Thomas Neumann is a full-time Professor at the Department of Database Systems within the TUM School of Computation, Information and Technology at the Technical University of Munich . Research Focus : Query optimization and processing in database systems Hardware-aware database engine design Scalable RDF/semantic data management Hybrid OLTP/OLAP processing Join optimization algorithms Scientific Recognition : Gottfried Wilhelm Leibniz Prize (2020), Germany's most prestigious research award Academic Contributions : His recent publications focus on high-performance query execution strategies, hybrid transactional/analytical systems using virtual memory snapshots, and scalable graph processing techniques. These works demonstrate consistent advancements in database engine optimization for modern hardware architectures and large-scale semantic data management.
Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.
Dr. Ablet Semet is a researcher at the University of Göttingen specializing in Turkology with expertise in Old Turkic language and cultural contacts among Turkic peoples. His institutional affiliation centers on Central Asian linguistic studies within the university's research framework, focusing on historical interactions between Turkic communities and neighboring civilizations. His research spans General Turkology, Turkish-Chinese and Turkish-Mongolian language contacts, and Islamic Turkic cultural exchanges. Semet's work emphasizes philological analysis of primary sources including the Xuanzang biography, Buddhist texts, and folklore traditions. His methodological approach combines textual criticism with historical linguistics to reconstruct cultural transmission routes across Eurasia. Analysis of Semet's 15 most recent publications reveals consistent focus on Old Turkic manuscripts (particularly Xuanzang-related texts), Buddhist narrative traditions, and comparative studies of measurement systems in Turkic cultures. His research demonstrates interdisciplinary integration of linguistics, religious studies, and historical anthropology with strong emphasis on manuscript evidence from Turfan and Central Asian collections. No scientific awards were documented in the available information. While no student advising or grant information appears in the provided materials, Semet's extensive collaborative work with scholars like Simone-Christiane Raschmann and Jens Wilkens indicates active participation in international research networks focused on Turkic manuscript studies.
Professor Peter Funke is a Senior Professor of Ancient History at the Westfälische Wilhelms-Universität Münster, where he leads the Cluster of Excellence 'Religion and Politics in Premodernity and Modernity' and directs the Seminar für Alte Geschichte. He has held significant academic leadership roles, including serving as Vice President of the German Research Foundation (DFG) and as a member of numerous international scholarly committees. His research focuses on ancient state systems, religious history, and historical geography, with particular expertise in ancient Greek federalism and interstate relations. Funke's academic journey includes a Habilitation from the University of Cologne (1985) and a PhD from the same institution (1978). His career spans over four decades, during which he has held professorships at the University of Siegen and Münster, and has directed major research initiatives like the SFB 1776 and the Cluster of Excellence. His research interests are centered on the political and religious dynamics of ancient Greek states, including studies on federal leagues like the Aetolian and Achaean confederations. He has authored or edited over 150 publications, including seminal works on Hellenistic political structures and the role of sanctuaries in governance. Funke has been awarded prestigious honors such as the Ausonius-Preis (2019) and the Kongress-Preis (2019), and is a member of institutions like the Berlin-Brandenburg Academy of Sciences. His advisory roles include leading the German Archaeological Institute's Athen branch and serving on editorial boards for journals like Klio and Digitales Classicist Online . In addition to his academic work, Funke has supervised numerous doctoral and postdoctoral researchers, contributing to the training of the next generation of ancient historians. His interdisciplinary projects incorporate digital humanities methodologies, exemplified by his work on the Inscriptiones Graecae corpus.
Jignesh M. Patel is a Professor at the University of Wisconsin, Madison, WI, USA , with over 25 years of contributions to database systems, data analytics, and hardware-aware query processing. His work bridges theoretical advancements with practical systems engineering. Research Interests span: Database systems optimization (query processing, transaction management) Hardware acceleration for analytics (eBPF, PIM, GPUs) Machine learning integration in databases (feature selection, model optimization) Efficient data structures (hashing, encoding, indexing) Multi-tenant and cloud database management Recent Work focuses on kernel-embedded databases (BPF-DB, 2025), memory-efficient dataframe processing (SplitDF, 2024), and algorithmic-hardware co-design for dense retrieval (DReX, 2025). He has pioneered techniques for adapting to data skew (VIP Hashing, 2022), leveraging static analysis in R optimization (ROSA, 2017), and rethinking benchmarking paradigms. Collaborations include key partnerships with: Systems researchers (Andrew Pavlo, José F. Martínez) Machine learning experts (Arun Kumar, Kevin Skadron) Education-focused colleagues (Adalbert Gerald Soosai Raj, Richard Halverson) Industry leaders (David J. DeWitt, Microsoft Research)
Johannes Waldmann is a Professor and Dean of Computer Science at the Faculty of Computer Science and Media, Leipzig University of Applied Sciences (HTWK Leipzig). He serves as a Member of the Faculty Council and maintains active engagement with students through regular office hours on Wednesdays from 10:00-10:30, requiring advance registration via email or the OPAL learning platform. Professor Waldmann's research spans theoretical computer science with practical applications, focusing on programming paradigms and languages, compiler construction, and computational complexity across different calculation models. His expertise includes declarative programming approaches (functional and constraint programming), automata theory, and formal languages. More recently, he has applied his theoretical knowledge to develop auto-grading systems for e-learning environments, bridging foundational computer science with educational technology. His teaching philosophy emphasizes mathematical foundations in computer science education, as reflected in his reference to G.H. Hardy's "A Mathematician's Apology," where he notes that "pure mathematics is distinctly more useful than applied" because "mathematical technique is taught mainly through pure mathematics." As Dean of Computer Science, he plays a pivotal leadership role in shaping academic programs and ensuring their alignment with both theoretical rigor and industry relevance at HTWK Leipzig.
Miryung Kim is a Professor and Vice Chair of Graduate Studies in UCLA's Computer Science Department, where she directs the Software Engineering and Analysis Laboratory. She is renowned for her pioneering work in software evolution, code clone management, and establishing the emerging field of Software Engineering for Data Intensive Computing (SE4DA and SE4ML). Her research focuses on automated testing and debugging for Apache Spark, developer tools for heterogeneous computing, and conducting systematic studies of refactoring practices in industry. She led the first large-scale study of data scientists in industry and developed JDebloat, a Java bytecode debloating tool that made significant tech transfer impact to the Navy. Her recent publications demonstrate strong trends in fuzz testing for big data analytics and heterogeneous computing, with a focus on natural input generation, co-dependence awareness, and leveraging hardware probes for acceleration. Her work bridges software engineering with data-intensive and heterogeneous computing paradigms. ACM SIGSOFT Influential Educator Award (2022) ICSME Most Influential Paper Award (2023 and 2020) NSF CAREER award Google Faculty Research Award Okawa Foundation Research Award Humboldt Fellow ACM Distinguished Member As an academic advisor, she has produced eight tenure-track faculty members at institutions including Columbia, Purdue, and Virginia Tech. Her research has been supported by National Science Foundation, Air Force Research Laboratory, Google, IBM, Intel, Okawa Foundation, Samsung, and Office of Naval Research. She previously served as Program Co-Chair of ESEC/FSE 2022 and has delivered keynotes at ASE 2019 and ISSTA 2022. She maintains active industry collaborations, serving as an Amazon Scholar at Amazon Web Services and having spent time as a visiting researcher at Microsoft Research.
Jinqiu Yang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. Her research focuses on improving software reliability and quality assurance, particularly in the context of machine learning systems and autonomous vehicles. She leads active research projects in software testing, automated program repair, and mining software repositories, with strong connections to both academic and industrial applications. Her research interests span software reliability, quality assurance of machine learning systems including autonomous vehicles, software testing, automated program repair, text analytics of software artifacts, and mining software repositories. She has developed novel approaches for testing deep learning libraries, evaluating robustness in autonomous driving systems, and tracking the evolution of static code warnings. Her work bridges traditional software engineering with emerging challenges in AI systems, addressing critical issues of reliability and safety in complex software environments. Yang's recent publications (2021-2025) demonstrate a clear trajectory toward AI/ML system reliability, with increasing focus on autonomous vehicles, concept drift detection, and security aspects of large language models. Her work spans both theoretical foundations and practical applications, often involving empirical studies of real-world systems and development of practical tools to address identified challenges. ACM SIGSOFT Distinguished Paper Award Dr. Yang actively mentors graduate students and is currently recruiting Master's and PhD candidates. She has secured significant research funding including NSERC Discovery Grants (2019-2025), Gina Cody Research and Innovation Fellowship (2024-2026), and participation in the NSERC CREATE Program SE4AI (2021-2026). Her research is supported by multiple grants including NOVA – FRQNT-NSERC PROGRAM (2024-2027) and Volt-Age Seed Grant (2024-2026). She leads research in the O-RISA Lab at Concordia University, focusing on reliability and security aspects of intelligent software systems. Her team collaborates with industry partners including IBM, where she previously worked at IBM Watson Research Lab and IBM CAS, bringing practical experience to her academic research.
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.
Shaohua Li is an Assistant Professor at The Chinese University of Hong Kong (CUHK), specializing in the correctness and security of critical software systems with emphasis on compilers. His research spans Software Engineering , Programming Languages , and Security , focusing on innovative compiler testing methodologies. Key areas include leveraging large language models for test generation, optimizing fuzzing techniques through prefix-guided execution, and decoupling sanitization mechanisms to reduce overhead in vulnerability detection. His work addresses fundamental challenges in ensuring reliability of systems programming infrastructure. Recent publications demonstrate a cohesive trajectory toward practical compiler validation: from empirical rustc bug analysis to SAND's low-overhead sanitization framework. The research consistently bridges theoretical formal methods with real-world implementation challenges in security-critical systems, showing particular strength in adapting AI techniques for traditional software testing problems.
Sarah Fakhoury is a Senior Researcher in the Research in Software Engineering (RiSE) group at Microsoft Research, Redmond. Her work bridges formal methods, empirical software engineering, machine learning, and human-computer interaction to optimize developer cognitive effort in AI-assisted programming tools. Her research focuses on trustworthy AI for code generation , leveraging formal verification to ensure correctness in LLM-generated outputs. Key areas include program comprehension, source code readability, and empirical evaluation of developer-AI interaction. She develops tools like 3DGen for provably correct binary parsers and NL2Fix for natural language-based code repair. Her publications reveal strong trends in formal methods integration with AI (60% of recent work), empirical developer studies (30%), and readability/metrics innovation (10%). Keywords cluster around program verification, LLM evaluation, and cognitive load measurement. ACM/SIGSOFT Distinguished Paper Award (ICPC 2018) Fakhoury actively contributes to the academic community as PC member for ASE, ICSE, and ESEC/FSE. She co-organizes workshops like Muslims in ML at NeurIPS and mentors through SMeW. Her RiSE group collaboration with Shuvendu Lahiri and Madanlal Musuvathi drives Microsoft's trustworthy AI4Code initiatives, focusing on verifiable developer tools.
Prof. Wolfgang Ecker is a Professor at the Technical University of Munich (TUM), affiliated with the Chair of Design Automation within the TUM School of Computation, Information and Technology . His research focuses on Electronic Design Automation (EDA), RISC-V processor architectures, and hardware-software co-design. He leads projects advancing EDA tools for embedded systems, neural network acceleration, and formal verification methodologies. Ecker's work bridges machine learning techniques with traditional EDA challenges, addressing topics like energy-efficient AI inference and automated documentation generation. His contributions span compiler optimization, FPGA implementations, and fault analysis in digital systems. Recent research highlights include contributions to the TRISTAN project for RISC-V ecosystem development, model-driven architecture frameworks, and AI-driven timing analysis. He actively collaborates on open-source EDA tools and explores Rust-based embedded systems development. Ecker’s lab emphasizes practical applications in edge computing and automotive microcontroller safety, with a strong emphasis on interdisciplinary collaboration across TUM’s CIT School. His publications (15 most recent listed) reflect a focus on EDA tool innovation, processor design, and leveraging machine learning for hardware optimization. While no specific awards are mentioned, his involvement in ERC-funded projects and leadership in international collaborations underscores his academic impact.
Wenguang Chen is a researcher affiliated with Tsinghua University and Pengcheng Laboratory , specializing in computer science and high-performance computing . His work bridges theoretical advancements with practical applications in domain-specific languages , parallel programming , and machine learning . Research Interests include: Development of modular DSLs for numerical methods (e.g., Mat2Stencil) Performance optimization in distributed and parallel systems Compiler frameworks for privacy-preserving AI (e.g., FHE-based neural network inference) Graph algorithms scaling to trillion-edge datasets Applications of Rust in memory-safe pointer analysis Recent Publications span 2014–2025, focusing on: Parallelization strategies for supercomputing Compiler automation tools Extreme-scale data processing Performance variance diagnosis in production environments
Prof. Jeronimo Castrillon serves as a Professor and holds the Chair for Compiler Construction at Dresden University of Technology. Based at Helmholtzstrasse 18, 3rd floor, Room BAR III68 in Dresden, Germany, his contact details include email jeronimo.castrillon@tu-dresden.de and phone +49 (0)351 463 42716. His research centers on Compiler Construction , focusing on compiler design, optimization techniques, and code generation for resource-constrained systems. This work extends to Embedded Systems where efficient compilation is critical, High-Level Synthesis for hardware design automation, and Computer Architecture through compiler-architecture co-design. His expertise bridges theoretical foundations and practical implementations in programming language ecosystems. As chairholder, he leads academic activities within the Compiler Construction unit, contributing to curriculum development and research supervision in computer engineering disciplines.
Mirela Alistar is an Assistant Professor at the ATLAS Institute and the Department of Computer Science at the University of Colorado Boulder. She leads the Living Matter Lab , focusing on cyber-physical systems based on biochips to revolutionize healthcare diagnostics. Her interdisciplinary work bridges computer science, engineering, biotechnology, and bioart. Education : PhD in Embedded Systems Engineering (2010-2014, Technical University of Denmark), Postdoc in Human-Computer Interaction (2015-2018, Hasso Plattner Institute). Her research advances digital microfluidics and fault-tolerant biochips , enabling at-home diagnostic tools like OpenDrop . She also explores bioart through installations such as Semina Aeternitatis and Perfume Distillation Machine . She co-founded >top , a Berlin-based art & science project space, and advises startups digi.bio and bold.health . Her UIST'16 Honorable Mention Award highlights her innovative contributions. The Living Matter Lab under her leadership investigates interactive biodesign , combining technical precision with creative exploration of living systems.
