Thomas Engelhardt is a Researcher and PhD student at the Computer Graphics Group of the Karlsruhe Institute of Technology (KIT) , Germany. His work focuses on real-time rendering techniques, including global illumination, participating media, and visibility culling. Research Interests: Interactive 3D graphics High-quality lighting simulation GPU-based rendering algorithms Occlusion culling and visibility optimization Participating media rendering Environment mapping techniques Publications demonstrate expertise in virtual point lights, epipolar sampling, hierarchical buffers, and scalable final gathering for global illumination.
Dr. Haojin Yang is a Professor and group leader of the Multimedia and Machine Learning Research Group at the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany. He works under the supervision of Prof. Dr. Christoph Meinel and leads research in efficient AI, edge computing, and deep learning applications. His research group includes several PhD students and scientific coworkers focusing on cutting-edge machine learning problems. Dr. Yang's research interests span Efficient AI systems with emphasis on edge computing, binary neural networks, deep model acceleration and compression, computer vision applications, and multimodal data retrieval. His work bridges theoretical advancements with practical applications in medical imaging, weather forecasting, and digital humanities. He has made significant contributions to the field of binary neural networks through frameworks like BMXNet and BITorch. His recent publications demonstrate a strong focus on optimizing neural networks for efficiency, particularly in binary and low-bit implementations, while maintaining performance. His work spans computer vision, natural language processing, and multimodal learning, with particular attention to real-world deployment constraints like computational efficiency and energy consumption. BEST PAPER AWARD at the 22nd IEEE International Symposium on Parallel and Distributed Processing with Applications (IEEE ISPA 2024) for 'Low-Bit CUTLASS GEMM Template Auto-Tuning Using Neural Network' Dr. Yang actively supervises numerous PhD and Master's students, with several having completed their degrees and moved to prominent positions at companies like Amazon AI, German Aerospace Center, and MILA-Quebec AI Institute. His research is supported by collaborations with SAP, Wildenstein Plattner Institute, and various academic institutions. He also contributes to the academic community as a reviewer and program committee member for top conferences including ICML, NeurIPS, and CVPR.
Jonas Unger is a Professor at Linköping University's Department of Engineering and Natural Sciences (ITN) and Media and Information Technology (MIT). As a research leader in computer graphics and image processing, he contributes to advanced visualization techniques, GPU computing, and AI applications in healthcare and climate adaptation. Specializes in BRDF modeling, spectral light field imaging, and augmented intelligence Active in AI4Climateadaptation and Wallenberg Autonomous Systems Program (WASP) Recipient of the 2023 Chester Carlson Research Prize Develops AI methods for medical imaging (SCAPIS dataset) and autonomous systems His recent work focuses on deep learning for material acquisition, real-time rendering optimizations, and visual analytics pipelines for disaster response systems. Current projects include multi-agent reinforcement learning for air traffic control and GPU-accelerated signal processing techniques. Scientific awards include: Chester Carlson Research Prize (2023) He collaborates with Visual Sweden's Augmented Intelligence platform, co-leads major visualization conferences, and contributes to Horizon 2020 Marie Skłodowska-Curie funded research. Contact: jonas.unger@liu.se
Yongluan Zhou is a Professor at the Department of Computer Science , University of Copenhagen , where he co-heads the Data Management Systems Lab (DMS Lab) and serves as Head of Studies for the MSc in Computer Science . His academic journey includes a PhD from the National University of Singapore (NUS) (2007), a postdoc at ETH Zürich (2007–2008), and prior roles as Associate Professor at University of Southern Denmark (SDU) (2008–2017). PhD in Computer Science, National University of Singapore (2002–2007) Postdoc, ETH Zürich (2007–2008) Zhou's research focuses on database systems and distributed systems , with recent emphasis on event-driven systems , scalable stream processing , and big graph analysis . His work bridges theoretical foundations and practical implementations, addressing challenges in data consistency, fault tolerance, and resource optimization in cloud and microservice environments. The trends in his 15 most recent publications (2025–2024) highlight advancements in asynchronous choreographies , blockchain consensus protocols , GPU-accelerated graph processing , and microservices data management . These works integrate formal methods with empirical validation, emphasizing scalability, security, and efficiency in distributed environments. He actively contributes to academic governance as a member of the DEBS Steering Committee (2024–), SSDBM Steering Committee (2022–), and the EDBT Association Executive Board (2020–).
Cosmin Eugen Oancea serves as an Associate Professor in the Programming Languages and Theory of Computation section at the Department of Computer Science (DIKU), University of Copenhagen, Faculty of Science. His academic profile shows active engagement in research, teaching, and collaboration within the computer science community. Education: PhD in Computer Science from Western University (completed November 20, 2005) Dissertation title: "Parametric Polymorphism for Software Component Architectures and Optimizations" Oancea's research spans multiple domains within computer systems, with primary focus on static, dynamic and hybrid techniques for automatic parallelization (optimizing compilers), programming language design and implementation, memory management, and computer algebra. His recent work demonstrates strong connections between functional programming, array languages, and high-performance computing, with applications in financial modeling, automatic differentiation, and time series analysis. He has made significant contributions to the Futhark programming language ecosystem, focusing on compiler optimizations and performance tuning for GPU architectures. His publication record shows consistent output in top-tier venues including SC (International Conference for High Performance Computing), IFL (Symposium on Implementation and Application of Functional Languages), and ARRAY (Workshop on Libraries, Languages and Compilers for Array Programming), with a notable concentration of publications from 2021-2023. His research demonstrates interdisciplinary connections between computer science theory and practical applications in finance, environmental data analysis, and machine learning infrastructure. Oancea maintains active international collaborations across multiple countries, as indicated by the network visualization of his research partnerships. His work bridges theoretical computer science with practical implementations, particularly in the domain of array programming and compiler optimization for parallel architectures.
Yue Shi serves as an Instructor in the Department of Computer Science at the University of Copenhagen, actively contributing to the Programming Languages and Theory of Computation (PLTC) section. This section drives research at the intersection of programming language theory and practical applications including security, privacy, and fintech. Research interests encompass: Programming Languages Theory of Computation Formal Verification Distributed Ledger Technology Compiler Technology Computer Security Within the PLTC ecosystem, Yue Shi engages with specialized research groups such as Decentralized Systems (blockchain/distributed ledgers), Security & Privacy, HOT Lab (higher-order typed languages), and FUTHARK (GPU-accelerated functional programming). The section supports BSc and MSc programs in Computer Science, Machine Learning, and related disciplines while maintaining industry collaborations in financial transparency and quantum programming.
Akshitha Sriraman is an Assistant Professor at Carnegie Mellon University with joint appointments in Electrical and Computer Engineering and Computer Science. Her research bridges computer architecture and software systems to enable sustainable, equitable, and efficient hyperscale web infrastructures. She holds a PhD in Computer Science from the University of Michigan (2021) and an M.S. in Embedded Systems from the University of Pennsylvania (2015). Research Focus: Dr. Sriraman redesigns computing stacks to achieve high-performance web systems while addressing critical ethical implications like sustainability (e.g., reducing hardware carbon footprint) and equity (e.g., eliminating demographic bias in web scheduling). Her solutions span from analytical models to production deployments at hyperscale, influencing industry hardware designs including Intel's CPUs and Infrastructure Processing Units. Publications: Her 15 most recent publications demonstrate consistent focus on optimizing data center systems through architectural innovations. Key themes include carbon-efficient cloud servers, hardware-accelerated metadata management, and profile-guided microarchitectural optimizations. Work appears in premier venues like ISCA, MICRO, ASPLOS, and IEEE Micro, with two papers receiving IEEE Micro Top Picks distinctions. Awards & Honors: Google ML and Systems Junior Faculty Award (2025) NSF CAREER Award (2023) Intel Rising Star Award (2023) George Tallman Ladd Research Award (2024) IEEE Micro Top Picks (2020, 2024) ACM SIGARCH/TCCA Outstanding Dissertation Award HM (2022) Meta Systems Research Award Facebook Fellowship Students & Funding: Advises 10+ PhD/Master's students working on sustainable systems, equitable computing, and hyperscale architectures. Student achievements include NSF Graduate Fellowships and K&L Gates Presidential Fellowships. Research supported by NSF, Google, Meta, Intel, VMWare, and AWS grants. Lab & Service: Leads the Ethical Computer Systems Lab focusing on hardware/software co-design for societal impact. Founded workshops like HotEthics and initiated DEI activities recognized by CMU's Service Award. Regularly serves on program committees for OSDI, ISCA, and ASPLOS.
Stefan Manegold is a Professor for Data Management (0.2 fte) at Leiden University's Faculty of Science within the Leiden Institute of Advanced Computer Science (LIACS), while also serving as a Senior Researcher (0.8 fte) and former Head (2011-2024) of the Database Architectures Research Group at Centrum Wiskunde & Informatica (CWI) in Amsterdam. His career spans over 27 years at CWI and 11 years as a professor at Leiden University, with prior experience at Humboldt-Universität zu Berlin. Professor Manegold's research focuses on innovative database architectures, particularly column-store systems and hardware-aware database technologies. His expertise spans database query optimization, parallel and distributed information systems, XML storage and processing, and scientific data management. He has pioneered work in adaptive indexing, progressive query processing, and main-memory database systems that leverage modern hardware capabilities. His research bridges theoretical database concepts with practical implementations, as evidenced by his involvement in the MonetDB open-source database system. His work shows a clear evolution from foundational database research toward addressing modern challenges in big data management, scientific data processing, and interactive analytics. Recent publications demonstrate his continued leadership in database indexing techniques, GPU-accelerated database operations, and geospatial data management. Professor Manegold has received significant recognition for his contributions to the database community, including the prestigious 2020 ACM SIGMOD Contributions Award, the VLDB'2011 Challenges & Visions Track Best Paper Award, and the VLDB'2009 10-year Best Paper Award. His work has had substantial impact on both academic research and practical database system design. He has been actively involved in the academic community through conference organization, particularly with SIGMOD and VLDB events, and has contributed to numerous workshops including the Data Management on New Hardware (DaMoN) series. His leadership extends to collaborative research projects such as SciLens, PROMIMOOC, and DAMIOSO, which address data management challenges in scientific domains. Professor Manegold leads the Database Architectures Research Group at CWI, which has been at the forefront of database system research for decades. The group's work on MonetDB has influenced modern column-store database systems and continues to push boundaries in areas like progressive query processing and hardware-aware database design.
Minlan Yu is the Gordon McKay Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). She leads the Harvard Theory and Systems group and co-leads the Harvard Power and AI initiative. Her research focuses on data networking, distributed systems, and software-defined networking, with recent emphasis on sustainable computing and AI-driven network management. She is also Assistant Director of the SRC/DARPA JUMP 2.0 ACE Center for Evolvable Computing. Key research interests include network optimization, edge AI serving, large-scale resource allocation, and fault tolerance in distributed systems. Recent work highlights include innovations in energy-efficient data centers, homomorphic encryption acceleration, and real-time network telemetry using FPGA coprocessors. Teaching responsibilities include advanced courses on networking (CS 145/243) and systems programming. She actively advises PhD students and postdocs across systems and networking domains. Her lab's work has led to impactful contributions in both academia and industry, with a focus on bridging theory and practical system implementations. Current research initiatives include the Harvard Power and AI initiative exploring energy-efficient AI workflows and the development of evolvable computing infrastructure through the ACE Center.
Cristina MARINESCU is an Associate Professor at the Politehnica University of Timisoara, Faculty of Automation and Computer Science. She holds a Dr. Eng. degree in Software Engineering from the same institution. Her research focuses on software quality assessment, empirical software engineering, and object-oriented design. She has contributed to projects like Methods and Tools for Continuous Quality Assurance in Complex Software Systems and Quality Assurance for Distributed Software Systems . Her academic roles include teaching Object-Oriented Programming and Algorithms for Parallel Computing labs. She has been involved in research groups like LOOSE and eAustria Institute Timisoara. Notably, she received the Best Reviewer Award at SCAM 2011. Her work spans publications in conferences like IEEE SCAM, ICSM, and WCRE, addressing topics from design flaws detection to cloud-based quality assessment. Her projects emphasize software evolution, design flaw mitigation, and distributed systems analysis. She has collaborated internationally, including with Swiss teams under the NOREX project. Her teaching and research activities reflect a commitment to advancing software engineering practices and education.
Fan Long is an Assistant Professor in the Department of Computer Science at the University of Toronto, affiliated with the Bahen Centre for Information Technology. His research focuses on programming languages, software engineering, systems security, and blockchain technology, with particular emphasis on smart contract security and automatic patch generation. He leads the Conflux project, a high-throughput blockchain platform, and has contributed to systems like Shrec (bandwidth-efficient transaction relay), Gosig (scalable Byzantine consensus), and CodePhage (cross-application code transfer). Education background includes a PhD in Computer Science from MIT (as evidenced by his former MIT homepage). His work spans foundational blockchain scalability improvements, runtime validation for smart contracts (Solythesis), and input sanitization techniques (SIFT/SOAP). Notable awards include a Distinguished Paper at ICSE 2024 and Best Paper at IEEE ICBC 2022. Research highlights include developing Prophet (machine learning-driven patch generation), RCV (crash recovery tool), and LVMT (authenticated storage for blockchains). His lab collaborates with industry partners to address challenges in decentralized systems, including transaction finality, oracle security, and gas optimization in DeFi applications. Grants and collaborations include projects funded by NSERC, MIT-IBM Watson AI Lab, and industry partnerships with blockchain firms. Active in open-source communities, maintaining repositories for Conflux, Prophet, and CodePhage. Recent work explores atomic state sharding (Möbius), flash loan attack synthesis (Flashsyn), and robust front-running methodologies.
William Steven Moses is an Assistant Professor at the University of Illinois at Urbana-Champaign, holding joint appointments in the Siebel School of Computing and Data Science, Electrical and Computer Engineering, and the Coordinated Science Lab. His research focuses on compiler design, automatic differentiation, machine learning systems, and high-performance computing. Moses has contributed to advancements in compiler transformations, GPU optimization, and parallel computing frameworks. His work emphasizes performance portability, compiler-based optimizations for heterogeneous architectures, and mitigating challenges in automatic differentiation. Key contributions include the MLIR Transform dialect for compiler extensibility and transparent checkpointing techniques for program loops. Recent publications span code generation, performance portability for GPU workloads, and transpilation strategies for cross-platform execution. Moses collaborates with industry and academic partners to address challenges in compiler frameworks and parallel computing systems. No formal advisees are listed, though his research group likely engages students in these areas. His work is supported by grants from NSF and industry partnerships, though specific funding details are not provided.
Pen-Chung Yew is a Professor in the Department of Computer Science and Engineering at the University of Minnesota at Twin Cities. His research focuses on computer architectures, compilers, and system security, with particular emphasis on dynamic binary translation (DBT), system virtualization, and leveraging machine learning for compiler optimization. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (1981), an M.S. from the University of Massachusetts Amherst (1977), and a B.S. from National Taiwan University (1972). Key research areas include enhancing security at micro-architectural and code levels, memory systems optimization, and parallel program debugging. Notable contributions include scalable emulators with retargetable DBT (DQEMU), efficient cross-ISA virtualization, and learning-based approaches for dynamic binary translation. He has been involved in NSF-funded projects exploring retargetable DBT and dynamic runtime optimization. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign (1981) M.S., Computer Engineering, University of Massachusetts Amherst (1977) B.S., Electrical Engineering, National Taiwan University (1972) His work has received recognition, including a Best Paper Award Finalist at CGO 2020. Collaborations span institutions like the National Science Foundation and international conferences, with contributions to journals such as IEEE Transactions on Parallel and Distributed Systems and ACM Transactions on Architecture and Code Optimization. Grants and projects include NSF support for dynamic binary translation research and petascale simulation of turbulent stellar hydrodynamics. His research bridges theoretical advancements with practical applications in high-performance computing and secure systems.
Ehsan Miandji is an Assistant Professor and Docent at Linköping University's Department of Science and Technology (ITN), part of the Faculty of Science and Engineering. His research focuses on computer graphics, computer vision, and machine learning, with a particular emphasis on BRDF modeling, light field imaging, compressed sensing, and sparse representation techniques. He is affiliated with the Computer Graphics and Image Processing group and the Wallenberg Autonomous Systems Program (WASP). His work spans both theoretical advancements and applied methodologies in visual data processing. Recent research includes optimizing BRDF acquisition via FROST-BRDF, advancing multidimensional compressed sensing for spectral light fields, and developing sparse representation frameworks for bidirectional texture functions (BTF). Miandji collaborates with interdisciplinary teams within the Media and Information Technology (MIT) division, contributing to projects that bridge computational imaging, algorithm design, and real-world applications. His publications reflect a strong commitment to pushing boundaries in visual data compression, rendering efficiency, and perceptual quality assessment of material models.
Santosh Pande is a Professor and Associate Chair for Graduate Studies at the School of Computer Science , Georgia Institute of Technology. He is affiliated with the Center for Experimental Research in Computer Systems (CERCS) and the Online Master of Science Computer Science (OMSCS) program. His research focuses on compiler analysis and optimizations, particularly in enhancing software properties through static and dynamic analysis. Key areas include hardware security (e.g., side-channel attacks on secure processors), real-time systems (converting interactive software behavior into quantifiable metrics), and parallel computing (optimizing SAT solvers and GPU scheduling). His work bridges compiler design with security, embedded systems, and machine learning, supported by grants from NSF, ONR, DARPA, and industry. Research Highlights: Side-channel attack mitigation on XOM secure processors Framework for soft real-time software analysis Compiler-guided security defenses (e.g., Pythia, Decker) High-performance RF emulation architectures His publications emphasize compiler-driven security, real-time computing, and algorithmic optimization. Over 100+ papers and open-source tools reflect his commitment to advancing compiler theory and practice. Current projects apply these techniques to machine learning algorithms, enhancing their efficiency and security.
