جستجوی استاد راهنمای دانشگاهی

Jean Anne Incorviaمشاهده پروفایل

دانشیار

• Nanotechnology
• Neuromorphic Computing
• Spintronics
• +۷ مورد دیگر

Jean Anne Incorvia is an Associate Professor in the Chandra Family Department of Electrical and Computer Engineering at The University of Texas at Austin, leading the Integrated Nano Computing (INC) Lab. She holds the Engineering Foundation Endowed Faculty Fellowship and has expertise in nanodevices leveraging spintronics, nanomagnetism, and bio-inspired computing. Her research spans neuromorphic systems, 2D materials, and computing in extreme environments. Education: Bachelor’s in Physics, UC Berkeley (2008) Ph.D. in Physics, Harvard University (2015) [cross-registered at MIT] Research Focus: Incorvia’s work integrates emerging materials and physics to advance low-power computing. Key areas include spintronic devices, magnetic tunnel junctions, and neuromorphic circuits for AI applications. She explores applications in health, energy, and security, with a focus on radiation-hardened systems. Grants & Collaborations: 9 NSF grants, 4 DOE grants, and partnerships with Sandia National Labs, Samsung, TSMC, and Lockheed Martin. Leadership roles in conferences like IEDM, DRC, and Intermag. Awards: NSF CAREER Award (2020) IEEE Magnetics Society Early Career Award (2020) Intel Rising Stars Award (2021) APL Rising Star Award (2023) Labs & Teams: Directs the INC Lab and participates in the Microelectronics Research Center. Her lab focuses on device codesign, AI-driven material discovery, and neuromorphic systems.

• Image Processing
• Signal Processing
• 3D Environments
• +۴ مورد دیگر

Stuart Perry is a Professor at the University of Technology Sydney (UTS), serving as Head of Discipline for Signal Processing and Analytics in the School of Electrical and Data Engineering. He holds affiliations with UTS' Faculty of Engineering and Information Technology, the Global Big Data Technologies Centre, and the Visualization Institute. With over 20 years of experience, his career spans roles at DSTO and Canon Information Systems Research Australia (CiSRA), focusing on image processing, signal processing, and perceptual quality measurement. Perry co-directs the Perceptual Imaging Laboratory (PILab), researching 3D environments, light field technologies, and human perception in immersive realities. He actively contributes to international standards committees like ISO/TC42 and ISO/SC29/WG7, leading JPEG's point cloud coding efforts. His research emphasizes adaptive image processing, machine learning-driven object detection, and medical imaging applications. Perry has authored 60+ publications, two books, and 20 patents. Current projects include VR empathy case studies, point cloud compression, and disaster management digital transformation. Education: PhD in Engineering, University of Sydney (1999) Research Interests: His work bridges computational imaging and human perception, addressing challenges in augmented/virtual reality (AR/VR), 3D scanning, and immersive media. Key areas include light field and point cloud coding, psychophysics of visual perception, and medical diagnostics via machine learning. He explores how perceptual principles can optimize interactive technologies for education, healthcare, and entertainment. Recent Research Trends: Recent articles focus on 3D Gaussian splatting, glaucoma detection via deep learning, and JPEG Pleno standards for plenoptic imaging. His work balances technical innovation (e.g., efficient point cloud compression) with human-centric design (e.g., reducing VR motion sickness through display lag analysis). Awards & Recognition: Member of IEEE and founding SPINet participant. Over 60 refereed publications and 20 patents highlight his industry-academic impact. Grants & Leadership: Leads SmartSat CRC projects on yield estimation and Aus4innovation-funded disaster response tech. Manages grants totaling millions AUD. Editorial roles include Associate Editor of SPIE/IS&T Journal of Electronic Imaging. Labs & Collaborations: PILab collaborates internationally on perceptual imaging standards. Active in ISO committees shaping future media technologies.

• Computer Science
• Cybersecurity
• Reliable Computing Systems
• +۵ مورد دیگر

Professor Zbigniew Kalbarczyk is affiliated with the Illinois Advanced Research Center at Singapore (Illinois ARCS), part of the University of Illinois. His research focuses on designing and validating reliable and secure computing systems, with an emphasis on software middleware (ARMOR), operating system-level error recovery (Reliability Microkernel, RMK), hardware reliability support (Reliability and Security Engine, RSE), and formal verification frameworks (SymPLFIED). He holds a Ph.D. in Computer Science from the Bulgarian Academy of Sciences (1992), and two M.S. degrees in Electronic Engineering (Technical University of Sofia, 1984) and Mechanical Engineering (Technical University of Warsaw, 1981). His research interests span fault injection techniques (NFTAPE), GPU resilience, autonomous system safety, and cybersecurity in industrial control systems. Recent work includes projects on hierarchical autoscaling for large language models (Chiron), power-aware DL serving (µ-Serve), and security testbeds for supercomputing infrastructure. He explores SLOs in resource-constrained environments and Kubernetes failure mitigation. His methodologies combine experimental validation with formal analysis to address both accidental errors and malicious attacks. Key contributions include frameworks like ARMOR middleware, Reliability Microkernel, and Symbolic Program-Level Fault Injection (SymPLFIED). His articles highlight trends in resilient AI/HPC systems, energy-efficient computing, and secure embedded systems (e.g., ARM TrustZone for PLCs). Current work addresses challenges in smart grid cybersecurity, machine learning-driven malware detection, and safety-critical fault mitigation in autonomous vehicles. No scientific awards are explicitly mentioned in the provided texts. Research grants and collaborations are inferred through project descriptions but not explicitly detailed. He is part of the Illinois ARCS team, collaborating on interdisciplinary projects involving hardware-software co-design and cybersecurity solutions for industrial and cloud systems.

Navid Asadiمشاهده پروفایل

دانشیار

• Hardware Security
• Reverse Engineering
• 3D Imaging
• +۷ مورد دیگر

Navid Asadi is an Associate Professor in the Electrical & Computer Engineering Department at the University of Florida, affiliated with the Materials Science & Engineering department. He leads the Security and Assurance (SCAN) Lab, equipped with $12M in advanced imaging tools, and serves as Associate Director of the Florida Semiconductor Institute and the Microelectronics Security Training (MEST) center. His research focuses on hardware security, reverse engineering, 3D imaging, and semiconductor assurance. Dr. Asadi has received NSF CAREER (2022) and multiple best paper awards, including from IEEE HOST and ASME ISFA. He pioneered the IEEE PAINE conference and collaborates with agencies like NSF, AFRL, and industry partners. Education: PhD, Mechanical Engineering, University of Connecticut, 2014 MS, Biomedical Engineering, Amir Kabir University of Technology, 2009 BS, Mechanical Engineering, K.N. Toosi University of Technology, 2007 Research Interests: Dr. Asadi’s work spans hardware security mechanisms, advanced packaging inspection, AI-driven defect detection, and semiconductor supply chain assurance. He develops novel techniques for X-ray compatibility metrics, PCB inspection via deep learning, and physical security of silicon photonics. His lab addresses challenges in heterogeneous integration, counterfeit detection, and runtime system-on-chip (SoC) validation. Awards: NSF CAREER Award (2022) Best Paper Award, HOST International Conference (2016) D.E. Crow Innovation Award (University of Connecticut) Labs & Initiatives: Directs the SCAN Lab and oversees the MEST center, training engineers in microelectronics security. His work integrates quantum sensing, THz spectroscopy, and optical neural networks to enhance hardware assurance. Current projects include AI-driven inspection systems, chiplet authentication (ChiPICA), and defense against probing attacks.

Ronald Wilsonمشاهده پروفایل

دانشگاهی

• Hardware Security and Trust
• Reverse Engineering
• Stylometry
• +۴ مورد دیگر

Ronald Wilson is a Research Assistant Professor at the University of Florida's Herbert Wertheim College of Engineering, Department of Electrical & Computer Engineering. His research focuses on hardware security, reverse engineering, stylometry, and psycholinguistics, leveraging machine learning and computer vision techniques. Education: PhD, Electrical and Computer Engineering, University of Florida (2021) MS, Electrical and Computer Engineering, University of Florida (2017) BS, Aviation Transport Engineering, Riga Technical University, Latvia (2015) Research Interests: Wilson’s work bridges AI and hardware security, emphasizing reverse engineering of integrated circuits using SEM imaging and machine learning. He explores applications in stylometry, psycholinguistics, and text-based AI content detection. His methodologies often combine knowledge-based systems with data-centric approaches. Article Trends: Recent publications (2023–2025) highlight advancements in AI-driven hardware assurance, automated segmentation of SEM images, and the integration of large language models (LLMs) for relation extraction and content analysis. His work addresses challenges in text-based threat detection and the synergy between psychology and AI. Labs/Teams: Affiliated with FINS (possibly a lab/team at the University of Florida).

Alexandre Megretskiمشاهده پروفایل

استاد

• Control Systems
• Signal Processing
• Machine Learning
• +۳ مورد دیگر

Alexandre Megretski is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), affiliated with the Laboratory for Information and Decision Systems (LIDS). He specializes in control systems, signal processing, and machine learning applications. Previously, he held roles at the Royal Institute of Technology (Sweden), University of Newcastle (Australia), and Iowa State University. His work focuses on system modeling, neural networks, and robust optimization techniques. Research interests include dynamical systems, reinforcement learning, beamforming algorithms, and analog circuit design. He has contributed to advancements in MIMO systems, uncertainty quantification, and certification-based training frameworks. Notable projects include ConCerNet for physics-informed machine learning and studies on passive intermodulation cancellation in RF systems. Awards and grants are not explicitly listed in the provided materials. Megretski collaborates extensively with industry and academic institutions, emphasizing practical applications of theoretical research. His laboratory work at LIDS integrates interdisciplinary approaches to address challenges in data science and systems engineering.

• Evolutionary Computing
• Adaptive Systems
• Artificial Intelligence
• +۳ مورد دیگر

Prof. Gusz Eiben is a Full Professor at Vrije Universiteit Amsterdam, affiliated with the Faculty of Science (Artificial Intelligence), Network Institute, and Computational Intelligence. His research focuses on evolutionary computing, adaptive systems, and robotics, particularly in controller optimization and AI-driven robotics. He holds an ORCID ID: 0000-0002-3106-4213 . His work explores evolutionary algorithms, reinforcement learning, and robotic morphologies, with applications in hardware-software integration and autonomous systems. Notable contributions include studies on robot curriculum learning and evolvable traits in robotics. Awards : 2001 IEEE Transactions on Evolutionary Computation Outstanding Paper Award Best Paper Award at EvoApps (2012) Best Paper Award at PPSN (2014) Activities : Active in academic memberships including IEEE Transactions on Evolutionary Computation, Journal of Evolutionary Optimization, and various conferences since 2000. Teaches the Evolutionary Computing course (2024–2025). Research datasets include studies on drone flocking optimization and modular robot skill learning, available via Dryad and Zenodo.

• Digital Forensics
• Cybersecurity
• Network Forensics
• +۵ مورد دیگر

Dr. Mark Scanlon is an Associate Professor in the School of Computer Science at University College Dublin (UCD), where he also serves as Founding Director of the UCD Forensics and Security Research Group and Programme Director of the MSc in Forensic Computing and Cybercrime Investigation. He holds a PhD in Remote Digital Forensic Evidence Acquisition and Analysis from UCD and is a Fulbright Scholar in Cybersecurity. His research focuses on digital forensics, cybersecurity, and IoT device analysis, with notable contributions in electromagnetic side-channel analysis, AI-driven forensic tools, and password cracking methodologies. Education: BA, University College Dublin MSc and PhD in Remote Digital Forensic Evidence Acquisition and Analysis, University College Dublin Professional Diploma in University Teaching & Learning, UCD Affiliations: Principal Investigator at CeADAR (National Centre for Applied AI) Senior Editor, Forensic Science International: Digital Investigation Scanlon’s research interests span digital forensics, cybersecurity, and AI applications in investigation. He has pioneered work in electromagnetic side-channel analysis for IoT devices, non-invasive cryptographic analysis, and leveraging LLMs for forensic efficiency. His work addresses challenges such as data deduplication, password cracking optimization, and improving practitioner workflows through automated tools like TraceGen and EMvidence frameworks. His articles highlight trends in AI integration (e.g., LLMs for evidence analysis), IoT forensic readiness, and practitioner challenges (via the DFPulse survey). Scanlon has secured grants including a Fulbright award for collaborative research and led initiatives in forensic education and ethical hacking training. His contributions bridge academic research and real-world forensic practices, emphasizing ethical considerations and tool development. Scientific Awards: Fulbright Scholar in Cybersecurity and Cybercrime Investigation In teaching, he coordinates modules such as Advanced Computer Forensics, Ethical Hacking, and OSINT Analysis, reflecting his commitment to advancing forensic education and cybersecurity training.

Ryan Grantمشاهده پروفایل

استادیار

• Cloud Computing
• High Performance Networks
• Low-level Hardware-Software Interfaces
• +۶ مورد دیگر

Dr. Ryan Grant is an Assistant Professor in the Department of Electrical and Computer Engineering at Queen’s University, Canada. He leads the Computing at Extreme Scale Advanced Research (CAESAR) lab and is affiliated with the Ingenuity Labs Research Institute. His expertise spans cloud computing, high-performance networks, low-level hardware-software interfaces, and energy-efficient supercomputing systems. Dr. Grant holds a PhD from Queen’s University (2012) and previously worked at Sandia National Laboratories (2012–2021), where he contributed to critical supercomputer communication protocols now deployed globally. He has authored over 80 peer-reviewed articles and received prestigious awards including the R&D100 Award and Queen’s University’s 125th Engineering Alumni Award. His research emphasizes advancing Canada’s supercomputing infrastructure to support AI, climate science, and national security applications. Education: PhD in Computer Engineering, Queen’s University (2012) MSc in Computer Engineering, Queen’s University (2005) BSc in Computer Engineering, Queen’s University (2004) Research Interests: Dr. Grant’s work focuses on optimizing supercomputing architectures for extreme-scale systems, with an emphasis on: High-performance networking and MPI communication protocols Power/energy management in HPC systems AI-driven network traffic prediction and resource disaggregation GPU-accelerated computing and cloud infrastructure integration National sovereignty in supercomputing for sensitive applications (e.g., defense, healthcare) Awards & Recognition: R&D100 Award (Oscars of Research) U.S. Defense Programs Awards Public Good Innovator Award Queen’s University 125th Engineering Alumni Award Grants & Labs: Dr. Grant directs the CAESAR lab, one of the world’s leading supercomputing architecture research groups. His work is supported by grants from Canadian and international agencies, focusing on sovereign supercomputing and HPC-AI convergence. Labs/Teams: CAESAR Lab (Queen’s University) Ingenuity Labs Research Institute

John R. Wagnerمشاهده پروفایل

استاد

• Mechatronics
• Nonlinear Control
• Intelligent Control
• +۷ مورد دیگر

Dr. John R. Wagner is a Professor in the Department of Automotive Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. He joined the department in August 1998 after industry roles at Delco Electronics, Hughes Electronics, and Delphi Automotive Systems, where he developed automotive control systems using hardware-in-the-loop and in-vehicle testing. Wagner established the Driving Simulator Laboratory and Rockwell Automation Mechatronics Educational Laboratory, and serves as faculty advisor for the Clemson SAE student chapter. His educational background includes: Ph.D. in Mechanical Engineering from Purdue University (1989) M.S. in Mechanical Engineering from SUNY at Buffalo (1985) B.S. in Mechanical Engineering from SUNY at Buffalo (1983) Wagner's research centers on Mechatronics , Nonlinear Control , and Intelligent Control systems with applications in automotive, energy, and manufacturing. He pioneers diagnostic/prognostic strategies and dynamic system modeling for turbine systems and vehicle safety, recently advancing digital twin technology for predictive maintenance and collaborative design. His work integrates AI-driven methodologies for autonomous vehicle safety and thermal management. Recent publications (2023-2025) reveal a dominant shift toward digital twin applications in ground vehicle maintenance, design validation, and tradespace exploration. Key trends include adversarial learning for highway safety, driver behavior analysis for traffic improvement, and PLM tool integration in engineering education, reflecting convergence of AI, systems engineering, and practical automotive solutions. His primary recognition includes: ASME Fellow Wagner actively mentors students through SAE chapter leadership and laboratory research, guiding capstone projects in mechatronics and vehicle dynamics. His educational initiatives include developing the Rockwell Mechatronics Laboratory and integrating PLM concepts into curriculum. Industry collaborations (e.g., Rockwell Automation) and editorial roles (IEEE/ASME Transactions on Mechatronics) underscore his impact on automotive engineering practice and pedagogy. He directs the Driving Simulator Laboratory and Rockwell Automation Mechatronics Educational Laboratory, enabling interdisciplinary research in vehicle control systems, energy harvesting, and human-vehicle interaction for next-generation automotive solutions.

Alessio Orsinoمشاهده پروفایل

پژوهشگر ارشد

• Big Data Analysis
• Distributed Computing
• Edge-Cloud Computing
• +۵ مورد دیگر

Alessio Orsino is a Research Fellow at the Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES) at the University of Calabria. His academic journey includes a Ph.D. in Information and Communication Technologies (ICT) awarded in January 2025, a Master’s in Computer Engineering (Big Data Analytics and Data Science) from 2020, and a Bachelor’s in Computer Engineering from 2018, all from the University of Calabria. He also served as a visiting researcher at the University of Cambridge’s Department of Computer Science and Technology (July–December 2023). His research focuses on big data analysis, distributed computing, machine learning, and edge-cloud integration. He contributes to projects like ASPIDE (Exascale programming models) and INSIDER (cloud-edge IoT deployment optimization), funded by EU Horizon 2020 and Italy’s MIUR. He is a contract professor at DIMES and a member of the Scalable Computing and Cloud Laboratory (SCALab). Orsino’s work bridges theoretical and applied computing, emphasizing efficient machine learning on resource-constrained systems and real-time adaptive models for edge environments. His publications address edge-cloud continuum challenges, adversarial robustness in AI, and scalable big data frameworks. Key grants include Horizon 2020 (ASPIDE) and PRIN 2022 (INSIDER), supporting his interdisciplinary research in hybrid cloud-edge infrastructures and data-intensive applications. He has authored/co-authored a book on big data programming and collaborates internationally through visiting positions and lab engagements.

Zakirul Alam Bhuiyanمشاهده پروفایل

دانشیار

• Cybersecurity
• Privacy-Preserving Systems
• Trustworthy AI/ML
• +۷ مورد دیگر

Dr. Md Zakirul Alam Bhuiyan is an Associate Professor in the Department of Computer and Information Sciences at Fordham University, New York. He leads the DependSys Lab (Dependable and Secure Systems Lab) and is affiliated with the Fordham Center for Cybersecurity. His research focuses on cybersecurity, data-driven dependability, trustworthy AI/ML, and IoT/CPS applications. He has authored over 250 publications, including top-tier journals and conferences like IEEE Transactions on Industrial Informatics and IEEE INFOCOM. Dr. Bhuiyan has held adjunct roles at Harvard University and Temple University. His work emphasizes securing healthcare monitoring systems, event detection in CPS, and privacy-preserving data collection. Education details are not explicitly stated, but his career trajectory includes prior positions at Temple University and Harvard. He has secured grants and awards, including the IEEE TCSC Award for Excellence, ScholarGPS recognition, and being named one of the world's top 2% scientists. His professional activities include editor roles for IEEE Transactions and ACM journals, and organizing major conferences like IEEE DASC and TrustCom. Research interests span cybersecurity, IoT, and AI/ML, with a focus on real-world applications like structural health monitoring and smart city infrastructure. His lab, DependSys, addresses challenges in dependable and secure systems. He has advised numerous students and collaborates globally, delivering keynote speeches at events like IEEE ISPA and ICECI. Key achievements include 25+ ESI Highly Cited Papers, 7 Best Paper Awards, and leadership in over 50 conferences. Current projects involve federated learning, edge computing, and blockchain for healthcare privacy. His lab explores innovative uses of Wi-Fi signals for sensing and security, including contactless health monitoring and intrusion detection.

Jesus Salidoمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Image Processing
• +۴ مورد دیگر

Jesus Salido is a Professor at the University of Castilla-La Mancha's School of Computer Science, affiliated with the Department of Systems Engineering and Automation (ISA). He holds a PhD in Industrial Engineering (Robotics and AI) from Universidad Politécnica de Madrid (UPM) and completed a PostDoc at Carnegie Mellon University's Robotics Institute in Pittsburgh, USA. Education: PhD in Industrial Engineering (Robotics and AI), Universidad Politécnica de Madrid (UPM) PostDoc at AML – The Robotics Institute, Carnegie Mellon University Research Interests: Focuses on Robotics, Artificial Intelligence, Medical Imaging, Computer Vision, and Educational Technology . His work includes developing low-cost microscopy systems (e.g., MicroHikari3D), AI-driven pathology tools, and educational resources in digital logic design using Logisim. He leads the VISILAB-UCLM research group, emphasizing Open Science and accessibility in technology. Key Projects: Eyes of Things (EOT): Smart sensor networks for environmental monitoring AIDPATH: Collaboration between academia and industry in digital pathology Bonseyes: Marketplace for edge device collaboration Publications & Contributions: Authored textbooks like Learning Image Processing with OpenCV and Lógica Digital y Tecnología de Computadores . Over 50 peer-reviewed articles on topics ranging from deep learning applications to automated microscopy systems. Actively contributes to open-source educational repositories on GitHub. Teaching: Teaches courses in Computer Technology, VLSI Design, and Artificial Intelligence at the undergraduate and master’s levels. Leads personalized tutoring initiatives to enhance student success and critical thinking.

Abdel-Karim Al-Tamimiمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Natural Language Processing
• Multimedia Networks
• +۳ مورد دیگر

Dr. Abdel-Karim Al-Tamimi is a Senior Lecturer in Computer Science and Software Engineering at Sheffield Hallam University (SHU), where he leads the Interactive Data Analytics Group (iDAG) and contributes to the Applied Software Engineering Research Group (ASERG). He previously served as an Associate Professor at Yarmouk University (Jordan), Director of the Entrepreneurship and Innovation Center (EIC), and Department Chair at the Higher Colleges of Technology (UAE). His roles include consultancy in digital transformation via the DIfG program and editorial board memberships for PLOS ONE and Research Reports on Computer Science . Education: MSc and PhD in Computer Engineering, Washington University in St. Louis (2007, 2010) Senior Fellow of the Higher Education Academy (SFHEA) Research Interests: Machine Learning applications in Natural Language Processing (NLP), Multimedia Networks, Computer Security, and IoT. Recent work focuses on conversational agents (e.g., Phyllis chatbot for adolescent health) and cybersecurity education frameworks. Projects include EU-funded initiatives (e.g., H2020, DFG) and interdisciplinary collaborations in AI-driven solutions. Publications: Over 50+ peer-reviewed articles, including recent contributions on optimization algorithms, game-based learning, and threat intelligence models. His work spans journals like Cluster Computing , ACM Transactions on Computing Education , and conferences such as IEEE BigComp. Grants & Awards: Received £10k for the Phyllis chatbot project and £9.8k for Sheffield’s population health initiative. Awards include SFHEA and industry certifications (Huawei HCIA-AI, Cisco CCNA). Advising & Teams: Oversees interdisciplinary projects and labs like the Orange-Yarmouk Innovation Lab. Active in academic entrepreneurship through roles like Co-Founder of Phyllis and mentor at global hackathons.

Wes Bethelمشاهده پروفایل

دانشیار

• Quantum Computing
• Machine Learning
• Scientific Computing
• +۴ مورد دیگر

Wes Bethel is an Associate Professor in the Department of Computer Science at San Francisco State University. His research focuses on quantum computing, machine learning applications in scientific modeling, and high-performance computing frameworks such as the SENSEI in situ visualization system. He leads efforts in optimizing quantum algorithms, real-time fusion energy modeling, and scalable data processing for large-scale simulations. Key research themes include quantum protocols for polynomial computation, GPU-based quantum circuit simulation, and machine learning-driven approaches to plasma physics and radio frequency (RF) heating analysis. His work bridges theoretical advancements with practical implementations in energy science and computational infrastructure. Recent projects emphasize accelerating RF modeling using AI surrogates, improving in situ visualization frameworks for heterogeneous architectures, and addressing challenges in classical-quantum system integration. Contributions span both methodological innovations and applied solutions for fusion energy research and distributed computing environments. No scientific awards are listed in the provided materials. Advising and grants information is not explicitly detailed here, though his publications indicate collaborative involvement in DOE-funded projects and fusion energy initiatives. He contributes to open-source tools like SENSEI and WarpVisIt, supporting community-driven advancements in scientific computing. Research labs and teams associated with his work likely include computational science groups focused on quantum computing, plasma physics modeling, and high-performance data management at San Francisco State University and collaborating institutions.