Find Academic Supervisors

• Computer Architecture
• Non-Volatile Memory Systems
• Deep Learning Systems
• +3 more

Jishen Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego (Jacobs School of Engineering). His research focuses on computer architecture, non-volatile memory systems, and deep learning acceleration. Dr. Zhao has published extensively in top venues including ISCA, MICRO, ASPLOS, and IEEE Transactions. He collaborates with researchers at UCSD and beyond to advance systems for emerging applications in AI and autonomous vehicles. Dr. Zhao's primary research areas include persistent memory systems, hardware/software co-design for deep learning, and safety-critical computing. He develops techniques for crash consistency, memory disaggregation, and efficient neural network deployment. His work on autonomous vehicles addresses scenario generation and perception-aware system design. Recent projects explore LLM applications for software engineering and hardware verification. Analysis of Dr. Zhao's 2024-2025 publications reveals a strong shift toward AI-integrated systems research. He applies large language models to tasks like RTL verification and software issue localization while continuing to innovate in memory systems for serverless computing. There is growing emphasis on safety-critical systems for autonomous vehicles and energy-efficient neural network training using novel hardware architectures. Information about Dr. Zhao's scientific awards, advising activities, grants, and laboratory facilities was not available in the provided documentation.

Sören LaueView profile

Professor

• Machine Learning
• Optimization Algorithms
• GPU Computing
• +3 more

Prof. Dr. Sören Laue is a Professor of Machine Learning at the University of Hamburg's Department of Informatics. His research focuses on optimization algorithms, machine learning frameworks, and high-performance computing. He leads the Machine Learning research group and developed the GENO optimization framework and the Matrix Calculus toolset. His work emphasizes GPU acceleration, tensor operations, and scalable solutions for classical machine learning problems. Projects: GENO solver (Python-based optimization), Matrix Calculus (derivative computation), and SQL-based tensor operations. Key Research Themes: Optimization frameworks, GPU computing, neural network scalability, and algorithm design. Selected recent publications highlight contributions to tensor calculus benchmarks, GPU-optimized machine learning pipelines, and novel optimization methods. His work bridges theoretical foundations and practical software tools for the machine learning community.

Frances YungView profile

Research Fellow

• Discourse Relations
• Computational Linguistics
• Natural Language Processing
• +4 more

Dr. Frances Yung is a Postdoctoral Researcher at Saarland University's Department of Language Science and Technology within the Department of Computer Science. She has been affiliated with Prof. Vera Demberg's research group since April 2017 and is currently working on the DFG-funded SFB-1102 project "Information Density and Linguistic Encoding," specifically on project B2 "Cognitive modelling of information density for discourse relations." She is pursuing her habilitation, indicating career progression toward a higher academic position in the German university system. Dr. Yung's research focuses on discourse relations at the intersection of NLP, corpus linguistics, and experimental psycholinguistics. Her work explores how information density affects discourse relation marking through cognitive modeling approaches. She has developed expertise in discourse parsing, resource construction, annotation aggregation, and experimental pragmatics, with particular attention to multilingual aspects of discourse phenomena. Her research combines computational modeling with experimental methods to understand how speakers produce and comprehend discourse relations. Analysis of Dr. Yung's recent publications reveals a strong focus on discourse relation resources, particularly multilingual corpora like DiscoGeM 2.0 covering English, German, French, and Czech. Her work increasingly incorporates crowdsourcing methodologies and examines how large language models can be leveraged for discourse annotation tasks. She has made significant contributions to understanding the challenges of implicit discourse relation annotation and the biases introduced by different task designs in crowdsourcing environments. Active reviewer for major computational linguistics conferences (ACL, EMNLP, NAACL, EACL, COLING, IJCNLP) and workshops since 2016 Served as area chair for Sigdial 2024 Regular service on program committees for discourse-related workshops Dr. Yung has supervised multiple Master's theses on topics related to discourse relations, implicit relation identification, and domain adaptation. Her teaching portfolio includes courses on crowdsourcing linguistic annotations, discourse relations from cognitive and NLP perspectives, and recent advances in discourse processing. She has also served as a teaching assistant for data science and AI courses, demonstrating her commitment to interdisciplinary education at the intersection of computer science and linguistics.

• GPU Computing
• Parallel Computing
• Domain-Specific Languages
• +5 more

Prof. Dr.-Ing. Richard Membarth is a Research Professor for System-on-a-Chip and AI at the Edge Computing at Technische Hochschule Ingolstadt (THI). He is affiliated with the Hardware-Software Co-Design group and holds a secondary position at the German Research Center for Artificial Intelligence (DFKI) Saarbrücken. Co-creator of DSL frameworks like AnyDSL and Hipacc Key contributor to MetaDL (AI metaprogramming) and PRIME (predictive rendering) His research bridges GPU computing , domain-specific languages , and compiler technology , with recent work on Vulkan SPIR-V compilation and device-driven SpMV algorithms . Notable awards include the HiPEAC Paper Award (2018) and multiple Best Paper Awards for his compiler frameworks.

Jingling XueView profile

Professor

• Programming Languages
• Compilers
• Program Analysis
• +5 more

Jingling Xue is a Scientia Professor at the School of Computer Science and Engineering at the University of New South Wales (UNSW) in Sydney, Australia. As an IEEE Fellow of the Computer Society, he leads the Programming Languages and Compilers research group, focusing on practical applications of compiler optimization and program analysis techniques. His work bridges theoretical foundations with real-world software systems, particularly in developing open-source tools for large-scale program analysis. Professor Xue received his B.Eng and M.Eng degrees from Tsinghua University in 1984 and 1987, respectively, followed by a PhD from the University of Edinburgh in 1992. His academic journey has established him as a leading figure in programming languages and compiler technology. Xue's research spans programming languages, compiler technology, and program analysis with emphasis on practical relevance. His current projects include compiler techniques for improving parallelism and locality, pointer/alias analysis for million-line-scale programs, and static/dynamic analysis for detecting bugs and security vulnerabilities in real-world applications like web browsers and Android apps. His group actively develops open-source tools to support scientific replicability and reproducibility in these areas. His recent publications demonstrate a strong focus on applying program analysis techniques to modern challenges including AI compilers, homomorphic encryption, security vulnerability detection, and graph processing systems. The work shows evolution from traditional compiler optimization to addressing emerging domains like privacy-preserving computation and deep learning systems while maintaining rigorous theoretical foundations. Scientific Awards: Best Paper Award at CGO'13 Best Paper Award at CGO'16 Distinguished Paper Award at ECOOP'16 Distinguished Paper Award at ICSE'18 Distinguished Paper Award at ISSTA'19 Distinguished Paper Award at ASE'19 Distinguished Artifact Award at ISSTA'23 Best Artifact Award at FSE'23 Distinguished Paper Award at ASE'23 Test-of-Time Award at CGO'21 Professor Xue has successfully supervised 30 PhD students to completion, many of whom now work as professors or researchers in academia and industry. He has served as Program Chair for major conferences including LCTES'13, CC'18, CGO'20, and General Chair for LCTES'20. His group currently focuses on memory safety in Rust, smart contract analysis, AI compilers, compilation for privacy-preserving computation, and adversarial attacks in deep learning. The Programming Languages and Compilers group maintains strong connections with industry partners, translating theoretical advances into practical tools for real-world software development challenges. Their work on pointer analysis, memory safety, and compiler optimizations continues to influence both academic research and industrial practice.

Julija ZavadlavView profile

Assistant Professor

• Multiscale Modeling
• Machine Learning
• Computational Chemistry
• +3 more

Prof. Julija Zavadlav is an Assistant Professor of Multiscale Modeling of Liquid Materials at the Technische Universität München (TUM), affiliated with the TUM School of Engineering and Design. Her research integrates physical modeling with machine learning and Bayesian techniques to develop multi-scale simulation frameworks for diverse applications in bioinformatics and engineering. Education: She earned her PhD in Physics from the University of Ljubljana (2015) and conducted postdoctoral research at ETH Zurich (2016–2019), where she received an ETH Postdoctoral Fellowship. Since 2019, she has held her current position at TUM. Research Interests: Her work focuses on advancing machine learning potentials, Bayesian uncertainty quantification, and multi-scale modeling for complex systems like ionic liquids, metal-organic frameworks, and biomolecules. Her ERC Starting Grant (2022) supports the SupraModel project, emphasizing scalable and interpretable models. Awards: Golden Teaching Award 2022 (Best Lecture), ERC Starting Grant 2022, and ETH Postdoctoral Fellowship. Her recent publications emphasize neural network potentials, transfer learning, and computational tools like JaxSGMC for Bayesian analysis. Grants and Labs: While specific lab names are not mentioned, her ERC grant underscores active funding. No formal student advisee list is provided, but her collaborative work suggests involvement in training next-generation computational scientists.

Birte GlimmView profile

Professor

• Knowledge Representation
• Automatic Reasoning
• Semantic Web Technologies
• +3 more

Prof. Birte Glimm is a full Professor and Chair of the Institute of Artificial Intelligence at the University of Ulm. She holds leadership roles in academic governance, including as Dean of Studies for Cognitive Systems and Computer Science programs. Her research focuses on knowledge representation, automated reasoning, and semantic web technologies, with applications in autonomous systems and intelligent assistants. She has led projects like the 'Companion-Technology for Home Improvement' and contributed to standards such as SPARQL 1.1 Entailment Regimes. Education: PhD (2008) from the University of Manchester under Prof. Ian Horrocks, BSc from Hamburg University of Applied Sciences (2004), and prior industry experience in communication design. Research Interests: Development of efficient reasoning algorithms for Description Logics, ontology-based data management, and explainable AI. Her work emphasizes scalability, dynamic knowledge processing (e.g., for autonomous vehicles), and natural language explanations of automated reasoning. She co-leads the DFG-funded KEMAI research training group and previously led the BMBF project 2LIKE. Awards: Google Faculty Award (2017), Mileva Einstein-Marić-Preis (2016), and multiple best paper awards. She has served on numerous conference committees and editorial boards, including the OWL 2 Conformance Specification. Teaching: Courses on Knowledge-Based AI, Semantic Web Foundations, and programming for cognitive systems. She coordinates curricula for Computer Science and AI programs at Ulm. Labs/Teams: Institute of Artificial Intelligence; contributions to Collaborative Research Centers SFB/TRR 62 and EU projects in AI for industry.

• High-Performance Computing
• Parallel Computing
• Data Compression
• +4 more

Kento Sato is a researcher specializing in High-Performance Computing (HPC), with a focus on checkpointing systems, data compression, and parallel computing optimization. His work addresses challenges in fault tolerance, algorithmic efficiency, and resource management in large-scale computing environments. Collaborating with institutions like the Joint-Laboratory of Extreme Scale Computing, he explores trade-offs between compression efficiency and hardware costs, as well as automatic variable identification for checkpointing. His contributions span theoretical advancements and practical implementations, with notable publications in venues such as SC, IPDPS, and the International Journal of High Performance Computing Applications. Key Research Themes: Checkpointing protocols, lossy compression for scientific data, HPC workflow optimization. Long-term Collaboration: Regular co-authorship with experts like Satoshi Matsuoka and Martin Schulz. Publications consistently highlight innovations in fault tolerance mechanisms, adaptive hardware design, and scalable algorithms for machine learning and distributed systems.

Volker HeunView profile

Professor

• Algorithmic Bioinformatics
• Protein Folding
• Data Structures
• +4 more

Volker Heun is a Professor of Bioinformatics at Ludwig-Maximilians-Universität München (LMU), holding positions within the Faculty of Mathematics, Computer Science, and Statistics since 2003. He currently serves as Vice Dean of the faculty and holds various academic coordination roles, including Bioinformatics program coordination and ERASMUS coordination. His academic journey includes a Ph.D. from TU München (1996) and research fellowships at the International Computer Science Institute (Berkeley, 1997–1998). Education: Studied Computer Science and Mathematics at Johann Wolfgang Goethe-Universität Frankfurt (1984–1991), followed by research roles at TU München and other institutions. Professional roles include Research Assistant (1991–1996) and Research Associate (1996–2003). Research focuses on algorithmic bioinformatics, protein folding, data structures, and computational biology. Key contributions include work on RNA sequence-structure analysis, chemical shift correction tools (e.g., CheckShift), and efficient algorithms for range queries and suffix arrays. He has authored multiple influential books on algorithms and bioinformatics. Publications span over two decades, emphasizing algorithm design, bioinformatics applications, and parallel computing. Supervised PhD students include Benjamin Albrecht (2016), Luca Chiarabini (2009), and Simon W. Ginzinger (2008). Teaching includes courses on Algorithmic Bioinformatics, Algorithms on Sequences, and bioinformatics-related seminars. Administrative roles include serving as Vice Dean (2004–2007, 2013–2017, 2019–present) and Dean (2011–2013, 2017–2019). His work bridges theoretical computer science and applied bioinformatics, with a focus on interdisciplinary research.

Regina StoddenView profile

Research Fellow

• Text Simplification
• Online Participation
• Web Accessibility
• +6 more

Regina Stodden is a Research Fellow at the Department of Computational Linguistics, Heinrich Heine University Düsseldorf, since January 2019. She is affiliated with the NRW Research College for Online Participation (second funding phase) and works under the supervision of Prof. Dr. Marc Ziegele. Education: B.A. in Educational Science, Text Technology, and Computational Linguistics from Bielefeld University M.A. in Information Science and Language Technology from HHU Düsseldorf Her research focuses on automatic text processing , particularly text simplification for online discussions. This includes: Enabling participation for people with limited German proficiency Reducing manual workload in text analysis Exploring accessibility in Open Data portals She has contributed to tools like TS-ANNO for corpus annotation and EASSE-DE for simplification evaluation, with recent work extending to CEFR-based language proficiency assessment . Her research intersects Natural Language Processing , Machine Learning , and Usability Studies , often addressing accessibility challenges in digital participation. Scientific awards: No explicit awards mentioned. Advising and grants: Participates in the NRW Research College for Online Participation funding program and collaborates under Prof. Dr. Laura Kallmeyer's supervision. Her work involves grants related to text simplification for online participation processes.

• Software Security
• Static Analysis
• Program Analysis
• +3 more

Dr. Dominik Helm is an interim professor for Software Engineering at the University of Duisburg-Essen . His research focuses on modularization and automatic parallelization of collaborative static analyses, particularly through the OPAL framework for Java VM bytecode. He is the lead maintainer of OPAL, which aims to enhance precision, soundness, and scalability in static analysis for bug and security vulnerability detection. Helm has also received the Ernst Denert Software-Engineering-Preis (2024) for his contributions. Education : Helm earned his PhD in Computer Science from Technische Universität Darmstadt in 2023, with his thesis titled Modular Collaborative Program Analysis . His academic background includes extensive work on purity analysis, call graphs, and modular static analysis techniques. Research Interests : Helm's work bridges theoretical advancements in program analysis with practical tools. His key areas include cross-language analysis (e.g., AXA framework), modular call-graph algorithms (Unimocg), and soundness theory for analysis architectures. He emphasizes improving static analysis tools to handle complex language features and ensure robust software quality. Publications & Awards : Helm has authored/co-authored over 15 papers in top-tier conferences like ASE, ISSTA, and ESOP. His 2024 work on AXA and Unimocg showcases cross-language interoperability and modular consistency. The Ernst Denert Preis recognizes his impactful contributions to software engineering research. Technical Leadership : As OPAL's lead maintainer, Helm drives collaborative research in static analysis frameworks. His projects often involve interdisciplinary teams, reflecting his commitment to practical software engineering solutions.

Thomas LeimkuehlerView profile

Researcher

• Neural Rendering
• Generative Models
• Computer Graphics
• +3 more

Thomas Leimkuehler is a Senior Researcher at the Max Planck Institute for Informatics in Saarbrücken, Germany, holding dual appointments in the Department of Computer Graphics and the Department of Visual Computing and Artificial Intelligence. His work bridges computer graphics and machine learning, focusing on neural rendering techniques for image synthesis and manipulation within the Saarland Informatics Campus ecosystem. Education: PhD in Computer Science, Saarland University (2019) Leimkuehler's research centers on neural signal representations and generative models for visual computing. He develops data-driven approaches to physically-based rendering, inverse rendering, and high dynamic range imaging, emphasizing efficient parallel algorithms. His work frequently integrates deep learning with traditional graphics pipelines to solve longstanding challenges in image synthesis and 3D scene representation, with applications spanning cinematic effects, automotive visualization, and computational photography. Analysis of his publication record reveals dominant trends in neural radiance fields (particularly Gaussian splatting), diffusion-based HDR generation, and multi-scale image synthesis. His research consistently addresses the tension between physical accuracy and neural efficiency, with recent work focusing on uncertainty modeling in radiance fields and real-time cinematic effects. Award highlights: Best Paper Award at SIGGRAPH 2023 for pioneering 3D Gaussian Splatting Eurographics PhD Award and Otto Hahn Medal (2019) Multiple student paper awards at EGSR, Graphics Interface, and ACM SAP Leimkuehler actively mentors through his Image Synthesis and Machine Learning research group, serving as primary advisor for PhD candidates and postdocs. He holds editorial positions at IEEE TVCG and Computer Graphics Forum while reviewing for major conferences including SIGGRAPH, CVPR, and ECCV. His group maintains strong collaborations with Snap, Princeton University, and the Visual Geometry Group at Oxford. Based at the Saarland Informatics Campus, his research group operates within the Max Planck Institute for Informatics' Department 4 and Department 6 frameworks, leveraging infrastructure from the Saarbrücken Research Center for Visual Computing and the European Laboratory for Learning and Intelligent Systems.

Sebastian KuckukView profile

Researcher

• High Performance Computing
• GPU Programming
• Parallel Computing
• +7 more

Dr. Sebastian Kuckuk is a researcher and head of training at the Erlangen National High Performance Computing Center (NHR@FAU), Friedrich-Alexander-Universität Erlangen-Nürnberg. He is affiliated with the Department of Computer Science and contributes to the Chair of System Simulation. His work bridges research, training, and software development in high-performance computing. Education: PhD in Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (2019) His research focuses on enhancing performance portability and programmer productivity using domain-specific languages, code generation, automatic parallelization, and GPU programming. These techniques are applied to develop massively parallel numerical solvers for computational fluid dynamics, particularly for the shallow water equations. He is a core developer of the ExaStencils framework, which enables automated generation of efficient multigrid solvers for structured and patch-structured grids. Analysis of his recent publications (2020–2025) reveals a consistent focus on code generation, GPU acceleration, and solver optimization for fluid dynamics problems. Key themes include heterogeneous computing, block-structured grids, and adaptive methods. His work integrates advanced compiler techniques with numerical mathematics to improve scalability and performance on modern HPC architectures. Scientific Recognition: NVIDIA Deep Learning Institute (DLI) University Ambassador Certified Instructor for DLI courses in GPU programming and CUDA He actively contributes to teaching and training through courses such as Programming Techniques for Supercomputers and High-End Simulation in Practice . He conducts workshops and tutorials on GPU programming and performance optimization. While no formal students are listed, his mentoring role is evident through collaborative research and training activities. He has no recorded grants in the provided text, but his involvement in NHR and KONWIHR projects indicates active participation in funded HPC initiatives. Laboratories and Projects: Lead developer of ExaStencils , a code generation framework for multigrid solvers Contributor to GHODDESS , a module for higher-order discretizations in shallow water modeling Active in NHR@FAU and KONWIHR projects focused on GPU computing and performance optimization

• GPU computing
• Parallel computing
• Domain-specific languages
• +9 more

Prof. Dr.-Ing. Richard Membarth is a faculty member at Technische Hochschule Ingolstadt , where he holds the professorship for System-on-a-Chip and AI for Edge Computing. He is also affiliated with the German Research Center for Artificial Intelligence (DFKI) as a Senior Researcher and Team Leader for Compiler Technologies and High-Performance Computing, and with the Saarland University Computer Graphics Lab . His research spans GPU computing, domain-specific languages, and compilers. PhD from Friedrich-Alexander University Erlangen-Nürnberg (2013) Postgraduate diploma from Auckland University of Technology His research focuses on: Parallel computer architectures and programming models Automatic code generation for embedded to HPC systems Image processing, computer graphics, and deep learning applications Domain-specific languages for performance-portable code Recent publications highlight compiler design, GPU acceleration, and parallel algorithms. Scientific awards include the HiPEAC Paper Award (2018) and GPCE Best Paper Award (2015) . Professional roles include organizing High-Performance Graphics conferences as Treasurer (2024-2025) and Papers Chair (2020).

• High Performance Computing
• Energy Efficiency
• Parallel Computing
• +6 more

Laura Carrington is a researcher at the University of California, San Diego, specializing in High Performance Computing (HPC) with a focus on energy efficiency, memory management, and performance optimization. She has contributed to the development of tools like PEBIL for binary instrumentation, ADAMANT for data movement analysis, and frameworks for power management in large-scale systems. Her research spans multiple domains including ARM processor evaluation, Xeon Phi vectorization, and communication reduction in graph algorithms. Key collaborations include work with Michael Laurenzano, Allan Snavely, Ananta Tiwari, and Pietro Cicotti. Laura's work addresses critical challenges in HPC such as DVFS configuration optimization, workload colocation, and energy-aware algorithm design. While no explicit academic rank is stated, her extensive publication record across 2002-2019 in top venues like SC, IPDPS, and IJHPCA establishes her as a significant contributor to HPC research. Her work has influenced practices in system-level power management, scientific application characterization, and energy-efficient computing for both CPU/DRAM domains and emerging memory technologies.