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Hanchen WangView profile

Research Fellow

• Artificial Intelligence for Science
• Biomedical AI Agents
• Multi-omics Analysis
• +7 more

Hanchen Wang is a Postdoctoral Research Fellow at Stanford AI Lab and Genentech, working under Jure Leskovec and Aviv Regev. He holds a PhD in Computer Science from Cambridge University completed in 3 years under Joan Lasenby, and a BS in Physics from Nanjing University where he was valedictorian. His research bridges artificial intelligence and biomedical discovery, with appointments spanning both academic and industry settings. Wang's research focuses on AI for Science , particularly developing autonomous agents for biomedical discovery. His work spans multi-omics analysis , spatial transcriptomics , live-cell imaging , and perturbation assays , with applications in cancer therapeutics , autoimmune diseases , and neurological disorders . He has pioneered multiple AI agent frameworks including Biomni (a general-purpose biomedical agent), SpatialAgent, and PerTurboAgent for specialized biological discovery tasks. His publication record demonstrates significant impact across both computer science and biology venues, with first-author papers in Nature , Nature Biotechnology , and NeurIPS . His research has been deployed by Anthropic, Amazon Web Services, and Genentech, and featured in Nature , The Economist , and DeepMind communications. Chan Zuckerberg Initiative Faculty Applicant Bootcamp participant UCSF Gladstone Institute Trainee-to-Tenure Track Program member OpenAI Researcher Access Program recipient Multiple conference travel awards Wang actively mentors early-career researchers including PhD students from institutions like CSHL, MIT, Harvard, and KAIST. He serves as Area Chair for ICLR 2026, organizes workshops on AI for Science at major conferences, and gives invited talks at leading institutions including Harvard, Yale, and the Broad Institute. His research is supported by Genentech internal funding ($500k/year) and OpenAI resources.

Ramsay TaylorView profile

Visiting Professor

• formal methods
• distributed systems
• Erlang
• +5 more

Dr. Ramsay Taylor is a Visiting Professor at the University of Sheffield's School of Computer Science, with dual affiliation to the Multidisciplinary Engineering Education (MEE) department. His research bridges theoretical computer science with practical engineering applications, focusing on formal verification of software systems. Dr. Taylor earned his BSc (Hons) in Computer Science from the University of Kent at Canterbury and completed his PhD in Computer Science at the University of Sheffield. Prior to academia, he worked at the Defence Science and Technology Laboratory (DSTL) in the Safety Critical Systems team, focusing on military aircraft systems and critical software across various domains. His research centers on formal methods for software verification, with particular expertise in distributed systems using Erlang. Dr. Taylor specializes in reverse engineering system models through grammar inference techniques and developing extended finite state machine representations. A distinctive aspect of his work involves verifying software that interacts directly with electronic or physical systems - an area often excluded from traditional formal verification approaches due to hardware dependencies. His involvement with the Diamond SMART building project provides practical applications for exploring these software-physical system interactions. Dr. Taylor has developed several significant open-source tools including Smother for MC/DC analysis of Erlang code, mu2 for mutation testing, and Synapse as an interface between testing tools and model inference systems. These tools demonstrate his commitment to bridging theoretical formal methods with practical software development needs. Member of the Verification and Testing research group at University of Sheffield Key contributor to the PROWESS project applying Property Based Testing to Erlang systems Active developer of StateChum system for grammar inference and model learning His teaching experience spans software engineering and electronic engineering across multiple engineering disciplines. The evolution of his work from early research on Z formalism (2008) through to recent automated verification frameworks (2022) demonstrates sustained contribution to advancing software verification techniques.

• Computer Architecture
• Security
• Systems and Networking
• +3 more

Dr. Theo Markettos is a Senior Research Associate at the Department of Computer Science and Technology, University of Cambridge. His research focuses on computer architecture, security, and systems & networking, with notable contributions to the CHERI capability system and FPGA-based hardware prototyping. He teaches the ECAD and Architecture Practical Classes (Part IB) for undergraduate students. His work spans secure hardware-software interfaces, memory protection mechanisms, and parallel computing architectures. He has contributed to projects like Bluehive (a FPGA-based neural network simulator) and CHERI, which enhances system security through compartmentalization and capability-based protections. Recent publications address heterogeneous accelerators, dynamic scalarization for GPGPUs, and termination detection in message-passing architectures. Markettos has collaborated extensively with researchers from academia and industry on topics such as FPGA cluster interconnect design, DMA vulnerability analysis (Thunderclap), and FPGA-based SoC prototyping. His teaching emphasizes hands-on exploration of full-stack computer design using FPGAs. He is based at the William Gates Building in Cambridge and leads efforts in secure computing systems through hardware innovation and interdisciplinary collaboration.

• High-performance computing
• Parallel computing
• Scientific computing
• +7 more

David E. Bernholdt is a Researcher at Oak Ridge National Laboratory in Tennessee, USA. With a prolific publication record spanning from 1995 to 2025, he has established himself as a leading expert in high-performance computing and scientific software development. His work focuses on advancing computational methods and software infrastructure for scientific applications across multiple domains. Dr. Bernholdt's research interests center around high-performance computing, parallel computing, and scientific software engineering. He has made significant contributions to component-based software architecture, fault tolerance in extreme-scale applications, and sustainable software ecosystems for scientific computing. His work bridges the gap between theoretical computer science and practical scientific applications, with particular emphasis on making scientific software more reliable, efficient, and maintainable. His recent publications demonstrate a strong focus on research software engineering practices, sustainability, and productivity. He has been actively involved with the IDEAS Productivity Project and Better Scientific Software (BSSw.io) initiative, working to establish community best practices and improve the professional recognition of research software engineers in scientific computing. Dr. Bernholdt has collaborated extensively with researchers across multiple institutions and has contributed to significant projects in exascale computing, climate modeling, and quantum chemistry computations. His work on the Common Component Architecture and related frameworks has influenced software development practices in scientific computing.

• High-Performance Computing
• Parallel I/O Systems
• Scientific Computing
• +3 more

Venkatram Vishwanath is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the College of Engineering. His research focuses on high-performance computing (HPC), parallel I/O systems, machine learning applications, and scientific computing. He has contributed to advancing scalable data management, performance optimization on supercomputers, and interdisciplinary projects such as the HACC cosmological simulation framework. Collaborations with institutions like Argonne National Laboratory highlight his work in bridging computational science with practical applications. Key areas of expertise include topology-aware data aggregation, GPU performance modeling, and accelerating I/O operations for large-scale simulations. His work on frameworks like vl3 and PIDX has improved parallel data handling and visualization. Awards and recognitions are not explicitly listed in the provided data, but his extensive publication record and leadership in HPC projects reflect significant contributions to the field.

• Combinatorics
• Probability
• Statistical Mechanics
• +3 more

Dr. Mustazee Rahman is an Associate Professor in the Department of Mathematical Sciences at Durham University. His research focuses on Combinatorics, Probability, and Statistical Mechanics, with contributions to stochastic processes, random matrix theory, and graph theory. He has published extensively on topics such as particle systems, growth models, and permutation patterns. His work bridges theoretical mathematics and applications in statistical physics. Key research interests include scaling limits in interacting particle systems, spectral properties of graphs, and algorithmic limitations in combinatorial optimization. Recent studies explore universality classes in KPZ models and local statistics in random sorting networks. His research also addresses percolation phenomena and meta-Fibonacci sequence dynamics. Publications span high-impact journals such as the Annals of Probability, Communications on Pure and Applied Mathematics, and Combinatorica. He has contributed to understanding phase transitions in random graphs and the geometry of permutation limits. His work demonstrates interdisciplinary connections between combinatorics, probability, and mathematical physics.

• Parallel Computing
• Java Virtual Machine (JVM) Optimization
• Performance Profiling
• +5 more

Walter Binder is a Full Professor in the Faculty of Informatics at the Università della Svizzera italiana (USI). He holds a MSc, PhD, and venia docendi from Vienna University of Technology. Previously, he was a senior researcher at the Artificial Intelligence Laboratory, EPFL. His research focuses on program transformations, virtual execution environments, aspect-oriented programming, profiling, and resource management. Education: MSc from Vienna University of Technology PhD from Vienna University of Technology Venia docendi (Habilitation) from Vienna University of Technology Research Interests: His work spans JVM optimization, parallel computing, and dynamic program analysis. Notable contributions include tools like Renaissance (a JVM benchmark suite), S2S (SQL-to-Stream translator), and Akkaprof (profiler for actor-based systems). He explores topics such as thread management, vectorization, and performance profiling in distributed systems. Articles Trends: Recent work emphasizes JVM performance (e.g., Java Vector API, Native Image optimizations), parallel execution policies (NAS benchmarks), and adaptive runtime systems (e.g., MPR framework). He also investigates big data systems (Spark, Node.js) and runtime verification tools. Grants & Labs: Leads projects on JVM profiling, polyglot runtimes, and large-scale program analysis. His lab develops tools like NodeMOP for Node.js and AccStream for stream processing systems.

• High-Performance Computing
• Monte Carlo Methods
• Grid and Distributed Computing
• +7 more

Vassil Alexandrov is a Professor at the School of Systems Engineering, University of Reading, specializing in high-performance computing, Monte Carlo methods, and grid-based scientific computing. His research integrates fault tolerance, virtual environments, and e-Science infrastructures, with strong collaborations in interdisciplinary projects such as eMinerals and P-GRADE. PhD in Computational Science (inferred) Extensive research in parallel and distributed algorithms His research interests lie at the intersection of computational science and distributed systems. He has pioneered the use of Monte Carlo methods for matrix computations and large-scale environmental modeling, particularly in air pollution simulation. His work extends into immersive virtual environments for medical training, psychological modeling, and collaborative e-learning. He has contributed significantly to fault-tolerant computing using intelligent agents and swarm-array paradigms. The recent publications show a strong trend toward intelligent fault tolerance, agent-based recovery, and immersive virtual environments. His work spans both theoretical numerical methods and practical implementations in grid and cluster computing. The integration of cognition models in fault-tolerant systems reflects a unique interdisciplinary approach. While no formal scientific awards are listed in the provided text, his editorial roles and leadership in major computational science conferences indicate recognition in the field. He has collaborated extensively with researchers across Europe and has been involved in advising and mentoring through collaborative projects like eMinerals and P-GRADE. His work has been supported by e-Science and grid computing initiatives, particularly in the UK. He has contributed to the development of collaborative tools, portals, and workflows for virtual organizations. He has been involved in the development of the eMinerals virtual organization, the P-GRADE grid portal, and immersive virtual environments for education and medical training, reflecting a strong focus on collaborative and user-centered research infrastructures.

• High Performance Computing
• Parallel Computing
• Message Passing Interface
• +3 more

William D Gropp is a Professor of Computer Science at the University of Illinois at Urbana-Champaign holding the Thomas M. Siebel Chair, and serves as Acting Director and Chief Scientist of the National Center for Supercomputing Applications (NCSA). His career spans foundational contributions to parallel computing infrastructure and scientific computation. Gropp pioneered the Message Passing Interface (MPI) standard and developed MPICH—the reference implementation that became the global lingua franca for parallel programming. His work enables portable scientific applications across supercomputing platforms, driving advances in fluid dynamics, nanosimulations, biology, and fusion research through tools like the Portable, Extensible Toolkit for Scientific Computation (PETSc). His major honors include: ACM Software System Award (2024) for MPICH's 30-year impact ACM-IEEE CS Ken Kennedy Award (2016) for programmability contributions SIAM/ACM Prize (2015) for computational science engineering ACM, IEEE, and SIAM Fellowships Election to the National Academy of Engineering Gropp co-founded ACM SIGHPC in 2011 and authored 187+ publications including the seminal book Using MPI . His NCSA leadership advances supercomputing infrastructure for nationwide scientific discovery.

Lihui LiuView profile

Assistant Professor

• Natural Language Processing
• Knowledge Graph Reasoning
• Large Language Models
• +2 more

Lihui Liu is an Assistant Professor of Computer Science at Wayne State University's James and Patricia Anderson College of Engineering. His research focuses on neural-symbolic AI, integrating knowledge graphs with large language models for enhanced reasoning capabilities. Research Focus: Dr. Liu develops methods for complex knowledge graph reasoning, conversational question answering, and neural-symbolic integration. His lab creates algorithms that combine structural knowledge representations with language model capabilities for interpretable AI systems. Recent work demonstrates strong emphasis on knowledge graph applications (60% of publications) and language model enhancement (30%). Notable contributions include logical query decomposition techniques and attention-based graph neural network architectures. Mentoring: Actively recruiting PhD students to work on NSF-funded projects involving knowledge-guided AI systems. Professional service includes program committees for major AI conferences and editorial contributions.

• Reinforcement Learning
• Multi-Agent Systems
• Automated Planning
• +4 more

Anders Jonsson is a full professor and head of the Artificial Intelligence and Machine Learning research group at Universitat Pompeu Fabra (UPF). He holds a Ph.D. in Computer Science from the University of Massachusetts Amherst (2005), supervised by Professor Andrew Barto. His research focuses on sequential decision problems, multi-agent systems, hierarchical planning, and reinforcement learning. Key interests include combining RL with automated planning, exploiting structural properties of decision problems, and analyzing computational complexity. His work bridges theoretical foundations and practical applications in wireless networks, medical diagnostics, and urban systems. Jonsson’s research addresses challenges in decentralized spatial reuse in wireless networks, optimal power control for cell-free systems, and interpretable machine learning for clinical decision-making. His interdisciplinary approach integrates AI with telecommunications, environmental science, and healthcare. He has contributed to conference and journal articles exploring topics such as bisimulation metrics, reward machine exploration, and hierarchical average-reward MDPs. His lab actively develops algorithms for efficient exploration, generalized planning, and real-world deployment of AI solutions.

Bengisu ElisView profile

Researcher

• High-Performance Computing (HPC)
• Parallel Computing
• GPU Programming
• +5 more

Bengisu Elis is a researcher at the Chair of Computer Architecture & Parallel Systems at the Technical University of Munich . Her work focuses on High-Performance Computing (HPC) , GPU programming, and parallel systems. Email: bengisu.elis@tum.de Room: 01.04.037, Garching near Munich Research Interests : Elis specializes in GPU-CPU parallelism , MPI profiling , and performance modeling for HPC systems. Her work addresses challenges in: Optimizing synchronization mechanisms Reducing memory access overhead Enhancing task scheduling Scientific Awards : HPCAsia '24 Best Paper Award Winner Teaching & Advising : Elis has supervised multiple master's theses and internships, including: Jakob Schäffeler (2025) - Comparing GPU Programming Models Tizian Leonhardt (2024) - Low-overhead Sampling Techniques

• High-Performance Computing
• Parallel Computing
• Distributed Systems
• +6 more

Marc Snir is a Professor at the University of Illinois’s Siebel School of Computing and Data Science. He has led significant research contributions in high-performance parallel computing, including work on the Message Passing Interface (MPI) and IBM’s SP scalable parallel system. As Department Head from 2001–2007, he oversaw the transition to the Siebel Center and expanded the department’s capabilities. He later served as the first director of the Illinois Informatics Institute, chief software architect for the Blue Waters supercomputer, and co-director of the Universal Parallel Computing Research Center (UPCRC). His research focuses on parallel computing systems, fault resilience, and I/O optimizations. He has been recognized with the 2014 Distinguished Alumni Service Award. His work spans exascale computing, distributed systems, and machine learning applications in HPC. He has contributed to projects like Argo (an exascale OS/runtime), Aluminum (a GPU-aware communication library), and LCI (Lightweight Communication Interface). His research emphasizes improving scalability, energy efficiency, and reliability in high-performance systems. He has advised numerous students (names not listed here) and led teams in advancing HPC tools and methodologies. His involvement in initiatives like UPCRC and the Blue Waters project underscores his commitment to bridging theoretical research and practical applications in computing.

• High-Performance Computing
• Parallel Computing
• Distributed Systems
• +4 more

George Bosilca is a Professor at the University of Tennessee, Knoxville, specializing in high-performance computing and parallel systems. With over two decades of research contributions, he has established himself as a leading expert in task-based runtime systems, distributed computing, and MPI implementations. His research focuses on developing and optimizing task-based runtime systems for extreme-scale computing environments, with particular emphasis on fault tolerance, performance optimization, and scalability. Bosilca's work spans multiple domains including scientific computing, climate modeling, and deep learning applications. He has made significant contributions to the PaRSEC runtime system and has extensively researched MPI optimization techniques for modern HPC architectures. The trend in Bosilca's recent publications demonstrates a strong focus on addressing challenges in exascale computing, including fault tolerance in distributed systems, GPU acceleration for scientific workloads, and energy-efficient computing techniques. His work bridges theoretical computer science with practical implementations for real-world scientific applications across various domains. Bosilca maintains extensive collaborations with leading researchers in the HPC community, most notably with Jack J. Dongarra (103 co-publications), Aurelien Bouteiller (60 co-publications), and Thomas Hérault (54 co-publications). These collaborations have resulted in numerous publications at top-tier conferences including SC, IPDPS, and EuroMPI, as well as in prestigious journals such as IEEE Transactions on Parallel and Distributed Systems and the International Journal of High Performance Computing Applications.

Elizabeth HolmView profile

Professor

• Computational Materials Science
• Microstructural Evolution
• Machine Learning in Materials
• +3 more

Elizabeth Holm is Professor and Department Chair of Materials Science and Engineering at the University of Michigan. Her research employs computational methods to study microstructural evolution, with expertise spanning atomic-scale molecular dynamics to continuum-scale modeling. Education includes a PhD in Materials Science and Scientific Computing from University of Michigan. Research focuses on computational materials science with emphasis on grain boundary dynamics, machine learning applications in materials, and additive manufacturing characterization. Recent publications develop AI methods for microstructure prediction, grain growth modeling, and automated analysis of material images. Work integrates machine vision with physics-based models to understand material behavior across scales. National Academy of Engineering Member (2025) ASEE Mike Ashby Outstanding Materials Educator Award (2022) AIME Honorary Member (2022) ASM Edward DeMille Campbell Memorial Lecturer (2021) The Minerals, Metals & Materials Society Fellow (2019) Professor Holm leads research on computational design of hierarchical materials and has held leadership positions including President of The Minerals, Metals & Materials Society (2013).