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Andrea Arpaci-Dusseau is the Susan Beth Horwitz WARF Professor of Computer Sciences and Catherine A. Erickson Professor in the School of Computer, Data & Information Sciences at the University of Wisconsin-Madison. She has been a professor at UW-Madison since January 2000 and currently serves as Graduate Advising Chair for the Computer Sciences Department. Her research focuses on computer systems with primary emphasis on file and storage systems, but also making significant contributions in distributed systems, virtualization, and scheduling. According to csrankings.org, she has published the fourth-most papers in premier systems conferences (SOSP and OSDI) and the most at the top file and storage conference (FAST). Professor Arpaci-Dusseau's recent publications demonstrate continued leadership in storage systems research, with increasing focus on cloud architectures, persistent memory technologies, and system reliability. Her work bridges theoretical foundations with practical implementations, consistently addressing real-world challenges in modern computing environments. ACM SIGOPS Mark Weiser Award 2018 (highest honor in systems field) UW-Madison Van Hise Outreach Teaching Award 2017 Multiple Best Paper awards at FAST, SOSP, OSDI, and EuroSys conferences Carolyn Rosner Award for Excellence in Teaching (2010, 2017) Co-chaired major conferences including USENIX ATC '04, FAST '07, OSDI'18, and SOSP'24 Professor Arpaci-Dusseau has co-advised 28 Ph.D. students and is deeply committed to educational outreach through the CaTaPuLT project, which connects UW-Madison students with K-12 schools to teach computational thinking using Scratch. This initiative has reached hundreds of students across Madison and earned her significant recognition for teaching excellence. She leads the Arpaci-Dusseau Systems Lab (ADSL) and is involved with the Wisconsin Institute on Software-defined Datacenters in Madison (WISDoM), where her research group continues to advance state-of-the-art solutions in computer systems.

• File and Storage systems
• Operating systems
• Distributed systems
• +1 more

Professor Knut Reinert is a leading figure in algorithmic bioinformatics at the Free University of Berlin, where he holds a professorship in the Department of Mathematics and Computer Science. He also maintains a significant affiliation with the Max Planck Institute for Molecular Genetics in Berlin, where he leads the Efficient Algorithms for Omics Data group. His research spans both institutions through the Reinert Lab, which focuses on developing novel computational approaches for biological data analysis. Reinert's educational background includes a Diploma in Computer Science (1994) and a Doctorate (Dr. Ing./Ph.D., 1999, with honors) from the Max-Planck-Institut for Computer Science and Universität des Saarlandes in Saarbrücken. Prior to his professorship, he worked as a computer scientist under Prof. Gene Myers at Celera Genomics in Rockville, USA (1999-2002). His primary research interests center on algorithmic bioinformatics with specific focus on developing novel algorithms and data structures for biomedical mass data analysis. This includes creating mathematical models for genomic sequence analysis and algorithms for mass spectrometry data to detect differential protein expression between normal and diseased samples. His work bridges the gap between computational tool development and practical biological applications, with particular emphasis on NGS and proteomics data. The publications and projects led by Prof. Reinert demonstrate a consistent focus on advancing computational methods in bioinformatics. His research spans genomic sequence analysis, RNA research (particularly long non-coding RNAs), parallel computing applications, and GPU acceleration for biological data processing. The work shows increasing sophistication in handling large-scale biological datasets through innovative algorithmic approaches. Intel® Parallel Computing Center designation for his lab CUDA Research Center status DFG funding of 530 thousand Euros for RNA research de.NBI funding of 2 million Euros BMBF funded projects 'LIVE-DREAM' and 'EssBar' Prof. Reinert leads multiple significant research projects and has established strong collaborations with international partners including Texas A&M, Kings College London, Eberhardt-Karls Universität Tübingen, Robert-Koch-Institute, and various Turkish institutions. His lab receives funding from major organizations including DFG, BMBF, and Intel. The Reinert Lab maintains active teaching responsibilities at FU Berlin, offering courses at BSc, MSc, and PhD levels using both traditional and innovative learning concepts like e-learning and inverted classrooms. The Reinert Lab consists of two interconnected research groups that work closely with experimental biologists and medical researchers to develop practical computational solutions for real-world biological problems. The lab has established itself as a key player in the German and international bioinformatics community through its development of the widely-used SeqAn library and participation in national infrastructure initiatives.

• Algorithmic Bioinformatics
• Genomic Sequence Analysis
• Proteomics
• +7 more

Peter Pietzuch is a Professor in the Department of Computing at Imperial College London, where he leads the Large-Scale Data & Systems (LSDS) group. He also serves as the Director of Research and is a Visiting Researcher at Microsoft Research Cambridge. Pietzuch holds a Ph.D. from the University of Cambridge and a B.A. from Girton College. His research spans distributed systems, cloud computing, big data processing, and systems security. Key interests include: Scalable architectures for cloud-native applications Efficient stream processing and machine learning systems Trusted execution environments and secure cloud infrastructure Optimization of serverless computing and distributed databases His recent publications focus on adaptive machine learning frameworks, secure cloud resource management, and high-performance stream processing systems. Trends show strong emphasis on hardware-software co-design, confidential computing, and fault-tolerant architectures. Awards include: Best Paper Award at Middleware'03 He actively advises PhD students and secures grants for projects like Faasm (serverless computing) and Teechain (blockchain security). His LSDS group collaborates with industry partners including Microsoft Research. Pietzuch teaches undergraduate and graduate courses including Scalable Systems for the Cloud and Operating Systems . He co-founded the ACM DEBS conference and serves on steering committees for EuroSys and Middleware.

• Distributed Systems
• Cloud Computing
• Big Data Processing
• +5 more

Jee Choi is an Assistant Professor in the Department of Computer and Information Science within the College of Arts and Sciences at the University of Oregon. His research focuses on developing high-performance algorithms for big data analytics, particularly in tensor decomposition and parallel computing systems. Education: PhD in Electrical and Computer Engineering, Georgia Institute of Technology, 2015 MS in Electrical and Computer Engineering, Georgia Institute of Technology, 2004 BS in Electrical and Computer Engineering, Georgia Institute of Technology, 2000 Research Interests: Dr. Choi specializes in High Performance Computing with expertise in parallel algorithm design, performance modeling, and energy efficiency. His work targets Tensor Decomposition & Data Mining for big data, developing scalable solutions for sparse and dense tensor computations. He advocates leveraging HPC systems to transform massive datasets into societal benefits through efficient data mining techniques. Publication Trends: Choi's recent publications (2021-2025) demonstrate consistent innovation in tensor decomposition methodologies, with increasing focus on adaptive storage (Alto), streaming data factorization, and energy-aware autotuning. His work spans GPU clusters, distributed systems, and emerging architectures, maintaining strong connections to real-world big data challenges while advancing theoretical algorithm design. Professional Background: After completing his PhD under Richard Vuduc at Georgia Tech (notable for SpMV GPU autotuning and Energy Roofline Model), Choi conducted DoD-funded research on tensor decomposition at IBM Watson Research Center (2015-2018) before joining the University of Oregon faculty.

• High Performance Computing
• Parallel and Scalable Algorithm Design
• Performance Analysis Optimization
• +2 more

Emmett Witchel is a Professor of Computer Science at the University of Texas at Austin , with research spanning computer architecture, systems, networking, security, and privacy . His work focuses on low-level systems optimization and secure concurrent execution. Research Interests: Concurrent systems, secure execution environments, GPU integration, distributed systems. Teaching: CS 380L (Advanced Operating Systems), CS 371M (Mobile Computing). Scientific Awards: Runner-up Best Paper, ASPLOS 2013 Runner-up Award for Outstanding Research, USENIX Symposium 2012 IEEE Micro Top Pick Award 2007 ACM Honorable Mention 2004 George M. Sprowls Award, MIT EECS 2004 Recent Publications demonstrate leadership in CXL pod databases ( Tigon ), stateful serverless computing ( Boki ), SmartNIC-accelerated file systems ( LineFS ), and GPU security ( Telekine ). His work bridges hardware-software co-design and practical systems implementation.

• Computer Architecture
• Systems and Networking
• Security and Privacy
• +2 more

Michela Becchi is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University. She specializes in computer architecture, systems software, and applications, with a focus on heterogeneous systems, parallel algorithms, and acceleration techniques for bioinformatics, pattern recognition, and quantum computing. Her work spans multi-core CPUs, GPUs, FPGAs, and distributed clusters, emphasizing the boundary between hardware and software design. Dr. Becchi holds a Ph.D. and Master’s degree in Computer Engineering from Washington University in St. Louis (2009) and a Bachelor’s degree in Computer Engineering from Politecnico di Milano, Italy (2000). Her research has been recognized with prestigious awards, including the NSF CAREER Award (2015) and the University of Missouri System President Award for Early Career Excellence (2016). Her research interests include compiler and runtime techniques for heterogeneous systems, acceleration of bioinformatics algorithms, and high-speed networking applications. She has pioneered frameworks for efficient data transformation, GPU-accelerated compression, and memory-efficient graph algorithms for quantum computing. Her work also explores thread coarsening, mixed-precision auto-tuning, and secure multi-core processor design. Key contributions include the PILOT runtime system for GPU memory management, the GPU-FPtuner auto-tuner for floating-point applications, and innovative approaches to automata processors for genomic analysis. Her publications emphasize reproducible accuracy in scientific simulations and the optimization of irregular applications on many-core platforms.

• Computer Architecture
• Systems Software
• Heterogeneous Computing
• +5 more

Andrew Warfield is an Adjunct Professor in the Department of Computer Science at the University of British Columbia (UBC) and a Senior Principal Engineer at Amazon. His research focuses on computer systems software, including virtualization, distributed storage, security, and high-availability systems. He previously held roles as Associate Professor at UBC, CTO at Coho Data, and Technical Director at Citrix Systems. His work has led to projects like Remus (high-availability replication), Tralfamadore (execution analysis), and secure hypervisor development with Xen. Warfield's education includes a PhD from the University of Cambridge's Computer Laboratory, where he researched I/O device virtualization under Steven Hand. He has held visiting roles at Intel Research Cambridge and internships at AT&T Research and Nortel Networks. Research Grants: Supported by Intel Research, NSERC, Network Appliance, and the Communications Security Establishment. Professional Activities: Technical Advisory Board Member at Teradici, and involvement in program committees for conferences like HotOS, EuroSys, and FAST. His research emphasizes practical systems, aiming to bridge the gap between theoretical computer science and real-world applications. Notable contributions include innovations in storage for virtualized environments, secure hypervisor architectures, and disaster-tolerant systems like SecondSite. Warfield is affiliated with UBC's Department of Computer Science and maintains active collaboration with industry partners. Though currently not actively recruiting students, his prior mentorship has influenced many in systems research.

• Computer Systems Software
• Virtualization
• Distributed Storage
• +3 more

William Gropp is the Grainger Distinguished Chair and Director of the National Center for Supercomputing Applications (NCSA) at the University of Illinois Urbana-Champaign. He holds a Ph.D. in Computer Science from Stanford University (1982) and has contributed extensively to parallel computing, software for scientific computing, and numerical methods for PDEs. His research focuses on high performance computing, programming models, and scalable algorithms. Education: B.S. Mathematics (Case Western Reserve, 1977), M.S. Physics (University of Washington, 1978), Ph.D. Computer Science (Stanford, 1982). Research Interests: Gropp's work spans HPC, parallel computing, and numerical methods. He co-developed the MPI standard and MPICH implementation, and contributed to the PETSc library. His current projects include the Delta and DeltaAI supercomputers, Illinois Computes, and exascale initiatives. Scientific Awards: AAAS Fellow (2018), ACM/IEEE-CS Ken Kennedy Award (2016), SIAM/ACM Prize (2015), and 2024 ACM Software System Award. Member of the National Academy of Engineering. Grants & Leadership: Director of NCSA, leader of the Midwest Big Data Hub, and contributor to NSF-funded projects. His teams support AI/ML infrastructure and exascale computing. He advises on HPC policy and serves on committees like the Computing Community Consortium. Labs/Teams: NCSA, Siebel School research groups, collaborations with DOE, NSF, and industry partners. His work drives advancements in cyberinfrastructure and computational science.

• High Performance Computing
• Parallel Computing
• Scientific Computing
• +3 more

Alan Sussman is a Professor and Associate Chair of Undergraduate Education in the Computer Science department at the University of Maryland. His research focuses on databases, high-performance computing, parallel systems, and educational curriculum development for computing disciplines. He holds a Ph.D. from Carnegie Mellon University (1991) and a B.S.E. from Princeton University (1982). His educational contributions include integrating parallel and distributed computing concepts into early undergraduate courses, supported by NSF-funded initiatives like the CyberTraining program. He has advised students such as Harshit Soora (Master's) and Xiaolong Tian (PhD). His research spans compiler optimizations for parallel programs, distributed data management systems, and scientific workflow frameworks like DYFLOW. He collaborates with UMIACS and contributes to interdisciplinary projects like the TASCS center. Key innovations include VeloxDFS for distributed dataset streaming, compiler techniques for irregular memory access in PGAS programs, and NetCDFaster for geospatial data optimization. His work emphasizes productivity improvements for high-performance applications and curriculum modernization to address emerging computational challenges. Awards: No individual awards explicitly listed; however, collaborator Jik-Soo Kim received a best paper award in 2006. Grants: NSF CyberTraining, TCPP Curriculum Initiative, and Center for Technology for Advanced Scientific Component Software (TASCS). Labs/Teams: Active in UMIACS and interdisciplinary collaborations, including the TASCS center and InterComm framework development.

• Databases and Big Data
• High Performance Computing
• Parallel and Distributed Systems
• +4 more

Walid G. Aref is a Professor of Computer Science at Purdue University since Fall 1999. His research focuses on database systems, spatial and spatio-temporal data management, query processing, and indexing. He has led projects funded by NSF, NIH, and industry partners. Aref is a Fellow of the IEEE and has received awards including the NSF CAREER Award (2001) and VLDB’s Ten-Year Best Paper Award (2016). He serves as Editor-in-Chief of ACM Transactions on Spatial Algorithms and Systems and has authored numerous influential papers. Education: BSc/MSc from Alexandria University (Egypt), PhD from University of Maryland (1993). Research Interests: Extending database functionality for emerging applications like spatial, graph, and sensor databases. Notable contributions include the Chameleon project, AQWA system for big spatial data, and privacy-preserving Casper framework. Selected Projects: NSF-funded work on multi-predicate spatial queries, in-memory graph relational systems, and adaptive spatio-textual processing. Systems like Tornado (spatio-textual streams) and LocationSpark (distributed spatial data) exemplify his contributions. Awards: Multiple best paper awards, IEEE Fellow, and leadership roles in ACM SIGSPATIAL.

• Databases
• Data Mining
• Spatial Databases
• +6 more

Saugata Ghose is an Assistant Professor in the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign (UIUC), with affiliate appointments in the Coordinated Science Laboratory and the Department of Electrical and Computer Engineering. His research focuses on data-centric computing, processing-in-memory architectures, memory systems, and hardware-software co-design. He holds a Ph.D. and M.S. in Computer Engineering from Cornell University and dual B.S. degrees in Computer Engineering and Computer Science from SUNY Binghamton. His academic positions include roles at Carnegie Mellon University (2016–2020) and postdoctoral research at CMU (2014–2016). Ghose has received notable awards such as the 2024 HPCA Hall of Fame, 2023 Intel Rising Star Faculty Award, and the 2019 CMU Wimmer Faculty Fellowship. His work has been supported by grants from NSF, Samsung, and Sandia National Laboratories. Research Interests: His group (ARCANA) explores data-centric architectures, processing-in-memory (PIM), and emerging memory technologies. Key areas include architectures for smart cities, autonomous systems, and genomics. He teaches courses on computer architecture and systems organization. Awards: HPCA Hall of Fame (2024) Intel Rising Star Faculty Award (2023) CMU Wimmer Faculty Fellow (2019) Cornell ECE Teaching Assistant Award (2013) Grants & Projects: NSF $2M for semiconductor advancements Samsung/Sandia grants for PIM programming models UIUC/ZJU DREMES collaboration on neuromorphic PIM Labs/Teams: Leads the ARCANA Research Group, focusing on reimagining computing around new applications. Collaborates with ASAP and HYBRID centers for co-design tools and neuromorphic architectures.

• Data-centric computing
• Processing-in-memory
• Memory and storage systems
• +3 more

Roger Chamberlain is a Professor in the Department of Computer Science & Engineering at Washington University in St. Louis , affiliated with the McKelvey School of Engineering . He has been on faculty since 1989, specializing in digital systems, parallel processing, computer architecture, embedded systems, and reconfigurable logic. Education: DSc, Washington University in St. Louis, 1989 MS, Washington University in St. Louis, 1985 BS, Washington University in St. Louis, 1983 Research Interests: His work focuses on architecturally diverse computing systems, including specialized architectures for astrophysics and biology. He explores high-performance parallel/distributed applications, energy-efficient computation, and high-capacity I/O systems. He collaborates with industry partners like Exegy, Inc., VelociData, Inc., and BECS Technology, contributing to commercialization of university-developed technologies for data analysis and microprocessor-based controls. Grants & Awards: In 2021, he was a co-investigator on a $5M gamma ray astronomy mission grant. In 2018, he led an NSF-funded project ($1.2M) to enhance streaming application efficiency on diverse architectures. His consulting and industry ties reflect a commitment to bridging academic research with practical applications. Professional Contributions: He co-founded Exegy, VelociData, and BECS Technology. His lab website and Google Scholar profile provide additional insights into his work.

• Computer Engineering
• Operating Systems
• Cyberphysical Systems
• +9 more

Dr. Song Jiang is a Professor in the Department of Computer Science and Engineering at The University of Texas at Arlington (UTA). He holds a PhD from the College of William and Mary (2004) and has held academic positions at institutions such as Wayne State University and Los Alamos National Laboratory. His research focuses on system infrastructure for large language models (LLMs) and big data processing, including GPU/CPU memory systems, file and storage systems, and high-performance computing (HPC) I/O systems. He has received significant funding from the National Science Foundation (NSF) and industry partners like VMware and Tencent. Education: B.S. and M.S. from University of Science and Technology of China (1993, 1996), Ph.D. in Computer Science from College of William and Mary (2004). Postdoctoral research at Los Alamos National Laboratory (2004–2006). Research interests include file and storage systems, data management, big data analytics, and optimizing computing architectures for AI/ML. Key contributions include the LIRS replacement algorithm (adopted in MySQL and NetBSD), CLOCK-Pro page replacement (used in Linux), and swap token algorithms (Linux kernel). Awards include the 2022 ACM SIGMETRICS Test of Time Award and 2009 NSF CAREER Award. His work has led to 15+ patents and impactful industry collaborations with Facebook, Baidu, and others. Advising: Supervised 14+ PhD/Master’s students, including current advisees Chen Zhong and Sujit Maharjan. Active roles in doctoral committees and thesis supervision. Grants: Over $2.5M in NSF funding for projects like 'Software Defined Cache for Index Search' and 'Taming Small Data Writes'. Industry grants include VMware’s $240K project on distributed key-value storage. Labs/Teams: Leads research on persistent memory systems, key-value stores, and LLM infrastructure through UTA’s CSE department and collaborations with industry partners.

• System Infrastructure for Big Data Processing
• High-Performance Computing (HPC)
• Storage Systems
• +5 more

Rachee Singh is an Assistant Professor of Computer Science at Cornell University, leading the sysphotonics research group. She concurrently serves as an Amazon Scholar within the SageMaker Hyperpod teams, specializing in large-scale machine learning infrastructure development for cloud environments. Her research focuses on photonic interconnect systems for server-scale, rack-scale, and long-haul communication networks, targeting performance optimization for distributed machine learning and planet-scale cloud workloads. Key specialties include optical network design, fault-tolerant WAN architectures, and energy-efficient datacenter interconnects, with strong emphasis on practical deployment in real-world systems. Her group bridges theoretical networking principles with applied AI infrastructure challenges. Recent publications demonstrate concentrated innovation in photonic network optimization for ML workloads, particularly in wavelength management, collective communication algorithms, and chip-to-chip photonic fabrics. This work spans optical physics, distributed systems, and machine learning, revealing a trajectory toward sustainable, high-performance AI infrastructure. Scientific recognition includes: Amazon Research Award (2023) Cisco Research Award Dr. Singh actively mentors graduate researchers including Jonathan Aimuyo, Byungsoo Oh, and Arjun Devraj, whose co-authored publications form the core of her group's output. Research funding is secured through competitive grants from the NSF (including a $1M award for chip-to-chip photonic fabrics), SRC/DARPA JUMP 2.0 program, Cisco, and Cornell's Atkinson Center for Sustainability. The sysphotonics group operates as Cornell's hub for photonic network systems research, developing programmable integrated photonics solutions and collaborating with Amazon on SageMaker Hyperpod for next-generation ML infrastructure.

• Photonic Interconnects
• Distributed Machine Learning
• Cloud Computing
• +3 more

Ada Gavrilovska is a Professor at Georgia Tech's School of Computer Science under the College of Computing. Her work focuses on systems software for emerging technologies, including hybrid memory systems, edge computing, and cloud infrastructure. She leads projects in the PRISM Center and ADA Center , with funding from NSF, DoE, SRC, and industry leaders like Cisco and VMware. Education: PhD in Computer Science, Georgia Tech (2004) Research Interests: Designing systems for new hardware and applications, including edge computing, heterogeneous memory management, and LEO satellite platforms. Her work bridges low-level OS mechanisms with high-level distributed systems challenges. Recent Publications highlight trends in LEO satellite resource scheduling Edge-based ML preprocessing Hybrid memory OS abstractions Disaggregated graph analytics Compiler-assisted performance optimization Scientific Awards: Best paper, NFV World Congress (2016) Spotlight paper, IEEE Transactions on Cloud Computing (2014) ISCA-50 25-year retrospective (2023) Advising & Grants: Ada has mentored over 15 PhD students and 10 MS students, with research supported by NSF, DoE, SRC, and industry grants. She serves as PI in the SRC/DARPA PRISM Center.

• Systems Software
• Emerging Technologies
• High-Performance Computing
• +7 more