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John MacLaren WalshView profile

Professor

• Information Theory
• Network Coding
• Distributed Computing
• +4 more

John MacLaren Walsh is a Professor in the Department of Electrical and Computer Engineering at Drexel University, where he leads the Adaptive Signal Processing and Information Theory Research Group. He holds BS, MS, and PhD degrees from Cornell University, all completed under Dr. C. Richard Johnson, Jr. His research spans information theory, network coding, distributed computing, and machine learning applications in patent analysis. His work focuses on: Bounding entropic vectors and their impact on communication networks Rate region computation for network coding and distributed storage Information theory for distributed function computation Machine learning-enhanced patent processing systems Publications emphasize entropy geometry, network coding complexity, distributed algorithms, and patent analysis, with consistent themes of optimization and combinatorial methods. Recent work (2016-2019) shows increased focus on probabilistic supports and computational efficiency in network coding. Awards: 2011 NSF CAREER Award for 'Entropy Geometry in Variational Inference Signal Processing' He has advised PhD students on topics like entropy region mapping, network coding, and distributed control. Key grants include NSF CAREER and AFOSR funding for wireless network overhead control. He directs the Adaptive Signal Processing and Information Theory Research Group, which develops algorithms for network coding, distributed storage, and patent analysis systems.

• Self-Driving Database Management Systems
• Machine Learning for Databases
• GPU-Accelerated Data Analytics
• +4 more

Lin Ma is currently an Assistant Professor at the University of Michigan, Ann Arbor in the Department of Electrical Engineering and Computer Science (College of Engineering). Previously, they served as a Post Doctoral Fellow at Carnegie Mellon University (2021-2022) and as a Software Engineer at Databricks, Inc. (2022-2023). Research Interests focus on the intersection of database systems and machine learning, particularly in developing self-driving database management systems . Key areas include workload forecasting , automated index optimization , query execution acceleration , and machine learning integration for database automation. Their work explores GPU-accelerated analytics, memory optimization, and transactional consistency models. Academic Contributions span 15+ publications in top venues like VKDB , SIGMOD , and CIDR , including recent 2025 papers on Vortex (GPU memory optimization) and Scompression (workload compression). Earlier work introduced QueryBot 5000 , a workload forecasting framework, and explored anti-caching for storage optimization in OLTP systems. Teaching includes courses like EECS 584: Advanced Database Management Systems and EECS 484: Database Management Systems at the University of Michigan (2023-2025), and 15-445/645 Database Systems at Carnegie Mellon University. Service involves program committee roles for SIGMOD (2023-2025), VLDB (2022-2025), and CIDR (2024-2025). They also served on admissions and search committees at both institutions. Advising includes supervising PhD and MS students: Siyuan (Doug) Dong , Zhongwei Xu , and Haotian (Jack) Gong (co-advised with Barzan Mozafari), among others.

Andrew RiceView profile

Professor

• Programming Languages
• Machine Learning
• Artificial Intelligence
• +4 more

Andrew Rice is a Professor of Computer Science at the University of Cambridge's Department of Computer Science and Technology, and holds the Hassabis Fellowship in Computer Science. He is also the Director of Studies in Computer Science at Queens' College. His research focuses on programming languages, software engineering, and machine learning applications in software development. He leads projects like Isaac Computer Science and ALTA (Automated Language Teaching and Assessment), advancing adaptive learning technologies. His work includes static analysis tools such as Error Prone at Google, energy efficiency studies in computing infrastructure, and contributions to the Computing for the Future of the Planet initiative. His teaching emphasizes practical skill development through flipped classrooms and video lectures, earning him the 2014 Pilkington Prize for teaching excellence. He has held visiting roles at Google and collaborated on energy consumption research for mobile devices and data centers. His research spans systems, networking, and natural language processing, with a strong focus on applying computational methods to real-world challenges. Key Projects: Isaac Physics/Computer Science, ALTA, Error Prone Static Analysis Research Themes: Programming Languages, Machine Learning, Energy Efficiency Awards: Pilkington Prize (2014)

• Cloud Computing
• Operating Systems
• File/Storage Systems
• +3 more

Haryadi S. Gunawi is a Professor in the Department of Computer Science at the University of Chicago where he leads the UCARE research group (UChicago systems research on Availability, Reliability, and Efficiency). His work focuses on improving the dependability of storage and cloud computing systems, with a particular emphasis on addressing performance stability, reliability, and scalability challenges in modern computing environments. Dr. Gunawi received his Ph.D. in Computer Science from the University of Wisconsin, Madison in 2009. Following his doctoral studies, he was a postdoctoral fellow at the University of California, Berkeley from 2010 to 2012 before joining the University of Chicago faculty. His research focuses on three main areas: (1) performance stability, where he builds storage and distributed systems robust to latency tails and "limping" hardware; (2) reliability and scalability, where he addresses concurrency and scalability bugs in cloud-scale distributed systems; and (3) the intersection of machine learning and systems, exploring how machine learning techniques can solve operating and storage system problems. His work often combines theoretical insights with practical system implementations that address real-world challenges in cloud and storage infrastructure. Dr. Gunawi's publication record shows a consistent focus on storage and cloud system reliability, with recent work increasingly incorporating machine learning techniques to address traditional systems challenges. His research spans the full stack from hardware interfaces to distributed system design, with a strong emphasis on practical solutions that can be deployed in production environments. His work often involves close collaboration with industry partners to ensure real-world relevance and impact. Dr. Gunawi has received numerous prestigious awards including the NSF CAREER award, NSF Computing Innovation Fellowship, Google Faculty Research Award, multiple NetApp Faculty Fellowships, and an Honorable Mention for the 2009 ACM Doctoral Dissertation Award. He has also received the Provost's Global Faculty Award and Facebook Faculty Research Award, highlighting the broad recognition of his contributions to the field. As an advisor, Dr. Gunawi has mentored several PhD students including Ruidan Li, Ray Andrew, Rani Ayu Putri, and William Nixon. His research has been supported by major grants from NSF, Google, Facebook, and NetApp, enabling his team to pursue ambitious research projects at the intersection of systems, storage, and machine learning. Dr. Gunawi leads the UCARE research group at UChicago, which focuses on improving the dependability of storage and cloud-scale distributed systems. He is also involved with the Chameleon cloud research infrastructure project and the broader Systems Group at UChicago, contributing to a vibrant research community focused on systems, programming languages, and software engineering.

Emilio ArtachoView profile

Professor

• Condensed Matter Physics
• Computational Physics
• Molecular Dynamics
• +4 more

Professor Emilio Artacho is a faculty member in the Department of Physics at the University of Cambridge, based at the Cavendish Laboratory. He transitioned from the Department of Earth Sciences in 2011, where he was granted a Professorship in 2006. His research focuses on computational simulations of non-equilibrium processes in condensed matter, particularly using first-principles molecular dynamics and density-functional theory. He co-developed the SIESTA program for linear-scaling electronic structure calculations, widely utilized in computational materials science. Artacho’s work spans far-from-equilibrium phenomena in irradiated matter, multiferroics, nanoconfined water systems, and surface chemistry. His contributions include studies of electronic stopping power in materials, 2D electron gas formation at ferroelectric interfaces, and the structural dynamics of water under confinement. His academic roles include adjunct positions at Ikerbasque (Nanogune, Spain) and visiting professorships at institutions like the University of California, Berkeley, and École Normale Supérieure de Lyon. Research interests are anchored in theoretical condensed matter physics, with applications to nanomaterials, radiation effects, and interfacial phenomena. His computational methods bridge quantum mechanics and classical dynamics, enabling insights into complex systems like proton-irradiated solar cells and confined water films.

Charles A. BoumanView profile

Professor

• Computational Imaging
• Statistical Image Models
• Tomographic Image Reconstruction
• +13 more

Charles A. Bouman is the Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering at Purdue University, with a courtesy appointment in Mathematics. He is a leading researcher in computational imaging, integrating statistical signal processing, physics, and computation for applications in healthcare, scientific, and industrial imaging. Education: B.S.E.E., University of Pennsylvania, 1981 M.S., University of California at Berkeley, 1982 Ph.D. in Electrical Engineering, Princeton University, 1989 His research focuses on computational imaging , including statistical image models, multiscale techniques, tomographic reconstruction, and fast algorithms. Key areas include Model-Based Iterative Reconstruction (MBIR), Plug-and-Play priors, document processing, and multiscale segmentation. His work has led to foundational contributions in total variation regularization and sparse-view reconstruction. The recent publications highlight a strong trend in integrating machine learning with physical models for image reconstruction, particularly through Plug-and-Play methods. His work spans optical tomography, halftoning, image scaling, and document compression, demonstrating consistent innovation in both theory and practical software implementation. Scientific Awards and Honors: Member, National Academy of Inventors Life Fellow, IEEE Fellow, IS&T; Honorary Member (2022); Service Award (2023) Fellow, SPIE and AIMBE IEEE Signal Processing Society Claude Shannon-Harry Nyquist Award (2021) Electronic Imaging Scientist of the Year (2014) SIAM Imaging Science Best Paper Prize (2020) Founder, IS&T Computational Imaging Conference (2003) Co-Founder, IEEE Transactions on Computational Imaging Vice President, IS&T; Former VP of Publications (2000–2004) Bouman has advised numerous graduate students and leads a vibrant research group developing open-source tools like MBIRJAX , SVMBIR , and OpenMBIR . His research has been supported by the National Science Foundation, General Electric, Intel, Xerox, Hewlett-Packard, and the State of Indiana 21st Century Fund. He maintains an active presence through tutorials, conference leadership, and educational resources including video lectures and a textbook on Foundations of Computational Imaging. Labs and Research Teams: His group develops cutting-edge software for tomographic reconstruction, clustering, segmentation, and dynamic sampling. Projects include Gaussian Mixture modeling (GMCluster), Plug-and-Play implementations, Sparse Matrix Transforms, and UAV sensing datasets. The research is highly interdisciplinary, bridging engineering, mathematics, and biomedical applications.

• Security
• Cryptography
• Theoretical Computer Science
• +3 more

Yan Huang is an Associate Professor in the Department of Computer Science at Indiana University Bloomington, focusing on security and cryptography. His work bridges theoretical foundations with practical systems, emphasizing cryptographic protocols with strong security guarantees for generic computation. University: Indiana University Bloomington School: College of Arts and Sciences Department: Computer Science Academic Rank: Associate Professor His research combines theoretical computer science , program analysis , artificial intelligence , and software engineering to address real-world security problems. Key areas include secure computation , zero-knowledge proofs , and privacy-preserving technologies . Recent publications, such as Phecda (SP'25) and Dubhe (USENIX Security'23), highlight advancements in post-quantum cryptography , AES verification , and privacy-preserving deep packet inspection , reflecting his focus on scalable and practical cryptographic solutions. He has advised notable students including Changchang Ding (PhD, Computer Science) and Ruiyu Zhu (PhD, now at Facebook), and served on program committees for top conferences like ACM CCS and CRYPTO .

Michael BaymView profile

Associate Professor

• Microbial Evolutionary Genomics
• Antibiotic Resistance
• Computational Biology
• +7 more

Dr. Michael Baym is an Associate Professor of Biomedical Informatics at Harvard Medical School with affiliate appointments in Microbiology and the Laboratory of Systems Pharmacology, and as an Associate Member of the Broad Institute. He leads the Baym Lab, which studies microbial evolutionary genomics and antibiotic resistance through a hybrid of experimental, computational, and theoretical approaches. His research focuses on: Antibiotic Resistance Evolution and practical interventions Mobile Genetic Elements (plasmids, phages, transposons) Computational Genomic Algorithms for big data analysis Synthetic Biology tools and technologies Key recent publications explore phage discovery systems , phylogenetic compression of microbial genomes, and RNA-guided gene drives in plasmids. His work is supported by multiple NIH/NIGMS and NSF grants including a MIRA award. Scientific honors include: Packard Fellowship (2018) Pew Biomedical Scholarship (2020) Sloan Research Fellowship (2020) A. Clifford Barger Excellence in Mentoring Award (2021) SSQBio Mentorship Award (2022) The lab actively trains PhD students and postdoctoral fellows with alumni occupying academic and industry positions globally. Current team members include researchers from interdisciplinary backgrounds working at the intersection of experiment, computation, and theory .

• Cybersecurity and Privacy
• Data Management and Data Science
• Distributed Computing and Systems Software

Professor Jun Zhang is a leading academic in cybersecurity at Swinburne University, Australia, where he directs the Cybersecurity Lab. He has been honored as Australia's top cybersecurity researcher and instrumental in establishing Swinburne as a globally recognized cybersecurity research institution. His work includes high-impact papers and multi-million-dollar R&D projects, culminating in awards like the 2021 'Top Cybersecurity Research Institution' accolade. As course director of the Bachelor of Cyber Security, he pioneered an industry-driven teaching model with Deloitte and CSIRO, significantly boosting course enrollment. His collaborations extend to Adobe's Curriculum Innovation Program and the Australian P-TECH initiative, promoting STEM education and cybersecurity awareness. He supervises doctoral candidates and leads grants focused on AI-driven cybersecurity, smart home security, and blockchain-based edge computing. His research spans vulnerability detection, GAN forensics, IoT security, and privacy preservation in OSNs. Research interests include cybersecurity fundamentals, data science applications, and distributed systems. Notable achievements include the PTFix framework for Java vulnerabilities, the IoTFuzz smart home testing system, and CTI mining methodologies. Awards reflect his mid-career research excellence and industry partnerships. His grants with CSIRO and defense organizations emphasize real-world impact, addressing challenges from malware detection to adversarial machine learning. The Cybersecurity Lab and collaborative projects like Artchain demonstrate his commitment to bridging academia and industry. Professional activities include supervising over 20 HDR students and securing grants totaling millions. His work on blockchain-based edge storage (CSEdge) and SDCCP congestion control highlights innovation in networking. Future directions include advancing AI for design collaboration with CSIRO and enhancing privacy in smart energy technologies. His contributions span technical, educational, and community outreach domains, positioning him as a pivotal figure in cybersecurity's evolution.

• Computer Architecture
• Non-Volatile Memory Systems
• Deep Learning Systems
• +3 more

Jishen Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego (Jacobs School of Engineering). His research focuses on computer architecture, non-volatile memory systems, and deep learning acceleration. Dr. Zhao has published extensively in top venues including ISCA, MICRO, ASPLOS, and IEEE Transactions. He collaborates with researchers at UCSD and beyond to advance systems for emerging applications in AI and autonomous vehicles. Dr. Zhao's primary research areas include persistent memory systems, hardware/software co-design for deep learning, and safety-critical computing. He develops techniques for crash consistency, memory disaggregation, and efficient neural network deployment. His work on autonomous vehicles addresses scenario generation and perception-aware system design. Recent projects explore LLM applications for software engineering and hardware verification. Analysis of Dr. Zhao's 2024-2025 publications reveals a strong shift toward AI-integrated systems research. He applies large language models to tasks like RTL verification and software issue localization while continuing to innovate in memory systems for serverless computing. There is growing emphasis on safety-critical systems for autonomous vehicles and energy-efficient neural network training using novel hardware architectures. Information about Dr. Zhao's scientific awards, advising activities, grants, and laboratory facilities was not available in the provided documentation.

Mohammad Mohammadi AmiriView profile

Assistant Professor

• Large Language Models
• Data Valuation
• Federated Learning
• +5 more

Mohammad Mohammadi Amiri serves as an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), appointed in Fall 2023. His research focuses on advancing artificial intelligence through strategic data utilization, with emphasis on large language models, data valuation, federated learning, and deep learning. Previously, he held postdoctoral appointments at Princeton University and MIT Media Lab, building on his strong educational foundation from Imperial College London, University of Tehran, and Iran University of Science and Technology. Education: Ph.D. in Electrical and Electronic Engineering, Imperial College London (2019) - Best Ph.D. Thesis Award recipient M.Sc. in Electrical and Computer Engineering, University of Tehran (2014) - Ranked 1st among all M.Sc. students B.Sc. in Electrical Engineering, Iran University of Science and Technology (2011) - Ranked 1st among all B.Sc. students Dr. Amiri's research centers on optimizing artificial intelligence systems through innovative data strategies. His work addresses critical challenges in large language models including efficiency, memory usage, alignment, and reasoning capabilities. In data valuation, he develops principled methods to quantify data worth for fair trading platforms. His federated learning research tackles privacy concerns, heterogeneous data distribution, and communication overhead in decentralized environments. The deep learning component explores theoretical foundations to improve model interpretability and robustness. Analysis of his recent publications reveals a strong focus on making AI systems more efficient and accessible, with particular emphasis on large language model optimization, federated learning advancements, and data valuation frameworks. His work bridges theoretical foundations with practical applications in wireless communications and distributed computing environments. Scientific Awards: IEEE Communications Society Young Author Best Paper Award (2022) Best PhD Thesis Award from IEEE Information Theory Chapter of UK and Ireland (2019) Eryl Cadwallader Davies Prize for Outstanding PhD Thesis (2019) EEE Departmental Scholarship at Imperial College London (2015-2019) Ranked 1st among M.Sc. students at University of Tehran (2014) Ranked 1st among B.Sc. students at Iran University of Science and Technology (2011) Dr. Amiri actively mentors graduate students, currently supervising five Ph.D. candidates and one M.Sc. student working on efficient LLM fine-tuning, inference, and storage. His research has attracted significant attention, evidenced by numerous keynote invitations at prestigious institutions including Bell Labs, MIT, King's College London, and various IEEE conferences. He serves on program committees for major conferences including IEEE Globecom and ICC, demonstrating his growing influence in the academic community. His research group operates at the intersection of machine learning and wireless communications, developing innovative solutions for resource-constrained environments while addressing fundamental theoretical challenges in AI systems. Current projects focus on making advanced AI more scalable and accessible through efficiency improvements in model training and inference.

Heiner LitzView profile

Associate Professor

• Computer Architecture
• Computer Systems
• Hardware-Software Interface
• +3 more

Heiner Litz is an Associate Professor in the Computer Science & Engineering Department at UC Santa Cruz's Baskin School of Engineering. He holds the Kumar Malavalli Endowed Chair of Storage Systems Research and serves as Director of the Center for Research in Storage Systems (CRSS). He is also a member of UCSC's Hardware Systems Collective (HSC). Litz earned his PhD from Mannheim University and previously held positions at MIT, Google, and Stanford University. His research focuses on computer architecture and systems optimization , specifically improving hardware-software interfaces for data center workloads. Key areas include microarchitectural mechanisms (branch prediction, prefetching, cache design), profile-guided optimizations, and storage/disaggregated memory systems. His work bridges compiler techniques and hardware efficiency for emerging cloud applications. Recent publications emphasize profile-guided optimization across microarchitecture layers, storage scalability, and resource allocation in distributed systems. Trends include hardware-software co-design for data centers, RDMA-based protocols, and real-time control systems. Awards & Honors: Kumar Malavalli Endowed Chair of Storage Systems Research Best Paper Award at MICRO (2022) IEEE Micro Top Picks (2019, 2023) Best Paper Awards at ICPP (2008) and ARC (2009) Advising & Grants: He currently advises 11 PhD students and has graduated 8 MS/PhD students. Research is supported by NSF, Intel, Samsung, Google, Meta, Nutanix, HPE, ARM, Marvell, Cerabyte, Western Digital, and Broadcom. Labs & Teams: Directs CRSS, focusing on storage systems innovation, and collaborates with HSC on hardware-software integration projects.

• Cognitive Neuroscience
• Working Memory
• Attention
• +3 more

Edward Awh is a Professor at the University of Chicago in the Department of Psychology, specializing in cognitive neuroscience, working memory, and attentional mechanisms. His research explores the neural basis of memory storage, spatial attention, and the interplay between cognitive systems using EEG and neuroimaging techniques. University of Chicago, Department of Psychology NIH R01 grants on working memory and ADHD Research Interests: Awh investigates discrete resource limits in working memory, the role of alpha oscillations in attention, and neural mechanisms underlying memory encoding and retrieval. His work addresses how the brain manages distractor suppression, spatial representations, and the relationship between attention and memory capacity. Scientific Trends: Recent publications focus on content-independent memory encoding, EEG decoding of attentional processes, and the intersection of sustained attention with memory performance. His studies frequently employ human behavioral experiments, EEG analysis, and computational modeling. Grants: Principal Investigator on multiple NIH R01 grants, including projects on working memory states (R01MH087214), perceptual interference in ADHD (R01MH077105), and attentional control mechanisms.

• Multimedia Signal Processing
• Computer Networks
• Communication Engineering
• +3 more

Jiro Katto is a Professor at Waseda University's School of Fundamental Science and Engineering, where he has been conducting research and teaching since 1999. He received his Ph.D. from the University of Tokyo and has established himself as a leading researcher in multimedia signal processing and computer networks. His academic journey includes positions as Associate Professor (1999-2004), Professor (2004-present), and Director at NEDO (2004-2008), along with research experience at NEC C&C Laboratories (1992-1999) and a Visiting Scholar position at Princeton University (1996-1997). Professor Katto's research interests focus on Multimedia Signal Processing and Computer Networks, with particular expertise in video compression, 5G network performance, and learned image compression techniques. His work bridges theoretical advancements with practical implementations, as evidenced by his extensive publications in top-tier conferences and journals. His research group has made significant contributions to point cloud compression, latency compensation in remote systems, and hardware-accelerated video encoding for UHD streaming. His publication record is impressive, with 276 papers cited 3,323 times in Scopus and 6,169 times in Google Scholar, reflecting his substantial impact in the field. His recent work shows a strong trend toward applying deep learning techniques to traditional signal processing problems, particularly in the areas of image and video compression, where his team has developed novel approaches to improve compression efficiency while reducing computational complexity. Electric Telecommunications Promotion Foundation Telecommunications System Technology Award (2023) Takayanagi Kenjiro Foundation Takayanagi Kenjiro Achievement Award (2020) Institute of Image Information and Television Engineers Fellow (2020) Institute of Electronics, Information and Communication Engineers Fellow (2015) IEICE Communications Society Activity Contribution Award (2006) IEICE Academic Encouragement Award (1995) SPIE VCIP 1991, Best Student Paper Award (1991) Professor Katto has served on numerous prestigious committees including IEEE ComSoC Tech News Editorial Board, IEEE Technical Program Committees for major conferences (Globecom, ICC, ICIP), and editorial boards for several academic journals. His leadership in the academic community extends to chairing conferences like IWAIT 2011 and serving as Editor-in-Chief for journals in his field. His research has practical applications in commercial 5G networks, video streaming services, and remote monitoring systems, demonstrating the real-world impact of his work.

John SerencesView profile

Professor

• Cognitive Psychology
• Neuroscience
• Cognitive Neuroscience
• +4 more

John Serences is a Professor in the Department of Psychology at the University of California, San Diego (UCSD). He leads the Perception and Cognition Lab, which participates in the Neuroscience Graduate Program. His research focuses on how behavioral goals and attention influence perception, memory, and decision-making, employing techniques like psychophysics, computational modeling, EEG, and fMRI. Key projects explore serial dependence, neural adaptation in visual cortex, and the interplay between sensory processing and mnemonic storage. Recent work highlights mechanisms reconciling repulsive neuronal adaptation with attractive behavioral biases. Affiliations: Department of Psychology, UCSD; Neuroscience Graduate Program. Research Themes: Visual perception, working memory, decision-making, neuroimaging. His lab investigates neural dynamics underlying cognitive processes, with particular emphasis on how attentional modulations and stimulus history shape neural representations. Notable contributions include studies on adaptive sensory coding and the role of top-down signals in perceptual stability.