جست‌وجوی استاد و پژوهشگر

Shuai Muمشاهده پروفایل

استادیار

• Distributed Systems
• Concurrency Control
• Large-Scale Storage Systems
• +۴ مورد دیگر

Shuai Mu is an Assistant Professor in the Department of Computer Science at Stony Brook University. Previously, they held a post-doctoral researcher and lecturer position at New York University. They earned a Ph.D. in computer science from Tsinghua University in 2015. Shuai's research focuses on distributed systems, particularly concurrency control in large-scale storage systems. They aim to enhance system efficiency without compromising correctness guarantees like serializability. Key areas include database systems, transactional memory, wide-area replication, and hardware/software optimizations. Their work addresses challenges in distributed/multicore architectures and fault-tolerant replication. Recent publications emphasize sustainability in data centers, concurrency control algorithms, and hardware acceleration techniques. They have explored innovative approaches such as the Causalmesh causal cache for serverless computing and NCC for natural concurrency control in datastores. Teaching responsibilities include CSE 535. While no awards are explicitly listed, their research contributions reflect a strong commitment to advancing distributed systems theory and practice.

• High Performance Computing
• Cloud Computing
• Computing Sustainability
• +۳ مورد دیگر

Roy Rohan Basu is an Assistant Professor at the Kahlert School of Computing at the University of Utah, joining in July 2025. His research focuses on High Performance Computing, Cloud Computing, and Computing Sustainability, with an emphasis on environmental impact mitigation and resource optimization in large-scale systems. Research interests include sustainable cloud architectures, serverless computing efficiency, and reducing carbon/water footprints in computing. He explores innovative solutions for cold-start mitigation, I/O bottlenecks, and energy-efficient resource allocation. Recent work highlights include analyzing forever chemicals' impact on computing infrastructure, optimizing carbon-aware serverless scheduling, and enhancing GPU-accelerated AI workloads. His research bridges theoretical models and practical implementations, addressing real-world challenges in scalable and sustainable computing. Articles from 2022–2025 reflect a growing focus on environmental sustainability in computing systems, with contributions to cloud resource management, quantum computing evaluation, and manufacturing process digital twins. No scientific awards or grants are explicitly listed in the provided information. His current role emphasizes advancing sustainable computing through interdisciplinary approaches.

Todd Eavisمشاهده پروفایل

دانشیار

• Programming Languages
• Code Generation
• Continuous Integration and Delivery
• +۳ مورد دیگر

Todd Eavis is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University. He received his PhD from Dalhousie University (2003) and completed an NSERC postdoctoral fellowship at Carleton University (Ottawa). His research focuses on programming language design, software development environments, and reducing complexity in build systems. Teaching includes courses on programming languages, operating systems, databases, and parallel programming at both undergraduate and graduate levels, such as COMP 228, COMP 248, COMP 6411, and COMP 6521. Research themes emphasize approachable software frameworks, 90/10 syntax, code generation, platform-agnostic development, and continuous integration/delivery. Key areas include query optimization in parallel DBMS, OLAP systems, and efficient data cube computation. Publications highlight contributions to database systems, parallel processing, and cloud-oriented software design. Notable works include work on real-time OLAP systems, NOX OLAP algebra optimization, and scalable data cube generation.

Geoff Hamiltonمشاهده پروفایل

دانشیار

• Program Transformation
• Automated Theorem Proving
• Formal Verification
• +۳ مورد دیگر

Dr. Geoff Hamilton is Associate Professor at Dublin City University's School of Computing. His research develops program transformation hierarchies for applications in theorem proving, verification, optimization, and energy-efficient computing. The distillation algorithm—a key contribution—achieves orders-of-magnitude efficiency gains over standard techniques. Work spans automated verification tools, termination provers, security protocol analyzers, and complexity bounds calculators. Recent publications focus on side-channel attacks, concurrency optimization, and tight complexity bounds for loop programs. Teaching includes comparative programming languages, cryptography, and formal methods. Research consistently advances transformation-based approaches for parallelization, verification, and resource analysis. Current directions include smart contract verification and energy-aware program optimization methodologies.

John J Collinsمشاهده پروفایل

دانشیار

• Compiler Optimization
• Software Parallelization
• Robotics
• +۴ مورد دیگر

John J Collins is an Associate Professor at the University of Limerick, affiliated with the Department of Computer Science & Information Systems and Lero – the Research Ireland Centre for Software. His research focuses on compiler optimization, software parallelization, robotics, and evolutionary algorithms. Notable contributions include work on grammatical evolution and empirical studies in software requirements engineering. He has authored 36 research outputs since 1997, including a comprehensive handbook on grammatical evolution. Research Interests: Compiler flag optimization using genetic engineering Robotics (mobile robot control, SLAM) Machine vision applications Parallelization techniques in multi-core architectures Grammatical evolution methodologies Key Publications: His recent work includes a 2019 study on requirement engineering challenges across 11 organizations and a 2017 case study on data decomposition for parallel software systems. The 2018 Handbook of Grammatical Evolution remains a foundational text in evolutionary computing. Advising & Grants: While no specific advisees are listed, his research has been supported through institutional affiliations with Lero and the Department of Computer Science. Active in both academic publishing and applied software research. Labs/Teams: Principal member of Lero – the Irish Software Research Centre, contributing to collaborative projects in software systems and AI-driven development.

• Loop Transformation and Scheduling
• Parallel Processing
• Computer Science Education
• +۳ مورد دیگر

Dr. Timothy W. O’Neil is a tenured Professor and Chair of the Department of Computer Science at The University of Akron. He joined UA in 2002 with prior teaching experience at institutions like the University of Notre Dame and Ohio State University. His research focuses on Loop Transformation, Parallel Processing, and Computer Science Education. He has authored/co-authored numerous publications in parallel computing and education, and serves as a member of IEEE. Education: Ph.D. (Notre Dame), M.S. (Ohio State in Computer Science and Mathematics), B.S./B.S.Ed. (Clarion University) Research Interests: Loop scheduling optimization, parallel processing architectures, and innovative teaching methodologies. He has advised graduate students and led grants as Principal Investigator. Teaching: Courses include Operating Systems, Parallel Processing, and Computer Science Education. Current courses (Spring 2025) include CPSC 4/526 (Operating Systems) and Topics in Computing Education. Grants and Service: Active in academic and industry committees. His work emphasizes reducing SWAP-C (Size, Weight, Power, and Cost) in microelectronic systems through novel fabrication techniques.

Lisa Lacherمشاهده پروفایل

دانشیار

• Computer Science Education
• Software Engineering
• eLearning Technologies
• +۳ مورد دیگر

Lisa Lacher is an Associate Professor of Information Technology and Program Director in the College of Science and Engineering at the University of Houston-Clear Lake. She specializes in computer science education, software engineering team dynamics, and innovative eLearning strategies. Her work emphasizes curriculum development using Scala, social sensitivity in collaborative environments, and leveraging modern tools like Discord for academic teamwork. Research interests include programming pedagogy, team collaboration metrics, and the integration of gaming technologies into higher education. She has explored topics such as eSports' impact on academic engagement, flipped classroom methodologies, and the role of philosophical belief systems in instructor effectiveness. Her publications since 2016 demonstrate a focus on improving technical education through empirical studies of teaching practices, team performance, and communication technologies. Notable themes include multithreading education, non-technical skills measurement in software teams, and the application of actor models in curricula. Lacher’s work bridges theory and practice, combining empirical research with actionable insights for educators and industry professionals. Her contributions highlight the evolving landscape of computer science education and collaborative learning environments.

Khondker Shajadul Hasanمشاهده پروفایل

دانشیار

• Distributed Computing
• Blockchain Security
• Internet of Things
• +۳ مورد دیگر

Khondker Shajadul Hasan is an Associate Professor of Computer Science in the College of Science and Engineering at the University of Houston–Clear Lake since Fall 2021, after serving as Assistant Professor from 2016. He teaches both undergraduate and graduate courses and directs research in distributed systems, blockchain security, GPU performance modeling, and IoT analytics. Education Ph.D. in Computer Science, University of Oklahoma, Norman, USA (2014) Dissertation: “Prediction Models for Estimating the Efficiency of Distributed Multi-core Systems” M.S. in Computer Science, Wichita State University, Kansas, USA (2001) Research Interests Dr. Hasan’s work bridges theory and practice in distributed computing , blockchain-based security frameworks , and GPU-accelerated high-performance computing . His group designs predictive models for multi-core CPU availability and energy-efficient scheduling, and explores private-blockchain solutions for securing data streams in IoT and aviation domains. Publications & Trends Across 10 refereed papers since 2017, he has advanced the state-of-the-art in secure IoT architectures, smart-farming analytics, and JVM-level concurrency optimization. His research consistently integrates machine-learning techniques with systems-level challenges, yielding practical tools for industry and academia. Awards & Honors Piper Award Semi-Finalist, University of Houston–Clear Lake (2017–2022) Distinguished Alumni Award, North South University (2017) Computer Science Advisory Board Award, University of Oklahoma (2013-14) Industry certifications from Schlumberger and InterSystems (2014-2015) Advising & Service Since 2016 he has mentored numerous graduate and undergraduate researchers and has served as faculty advisor for the Computer Club. He is an active member of editorial boards and technical program committees for IEEE and Elsevier journals/conferences worldwide. Laboratory & Collaborations Dr. Hasan directs the Parallel and Distributed Systems Lab at UHCL, collaborating with industry partners such as Schlumberger and academic groups across the USA and Asia on blockchain security, GPU performance modeling, and smart-IoT systems.

LillAnne Jacksonمشاهده پروفایل

عضو هیئت علمی

• Memory Consistency for Multiprocessor Architectures
• Computational Geometry
• Teaching Concurrency
• +۱ مورد دیگر

LillAnne Jackson is an Associate Teaching Professor and Associate Dean of Undergraduate Studies in the Department of Computer Science at the University of Victoria. She holds a PhD from the University of Calgary and has dual roles in academic leadership and teaching innovation within the Faculty of Engineering and Computer Science. Dr. Jackson’s research focuses on memory consistency for multiprocessor architectures, computational geometry, and pedagogical strategies for teaching concurrency in computer science. Her educational work emphasizes improving retention in STEM fields through innovative teaching methods and curriculum design. She has contributed to accreditation-driven curricular reforms and explored the efficacy of video-based instruction in technical education. Her technical research addresses challenges in parallel computing systems, including memory coherence models for architectures like Itanium and Sparc. Earlier work includes computational geometry projects such as polygon reconstruction algorithms and visibility analysis. Her articles span both technical computer science topics and educational methodologies, reflecting her dual expertise in technical systems and academic innovation. In her administrative role as Associate Dean, she oversees undergraduate academic policies and program development. She collaborates with faculty to enhance student experiences through curriculum modernization and pedagogical research. Dr. Jackson’s work bridges theoretical computer science with practical educational strategies to strengthen undergraduate education and technical workforce development.

• High-Performance Computing
• Parallel Processing
• Code Optimization
• +۳ مورد دیگر

Georgios Goumas is an Associate Professor in the Division of Computer Science at the School of Electrical and Computer Engineering, National Technical University of Athens. His research focuses on high-performance computing, parallel systems, and efficient resource management for modern computing platforms. Research domains: Serverless computing scalability and memory management Quantum algorithm design for classification Virtual memory optimization Hardware acceleration for sparse computations NUMA-aware concurrent data structures Recent innovations include DaeMon for disaggregated systems, eBPF-based Linux memory management, and multi-GPU matrix multiplication optimizations. Publications demonstrate consistent advancement in low-level system efficiency across quantum, serverless, and heterogeneous computing domains. Dr. Goumas teaches undergraduate and graduate courses on Operating Systems, Parallel Processing, and Code Optimization Techniques. His lab develops open-source tools like Paralia runtime for auto-tuning linear algebra and FPGA-based accelerators for scientific computing.

Anant Agarwalمشاهده پروفایل

استاد

• Computer Architecture
• Programming Languages and Software Engineering
• Systems and Networking
• +۴ مورد دیگر

Anant Agarwal is a Professor of Electrical Engineering and Computer Science at MIT, holding the rank of 'Professor Post-Tenure' in both CS and EE disciplines. He serves as CEO of edX, a leading platform for open online education. His primary affiliation is with the MIT Electrical Engineering & Computer Science Department within the MIT College of Engineering. Agarwal's research focuses on computer architecture, parallel processing, caching systems, and the integration of AI in clinical information systems. He has pioneered work on multicore processors, memory management, and self-optimizing computing systems. His academic contributions span over two decades, with notable advancements in tiled processor architectures, cache coherence protocols, and the design of energy-efficient embedded systems. Agarwal has been instrumental in developing MITx and edX, revolutionizing online education through scalable platforms like the 6.002x Circuits course. His work emphasizes blending traditional and digital learning modalities to enhance global access to education. Key research themes include parallel computing, machine learning applications in healthcare, and the future of higher education. His publications reflect a sustained focus on improving system performance, optimizing data management, and advancing educational technologies. Agarwal's leadership at edX underscores his commitment to democratizing access to world-class learning resources.

Mohammad Alizadehمشاهده پروفایل

دانشیار

• Computer Networks
• Systems
• Machine Learning
• +۶ مورد دیگر

Mohammad Alizadeh is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT, affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). He holds additional roles as Director of the 6-A MEng Thesis Program and Industry Officer for EECS, overseeing the EECS Alliance industry outreach program. His research focuses on computer networks, systems, and their applications in datacenters, cloud computing, and edge computing. Notable areas include machine learning for systems, congestion control, and network protocols. Education: PhD (Stanford University), MS in Electrical Engineering (Stanford), BS (Sharif University of Technology). Industry Experience: Former roles at Microsoft Research, Insieme Networks, and Cisco Systems. Research Interests: Alizadeh’s work emphasizes improving network performance, robustness, and management in large-scale systems. Key projects include DCTCP, CONGA, and Pensieve (adaptive video streaming with reinforcement learning). His contributions span network architecture, programmable switches, and learning-based systems. Awards: NSF CAREER Award Alfred P. Sloan Research Fellowship SIGCOMM Rising Star Award Microsoft Research Faculty Fellowship Teaching: Courses include 6.888 (Advanced Topics in Networking), 6.829 (Computer Networks), and 6.02 (Digital Communication Systems). His teaching emphasizes cutting-edge networking research and practical system design. Labs/Teams: Active member of CSAIL, leading projects in distributed systems, network protocols, and cloud computing.

Ilya Sergeyمشاهده پروفایل

دانشیار

• Programming Languages
• Software Verification
• Distributed Systems
• +۵ مورد دیگر

Associate Professor Ilya Sergey leads the Verified Systems Engineering (VERSE) lab at the School of Computing, National University of Singapore (NUS). He holds a PhD in Computer Science from KU Leuven and an MSc in Mathematics from Saint Petersburg State University. His research focuses on programming languages, software verification, distributed systems, and program synthesis. He is a recipient of the AITO Dahl-Nygaard Junior Prize (2019) and multiple ACM SIGPLAN Distinguished Paper Awards. His work includes developing the Scilla smart contract language and the Veil verification framework, emphasizing formal methods for ensuring software correctness. **Education**: PhD in Computer Science, KU Leuven, Belgium (2012) MSc in Mathematics and Computer Science, Saint Petersburg State University, Russia (2008) **Research Interests**: Design and Implementation of Programming Languages Formal Verification using Separation Logic Distributed Systems and Concurrency Program Synthesis and Automated Proof Repair **Recent Articles Trends**: His recent work emphasizes automated verification tools (e.g., Veil), exploit generation via synthesis, and compositional verification of Byzantine protocols. These contributions bridge foundational theory with practical applications in distributed systems and smart contracts. **Awards**: AITO Dahl-Nygaard Junior Prize (2019) ACM SIGPLAN Distinguished Paper Awards (PLDI'23, PLDI'21, POPL'19) Google, Facebook, and Amazon Research Awards **Advising & Grants**: He actively mentors PhD students in formal methods and verification. His grants include support from Google and industry partnerships. He organizes the ICFP Programming Contest and co-chairs ICFP'25. **Labs & Teams**: Leads the VERSE lab, part of the PLSE@NUS group, focusing on verified software systems and program synthesis.

Bruno Loic Alexandre Bodinمشاهده پروفایل

استادیار

• Dataflow Programming
• Compilers
• Static Analysis
• +۳ مورد دیگر

Bruno Bodin is an Assistant Professor at Yale-NUS College, Singapore, with a joint appointment at the National University of Singapore (NUS) School of Computing. He previously held roles as Senior Research Associate at the University of Edinburgh (2014–2018), Visiting Research Fellow at INRIA ENS Paris, and Software Engineer at Kalray (2011–2014). He earned his PhD and M.S. in Computer Science from Université Pierre & Marie Curie, Paris. His research focuses on dataflow programming , compilers , and static analysis , with applications in signal processing and robotics . Notable contributions include benchmarking frameworks for SLAM systems and compiler techniques for heterogeneous computing. Teaching includes courses on Operating Systems, Programming for Data Science, and Software Engineering at Yale-NUS. Active in open-source projects, he maintains repositories on GitHub related to dataflow graph generation and SLAM evaluation.

Phil Gibbonsمشاهده پروفایل

استاد

• Big Data
• Parallel Computing
• Databases
• +۵ مورد دیگر

Phil Gibbons is a Professor in the Electrical & Computer Engineering and Computer Science Departments at Carnegie Mellon University. He holds a Ph.D. from UC Berkeley (1989) and has extensive industry experience at AT&T Bell Labs, Lucent Bell Labs, and Intel Research. His research focuses on parallel computing, distributed systems, databases, and machine learning, with a emphasis on algorithmic and systems-level innovations. He has led major initiatives like the Intel Science and Technology Center for Cloud Computing and contributed to projects such as IrisNet (a planetary-scale sensor network). Education : Ph.D. in Computer Science, University of California at Berkeley (1989) Research Interests : Gibbons' work spans big data analytics , high-performance computing , and cloud systems . He develops scalable algorithms and systems for emerging memory technologies, distributed ML, and robotics. Notable contributions include processing-in-memory (PIM) optimizations, pipeline parallelism for DNN training, and system architectures for robotic processors. Awards : IEEE Fellow (2014) ACM Fellow (2006) ACM Paris Kanellakis Theory and Practice Award (2019) Best Paper Award at NSDI 2006 Grants & Leadership : Co-PI of the $15M Intel STC for Cloud Computing (2011-2015) Editor-in-Chief of ACM Transactions on Parallel Computing (2012-2018) Leadership roles in conferences like SPAA, EuroSys, and MLSys Teams & Labs : Active in robotics computing (RobotPerf benchmark), distributed ML systems, and hardware-software co-design initiatives.