Find Academic Supervisors

• Quantum Key Distribution
• Neuroscience
• Machine Learning
• +3 more

Dr. Yijun Wang is a researcher affiliated with the Chinese Academy of Sciences, State Key Laboratory on Integrated Optoelectronics, and Institute of Semiconductors in Beijing, China. His work spans multiple disciplines including quantum key distribution, brain-computer interfaces, and machine learning applications in cybersecurity and biomedical imaging. Key Research Areas: Quantum cryptography, Neural signal processing, Transformer-based models, Network security, and Digital health systems. Recent publications highlight advancements in EEG authentication systems 2026 , radar-based activity recognition 2025 , and quantum communication security 2025 . His work demonstrates interdisciplinary expertise bridging theoretical algorithms and practical engineering solutions. Scientific contributions include: Developing attention mechanisms for image restoration 2025 Creating reliability-enhanced BCIs via graph-driven fusion 2025 Designing semi-supervised solvers for CAPTCHA breaking 2021 Dr. Wang's collaborations appear across journals like Pattern Recognition , IEEE Transactions , and conferences including AAAI 2025 , ICLR 2025 , and EMBC 2023 . Current projects integrate LLMs with digital phenotyping for hypertension management 2025 and explore self-taught reasoning mechanisms 2025 .

• Computer Science
• Artificial Intelligence
• Machine Learning
• +4 more

Jiawei Zhang is an active researcher with primary affiliations at institutions like the University of California, Davis and other universities in China and the USA. His work spans Computer Science , Artificial Intelligence , and Biomedical Engineering , focusing on applications in image processing , autonomous vehicles , and remote sensing . Research interests include deep learning , graph neural networks , 3D reconstruction , and medical image analysis . Recent publications emphasize transformer models , lightweight AI frameworks , and multimodal data fusion . His work has been published in prestigious venues such as IEEE Transactions on Biomedical Engineering , CVPR , and IEEE Internet of Things Journal . Collaborations include researchers from institutions in China, the US, and Europe. Key trends in his 2024-2025 articles involve industrial anomaly detection , liver tumor segmentation , autonomous vehicle control , and AI-driven sensor networks . Methodologies often integrate attention mechanisms , generative models , and real-time optimization .

• Relational Machine Learning
• Graph Neural Networks
• Complex Networks
• +4 more

Dr. Rebekka Burkholz is a tenured faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. Her work bridges relational machine learning with biological applications , focusing on theoretical improvements to deep learning through complex network analysis. Education: Studied Mathematics and Physics at TU Darmstadt, followed by a PhD at ETH Risk Center under Frank Schweitzer and Hans J. Herrmann. Postdoctoral Experience: Worked at Harvard's Biostatistics Department (2019-2021) and ETH Zurich's Machine Learning Institute (2017-2019). Her research explores graph neural networks , sparse training , and biologically informed models , aiming to enhance algorithm robustness and interpretability. Recent publications analyze: 2025 : Implicit sparsification, hyperbolic regularization, and network balance techniques. 2024 : Spectral pruning, gene regulatory modeling, and dynamic rescaling for GNNs. Scientific recognition includes: Zurich Dissertation Prize for systemic risk research (2016). CSF Best Contribution Award for maize trade analysis (2016). She collaborates with institutions like Harvard T.H. Chan School of Public Health and ETH Zurich, focusing on trustworthy information processing and empirical security at CISPA.

• Stochastic Optimization
• Machine Learning
• Distributed Optimization
• +7 more

Eduard Gorbunov is a Tenure-Track Assistant Professor at the Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) , where he focuses on Stochastic Optimization, Distributed Optimization, Derivative-Free Optimization, and Variational Inequalities. His work bridges theoretical advancements with practical applications in Machine Learning and Federated Learning. Education: PhD in Computer Science (2021), Moscow Institute of Physics and Technology (MIPT), advised by Alexander Gasnikov and Peter Richtárik MSc in Applied Mathematics (2020), MIPT BSc in Applied Mathematics (2018), MIPT Research Trends: Key contributions to Heavy-Tailed Noise Analysis in Stochastic Optimization Communication-Efficient Methods for Distributed/Federated Learning Byzantine-Robust Algorithms with Partial Participation Gradient Compression and Adaptive Optimization Techniques Awards & Grants: Ilya Segalovich Award (2019) A. M. Raigorodskii Scholarship (2020, 2021) Outstanding Reviewer at NeurIPS 2022 and ICML 2022 Huawei Scholarship for Academic Achievements (2019) Leadership: Action Editor at Transactions on Machine Learning Research (TMLR) (2024) Area Chair at NeurIPS (2025)

Jongse ParkView profile

Associate Professor

• AI Serving Systems
• On-Device AI
• Processing-in-Memory (PIM)
• +4 more

Jongse Park is currently an Associate Professor at the School of Computing (SoC), KAIST , and a core member of the Computer Architecture and Systems Laboratory (CASYS) . He holds co-affiliations with the School of Electrical Engineering , Graduate School of AI Semiconductor , Graduate School of System Architect , and Department of Semiconductor System Engineering at KAIST. Since 2025, he has been serving as a Visiting Associate Professor at Stanford University's Pervasive Parallelism Lab within the EECS department. PhD in Computer Science, Georgia Institute of Technology (2018), advised by Prof. Hadi Esmaeilzadeh MS in Computer Science, KAIST (2012), advised by Prof. Seungryoul Maeng BS in Computer Science and Engineering, Sogang University (2010) His research focuses on accelerating AI serving systems , enabling on-device AI , processing-in-memory (PIM) architectures , and flexible AI compiler frameworks . Recent work explores transformer optimization, heterogeneous AI semiconductors, and efficient video-language processing. Recent publications include MICRO 2025 work on PIM for LLMs, VLDB 2025 research on video-language engines, and ISCA 2025 contributions to LLM quantization. His team's projects have received funding from the K-Cloud Project , NRF Young Researcher Program , and IITP Core Technology Development grants . Teaching Innovation Award Excellence Prize, KAIST (2025) Samsung Humantech Paper Award Gold Prize (2025) IEEE Senior Member (2024) Best Paper & Distinguished Artifact Awards at IISWC (2024) and ISCA (2024) He actively supervises PhD and MS students in AI systems research and serves on program committees for top conferences like ASPLOS , ISCA , and MICRO , including organizing roles as Sponsorship Chair for MICRO 2025.

• Program Specification and Verification
• Concurrent Separation Logics
• Symbolic Analysis Tools
• +3 more

Philippa Gardner is a Professor in the Department of Computing at Imperial College London, holding a UK Research and Innovation Established Career Fellowship (2018–2023). She leads the Research Institute on Verified Trustworthy Software Systems (VeTSS, 2017–2023) and is renowned for her work on program specification and verification, particularly in concurrent separation logics and the development of the Gillian platform for symbolic analysis of real-world languages like C and JavaScript. PhD from the University of Edinburgh (1992), supervised by Professor Gordon Plotkin FRS. EPSRC Advanced Fellowship at Cambridge (1998), followed by a lectureship at Imperial (2001) and promotion to Professor (2009). Her research bridges program verification, symbolic execution, and bi-abduction, focusing on robust mechanised language specifications. She has pioneered library specifications for JavaScript and WebAssembly, emphasizing environment robustness and scalable compositional reasoning. The Gillian platform unifies classical symbolic execution, separation-logic verification, and bi-abduction-based testing. Philippa has received prestigious awards, including the Royal Academy of Engineering Fellowship (2020), President and Rector’s Award for Teaching (2013), and Microsoft Research — Royal Academy of Engineering Senior Fellowship (2005–2009). She has secured significant grants from Amazon, Facebook, EPSRC, and GCHQ for projects like Gillian and JSVerify. She actively supervises PhD students and teaches the Scalable Software Verification course. As General Chair of POPL ’24 and organizer of the Isaac Newton Institute’s Verified Software program (2022), she shapes academic discourse. Her editorial roles and conference committee memberships further underscore her influence in programming languages and formal methods.

Harry XuView profile

Professor

• Programming Languages
• Compilers
• Computer Systems
• +3 more

Harry Xu is a Professor in the Computer Science Department at the University of California, Los Angeles (UCLA), with a research focus on computer systems spanning programming languages, compilers, runtime/operating systems, distributed systems, and computer architecture. Prior to UCLA, he was an Associate Professor at UC Irvine (UCI). His current work centers on infrastructures for future cloud computing (user-defined clouds) and scalable, cost-effective AI/ML systems. Education : Ph.D. in Computer Science and Engineering from Ohio State University (2011) His research has pioneered techniques for combating software bloat, optimizing data analytics through programming language innovations, and developing systems like Yak GC, VQPy, and Niijima. He co-founded BreezeML, a startup for GenAI risk management, and has held visiting roles at Microsoft Research and IBM Watson Research Center. Recent publications include work on resource-disaggregated datacenters, cloud computing fault tolerance, and ML system scalability, presented at top venues like SOSP, OSDI, and NSDI. He has advised numerous Ph.D. and M.S. students, many of whom now hold academic or industry roles. Awards : 2018 Dahl-Nygaard Junior Prize, ACM Distinguished Scientist Harry actively contributes to academic service as a committee member and co-chair at conferences like PLDI, SPLASH, and VMCAI. He maintains the VQPy project and other open-source systems.

• Medical Image Analysis
• Deep Learning for Biomedical Applications
• Edge Computing and Real-Time Segmentation
• +5 more

Jian Zhuang is a Professor at the Department of Artificial Intelligence, School of Computer Science and Engineering, University of Electronic Science and Technology of China. With over 30 years of academic contributions, he leads research at the intersection of medical image analysis , deep learning , and biomedical engineering . His work focuses on real-time medical imaging solutions for cardiac disease diagnosis and surgical planning. Research highlights include: Pioneering 3D CT and MRI segmentation frameworks for congenital heart disease Developing domain-adapted neural networks for clinical applications Advancing edge computing techniques for low-latency medical image processing Creating large-scale medical datasets like ImageCAS and ImageCHD His publications (90+) in venues such as Medical Image Analysis , IEEE Transactions , and MICCAI demonstrate technical leadership in medical AI and computer vision . Recognized with multiple awards including the MICCAI Best Paper Award and IEEE Access Outstanding Paper Award , he mentors a research group that has produced key tools for cardiac surgical telementoring and image-guided interventions .

Almuth SchollView profile

Professor

• Macroeconomics
• International Macroeconomics
• Sovereign Debt
• +3 more

Almuth Scholl is a Professor of Economics at the University of Konstanz and serves as a principal investigator at the Cluster of Excellence “The Politics of Inequality”. Her research focuses on macroeconomic policy with emphasis on sovereign debt dynamics, fiscal policy, and their intersection with political economy and economic inequality. Her research interests center on macroeconomics and international macroeconomics , particularly examining how sovereign debt crises interact with political polarization and economic inequality . Current work investigates fiscal policy responses during financial crises through her Cluster project “From Bad to Worse? Financial Crises, Polarization, and Inequality”, analyzing politico-economic feedback loops between debt distress and social fragmentation. Analysis of her 14 recent publications (2025-2008) reveals consistent focus on sovereign debt sustainability, taxation mechanisms, and political economy drivers. Work spans Journal of International Economics (7 publications), Review of Economic Dynamics , and International Economic Review , demonstrating methodological rigor in quantitative macroeconomic modeling applied to real-world policy challenges like tax avoidance, bailout conditionality, and debt relief effectiveness. Scientific recognition includes: CEPR Research Fellow As principal investigator in the Cluster of Excellence, she leads interdisciplinary research connecting macroeconomic policy with social inequality dynamics. Her work integrates theoretical modeling with empirical analysis of sovereign default risks, political turnover, and fiscal policy impacts on distributional outcomes, contributing to policy debates on inequality mitigation during economic crises.

• Secure and robust distributed systems
• Computer-supported group work
• Internet-based democracy
• +4 more

Prof. Dr. Martin Mauve has been heading the Chair of Computer Networks and Communication Systems at Heinrich Heine University Düsseldorf since August 2003. He served as Head of the Computer Science Department from 2005 to 2010 and has been Dean of the Faculty of Mathematics and Natural Sciences since 2015. His interdisciplinary work focuses on enhancing democratic processes through internet technology, and he is Vice Spokesperson of the Düsseldorf Institute for Internet and Democracy (DIID) and a member of the Center for Advanced Internet Studies (CAIS) advisory board. Education University of Mannheim: Computer Science & Business Administration (1991-1997) Ph.D. in Computer Science (2000) with Siemens grant Habilitation (2003) on "Algorithms and Protocols for Distributed Multimedia Systems" His research spans two major phases: pre-2013 work on wireless networking, vehicular networks, and multimedia systems, and post-2013 focus on internet-enabled democratic processes. Current interests include scalable online discussion platforms and interdisciplinary graduate education in digital participation. He leads projects like DiscourseData4Policy (DD4P) , aiming to create functional systems for dialogue-based online participation. His technical work addresses consistency in replicated multimedia systems, congestion control, and networked gaming. As a faculty leader, he oversees academic programs and collaborates with institutions like the Center for Advanced Internet Studies (CAIS). His visiting hours are flexible, arranged via email contact.

Thomas EickermannView profile

Researcher

• Networking
• Cybersecurity
• Project Management
• +4 more

Dr. Thomas Eickermann is Deputy Director of the Jülich Supercomputing Centre (JSC) and Head of the Communication Systems and Services division at Forschungszentrum Jülich GmbH. With a PhD in Physics from the University of Düsseldorf (1994), his career at JSC has spanned system administration, grid computing, networking, and IT security. He led PRACE preparatory and implementation phase projects (2008–2015) and served on the PRACE aisbl Board of Directors. Current role: Deputy Director, JSC Leadership in Communication Systems and Services division since 2002 Key expertise: Networking, Cybersecurity, Project Management Actively engaged in JUPITER EuroHPC Exascale computer and Modular Data Centre projects His research interests focus on high-performance computing infrastructure, cybersecurity frameworks, and distributed computing systems. As a Principal Investigator in the Helmholtz Information Program 1 (Topics 1, 2, and 3), he contributes to advancing computational science infrastructure.

Stefan BrunthalerView profile

Professor

• Software Security
• Programming Languages
• Compiler Design
• +6 more

Stefan Brunthaler is a Professor at the University of the Federal Armed Forces Munich , leading the uCSRL research group at the Institute for System Security (INF 6). He previously held a chair for compilers and programming languages at the University of Paderborn and was a postdoctoral scholar at the University of California, Irvine (2011–2015). His academic journey began with a Dr.techn. degree from the Vienna University of Technology in 2011. Research interests include: Language-based security (software diversity, language-based privacy) Formalization and verification of language-based transformations Fuzz testing of complex software systems Language-oriented programming concepts for compiler construction Dynamic compiler optimization (JIT compilers) for Python/JavaScript Energy-efficient programming language implementations Design of holistic data center architectures Scientific contributions include critical optimizations integrated into the official Python 3.10 distribution in 2021, impacting hundreds of millions of users daily. He has secured over €45 million in third-party funding from organizations like DARPA, NSF, and the German Federal Ministry of Defense. Grants & affiliations include collaborations with CODE research institute and memberships in IFIP Working Group 2.4 .

Thomas MundtView profile

Academic

• Security in fieldbuses
• Industrial network security
• IoT system security
• +3 more

Dr.-Ing. Thomas Mundt is a Research Associate in the Department of Computer Science at the University of Rostock , Germany, where he has been affiliated since 2003. Born in 1974, he completed his studies in Computer Science at the same university from 1994 to 1999, followed by a doctorate in Engineering (Dr.-Ing.) in 2003. Education: 1994-1999: Study of Computer Science, University of Rostock 2003: Doctorate in Engineering (Dr.-Ing.), University of Rostock Research Focus: Dr. Mundt's research centers on cybersecurity in industrial and building automation systems , with particular emphasis on securing fieldbus networks and IoT devices. His work spans both theoretical foundations and practical implementations, addressing emerging threats in connected industrial environments. His primary research interests include: Security in fieldbuses for industrial networks and building automation Security of IoT systems Anomaly detection in network traffic Intrusion detection systems for industrial control systems LoRaWAN security for IoT deployments Research Trends: From 2002 to 2024, Dr. Mundt's publications demonstrate a clear evolution from foundational work in wireless mesh networks and positioning systems to cutting-edge research in industrial cybersecurity. His recent work (2018-2024) focuses heavily on practical security implementations for Industry 4.0 environments, including anomaly detection in maritime fieldbus systems and threat intelligence through honeypots in building automation. Current Projects: Dish-O-Klin GES 2021 (Current Project Manager) Former Projects: EMERGE IoT TopoTool SeBuNet Sindabus Wavehopper Contact Information: Room 363, Albert-Einstein-Str. 22, 18059 Rostock, Germany Phone: +49 (0)381 498 7505 Email: thomas.mundt@uni-rostock.de

Peter DanielisView profile

Professor

• Parallel Systems
• Distributed Computing
• Time-Sensitive Networking
• +4 more

Prof. Dr.-Ing. habil. Peter Danielis holds an Extraordinary Professorship in Parallel Systems at the Institute of Computer Science, University of Rostock. His research focuses on parallel and distributed systems with applications in time-sensitive networking, autonomous underwater vehicles, and industrial IoT. He serves as Deputy Director of the Institute of Computer Science (IFI) and participates in editorial boards for multiple conferences and journals. His primary research interests span Parallel Systems , Distributed Computing , and Time-Sensitive Networking , with significant contributions to mobile opportunistic networks and real-time communication in industrial settings. Current projects include cooperative navigation of autonomous underwater vehicles (AUVs) and distributed communication networks for IoT applications. His work bridges theoretical research with industrial implementation, particularly in deterministic networking for critical infrastructure. Analysis of his recent publications (2020-2025) reveals strong emphasis on Time-Sensitive Networking (TSN) for industrial automation, with 7 of 15 recent papers directly addressing TSN scheduling, reliability, and traffic management. Secondary focus areas include Autonomous Underwater Vehicle coordination (3 papers) and Mobile Opportunistic Networks for urban sensing. His methodology combines simulation, protocol design, and real-world validation. Professional affiliations include membership in the Institute of Electrical and Electronics Engineers (IEEE). He serves as TPC member and editorial board member for various conferences and journals. His teaching portfolio covers High Performance Computing, Operating Systems, and Complex Software Systems. As Deputy Director of the Institute of Computer Science, he oversees academic operations while maintaining active research leadership. His laboratory work focuses on real-time communication systems and underwater vehicle networks, utilizing simulation frameworks like OMNeT++ for protocol validation. Current research directions include adaptive TSN scheduling and cooperative AUV navigation in dynamic environments.

• Labor Economics
• Empirical Public Economics
• Microeconometrics
• +3 more

Dr. Maximilian Joseph Blömer is a Research Associate and scientific Assistant Head of Ex-Ante Policy Evaluation at the ifo Center for Macroeconomics and Surveys in Germany. He holds a doctorate in economics and has been with ifo since 2017, initially as a doctoral student before advancing to his current leadership role. His research focuses on labor economics, empirical public economics, and microeconometrics, with specialization in tax policy, social security systems, and income inequality. Blömer's research interests center on policy evaluation through microsimulation modeling. Key areas include: Labor market incentives in welfare systems Tax-benefit reforms and household behavior Income inequality trends and measurement Social transfer program design Minimum wage impacts Child support and basic income schemes His work combines theoretical frameworks with empirical data analysis to assess policy effectiveness. His publication portfolio (2024-2025) shows strong focus on German economic policy reforms, particularly: Tax system restructuring and efficiency Welfare program interactions (Bürgergeld, Kindergrundsicherung) Labor market disincentives in social transfers Long-term inequality trends Electoral policy analysis Methodologically, he employs advanced econometric techniques and microsimulation models for policy impact forecasting. Blömer leads significant government-commissioned projects including: Quantification of basic child benefit systems (BMFSFJ) Reform of transfer withdrawal rates (BMAS) Housing allowance optimization (BMWK) Tax system simulations (Federal Finance Ministry) Income inequality analysis (BMBF) He coordinates research teams for these policy-focused studies.