جست‌وجوی استاد و پژوهشگر

David Thomasمشاهده پروفایل

استاد

• Event-driven Computing
• FPGAs
• Digital Architectures
• +۳ مورد دیگر

Professor David Thomas holds the position of Professor in Computer Engineering at the University of Southampton's Electronics and Computer Science Department. His research focuses on the intersection of software and hardware, particularly leveraging FPGAs for novel digital architectures and event-driven computing. He has a notable academic trajectory, having previously served as a Lecturer and Senior Lecturer at Imperial College London before joining Southampton in 2021. Dr. Thomas is actively involved in supervising PhD students and contributes to interdisciplinary research projects funded by the EPSRC, such as the SONNETS initiative exploring scalable event-triggered systems. Education: BSc in Computer Science (Imperial College London), PhD in Digital Architectures (Imperial College London). Postdoctoral roles included Research Associate and Research Fellow at Imperial's Department of Computing. Research Interests: Event-driven computing, FPGA-based systems, high-level synthesis, and high-performance computing. His work emphasizes practical implementations of theoretical models, such as custom processors and application-specific accelerators. Current projects include optimizing random number generation for FPGAs and exploring meta-programming techniques for hardware design. Advising and Grants: Supervises multiple PhD students in areas like neuromorphic computing and algorithm optimization. Active in securing funding for distributed system architectures and FPGA-based solutions. Labs/Teams: Member of the Cyber Physical Systems research group. Collaborates with interdisciplinary teams on projects like POETS (Partially Ordered Event-Triggered Systems) for large-scale parallel computing.

Rabi N. Mahapatraمشاهده پروفایل

استاد

• Embedded systems
• System-on-chip
• Reconfigurable architectures
• +۸ مورد دیگر

Rabi N. Mahapatra is a Professor in the Department of Computer Science & Engineering at Texas A&M University, within the College of Engineering. His research focuses on embedded systems, reconfigurable architectures, real-time systems, and semantic networks. He holds a Ph.D. in Computer Engineering from the Indian Institute of Technology (1992), an M.S. in Electrical Engineering (Sambalpur University, 1984), and a B.S. in Electronics & Communication (Sambalpur University, 1979). His research interests include Network-on-Chip (NoC), data analytic co-design, IoT protocols, and temperature-aware energy management. His work emphasizes hardware-software co-design for complex systems, with applications in many-core processors, semantic search engines, and real-time embedded systems. Key publications highlight contributions to collaborative filtering on many-core architectures, low-jitter clock distribution circuits, and energy-efficient scheduling. He has been recognized as an IEEE Computer Society Distinguished Visitor (2005–2007) and received the BOYS-CAST Indo-US Young Scientist Award. He leads the Codesign Embedded Systems group at Texas A&M, exploring cutting-edge topics such as photonics NoC, reservoir computing, and IoT security. His research bridges theory and practice, addressing challenges in scalable systems and embedded applications.

Abdullah Muzahidمشاهده پروفایل

دانشیار

• Computer Architecture
• Systems
• Artificial Intelligence
• +۵ مورد دیگر

Abdullah Muzahid is an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University (since August 2024), previously serving as an Assistant Professor there since August 2018. Prior, he held an Assistant Professor role at the University of Texas at San Antonio (2012-2018). He earned his Ph.D. from the University of Illinois at Urbana-Champaign (2012), focusing on architectural support for debugging concurrency bugs under Prof. Josep Torrellas. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign (2012) M.S., Computer Science, University of Illinois at Urbana-Champaign (2009) B.S., Computer Science and Engineering, Bangladesh University of Engineering and Technology (2005) Research Interests: His work spans Computer Architecture , Systems , and Artificial Intelligence , with focus on multiprocessor architecture, parallel programming, debugging, and applying machine learning to system optimization. Recent projects include cache indexing via entropy estimation, DNN training acceleration, and hardware-software co-design for security. Awards: NSF CAREER Award (2017) Excellence in Research Award (UTSA, 2015 & 2017) W. J. Poppelbaum Award (UIUC, 2012) Intel Ph.D. Fellowship (2011) Grants & Advising: He leads NSF-funded projects on robust deep learning and stream processing systems. Advised 5 PhD graduates and currently mentors 4 PhD students. Served on program committees for ISCA, HPCA, MICRO, and as NSF panelist. Labs/Teams: Active in Texas A&M’s Computer Architecture group, collaborating on machine programming, hardware security, and AI-driven systems optimization.

Lan Guanمشاهده پروفایل

استاد

• Membrane transport
• Membrane proteins
• Solute transport mechanisms
• +۶ مورد دیگر

Lan Guan is a Professor at Texas Tech University Health Sciences Center in the Department of Cell Physiology and Molecular Biophysics within the School of Medicine. He also serves as Co-Director of the Center for Membrane Protein Research. His research focuses on membrane proteins, which constitute approximately 30% of all eukaryotic proteins and play crucial roles in many aspects of cell function. Dr. Guan's research seeks to understand the mechanisms of solute transport and lay the foundation for advances in disease treatment and human health. He employs an integrated approach including cryo-EM single-particle analysis, X-ray crystallography, ligand binding, molecular dynamics simulations, thermodynamics, genetic engineering, novel amphiphiles, and many other biochemical & biophysical analyses. His current research focuses on cation-coupled bacterial and human transporters. Dr. Guan is currently supported by an NIGMS MIRA R35 Award (2024). His publication record demonstrates expertise in membrane protein structure and function, particularly with melibiose transporters (MelB) and their mechanisms. His work spans structural biology, biochemistry, and biophysics, with significant contributions to understanding membrane transport mechanisms. His research has led to important insights into membrane protein structure-function relationships, particularly in sugar transporters. Dr. Guan's work has implications for understanding fundamental biological processes and potential therapeutic applications related to membrane transport. NIGMS MIRA R35 Award 2024 Dr. Guan actively collaborates with researchers across disciplines and institutions, as evidenced by his extensive publication record with numerous co-authors. His work bridges structural biology, biochemistry, and biophysics to advance our understanding of membrane protein function. He is affiliated with the Center for Membrane Protein Research, where he contributes to advancing methodologies for studying these challenging but critically important biological molecules. His work on novel amphiphiles and detergent design has helped overcome technical barriers in membrane protein research.

Ali Shojaieمشاهده پروفایل

استاد

• Biostatistics
• Statistics
• Machine Learning
• +۴ مورد دیگر

Ali Shojaie is a Professor of Biostatistics and Statistics at the University of Washington, serving as Associate Chair for Strategic Research Affairs in the Department of Biostatistics. He leads the Summer Institute for Statistics in Big Data (SISBID) and the Data Management and Statistics (DMS) Core for the UW Alzheimer's Disease Research Center. His research focuses on developing statistical and machine learning methods for high-dimensional data, with applications in genomics, neuroscience, and public health. Shojaie's work includes advancements in graphical models, Granger causality, and spatial statistics. He has contributed to methodologies for analyzing networks from time series and spatial data, with applications in understanding gene regulatory networks and brain connectivity. His recent projects involve NIH-funded grants exploring gene-phenotype associations using omic data and explainable machine learning for brain stimulation research. Scientific awards include the 2022 Leo Breiman Award from ASA's Statistical Learning and Data Science section, and election as a Fellow of the Institute of Mathematical Statistics (IMS) and American Statistical Association (ASA). He serves on editorial boards for journals like the Journal of the American Statistical Association and Biometrika. Shojaie advises numerous PhD students and postdocs, many of whom have secured academic and industry positions. His lab develops open-source software tools, including the netgsa and ngc packages for network analysis and Granger causality estimation.

Anthony D. Josephمشاهده پروفایل

استاد

• Computer and Network Security
• Distributed Systems
• Mobile Computing
• +۶ مورد دیگر

Anthony D. Joseph is a Chancellor's Professor in the Department of Computer Science at the University of California, Berkeley, within the College of Engineering. He is a faculty member in the Computer Science Division and part of the RISE Lab and AMP Lab at UC Berkeley. His research spans multiple domains in computer science with a focus on security, distributed systems, and networking. Education: 1998, Ph.D., Computer Science, MIT 1988, S.M./S.B., Electrical Engineering and Computer Science/Computer Science and Engineering, MIT Professor Joseph's primary research interests include Computer and Network Security, Distributed Systems, Mobile Computing, Wireless Networking, Software Engineering, Operating Systems, Genomics, Secure Machine Learning, and Datacenters. His work has significant implications for both theoretical computer science and practical applications in industry. He leads multiple research projects including Mesos, SecML (Secure Machine Learning), D-Trigger, DETER, and Tapestry/Brochure. His research has been instrumental in advancing the fields of distributed systems and security, particularly in the context of machine learning applications. His publications reflect a strong focus on the intersection of security and distributed systems, with recent work emphasizing secure machine learning techniques and resource management in data centers. Professor Joseph's research has evolved from foundational work in networking and distributed systems to addressing contemporary challenges in cloud computing and AI security. Scientific Awards: Diane S. McEntyre Award for Excellence in Teaching Computer Science (2007) NSF Faculty Early Career Development Award (CAREER) (2000) Okawa Research Grant (1999) Professor Joseph has advised numerous graduate and undergraduate students, many of whom have gone on to make significant contributions in academia and industry. His research has been supported by various grants, including the NSF CAREER award. He has been actively involved in teaching core computer science courses including CS162: Operating Systems and Systems Programming and CS262: Advanced Topics in Computer Systems. He leads several research groups including the AMP Lab (which focuses on data analytics) and has been instrumental in projects like Mesos (for resource sharing in data centers) and SecML (focusing on the security of machine learning systems). His labs work on cutting-edge problems at the intersection of systems, networking, and security, with applications ranging from cloud computing to critical infrastructure protection.

• Embedded Systems
• Hardware-Software Co-Design
• Invasive Computing
• +۷ مورد دیگر

Prof. Dr.-Ing. Jürgen Teich is a full Professor and Chair for Hardware-Software Co-Design at the Department of Computer Science, Friedrich Alexander University Erlangen-Nuremberg (FAU). He serves as Head of Department Computer Science and Vice Dean of the Technical Faculty since August 2024, and has been Speaker of the FAU Research Center Embedded System Initiative (FAU ESI) since 2023. His educational background includes: Diploma degree in Electrical Engineering, University of Kaiserslautern (1989) Dr.-Ing. degree in Electrical Engineering, University of Saarland (1993) Habilitation (PD Dr.-Ing.) entitled "Synthesis and Optimization of Digital Hardware/Software Systems" (1996) Prof. Teich's research focuses on Embedded Systems , Invasive Computing , Hardware-Software Co-Design , and Reconfigurable Computing . His work spans from theoretical foundations to practical implementations, with particular emphasis on resource-constrained systems, many-core architectures, and energy-efficient computing. He has pioneered research in invasive computing paradigms that enable more efficient use of many-core processors by allowing applications to dynamically claim resources. His recent publications reveal a strong trend toward energy-efficient AI deployment on embedded devices , security of embedded systems , and novel memory technologies . There's a clear focus on practical implementations of machine learning on microcontrollers (TinyML), hardware acceleration for data processing, and innovative approaches to power management in self-powered systems. Among his notable scientific awards are: IEEE Fellow (since 2018) Member of Academia Europaea, Section Informatics (since 2011) Member of the National Academy of Science and Engineering (acatech) (since 2018) Member of the German Society of Humboldtians (since 2021) Prof. Teich has been Principal Investigator for numerous DFG-funded projects including SFB/Transregio 89 "Invasive Computing" (2010-2022), SFB 694, and multiple priority programs. He has coordinated large collaborative research efforts across Germany and internationally, with significant funding from DFG and other sources. His research group has produced influential work in embedded systems design and co-design methodologies. He leads the Hardware-Software Co-Design research group at FAU, which focuses on innovative approaches to embedded system design, invasive computing architectures, and efficient implementation of machine learning on resource-constrained devices. The group maintains strong collaborations with industry partners including Intel, Xilinx, and automotive companies.

• Theoretical Particle Physics
• Nuclear Physics
• Cosmic Ray Physics
• +۷ مورد دیگر

Masatoshi Takano is a Professor at the Faculty of Science and Engineering, Waseda University, specializing in theoretical studies of nuclear physics, particle physics, and astrophysics. His work focuses on nuclear equations of state (EOS) for neutron stars and core-collapse supernovae, incorporating realistic nuclear forces like the Argonne v18 and Urbana IX potentials. He has developed variational methods with explicit energy functionals to model hyperonic nuclear matter, spin-orbit forces, and finite-temperature effects. Education : PhD in Science, Waseda University Professional Memberships : American Physical Society, Japan Physical Society Research spans neutron star structure, supernova simulations, and nuclear matter phase transitions. His recent presentations address neutrino emission rates, braking radiation in nuclear matter, and cluster variational methods. Key collaborations include H. Togashi, K. Nakazato, and K. Sumiyoshi. Scientific contributions involve refining variational energy expressions for asymmetric nuclear matter, incorporating three-body forces, and studying pion condensation effects on neutron star cooling. He has applied his EOS models to multidimensional supernova simulations and cosmic ray detector design.

Vahe Permzadianمشاهده پروفایل

استادیار

• Industrial-Organizational Psychology
• Organizational Behavior
• Work Motivation
• +۵ مورد دیگر

Vahe Permzadian is an Assistant Professor in the Department of Psychological Science at Missouri University of Science and Technology, where he has been employed since 2020. Prior to this position, he served as a Lecturer of Management in the School of Business and Economics at the State University of New York at Plattsburgh for one year. His academic background includes a Ph.D. in Industrial-Organizational Psychology from the University at Albany, State University of New York. Education Ph.D. in Industrial-Organizational Psychology from University at Albany, State University of New York M.A. in Experimental Psychology from Brooklyn College, City University of New York B.A. in Psychology from Queens College, City University of New York Research Interests Dr. Permzadian's research focuses on three key domains: adjustment and socialization of organizational newcomers, goal setting and motivation in work and achievement settings, and scholarship of teaching and learning. His work bridges industrial-organizational psychology with educational psychology, examining how psychological principles apply to both workplace and academic settings. He has conducted extensive research on assessment formats, open educational resources, and the relationship between personality traits and performance outcomes, with particular attention to motivational processes and their impact on individual achievement. Publication Trends Dr. Permzadian's recent publications show a strong methodological emphasis on meta-analyses and systematic reviews examining educational interventions and psychological processes. His work spans both organizational psychology (focusing on workplace motivation, social efficacy, and personality) and educational psychology (focusing on student learning, assessment formats, and educational resources). A notable trend is his investigation of how test formats (closed-book vs. open-book) affect learning outcomes, as well as the impact of open educational resources on student achievement, reflecting his commitment to evidence-based educational practices. Teaching Dr. Permzadian has taught a comprehensive range of courses at both undergraduate and graduate levels since 2013, including Industrial-Organizational Psychology, Organizational Psychology, Principles of Statistics, Psychology of Personality, Introduction to Psychology, Principles of Management and Organizational Behavior, Human Resource Management, and Training and Development. His teaching portfolio demonstrates expertise across core psychology domains and business applications, with many courses taught repeatedly over multiple years, indicating established curriculum development and instructional expertise.

Debanjan Mitraمشاهده پروفایل

استاد

• Innovation
• Consumer Perception
• Quality Management
• +۴ مورد دیگر

Debanjan Mitra is the Voya Financial Fellow and Professor of Marketing at the University of Connecticut School of Business. His research focuses on consumer perception processes, innovation, and the development of metrics for organizations, products, and customers to evaluate their short-term and long-term impact. He employs analytical and econometric methods to study these effects across organizations, product categories, and brands. His educational background includes a Ph.D. in Business Administration (Marketing) from New York University (2004), a Post Graduate Diploma in Management (P.G.D.M.) from the Indian Institute of Management (1990), and a Bachelor of Technology (B.Tech.) from the Indian Institute of Technology (1988). Prior to his doctoral studies, he gained industry experience in marketing positions at Coca Cola and Diageo. Professor Mitra's research interests are multifaceted, covering Innovation, Consumer Perception, Quality Management, New Product Development, Marketing Analytics, Marketing Strategy, and Regulatory Marketing. His work in marketing analytics has provided insights into customer behavior and brand performance, while his research on regulatory marketing explores how firms navigate marketing regulations, particularly in financial markets. He has also made significant contributions to understanding quality from a customer perspective and its role in merger analysis. His publications appear in premier journals such as the Journal of Marketing, Journal of Marketing Research, Marketing Science, and Management Science. The trajectory of his recent work (from 2012 to 2021) shows a consistent focus on the interplay between marketing metrics, regulatory constraints, and innovation, with increasing emphasis on data-driven approaches to marketing strategy. Professor Mitra's scholarly contributions have been recognized with numerous awards: Marketing Science Institute Scholar American Marketing Association’s Varadarajan Award for Early Career Contributions to Marketing Strategy Research Harold H. Maynard Best Paper Award Excellence in Global Marketing Research Award Marketing Science Institute’s Robert D. Buzzell Award INFORMS’ John D. C. Little Award Finalist INFORMS’ Frank M. Bass Award Finalist Academy of Marketing Science’s Emerging Distinguished Scholar He has served as an Associate Editor for the Journal of Marketing and his research has received media coverage in The Wall Street Journal, The Financial Times, Business Week, Forbes, and CNN. Professor Mitra has consulted for many Fortune 500 firms and high-tech start-ups, applying his academic insights to real-world business challenges. In the classroom, he teaches graduate courses including 'Customer Insights', 'Brand Management', and 'Marketing Analytics', as well as PhD courses on 'Marketing Strategy Research' and 'Multivariate Analysis'. His industry experience and academic rigor provide a unique perspective for his students. At the University of Connecticut, Professor Mitra is a core faculty member in the Marketing department within the School of Business, where he continues to advance research in marketing strategy and analytics while mentoring the next generation of marketing scholars and practitioners.

Simon Mooreمشاهده پروفایل

استاد

• Computer Architecture
• Computer Security
• Hardware Security
• +۳ مورد دیگر

Simon Moore is a Professor of Computer Engineering at the University of Cambridge's Department of Computer Science and Technology. He leads the Computer Architecture research group, focusing on secure processors and subsystems, particularly the CHERI project. His work emphasizes formal verification, hardware-software co-design, and scalable security solutions. He is a Fellow and Director of Studies at Trinity Hall, overseeing undergraduate admissions and mentoring in Computer Science. Research Interests: Moore's primary focus is the CHERI secure processor architecture, integrating RISC-V cores with formal verification. His work spans secure hardware design, memory safety, and embedded systems. Notable contributions include the CHERI-RISC-V microarchitecture, CheriABI, and formal verification frameworks. Key Projects: CHERI, CheriBSD, Morello (ARM collaboration) Recent Achievements: Test of Time Award (IEEE Security & Privacy 2025), finalist for Bhattacharyya Award (2022) Grants: Innovate UK Digital Security by Design, DARPA Mission Oriented Resilient Clouds Publications: Over 200 papers on secure architectures, including influential work on CHERI's capability model, formal verification, and hardware security. Recent focus areas include temporal memory safety, embedded system security, and GPU-based capability systems. Labs/Teams: Directs the Computer Architecture Group and collaborates with industry partners like ARM and Microsoft on CHERI implementations.

Graeme Braggمشاهده پروفایل

استاد آموزشی

• Parallel Computing
• Event-driven Systems
• Bioinformatics
• +۵ مورد دیگر

Dr Graeme Bragg is a Senior Teaching Fellow at the University of Southampton within the Department of Electronics and Computer Science . His work spans teaching, research, and technical development with a focus on event-driven computing, bioinformatics, and computational modeling. He actively supervises PhD students and collaborates on interdisciplinary projects. Research Interests: Parallel computing, event-driven systems, genotype imputation, Petri net simulations, subglacial hydrology modeling Teaching: Specializes in hardware description languages and computational methods for engineering students Technical Expertise: RISC-V architecture, FPGA acceleration, bespoke compute fabric development His recent publications demonstrate expertise in applying event-driven computing to diverse problems including: 2025: Automated marking systems for SystemVerilog labs 2025: Seasonal dynamics in subglacial hydrology 2023: Genotype imputation using custom hardware 2022: Optimization algorithms and graph analysis Current research explores: Custom RISC-V FPGA clusters for bioinformatics Event-triggered systems for scientific simulations Parallel computing solutions for molecular modeling Contact: gmb@ecs.soton.ac.uk | +44 23 8059 2784

Reetuparna Dasمشاهده پروفایل

دانشیار

• Computer Architecture
• In-Memory Computing
• Precision Health
• +۷ مورد دیگر

Reetuparna Das is an Associate Professor at the University of Michigan in the Department of Computer Science and Engineering, School of Electrical Engineering and Computer Science (EECS). She previously worked as a research scientist at Intel Labs and as researcher-in-residence for the Center for Future Architectures Research (C-FAR). She co-founded the precision medicine start-up Sequal Inc. and leads the M-Bits research group, which is part of the Computer Engineering Lab at Michigan. Her research focuses on computer architecture and its intersections with software systems and device/VLSI technologies . Key projects include in-memory computing for BigData and ML, fine-grain heterogeneous architectures for mobile systems, and energy-efficient network-on-chip (NoC) designs for many-core processors. Her work has been funded by the NSF, C-FAR, Semiconductor Research Corporation, and Intel. She has authored over 50 papers, filed 7 patents, and received numerous awards, including the Sloan Foundation Faculty Fellowship CRA-W Borg Early Career Award NSF CAREER IEEE Top Picks MICRO/ISCA Hall of Fame inductions Das has served on over 40 program committees, is associate editor for TACO, and co-founded initiatives like WiCArch (Women In Computer Architecture) to promote diversity. She mentors students through outreach programs like Girls Encoded and Ada Lovelace opera events.

Jonathan Huمشاهده پروفایل

استاد

• Optics
• Photonics
• Nanophotonics
• +۱۱ مورد دیگر

Dr. Jonathan Hu is a Professor in the Department of Electrical and Computer Engineering at Baylor University's School of Engineering and Computer Science. He holds a PhD from the University of Maryland Baltimore County (2008) and completed a postdoctoral fellowship at Princeton University (2009–2011). He is an active researcher in optics and photonics, leading the Photonics Research Laboratory and advising both graduate and undergraduate research assistants. Research Interests: Nanophotonics and metamaterials for photovoltaic and biomedical applications Mid-IR supercontinuum generation using chalcogenide photonic crystal fibers 2D materials such as graphene and their alignment via magnetic fields Coherent optical communication and quantum optical Fredkin gates Numerical simulation of electromagnetic problems and leaky mode analysis His recent publications (2019–2024) demonstrate a strong focus on quantum plasmonics, specialty optical fibers, optofluidics, and nonlinear optical phenomena, with high-impact work in journals like Science Advances , ACS Photonics , and Advanced Materials . The research shows a clear trend toward integrating photonics with 2D materials and quantum systems, with applications in sensing, communication, and materials characterization. Scientific Awards and Recognition: 35 Baylor faculty named among top 2% most cited researchers (2023) Editor’s Pick, Journal of Applied Physics (2018) Top three downloads in OSA journals for three consecutive months (2009) NSF Graduate Research Fellowship (awarded to advisee) Chinese Government Award for Outstanding Self-Financed Students Abroad (awarded to advisee) Second Place in FiO + LS Student Competition (awarded to advisee) Advising and Grants: Dr. Hu actively mentors students at all levels, with current graduate research assistants including Wei Zhang, Zhihao Hu, and Sterling Walzel. His lab is supported by external funding, though specific grants are not detailed in the text. He has advised PhD students such as Joshua Young, Chao Niu, and Chengli Wei, many of whom have gone on to successful academic and industry careers. His teaching includes core courses like EGR 1302, ELC 2320, and ELC 4320, as well as advanced topics in computational photonics and integrated photonics. Labs and Teams: He leads the Photonics Research Laboratory at Baylor University, located at the BRIC facility. He is also involved with the Baylor University Optica Student Chapter, promoting optics outreach and networking among students and researchers.

Ioan Raicuمشاهده پروفایل

استاد

• Distributed Systems
• Cloud Computing
• Grid Computing
• +۳ مورد دیگر

Ioan Raicu is a Professor in the Department of Computer Science at Illinois Institute of Technology (IIT) and a guest research faculty at Argonne National Laboratory's Math and Computer Science Division. He leads the Data-Intensive Distributed Systems Laboratory (DataSys) at IIT, focusing on distributed systems, cloud computing, and high-performance computing. His work is primarily funded by the NSF and DOE. Research interests include distributed systems, many-task computing, and data-intensive applications. Over 140 peer-reviewed publications have yielded a H-index of 46, with top papers addressing cloud vs. grid computing comparisons, Globus GridFTP, Swift workflow systems, and Falkon frameworks. Recent projects explore fine-grained parallelism, scalable indexing, and energy-efficient blockchain algorithms. Awards include NSF grants and recognition for lab innovations. Advised PhD students include Alexandru Orhean and Poornima Nookala. The DataSys lab has won the Grainger Computing Innovation Prize and leads initiatives like the BigDataX REU program. Active in conferences such as SC and IEEE IPDPS, Raicu's work bridges theory and practice in extreme-scale computing systems.