Find Academic Supervisors

Donald YeungView profile

Professor

• Computer Architecture
• Computer Engineering
• Memory Systems
• +5 more

Donald Yeung is a Professor and Associate Chair for Undergraduate Education in the Department of Electrical and Computer Engineering at the University of Maryland , with an additional appointment as Affiliate Professor in the Department of Computer Science . His research focuses on Computer Architecture , particularly in memory systems , 3D integration , energy-efficient processors , and parallel processing . He leads projects like Monolithic 3D Integration of CPU and Main Memory and Approximate Computing . Recent work emphasizes ReRAM-based memory architectures , extreme-scale processor design , and micro-fluidic cooling solutions for 3D CPUs. His teaching includes courses like ENE 646: Computer Architecture and ENE 150: Intermediate Programming Concepts . Key achievements include the Best Paper Award at MULTIPROG-2017 and contributions to IEEE Micro and ACM Transactions . His research spans cache optimization , reuse distance analysis , and directory coherence protocols . Current grants include funding for heterogeneous microprocessor parallelism and low-power system design . Advises graduate students Yinuo Wang and Hung-Yu Yeh, and collaborates with teams like the UMIACS Technical Report Group . His lab focuses on memory-centric computing and scalable multicore systems .

Stephen ChongView profile

Professor

• Programming Languages
• Information Security
• Language-based Security
• +3 more

Stephen Chong is a Gordon McKay Professor in the Harvard John A. Paulson School of Engineering and Applied Sciences , where he co-directs the Undergraduate Studies in Computer Science program. His research intersects programming languages and information security , focusing on language-based security frameworks. Education : PhD in Computer Science from Cornell University (2008), B.Sc.(Hons) and B.A. from Victoria University of Wellington (New Zealand). Research : Develops tools like Formulog (Datalog + SMT for static analysis) and Accrue (Java interprocedural analysis), emphasizing security guarantees proportional to programmer effort. Grants : Funded by NSF , DARPA , AFOSR , and Google Faculty Research Award . Recent publications focus on neurosymbolic approaches (e.g., Guess & Sketch ), Datalog synthesis (e.g., Making Formulog Fast ), and quantitative robustness in cyber-physical systems. His group has pioneered formal methods for secure assembly transpilation and sensor attack modeling. Awards : NSF CAREER Award AFOSR Young Investigator Award Sloan Research Fellowship Advising : Supervised numerous PhD and senior thesis students, including Aaron Bembenek , Anitha Gollamudi , and Lucas Waye . Mentored projects like AbcDatalog (multi-threaded Datalog engine) and Shill (secure shell scripting). Labs/Teams : Leads the Programming Languages at Harvard group, collaborating with institutions globally. Organized workshops (e.g., NSF Workshop on Formal Methods for Security ) and chaired committees at conferences like CSF , POPL , and PLDI .

Hanjun KimView profile

Researcher

• Compiler Design
• Machine Learning
• Parallel Computing
• +3 more

Hanjun Kim is a researcher at Yonsei University, focusing on compiler design, machine learning optimization, and hardware-aware programming techniques. His work bridges theoretical research with practical implementations in embedded systems and security domains. Research Interests Compiler-driven optimization for PIM (Processing-in-Memory) architectures Homomorphic encryption compiler design Parallel computing for DNN/LLM inference Network function program analysis Recent research trends include: application of compiler techniques to optimize resource utilization in heterogeneous computing environments, particularly for AI workloads and secure computation. His publications demonstrate expertise in tackling performance bottlenecks through architectural and compiler co-design. Conference Service 2025 SPLASH OOPSLA Review Committee 2025 LCTES Program Committee 2024 CGO Program Committee 2023 LCTES Program Committee 2022 CGO Organization Committee 2020 LCTES Program Committee

• Optimization methods
• Metaheuristics
• Heuristics
• +12 more

Mina Mirhosseini is a Research Fellow at the Faculty of Logistics, Molde University College, Norway. She holds a PhD in Computer Science from Shahid Beheshti University of Tehran, Iran, and has prior academic experience as a faculty member and lecturer in Iran and as a remote teaching assistant at the University of Hertfordshire, UK. Her primary research interests include Optimization Methods, Metaheuristics, Heuristics, Linear Integer Programming, Parallel Processing, Machine Learning, Artificial Intelligence, and Logistics. She has made significant contributions to solving complex computational problems such as the n-similarity problem and Mixed Integer Linear Programming (MILP) models using hybrid and parallel algorithms, particularly in the context of high-level synthesis and wireless sensor networks. The analysis of her recent publications reveals a strong focus on developing and applying advanced optimization techniques, especially quantum-inspired gravitational search algorithms and parallel genetic algorithms, to real-world engineering and computational challenges. Her work consistently emphasizes performance improvement, scalability, and load balancing in distributed and heterogeneous computing environments. Mina Mirhosseini has an extensive publication record in high-impact journals such as IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel and Distributed Computing, Journal of Supercomputing, and Computers and Electrical Engineering. Her research has been published across a range of venues, reflecting interdisciplinary work at the intersection of computer science, electrical engineering, and applied optimization. She has actively contributed to the academic community through roles such as program committee member and executive committee member for conferences on fuzzy systems, swarm intelligence, and evolutionary computation. Her academic journey includes teaching and research roles in Iran, demonstrating a sustained commitment to higher education and scientific inquiry. Mina Mirhosseini is part of the research group focused on Planning, Optimization and Decision Support at Molde University College. Her current work continues to advance the state-of-the-art in parallel and metaheuristic optimization methods, with applications in logistics, synthesis, and sensor network design.

Miroslaw MalekView profile

Professor

• Parallel and distributed systems
• Dependability engineering
• Real-time systems
• +4 more

Prof. Dr. Miroslaw Malek is a Professor and Chair of Computer Architecture and Communication at the Humboldt University of Berlin's Institute of Computer Science. His research focuses on parallel/distributed systems, dependability, real-time systems, and embedded systems. He leads the Computer Architecture Research Group (ROK) and has supervised numerous PhD students across multiple institutions. Malek's work emphasizes fault management, network reliability, and proactive system design. His academic roles include advising on over 25 PhD theses and co-authoring influential books like Responsive Computer Systems . He holds a prominent position in the field, with extensive contributions to service availability, failure prediction, and fault-tolerant computing. Key affiliations include the Faculty of Mathematics and Natural Sciences and the Institute of Computer Science at Humboldt University. Research Interests: - Parallel/distributed/embedded systems - Dependability and real-time responsiveness - Web service architecture and fault tolerance - Network reliability and failure prediction Notable Achievements: - Developed consensus-based frameworks for responsive systems - Pioneered failure prediction methods using hidden Markov models - Authored/co-authored over 30 books and 200+ peer-reviewed publications - Supervised 25+ doctoral students at UT Austin and Humboldt University - Active in international workshops and symposiums on dependability and fault tolerance Labs/Teams: - Computer Architecture Research Group (ROK) at Humboldt University - Collaborations with TU Berlin, Duke University, and University of Minnesota

Thomas OttoView profile

Lecturer

• Perceptual Neuroscience
• Cognitive Neuroscience
• Multisensory Decision Making
• +3 more

Dr Thomas Otto is a Lecturer at the University of St Andrews' School of Psychology and Neuroscience. His research focuses on the interface between perceptual and cognitive neuroscience, employing mental chronometry and mathematical modeling to investigate multisensory decision-making processes. Current work examines cognitive architectures underlying human decision-making in multisensory contexts, logical decision gates, and variability in response time tasks. He has supervised four PhD students and led a BBSRC-funded project on multisensory decision frameworks (2016–2019). Research interests include multisensory integration mechanisms, response time analysis, and decision-making models. Key contributions involve testing race models for audiovisual motion and semantic congruency effects in multisensory responses. Recent studies explore contextual influences on decision processes and subjective visual awareness effects. Publications span 20 years, focusing on perceptual decision-making, race models, and multisensory integration. Developed the RSE-box analysis tool for response time studies. Active in presenting at conferences and leading collaborative datasets (e.g., audiovisual decision-making projects). Labs/Teams: Directs the Cognitive Chronometry Lab (http://lclab.wp.st-andrews.ac.uk/), focusing on temporal aspects of perception and decision-making.

Anjali SandipView profile

Academic

• Computational Mechanics
• High-Performance Computing
• Machine Learning
• +5 more

Anjali Sandip is a Teaching Assistant Professor in the Mechanical Engineering Department at the University of North Dakota's College of Engineering and Mines. She holds a Ph.D. in Mechanical Engineering from the University of Kansas and maintains an active research program in computational mechanics, high-performance computing, and machine learning. Her educational background includes a Doctor of Philosophy and Master of Science in Mechanical Engineering from the University of Kansas, and a Bachelor of Engineering in Mechanical Engineering from Osmania University in Hyderabad, India. She previously served as a post-doctoral researcher at the University of Nebraska, where she developed patient-specific computational models for peripheral artery disease treatment. Dr. Sandip's research spans computational mechanics, high-performance computing, uncertainty quantification, physics-informed machine learning, and multi-physics modeling. Her work has significant applications in ice sheet dynamics, medical device modeling, and multi-phase flow simulations. She has developed open-source software frameworks that integrate finite element and finite volume methods with uncertainty quantification tools. Her recent publications demonstrate a strong focus on developing computational frameworks for multi-physics problems, with particular emphasis on GPU acceleration, uncertainty quantification, and machine learning integration. The research shows consistent application of these methods to challenging problems in earth sciences, biomedical engineering, and traditional mechanical engineering domains. Scientific Awards: NSF EPSCoR Research Fellow (2024-25) Dr. Sandip actively mentors both undergraduate and graduate researchers, and she is currently seeking Master's and Ph.D. students interested in computational mechanics, applied mathematics, scientific machine learning, and earth sciences. She serves as an active member of the Association of Computational Mechanics (USACM & IACM) and has delivered numerous presentations at professional conferences. Her research has received support from prestigious organizations including the National Science Foundation (NSF), Department of Energy (DOE), and NVIDIA. She teaches courses including Introduction to Mechanical Engineering, Thermodynamics, Machine Component Design Laboratory, Advanced Finite Element Methods, Modeling Glaciers and Ice Sheets, Statics, and Engineering Ethics, demonstrating her broad expertise across mechanical engineering disciplines.

Samuel ThibaultView profile

Professor

• Parallel Computing
• High Performance Computing
• Task-Based Runtime Systems
• +3 more

Samuel Thibault is a Professor at University of Bordeaux, affiliated with Laboratoire Bordelais de Recherche en Informatique (LaBRI) and Inria Bordeaux -- Sud-Ouest. He is a member of the SATANAS team at LaBRI (theme: High Performance Runtime Systems for Parallel Architectures) and the STORM research team at Inria Bordeaux (previously RunTime). His work bridges academic research and practical implementation of high-performance computing systems. Thibault's research focuses on task-based runtime systems, particularly StarPU, for heterogeneous and parallel computing architectures. His work addresses critical challenges in scheduling algorithms, memory management under constraints, data locality optimization, and performance modeling for complex NUMA architectures. He has made significant contributions to the field of parallel computing through the development and analysis of runtime systems that efficiently manage tasks across diverse hardware resources including CPUs, GPUs, and other accelerators. His research has practical applications in scientific computing, deep learning inference, and large-scale simulations requiring extreme computing power. His recent publication trends show a strong emphasis on optimizing task-based runtime systems for heterogeneous architectures with particular attention to memory constraints and data locality. There's a clear progression toward applying these techniques to deep learning inference workloads, as evidenced by his StarONNX project. His work consistently addresses the challenge of balancing throughput and latency in complex computing environments, with increasing focus on recursive task graphs and dynamic adaptation strategies. Thibault is actively involved in European research initiatives including TEXTAROSSA (focusing on exascale technologies) and EXA2PRO (high development productivity on heterogeneous systems). His work has been published consistently in top-tier conferences and journals in parallel and distributed computing. As part of the STORM team at Inria Bordeaux, Thibault contributes to advancing the state of the art in runtime systems for high-performance computing. His team's work on StarPU has become a reference implementation in the field, enabling researchers and practitioners to develop applications that can efficiently utilize heterogeneous computing resources without needing to manage the complexity of different hardware architectures directly.

Daniel J. CrossView profile

Professor

• Mathematical Physics
• Computational Physics
• Topology
• +5 more

Dr. Daniel J. Cross is a Professor in the Physics and Mathematics department at Geneva College. He teaches courses including Acoustics, Classical Mechanics, College Physics, and Modern Physics. He holds a Ph.D. in Physics from Drexel University (2010) and dual bachelor's degrees in Mathematics and Physics from Cedarville University (2002). His research integrates mathematical and computational approaches, with emphasis on: Topology and group theory in nonlinear dynamics Chaos theory and electromagnetism Relativity and GPU-accelerated computations Current projects investigate magnetic field line topology, gravitational dynamical systems, and parallel computing applications. He received the AAPT Reviewer Excellence Award (2020) and mentors students including NOYCE scholars Emerson Spear and Peter Lewis (2023). He serves as faculty advisor for the Anime Club and is a member of the American Physical Society (APS), American Scientific Affiliation (ASA), and Sigma Pi Sigma (ΣΠΣ) Physics Honor Society.

• Computer Architecture
• Programming Models
• Parallel Computing
• +5 more

Marc Gonzalez Tallada is a researcher at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the Barcelona School of Informatics (FIB). He is a member of the MGT Research Group, focusing on Programming Models and high-performance computing systems. His research interests lie in the domain of computer architecture and programming models, with a strong emphasis on parallel and distributed computing, performance optimization, and system-level software. His work contributes to the design and implementation of efficient programming models that exploit modern parallel architectures. The recent publications (2010–2024) reflect a consistent focus on programming models, particularly in the context of high-performance computing. Key themes include task-based programming, runtime systems, compiler optimization, memory management, and performance analysis. The research spans computer architecture, system software, and compiler design, demonstrating a multidisciplinary approach to solving challenges in parallel computing. Scientific Awards and Recognitions: Premi o reconeixement Advising and Grants: While specific student names are not listed, his involvement in competitive R&D+i projects indicates active participation in funded research. These projects suggest leadership or collaboration in grant-supported initiatives related to programming models and computer systems. His role in innovation and research projects highlights contributions to both academic and applied domains. Labs and Research Teams: He is a core member of the MGT (Programming Models) research group at UPC, which focuses on advancing the state-of-the-art in programming models for parallel and distributed systems.

• Computer Architecture
• High-Performance Computing
• Distributed Systems
• +12 more

Yolanda Becerra Fontal is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the Barcelona School of Informatics (FIB). She is actively involved in research projects and collaborations, notably with the Barcelona Supercomputing Center, and is a member of prominent research groups such as the High Performance Computing Group (CAP) and CROMAI (Computing Resources Orchestration and Management for AI). Research Interests: Her research spans a broad spectrum of computer systems, with a consistent focus on performance, efficiency, and scalability. Key areas include Computer Architecture , High-Performance Computing (HPC) , Distributed and Cloud Systems , Resource and Energy Management in virtualized environments, and Data-Intensive Computing . More recently, her work has centered on innovative time-series database systems and data management for edge and cloud analytics. Publication Trends: Her recent scholarly output (2020-2022) shows a strong emphasis on time-series data management, proposing novel database architectures like NagareDB and strategies for polyglot persistence. Earlier work (2009-2013) was pivotal in MapReduce workload management, energy accounting for virtualized systems, and optical data center networks, demonstrating a long-standing contribution to foundational distributed computing challenges. Scientific Contributions: Her work has been published in top-tier journals and conferences such as Future Generation Computer Systems , IEEE Transactions , and Nucleic Acids Research . She has also contributed to significant competitive R&D projects and holds patents related to data flow management and distributed indexing. Advising and Grants: Dr. Becerra Fontal has served as a thesis advisor for doctoral students. Her research has been funded through competitive grants from national and regional programs, including Spanish State Research Plans (Plan Estatal de Investigación) and Catalonia's RIS3CAT strategy, supporting projects on high-performance computing and data management. Research Groups and Labs: She is a core member of the CAP - High Performance Computing Group and the CROMAI - Computing Resources Orchestration and Management for AI group at UPC. Her work is closely associated with the Barcelona Supercomputing Center (BSC) , one of Europe's leading supercomputing facilities, indicating access to advanced computational infrastructure.

• Multi-Agent Systems
• Computational Intelligence
• Smart Energy Systems
• +4 more

J.A. La Poutré is a Full Professor at Delft University of Technology and a Scientific Staff Member at CWI (Centrum Wiskunde & Informatica) in the Intelligent and Autonomous Systems group. He holds part-time positions and is involved in research on multi-agent systems and computational intelligence for smart energy systems, with a focus on market-based optimization and fairness in network congestion management. PhD in computer science (Utrecht University, 1991) MSc in mathematics (TU Eindhoven, 1986) His research spans algorithm design, game theory, and reinforcement learning applied to energy systems, particularly smart grids and market mechanisms. Recent publications address cybersecurity threats in power networks, auction-based energy trading, and AI integration in media sectors. The 2019–2025 publications highlight his work in combining game theory with smart grid optimization , covering topics such as topology attacks , demand-side bidding , and fair congestion management . Papers frequently involve reinforcement learning , metaheuristics , and market mechanisms . Scientific Awards : Best paper award at GECCO-2015 KNAW fellow (Utrecht University, 1991–1997) He has led funded projects like Computational Capacity Planning in Electricity Networks (STW program) and co-chairs the Commit2Data Energy division. La Poutré also serves as Vice President of ERCIM (European Research Consortium for Informatics and Mathematics).

Ole MadsenView profile

Professor

• Robotics
• Automation
• Industry 4.0
• +6 more

Ole Madsen is a Professor at the Department of Materials and Production within The Faculty of Engineering and Science at Aalborg University . His research focuses on Robotics and Automation , particularly in 5G Smart Production , AI for Manufacturing , and Industry 4.0 applications. He also holds a part-time position at Adding Robotics , applying his expertise in robot integration for industrial and healthcare settings. Research Interests : Robotics, Automation, AI, Sensor Systems, Modular Manufacturing, Welding Technology, Digital Twins, Human-Centered Robotics, Industry 4.0 His work spans 35+ years with over 205 publications , emphasizing smart production systems and robot-assisted processes . Key contributions include Swarm Production Architectures , 5G-Enabled Robotics , and Human-Robot Collaboration frameworks. He has supervised 10 PhD students and led major projects like RAU (Robot-Assisted Ultrasound) and GINP: Robotics & AI Innovation Network . Scientific Awards : SCAP2020 Best Presentation Award (2020) Ole's 31 projects include AP2030: Aseptic Factory 2030 (pharma production), AddSmart (robotics R&D), and 5G-Enabled Autonomous Systems . His 127 press/media mentions highlight advancements in robotic welding , industrial metaverse , and swarm production . He maintains an ORCID: 0000-0003-2133-2541 with extensive publication records across Swarm Robotics , Modular Manufacturing , and AI-Driven Production .

Yuanchao XuView profile

Assistant Professor

• Computer Architecture
• Security
• Machine Learning Systems
• +3 more

Yuanchao Xu is an Assistant Professor in the Department of Computer Science and Engineering at the University of California Santa Cruz. He earned his Ph.D. from North Carolina State University, advised by Dr. Xipeng Shen and Dr. Yan Solihin, and is a student researcher at SystemResearch@Google since 2021. His research spans computer architecture, security, and ML systems.

• Database Systems
• Distributed Data Management
• Concurrency Control
• +5 more

Alan D. Fekete is a Professor at the University of Sydney's Department of Computer Science, specializing in database systems, distributed data management, and consistency models. His work spans transaction processing, cloud computing, and query optimization, with recent focus on enhancing database concurrency and serializable execution. Key Research Areas: Database Concurrency & Transaction Isolation Multicore Scalability & Distributed Systems Cloud Data Consistency & Replication Query Optimization & NoSQL Performance Recent publications (2023-2025) explore transactional frameworks for analytical interfaces, DB-OS co-design for data ingestion, and mixed isolation levels for serializable execution. Earlier works (2018-2014) address scalable lock managers, coordination avoidance in databases, and consistency properties in cloud storage. He has contributed to educational initiatives, including a data-centric computing curriculum (2021) and teaching threading concepts (2008). Collaborations include co-authors like Nancy Lynch, Uwe Röhm, and Joseph Hellerstein.