Mark Smotherman is an Associate Professor in the School of Computing at Clemson University, specializing in computer architecture, reliability modeling, and computer science education. He has held roles such as Associate Director of the School of Computing and Director of Graduate Affairs. He earned his Ph.D. in Computer Science from the University of North Carolina at Chapel Hill and a B.S. in Physics from Middle Tennessee State University. His research focuses on computer architecture history, superscalar processors, reliability analysis, and educational methodologies. Notable contributions include the Hybrid Automated Reliability Predictor (HARP) and work on IBM's ACS project. He has received awards like NASA Langley's Space Act Award (1997) for HARP's impact. Smotherman teaches courses like Operating Systems (CPSC/ECE 3220) and Computer Systems Organization (CPSC 3300), and has advised numerous graduate students. He has contributed to over 100 publications and served on committees for conferences like MICRO and ACSAC. His work bridges historical computing insights with modern systems design.
James Plank is a Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, within the Tickle College of Engineering. He has been at UT since 1993 and holds a PhD from Princeton University (1993), with prior degrees from Princeton (MS, 1990) and Yale (BS, 1988). His research focuses on neuromorphic computing , including models, software, and hardware applications, alongside expertise in erasure codes for storage systems, fault-tolerance, and distributed computing. His work spans neuromorphic hardware design, spiking neural networks, and storage reliability. He also explores interdisciplinary interests like duplicate bridge , computer-mediated music performance , and origami . Plank's publications emphasize neuromorphic computing benchmarks (e.g., Cart-Pole), neuromorphic processor architectures (RISP Neuroprocessor), and storage systems (RAID-6 codes). His research has applications in embedded systems, control systems, and energy-efficient hardware. Awards: Multiple teaching awards including the 2021 Brooks Distinguished Professor Award and Chancellor's Excellence in Teaching (2008). Grants: Supported by NSF and industry partnerships, focusing on neuromorphic computing and storage reliability. Labs: Leads TENNLab for neuromorphic control applications and collaborates on DANNA and RISP neuroprocessor frameworks.
Edward A. Freeman serves as Professor of Biology at St. John Fisher University, where he teaches courses including Fundamentals of Nutrition, General Biology II, Advanced Human Anatomy, Biopharmacology, and Endocrinology. His research program investigates environmental and dietary impacts on animal biology through zebrafish and fruit fly models. His educational background comprises: Ph.D. from the University of South Carolina M.A. from Kent State University B.S. from Ohio University Dr. Freeman's research centers on endocrine disrupting chemicals (EDCs) and diet-related metabolic impacts. His lab examines how bisphenols affect ovarian follicle maturation, primordial germ cell migration, and larval behaviors in zebrafish embryos, while parallel studies in fruit flies explore EDC effects on fat body formation and dietary influences on glucose levels and reproductive function. This dual approach aims to uncover transgenerational epigenetic mechanisms linking environmental exposures to conditions like type II diabetes. His publication record reveals two distinct scholarly threads: environmental toxicology research using zebrafish/fruit fly models to assess EDC impacts on development and reproduction, and educational scholarship focused on STEM career development, academic advising, and information literacy. The toxicology work demonstrates methodological rigor through techniques like immunofluorescence microscopy and larval buoyancy assays, while the educational publications emphasize institutional collaboration between faculty, career centers, and alumni. Dr. Freeman has not received any listed scientific awards. He has secured significant external funding to support undergraduate research: Wyman-Potter Foundation ($23,250 for 2024-2025 academic year) Wyman-Potter Foundation ($10,000 for summer 2020) The Endocrine Society ($4,000 Summer Research Fellowship, 2019) NSF Noyce grant ($1.4 million as Co-PI for INSPIRE project) These awards directly fund stipends and supplies for undergraduate researchers, with student co-authors appearing on multiple publications. His Freeman lab operates as a training ground for undergraduates through hands-on research in developmental toxicology. The Freeman lab maintains specialized facilities for zebrafish and fruit fly research, employing techniques including quantitative PCR, immunofluorescence microscopy, and larval behavioral assays. Current projects focus on transgenerational epigenetic studies of diet-induced metabolic disorders and comparative analyses of bisphenol analogs, with ongoing collaboration through the NSF-funded INSPIRE initiative.
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.
Jay McClelland is the Lucie Stern Professor in the Social Sciences at Stanford University, where he serves as Professor of Psychology and, by courtesy, of Linguistics and of Computer Science. He directs the Center for Mind, Brain, Computation and Technology (MBCT) and is affiliated with multiple interdisciplinary institutes including Bio-X, the Wu Tsai Neurosciences Institute, and the Institute for Human-Centered Artificial Intelligence. Dr. McClelland's research spans multiple domains of cognitive science, with a focus on understanding how cognition emerges from distributed neural processing. His work addresses fundamental questions in perception and decision making, learning and memory, language and reading, semantic cognition, and cognitive development. A newer direction in his laboratory investigates mathematical cognition and reasoning across humans and artificial neural networks. His publications reveal a consistent theme of applying connectionist approaches to understand cognitive phenomena, with recent work exploring the intersection of human cognition and artificial intelligence. McClelland's research demonstrates how neural network models can illuminate both human cognitive processes and the potential pathways for developing more human-like AI systems. Distinguished Scientific Contribution Award, American Psychological Association (1996) Member, National Academy of Sciences (2001-) As an educator, McClelland teaches foundational courses in cognition and mentors doctoral students in psychology and related disciplines. His laboratory provides research opportunities for students interested in computational approaches to cognitive science, with current advisees working on topics ranging from language processing to mathematical cognition. McClelland leads the PDP Lab (Parallel Distributed Processing Lab) and the Center for Mind, Brain, Computation and Technology, which serve as hubs for interdisciplinary research connecting cognitive science, neuroscience, and artificial intelligence.
Benjamin F. Goldberg is an Associate Professor in the Computer Science Department at New York University (NYU), affiliated with the College of Arts and Science. His research focuses on compiler design, programming languages, and their verification, with notable contributions to compiler optimizations and validation frameworks like TVOC. He holds a Ph.D. in Computer Science from Yale University (1988), an M.S./M.Phil. from Yale (1984), and a B.A. in Mathematical Sciences from Williams College (1982). Education: Ph.D., Computer Science, Yale University, 1988 M.S./M.Phil., Computer Science, Yale University, 1984 B.A., Mathematical Sciences, Williams College, 1982 His research interests include compiler verification, compiler optimizations, and functional programming. He has developed the Trimaran compiler research infrastructure for instruction-level parallel architectures and pioneered work in translation validation (TVOC framework). Recent projects involve data-driven stroke rehabilitation using AI and wearable sensors, as well as climate modeling collaborations with the M2LInES initiative. Grants and Collaborations: NIH Grant R01 LM013316 (Stroke Rehabilitation) NSF Grant IIS 2404476 (Data-Driven Medicine) Climate Modeling: Part of the M2LInES international project Labs/Teams: Collaborates with the Mobilis Lab at NYU School of Medicine and the NYU Courant Institute. Active in interdisciplinary projects merging AI, healthcare, and environmental science.
Frank Bellosa is a Professor and head of the Operating Systems Group at the Karlsruhe Institute of Technology (KIT). Previously, he held roles at the University of Erlangen, including Assistant Professor and researcher in the Operating Systems Department. He earned his PhD from the University of Erlangen in 1998, focusing on memory-conscious scheduling in multiprocessor systems. His research interests center on energy-aware systems, including OS-directed power management, thermal management in distributed systems, and flexible operating system architectures. Key projects include Event-Driven Clock Scaling (Process Cruise Control) and Energy-Aware Memory Management. He has advised numerous students on topics like temperature-aware scheduling and power management for embedded systems. Bellosa's publications span dynamic thermal management, cooperative I/O, and energy-efficient file systems. His work emphasizes reducing energy consumption while maintaining performance through innovative OS mechanisms. He has contributed to international conferences and workshops, including EuroSys and USENIX, and serves on program committees for major systems conferences. Current roles include leading the Operating Systems Group at KIT and contributing to initiatives like the Disruptive Memory Systems workshop. His research bridges hardware and software, addressing challenges in modern computing systems' efficiency and scalability.
Wonsun Ahn is a Teaching Associate Professor in the Department of Computer Science at the University of Pittsburgh's School of Computing and Information. He holds a PhD from the University of Illinois at Urbana-Champaign (2012). His research focuses on compilers, computer architecture, and their co-optimization for parallel machines, GPUs, and quantum computing. He has authored numerous papers on topics like compiler/hardware integration and parallel processing, and has served on over a dozen conference program committees and journal editorial boards. His research interests include compiler design, hardware architecture, and improving programmability for parallel systems. Notable areas of work include deterministic execution, atomic block optimization, and energy-efficient IoT device updates. Dr. Ahn actively contributes to both academic and industrial advancements in high-performance computing and parallel processing. Dr. Ahn has no listed awards but maintains an active presence in the academic community through conference involvement and editorial work. His contact information includes an office at 5423 Sennott Square and an email at wahn@pitt.edu . He oversees no listed student advisees or grants but is affiliated with the Department of Computer Science's research initiatives.
Han Tran is an Instructional Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. in Computer Science from the University of Utah (2024), an M.S. in Mechanics of Construction from the University of Liège (2003), and a B.S. in Civil Engineering from the University of Architecture in Ho Chi Minh City (1997). His research focuses on high-performance parallel algorithms, scientific computing, and computational mechanics, with applications in materials science and environmental modeling. His work emphasizes scalable parallelization techniques for solving complex equations, such as the phonon Boltzmann Transport Equation and boundary integral methods for fracture mechanics. Recent contributions include advancing adaptive-matrix algorithms for heterogeneous architectures and developing isogeometric analysis frameworks for multi-field problems. His publications span computational physics, mechanical engineering, and environmental science, reflecting interdisciplinary collaborations. No scientific awards are explicitly mentioned. His advising and grants sections remain unlisted. He is affiliated with the Department of Computer Science & Engineering at Texas A&M University, located in College Station, Texas.
Jianling Liao is an Associate Professor of Chinese and Director of the Chinese Language Flagship Program at Arizona State University (ASU), affiliated with the School of International Letters and Cultures and the Center for Asian Research. She holds a Ph.D. in Second Language Acquisition from the University of Iowa and multiple master's degrees in fields including Teaching Chinese as a Foreign Language and Chinese Linguistics from the University of Iowa and Wuhan University. Her research focuses on second language writing and speaking, assessment methodologies, computer-assisted language learning (CALL), and curriculum design. She has authored books such as Principles of Language Testing and Assessment and Curriculum Design for International Chinese Language Teaching , and published extensively in journals like Journal of Second Language Writing and System . Liao has developed and managed language programs both domestically and internationally, served as a program evaluator, and provided teacher training across diverse groups. Her teaching spans L2 methodologies, assessment, curriculum design, and Chinese language instruction at all levels. Her service contributions include roles on the Board of the Chinese Language Teachers Association (CLTA) and peer reviewing for over 20 academic journals. She actively contributes to national and international language education initiatives, emphasizing teacher training and assessment literacy frameworks.
Peter Kramlinger is affiliated with the University of California, Davis, specifically within the Department of Statistics under the College of Letters and Science. He is actively involved in teaching and developing course materials for undergraduate and graduate-level statistics and data science courses. University: University of California, Davis School: College of Letters and Science Department: Department of Statistics Role: Lecturer His research and instructional interests center on modern statistical computing, Bayesian methods, and data science education. He emphasizes computational tools and reproducible workflows in teaching. The recent course repositories and tutorials reflect a strong focus on practical data analysis, with an emphasis on MCMC methods, statistical programming, and large-scale data processing. These materials span topics in both foundational and advanced data science, indicating a commitment to evolving pedagogical practices in statistics. Although no formal scientific awards are listed, his open-source contributions to educational materials demonstrate scholarly engagement and a dedication to accessible learning resources. Peter Kramlinger advises no listed students, but his instructional role suggests mentorship of students through coursework. There is no mention of external grants or funded research projects in the available data. He maintains an active GitHub presence with publicly shared teaching materials, indicating participation in open academic communities and collaborative learning environments.
Matteo Basso is a researcher at Università della Svizzera italiana (USI), Switzerland, specializing in compiler design, virtual machines, and WebAssembly technologies. His work bridges high-level programming languages with efficient execution on modern hardware platforms, with a strong focus on practical systems that improve performance and efficiency. His research interests include: WebAssembly and its applications in web and server environments Compiler optimization techniques for virtual machines Memory management and binary serialization formats Performance analysis of programming language runtimes Java Virtual Machine enhancements and thread management Matteo's publication record shows consistent contributions to top-tier conferences including SPLASH, OOPSLA, and CGO, with recent work emphasizing WebAssembly technologies and Java performance optimization. His research on heap management, native-image startup performance, and compiler-level profiling demonstrates practical impact in language implementation. Notable contributions include: Heap snapshot matching using context-augmented heap-path representations Improvements to native-image startup performance in GraalVM Optimization-aware compiler-level event profiling techniques Analysis of Java Vector API performance characteristics As an active open-source contributor, Matteo maintains several popular repositories including asm-dom (a WebAssembly virtual DOM implementation with 2.8k stars) and awesome-wasm (a curated WebAssembly resource list with 9.4k stars).
Alastair Donaldson is a Professor and Director of Research in the Department of Computing at Imperial College London, where he has been a faculty member since 2011. He leads the FastPL research group (formerly Multicore Programming Group), focusing on formal analysis, software testing, and programming languages techniques for improving software reliability, with special emphasis on high-performance systems. Donaldson earned his BSc (First Class Honors) in Computing Science and Mathematics from the University of Glasgow in 2003, followed by a PhD in Computing Science from the same institution in 2007 under Alice Miller. His academic journey includes positions as an EPSRC Postdoctoral Research Fellow at Oxford, Visiting Researcher at Microsoft Research Redmond, and Research Engineer at Codeplay Software Ltd. His research spans automated reasoning, compiler verification, and GPU programming with significant contributions to software reliability. He pioneered metamorphic testing for graphics drivers through GraphicsFuzz (acquired by Google in 2018) and developed innovative compiler fuzzing techniques. His work addresses critical challenges in memory models, concurrency, and verification of complex systems. Recent publications show increasing focus on applying these techniques to modern challenges including AI-generated code and verification-aware programming languages like Dafny. Donaldson's scientific contributions have been recognized with the 2017 BCS Roger Needham Award, an EPSRC Early Career Fellowship, and multiple best paper awards including EuroSys 2024 (Best Paper), ICST 2024 (Best Industry Paper), and ISSTA 2023 (Distinguished Paper). His 2012 GPUVerify paper received the ACM SIGPLAN Most Influential OOPSLA Paper Award in 2022. As an advisor, Donaldson has mentored numerous PhD students and postdocs, many now in prominent academic and industry positions. His research is supported by Amazon Research Awards (2022-2023) for Dafny ecosystem testing and compiler validation. He previously served as Senior Software Engineer and Visiting Researcher at Google following the GraphicsFuzz acquisition. The FastPL group maintains strong industry connections with Google, Microsoft, and Amazon, ensuring practical relevance of their theoretical work. Donaldson currently serves as Editor-in-Chief of ACM TOPLAS (2025-present) and on program committees for major conferences including PLDI, ICSE, and ASPLOS.
Drew Hilton is Professor of the Practice in the Department of Electrical and Computer Engineering at Duke University . His academic appointment focuses on teaching and practical applications in computer engineering, with particular emphasis on bridging theoretical concepts with real-world implementation. His research spans several critical areas in computer systems and architecture: Computer Architecture & Microarchitecture - Advanced processor designs including hybrid pipeline systems Memory Systems & Security - Secure memory architectures and metadata access patterns Hardware Description Languages - Development of functional programming languages like Gemini for hardware design Parallel Computing Systems - CPU/GPU task-parallel runtimes and synchronization mechanisms Disaster Response Computing - Optimization of 3D reconstruction systems for emergency scenarios His publication record demonstrates a consistent focus on improving processor performance, security, and efficiency. The research trajectory shows evolution from foundational work on branch prediction and memory systems (2007-2012) to more recent applications in disaster response and secure computing architectures (2016-2022). Teaching Contributions: Professor Hilton teaches the MIDS Bootcamp course, indicating his involvement in data science education and practical training programs at Duke University. Contact Information: He can be reached at adh39@duke.edu .
Dr. Carlos Benavides is Professor of Spanish/Linguistics and Chair of the Department of Global Languages and Cultures at the University of Massachusetts Dartmouth. He has been an educator for over 40 years, teaching a wide range of Spanish language, culture, literature, and linguistics courses at graduate, advanced, intermediate, and elementary levels. His contact information includes cbenavides@umassd.edu and phone number 508-910-6469, with his office located in Spruce Hall. Dr. Benavides' educational background includes: PhD in Linguistics from the University of Iowa (1999) MA in Linguistics from the University of Texas at El Paso (1993) BA in Business Administration from Universidad Nacional Autónoma de Honduras (1986) His primary research interests focus on Hispanic linguistics, with particular expertise in morphology, lexical semantics, voseo in Latin America, corpus linguistics, and conceptual structure. Dr. Benavides has made significant contributions to understanding Spanish derivational morphology and the semantic component of motion verbs in Spanish. His work bridges theoretical linguistics with practical applications in language teaching, particularly through the use of corpus linguistics in Spanish grammar instruction. Dr. Benavides has received recognition for his teaching excellence, including the 2018 Faculty Civic Leadership Award for his contributions to advance UMass Dartmouth's mission of promoting engaged learning. He was also nominated by a student for the 2018 Manning Prize for Excellence in Teaching for his "unique teaching strategies and great enthusiasm for education and multiculturalism." He has consistently incorporated service-learning projects in his courses for over 18 years, and has made significant contributions in promoting the institutionalization of this teaching methodology at UMass Dartmouth. His professional affiliations include membership in the American Council on the Teaching of Foreign Languages (ACTFL) and the American Association of Teachers of Spanish and Portuguese (AATSP), where he served as College/University Representative on the Board of Directors. Dr. Benavides is also actively involved in non-profit community organizations. As an educator, Dr. Benavides has taught courses such as Applied Linguistics for Teachers of Spanish, Medical Spanish, Spanish Composition and Conversation, and Business Spanish, demonstrating his commitment to both language instruction and practical application in various professional contexts.
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