Find Academic Supervisors

• Quantum Computing
• Quantum Foundations
• Diagrammatic Reasoning
• +3 more

John van de Wetering is an Assistant Professor at the Theoretical Computer Science group of the Informatics Institute at the University of Amsterdam . He works with the QuSoft research center and co-develops the PyZX open-source quantum compiler. His research spans quantum computation, diagrammatic reasoning, and quantum foundations. Education: PhD in Computer Science (2022, Radboud University) Research: Focuses on ZX-calculus for quantum circuit optimization, verification, and simulation. Explores quantum foundations through algebraic and compositional methods. Recent work includes quantum circuit optimization with AlphaTensor, completeness proofs for ZH-calculus, and scalable spider nets for transversal non-Clifford gates. He directs the new Master's program in Quantum Computer Science at UvA and co-authored the book Picturing Quantum Software . Notable collaborations include PyZX development and EU Gender Equality Working Group participation. Supervision includes current PhD students Lia Yeh (Oxford), Sarah Li (UvA), and Marc Farreras (Leiden). Former students include Boldizsár Poór (Quantinuum), Julien Codsi (Princeton), and Yanbin Chen (TUM). Tools & Outreach: Maintains the ZX-calculus Wikipedia page, co-lectured courses like Quantum Processes and Computation , and develops the ZX-calculus educational website. Organized QPL2026 conference and contributed to open-access journal Quantum .

Artur WideraView profile

Professor

• Quantum Physics
• Ultracold Atoms
• Quantum Gases
• +5 more

Prof. Dr. Artur Widera is a Professor at RPTU Kaiserslautern in the Department of Physics, where he leads a research group focused on quantum physics and ultracold atoms. He has held this position since 2010 and previously served as a scientific assistant in the group of D. Meschede in Bonn (2007-2010). His academic background includes a PhD from University Mainz (2003-2007) under I. Bloch, a diploma thesis with T.W. Hänsch in München (2002-2003), and studies in physics at University Würzburg (1997-2002). Prof. Widera's research spans quantum physics, ultracold atoms, quantum thermodynamics, quantum information, and atomic physics. His work explores quantum gases in optical lattices, quantum simulation, quantum sensing with nitrogen-vacancy centers, and quantum thermodynamics. He has made significant contributions to understanding quantum phase transitions, quantum transport phenomena, and quantum metrology. His publication record shows a consistent output of high-impact research, with recent work focusing on quantum sensing applications, quantum thermodynamics, quantum education, and multiqubit quantum systems. The publications demonstrate a trend toward increasingly interdisciplinary research, connecting fundamental quantum phenomena with practical applications in sensing and quantum information processing. Teaching award of the state Rhineland-Palatinate (2020) APS Outstanding Referee (2020) Teaching award of the state Rhineland-Palatinate (2016) Teaching award of TU Kaiserslautern (2013) ERC Independent Starting Researcher Grant (2011) Prof. Widera has supervised numerous PhD students including Daniel Adam, Sian Barbosa, and Jennifer Koch. His research has been supported by significant grants including an ERC Starting Grant in 2011 and leadership of an independent research group funded by the state of Northrhine-Westphalia (2009-2010). He serves as Dean of Studies for the physics department and chairs the committee for the international Master's program 'Advanced Quantum Physics' at RPTU Kaiserslautern. His workgroup at RPTU Kaiserslautern includes researchers focusing on quantum gases, quantum sensing with NV centers, quantum thermodynamics, and quantum information processing. The team maintains strong collaborations with other institutions in Germany and internationally, particularly in the areas of quantum simulation and quantum metrology.

Andreas BothView profile

Professor

• Software Engineering
• Web Engineering
• Artificial Intelligence
• +7 more

Andreas Both is a Professor at the Faculty of Computer Science and Media at Leipzig University of Applied Sciences (HTWK Leipzig), where he leads the Web & Software Engineering (WSE) research group. His work spans multiple domains of computer science with a strong focus on bridging theoretical foundations with practical applications in software engineering, web technologies, and artificial intelligence. His research interests primarily revolve around Software Engineering (particularly test automation with AI and source code analysis), Web Engineering , Applied Artificial Intelligence (including Machine Learning, Deep Learning, and Large Language Models), Question Answering & Chatbots , and Data-driven Applications . He has developed innovative approaches in knowledge graph question answering systems, multilingual NLP applications, and privacy-preserving data sharing technologies using the Solid protocol framework. The analysis of his recent publications reveals a strong trajectory toward leveraging Large Language Models for knowledge graph applications, with particular emphasis on multilingual capabilities, explainability, and quality improvement in question answering systems. His work increasingly integrates privacy considerations with advanced AI techniques, particularly through Solid protocol implementations for data sovereignty. Best Paper Award at ICWE 2024 for AuthApp - a GDPR-compliant access granting system Outstanding Paper Award at ICWI 2024 for LLM-generated explanations in question answering systems Multiple first-place awards at the TEXT2SPARQL Challenge 2025 Best Paper Awards at ICWE 2025 and IEEE ISI 2025 CHI 2015 Honorable Mentions for search interface research Professor Both actively mentors students through the Google Summer of Code program and serves on the leadership board of the Architecture (ARC) working group of the German Computer Science Society (Gesellschaft für Informatik). His teaching portfolio includes Software Engineering, Question Answering & Chatbots, Software Projects, Project Management Practicum, Web Engineering, and Software Engineering & AI courses. His office hours are Thursdays from 11:15-12:15, requiring advance email appointment with topic specification.

Giovanni FantuzziView profile

Assistant Professor

• Optimization
• Dynamical Systems
• Fluid Mechanics
• +4 more

Giovanni Fantuzzi serves as a W1 Professor (equivalent to Assistant Professor) in the Department of Mathematics at Friedrich-Alexander University Erlangen-Nuremberg. He leads research within the FAU DCN-AvH Chair for Dynamics, Control, Machine Learning and Numerics under the Alexander von Humboldt Professorship framework, holding office in Room 03.318 with contact details including giovanni.fantuzzi@fau.de and +49 9131 85-67134. His educational background includes a PhD and Master of Engineering in Aeronautics from Imperial College London, supplemented by a research position in Engineering Science at the University of Oxford during his doctoral studies. Key academic milestones are documented through his ORCID, Google Scholar, and LinkedIn profiles. Fantuzzi's research program integrates mathematical analysis with computational optimization to solve nonlinear differential equations, focusing on deriving a priori scaling laws for heat transport and developing provable numerical schemes for PDE-constrained optimization. His methodology bridges convex optimization, polynomial optimization, and dynamical systems theory, with recent applications extending to transformer neural networks and sentiment analysis through hardmax mechanisms. Current teaching includes Data-driven methods for dynamical systems and Polynomial optimization and applications for WS 24/25. Analysis of his 15 most recent publications reveals dominant trends in fluid mechanics (particularly convection and heat transfer), polynomial optimization techniques, and data-driven dynamical systems analysis. His work consistently applies convex optimization frameworks to derive rigorous bounds in physical systems while expanding into machine learning applications like transformer model analysis. Geophysical Fluid Dynamics Fellowship at WHOI (2015) EPSRC Doctoral Prize Fellowship (2018) Imperial College Research Fellowship Fantuzzi's research program is supported by prestigious fellowships including the Imperial College Research Fellowship and EPSRC Doctoral Prize. His academic service includes organizing the FAU MoD Lecture & Workshop on AI for maths and maths for AI (June 2025) and co-hosting the #MLPDES25 Machine Learning and PDEs Workshop. He actively supervises research within the FAU DCN-AvH group, focusing on polynomial optimization applications in dynamical systems and PDEs. As core faculty in the FAU DCN-AvH Chair, Fantuzzi collaborates within a multidisciplinary team specializing in dynamics, control, machine learning, and numerical methods. The group maintains strong international connections through workshops like the Oberwolfach Seminar on Polynomial Optimization for Nonlinear Dynamics and participates in conferences including CIN-PDE and Nečas Seminar on Continuum Mechanics, driving innovation at the intersection of mathematics and computational physics.

• Machine Learning
• Collaborative Robotics
• Software-driven Robotics
• +5 more

Daniel Bermuth is a Researcher at the Chair of Software Engineering within the Institute for Software & Systems Engineering at the University of Augsburg . His work focuses on the intersection of Machine Learning and Collaborative Robotics , with a particular emphasis on Software-driven Robotics and Automation . Education Master in Computer Science (2017-2020), University of Augsburg Bachelor in Computer Science (2014-2017), University of Augsburg Research Interests Development of AI-based safety mechanisms for human-robot collaboration Real-time human pose estimation using RGBD imaging and voxel fusion techniques Efficient spoken language understanding architectures via n-grams, tries, and finite state transducers Privacy-aware voice assistants operating in offline environments Fast speech-to-text models for multilingual applications Publications demonstrate expertise in Robotics , Artificial Intelligence , Computer Vision , and Natural Language Processing . Key subfields include Human-Robot Interaction , 3D Pose Estimation , Speech Recognition , Privacy-preserving AI , Efficient Parsing Algorithms , and Multilingual Language Models . Laboratory Works within the Institute for Software & Systems Engineering under Prof. Dr. Wolfgang Reif Active in developing software solutions for robotics and speech processing

Johanna SchwabView profile

Researcher

• Special Education
• Inclusive Education
• Autism Spectrum Disorders
• +3 more

Johanna Schwab is a researcher at the Chair of Special Education IV - Pedagogy for People with Intellectual Disabilities within the Faculty of Human Sciences at the University of Würzburg. Her work focuses on inclusive education, autism spectrum disorders, and media literacy in special education contexts. Current affiliation: University of Würzburg (since 2019) Prior: Ludwig-Maximilians-Universität Munich (2017-2019) Research Interests: Dr. Schwab specializes in pedagogical approaches for students with intellectual disabilities, particularly examining teacher attitudes toward autism, inclusive schooling during pandemics, and digital storytelling in special education. Her projects include Bewegte inklusive Schule (movement in inclusive education) and CoTeach: Digital Storytelling in inclusive classrooms, with a focus on the Würzburg-Bielefeld Study on Teacher Professionalization for science instruction. She has investigated effects of school closures on special education during the Corona pandemic through multiple studies. Publications: Her recent work spans media psychology, suicide prevention in media, and social behavior analysis. Key topics include evolutionary media theory, facial emotion recognition algorithms, and digital communication dynamics.

• Machine Learning
• Computer Vision
• Autonomous Driving
• +4 more

Ross Greer is an Assistant Professor in the Department of Computer Science & Engineering at the University of California Merced. He leads the Mi³ Lab, focusing on machine intelligence, human-agent interaction, and safe autonomous systems. Education : B.S. and B.A. in EECS, Engineering Physics, and Music from UC Berkeley (2015), M.S. in Electrical & Computer Engineering from UC San Diego (2018), Ph.D. in Electrical & Computer Engineering at UC San Diego (2021) under Mohan Trivedi and Shlomo Dubnov. His research explores computational intelligence for open-world adaptability, robustness to rare events, and safety in chaotic environments. Key applications include autonomous driving, driver state analysis, trajectory prediction, and AI-assisted musical creativity. Recent publications emphasize vision-language models, active learning for 3D object detection, and safety metrics. Awards include the 2024 Interdisciplinary Research Award, Henry Booker Award for Ethical Engineering, and multiple best poster/grand prizes. Scientific Awards : 2024 Interdisciplinary Research Award 2024 Henry Booker Award for Exemplary Ethical Engineering Postdoctoral Networking Fellowship (Germany's Academic Exchange Service) Grand Prize (AWS Automotive Day competition at IEEE Intelligent Vehicles Symposium, 2023) Best Poster Awards (2021/2023 Jacobs Research Expo) He also co-authored the textbook Deep and Shallow: Machine Learning in Music and Audio (Chapman & Hall, 2023) and serves as a music director for UCSD's Symphonic Student Association and UC Merced's marching band.

• Artificial Intelligence
• Natural Language Processing
• Speech Technology
• +4 more

Sven Schmeier is a researcher at the German Research Center for Artificial Intelligence (DFKI) in Berlin, affiliated with the Speech and Language Technology department. His work focuses on applying artificial intelligence to healthcare and social assistance, particularly in emergency medicine and support for the elderly. His research interests span Artificial Intelligence , Natural Language Processing , and Speech Technology , with applications in Medical Informatics , Assistive Technology , and Emergency Care . He is particularly interested in the ethical implications of AI in clinical decision support and the development of systems to overcome language barriers for elderly populations. Recent work includes leading the KiBATIN project for AI-based triage in emergency rooms and the SmartSenior_HU project for multilingual assistance for seniors. His publications reflect a multidisciplinary approach, combining machine learning with clinical practice and ethical considerations. Schmeier actively collaborates on projects such as WIK (We in the neighborhood) which aims to support elderly individuals in daily tasks through community-based assistance systems.

Rajendra AkerkarView profile

Professor

• Artificial Intelligence
• Big Data Analytics
• Semantic Technologies
• +5 more

Rajendra Akerkar is a Professor and coordinator of the Big Data and Emerging Technologies subgroup at the Western Norway Research Institute. With over 30 years in academia across Asia, Europe, and North America, he specializes in combining theoretical AI research with practical applications for societal benefit. Research Focus: Artificial Intelligence for crisis management (resilient communities, situational awareness) Social cybersecurity (hate speech prevention, misinformation) Urban transport systems optimization Energy analytics through data-driven approaches Semantic technologies and intelligent information systems Scientific Awards: DAAD Exchange Fellowship BOYSCASTS Young Scientist Award Leadership & Publications: Associate Editor for International Journal of Metadata, Semantics and Ontologies and Editor for Web Intelligence . Published 16 monographs, 154 scientific articles, and coordinated international networks like ISO15926 Semantic Web Technologies Network.

Mengshan XuView profile

Assistant Professor

• Econometrics
• Applied Econometrics
• Semi-nonparametric Econometrics
• +1 more

Mengshan Xu is an Assistant Professor of Applied Econometrics at the University of Mannheim's Department of Economics since August 2021. His academic journey includes an M.Sc. from Humboldt University of Berlin (2015) and a Ph.D. from the London School of Economics and Political Science (2021). Education: M.Sc., Humboldt University of Berlin (2015) Ph.D., London School of Economics and Political Science (2021) His research focuses on Econometrics, Semi-nonparametric Econometrics, and Statistical Learning. Despite his primary affiliation with economics, his recent publications suggest interdisciplinary work spanning Artificial Intelligence, Robotics, and Computer Vision , including human-aware navigation frameworks, attention mechanisms for LLMs, and skeleton-based action recognition systems. Article trends reveal expertise in vision-and-language navigation , anomaly detection , and multi-modal learning , blending econometric theory with computational methods. Notable subfields include dynamic human interactions, deep invertible networks, and hypergraph transformers. His professional contact details include a direct email ( mengshan.xu@uni-mannheim.de ) and office location in Mannheim. No scientific awards or student advisement details are publicly listed in the provided materials.

• Deep Learning
• Internet of Things (IoT) Security
• Software Defined Networking (SDN)
• +4 more

Adnan Akhunzada is a prolific researcher with extensive contributions to computer science, particularly in artificial intelligence, cybersecurity, and internet of things. His work spans deep learning architectures, software defined networks, and security frameworks for emerging technologies. Research Interests include: Deep learning for micro-expression and image analysis Quantum control systems with reinforcement learning AI-based phishing and malware detection Federated learning for drone services Cryptographic protocols for UAV communications Publication Trends show expertise in: Hybrid neural network architectures Cybersecurity for industrial IoT Privacy-preserving crowdsourcing Sign language recognition datasets 5G-assisted cognitive communication

Lena Maier-HeinView profile

Professor

• Machine Learning
• Biomedical Image Analysis
• Surgical Data Science
• +4 more

Lena Maier-Hein serves as one of Helmholtz Imaging's center coordinators at the German Cancer Research Center (DKFZ), head of the Intelligent Medical Systems division, managing director of the Data Science and Digital Oncology cross-topic program, and is a full professor at Heidelberg University. She also holds the position of managing director at the National Center for Tumor Diseases (NCT) Heidelberg. Her research focuses on machine learning-based biomedical image analysis with specific emphasis on surgical data science, computational biophotonics, and validation of machine learning algorithms. As a fellow of both the Medical Image Computing and Computer Assisted Intervention (MICCAI) society and the European Laboratory for Learning and Intelligent Systems (ELLIS), she leads significant initiatives including presidency of the MICCAI special interest group on challenges and chairing the international surgical data science initiative. Her publication portfolio demonstrates strong focus on algorithm validation, surgical data science, and medical imaging AI, with recent work addressing critical issues in reproducibility, bias in medical AI, and practical clinical implementation. The articles reveal consistent themes around establishing rigorous validation frameworks, developing practical surgical AI tools, and addressing real-world deployment challenges in medical imaging. Her notable scientific recognition includes: 2013 Heinz Maier Leibnitz Award of the German Research Foundation (DFG) 2017/18 Berlin-Brandenburg Academy Prize European Research Council (ERC) starting grant (2015-2020) ERC consolidator grant (2021-2026) She serves on the editorial boards of prestigious journals including Nature Scientific Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, and Medical Image Analysis, demonstrating her leadership in establishing standards for medical imaging research. Her work bridges fundamental AI research with practical clinical applications, particularly in surgical settings where real-time decision support is critical.

Manolis ZampetakisView profile

Assistant Professor

• Theoretical Machine Learning
• Statistics
• Optimization
• +3 more

Manolis Zampetakis is an Assistant Professor of Computer Science at Yale University. Previously, he was a postdoc at UC Berkeley's EECS Department working with Michael Jordan, and earned his PhD from MIT's EECS Department under Constantinos Daskalakis. His research spans Theoretical Machine Learning, Statistics, Optimization, Computational Complexity, Game Theory, and Mechanism Design. He has received the ACM SIGEcom Doctoral Dissertation Award and a Google PhD Fellowship. Current affiliation: Yale University (Assistant Professor) Prior affiliations: UC Berkeley (Postdoc), MIT (PhD student), NTUA (Undergraduate) His research focuses on algorithmic game theory, robust statistics, and optimization challenges in machine learning. He explores computational complexity in multi-player games, truncated linear regression, and strategy-proof mechanisms. Recent work includes backdoor attacks in neural networks and jailbreaking black-box LLMs, with publications in top venues like NeurIPS, COLT, FOCS, and STOC. Notable scientific contributions have been recognized through awards and special issues. He co-organized workshops at FOCS 2018, WALE 2019, and WALE 2022. His students include Anay Mehrotra, Jane Lee, Katerina Mamali, Shuchen Li, and Nikolaos Koumpis, often co-advised with prominent researchers like Amin Karbasi and Tuomas Sandholm.

Kim VöllingerView profile

Researcher

• Formal Methods
• Trustworthy Machine Learning
• Distributed Systems
• +12 more

Dr. Kim Völlinger is a Researcher at the Technical University of Berlin in the Models and Theory of Distributed Systems group. Her academic career spans formal methods, trustworthy machine learning, and distributed systems, with a focus on integrating interactive proof assistants like Coq for neural network verification. Education: Computer Science with a minor in Cognitive Psychology at Humboldt University of Berlin and ENSEEIHT in Toulouse, France PhD Supervisors: Wolfgang Reisig (HU Berlin), Kurt Mehlhorn (MPI-INF Saarbrücken), Holger Schlingloff (Fraunhofer FOKUS) Her research bridges theoretical computer science and practical verification, exploring witness-based runtime verification for asynchronous systems, hybrid system formalization, and LLM-supported proof synthesis. She also contributes to interdisciplinary collaborations, particularly evident in her microbiology-related publications. Recent publications on Google Scholar highlight her work in environmental microbiology, including microbial community dynamics in petroleum reservoirs, DNA extraction from crude oil, and bacterial stress responses in extreme saline environments. These studies reflect cross-disciplinary applications of computational modeling to environmental systems. Teaching activities include formal languages, automata theory, and interactive theorem provers. She actively mentors doctoral students, leads research-oriented master's projects, and supervises student theses. The Models and Theory of Distributed Systems group at TU Berlin serves as her primary research environment, where she continues to develop tools for computational verification and machine-reviewed proofs.

• Database Systems
• In-Database Machine Learning
• GPU Acceleration for Databases
• +3 more

Maximilian Schüle serves as Assistant Professor in the Department of Data Engineering at the University of Bamberg's Faculty of Information Systems and Applied Computer Sciences since October 2022. Previously, he held research positions at Technical University of Munich (2017-2022). His research bridges database systems and machine learning through compiler-based approaches. His research focuses on in-database machine learning , GPU-accelerated query processing , and recursive SQL extensions . Key contributions include: Developing MLIR-based compilers for automatic differentiation in SQL (DuoLingo-AutoDiff) Creating GPU code generators for database kernels using NVRTC Designing higher-order lambda functions for expressive query languages Implementing end-to-end neural network training within database engines His recent publications (2023-2025) demonstrate consistent output in top venues including ICDE, VLDB workshops, and BTW conferences, with growing emphasis on hardware-aware optimization and compiler techniques for analytical workloads. He currently leads a DFG-funded project on elastic memory hierarchies for memory-intensive applications (2025-2028), supporting multiple PhD researchers. His supervision emphasizes open-source contributions to database systems like Umbra and practical implementation skills alongside theoretical foundations. As an active member of the database community, he serves as workshop chair for BTW 2025 and regularly reviews for ACM TODS, VLDB Journal, and Information Systems. His work on public transport analytics demonstrates real-world impact through collaborations with urban mobility initiatives in Bamberg.