is supported by major grants, including funding from the German Federal Ministry of Education and Research (BMBF). He is actively involved in collaborative projects across mathematics, physics, and engineering disciplines. He leads the Computational Imaging Group at DESY, fostering a collaborative environment for developing novel mathematical tools in imaging science. The group works on both theoretical foundations and practical implementations, contributing to advancements in tomography, machine learning, and data analysis.

Felix KrahmerView profile

Assistant Professor

• Compressed Sensing
• Signal Processing
• Optimization
• +4 more

Prof. Felix Krahmer is a TUM Tenure Track Assistant Professor of Optimization and Data Analysis at the Department of Mathematics, Technische Universität München (TUM), part of the School of Computation, Information and Technology. Previously, he held a Junior Professorship (W1) for Mathematical Data Analysis at the University of Göttingen (2012–2015). His research focuses on mathematical foundations of data science, including compressed sensing, signal quantization, uncertainty quantification, and optimization algorithms. He has led an Emmy Noether research group and contributed to the ZeMat interdisciplinary project. Education : PhD in Mathematics (2009), New York University, advisors: Percy Deift & Sinan Güntürk MSc in Mathematics (2007), New York University BSc in Mathematics (2004), Jacobs University Bremen Research Interests : Krahmer’s work bridges theoretical mathematics and applied data science. Key areas include compressed sensing algorithms, high-dimensional signal reconstruction, quantization theory for analog-to-digital conversion, and optimization methods for inverse problems. He also explores applications in imaging (e.g., MRI reconstruction) and stochastic processes. Key Contributions : His research on phase retrieval, sigma-delta quantization, and compressed sensing recovery has advanced signal processing techniques. Recent efforts focus on uncertainty quantification for high-dimensional inverse problems and the mathematical analysis of consensus-based optimization. Grants & Awards : Emmy Noether Research Group Grant (DFG) Funding from BMBF (ZeMat project) Teaching & Outreach : Krahmer has taught advanced courses on compressed sensing, functional analysis, and random matrix theory. He co-organized workshops on probabilistic techniques and clinical risk prediction, emphasizing interdisciplinary collaboration. Labs/Teams : Member of the TUM Data Science Research Group, focusing on optimization, imaging, and uncertainty quantification. Active in the Munich Center for Quantum Science and Technology (MCQST).

Thomas HermannView profile

Researcher

• Auditory Data Science
• Interactive Sonification
• Smart Environments
• +5 more

Dr. rer. nat. Thomas Hermann is a faculty member at Bielefeld University's Faculty of Engineering, leading the Ambient Intelligence Group and coordinating the Computer Science program. He specializes in sonification, auditory data science, and smart environments. Head of Ambient Intelligence Working Group Computer Science Program Coordinator Member of multiple academic advisory boards His research focuses on interactive sonification for biomedical applications, quantum systems, and smart environments. Key projects include ECG sonification for cardiac diagnosis, real-time auditory feedback in swimming, and sonic interfaces for AR cooperation. Recent publications span 2025 with Python-based sonification tools ( pya AGen ), quantum system sonification, and ST-elevation myocardial infarction monitoring. He contributes to open-access supplementary materials and interdisciplinary workshops. As a researcher , Hermann develops practical sonification frameworks like Panson for facial behavior analysis, CardioScope for portable ECG monitoring, and Base Cube One for smart environments. His work bridges academic research with industry applications.

• Multisensory Perception
• Predictive Processing
• Speech Perception
• +3 more

Prof. Helen Blank is a Professor leading the Multisensory Perception Group and the Prediction in Communication Lab at the Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf. Her work focuses on understanding how sensory information is integrated and predicted in contexts like speech perception and face recognition. She holds a Marie Curie Fellowship for her research on prior information's role in human communication. Fluent in German, English, and French, she contributes to experimental medicine and systems neuroscience. Her research spans predictive coding, neuroimaging, and clinical applications in Parkinson’s and developmental disorders. Education: Not explicitly stated in text, inferred as advanced degrees in neuroscience or related fields. Her research interests emphasize multisensory integration, predictive processing in speech and vision, and the neural bases of perception. Recent articles explore topics such as pupil responses to auditory surprise, face expectation hierarchies, and audio-visual speech processing. Awards include the Marie Curie Fellowship supporting her predictive communication work. She leads interdisciplinary teams within the Center for Experimental Medicine, advancing knowledge on perceptual mechanisms and their clinical implications.