جست‌وجوی استاد و پژوهشگر

Hossein Pedramمشاهده پروفایل

استادیار

• Computer Networking
• Computer Architecture
• Distributed Systems
• +۴ مورد دیگر

Hossein Pedram is an Assistant Professor in the Department of Electrical and Computer Engineering at The University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science. His research focuses on computer networking, computer architecture, and distributed systems, with contributions to IoT, fog computing, and energy-efficient resource management. Pedram holds a PhD in Computer Engineering from Washington State University. He leads research initiatives in emerging areas such as fog-based IoT optimization and bioleaching applications of microbial systems. Education : PhD in Computer Engineering, Washington State University Research Interests : Pedram’s work bridges theoretical computer science and practical system design. Key areas include: - Computer Networking : IoT protocols, fog computing architectures, and energy-efficient resource allocation. - Computer Architecture : Fault-tolerant systems, error-correcting codes, and synchronization techniques. - Distributed Systems : Multi-cloud workflow scheduling, grid service composition, and wireless sensor networks. Research Trends : His publications reflect a strong focus on network optimization (e.g., fog-based task offloading) and interdisciplinary work in bioleaching (using microbial strains for industrial waste remediation). Recent work emphasizes real-time systems and cybersecurity in distributed environments. Awards : No specific awards listed in available materials. Advising & Grants : Several faculty positions are open in Pedram’s department, offering opportunities to collaborate on nascent research directions. Specific grant-funded projects or student advisees are not detailed in the provided data. Labs & Teams : Pedram maintains an active research website outlining current projects, though no specific lab names or team structures are documented here.

Xiaokun Yangمشاهده پروفایل

دانشیار

• FPGA Acceleration
• AI/ML Hardware
• System-on-Chip Design
• +۲ مورد دیگر

Dr. Xiaokun Yang is an Associate Professor of Computer Engineering in the College of Science and Engineering at the University of Houston-Clear Lake. With extensive industry experience at AMD and CEC, his research bridges hardware design and AI acceleration. Research focuses on FPGA acceleration for AI/ML, hardware/software co-design, and IoT system architectures. Publications demonstrate innovations in neural network accelerators, hardware-efficient AI, secure SoC design, and edge computing solutions. Recent work explores ReRAM-based accelerators, blockchain-secured hardware, and federated learning systems. Awards include Best Ph.D Forum Paper at ISVLSI 2014. Education includes a Ph.D. in Electrical and Computer Engineering from Florida International University (2016) and dual M.S. degrees from FIU and Beihang University. Courses taught include Advanced Digital Systems Design, Electronics, and Senior Project. Current research projects investigate FPGA-based AI acceleration and IoT architectures.

JP Legerskiمشاهده پروفایل

استاد بالینی

• Child Psychopathology
• Memory Development
• Social & Emotional Development
• +۳ مورد دیگر

JP Legerski is a Clinical Professor and NPCC Clinical Director at the Department of Psychology, University of North Dakota. He specializes in child psychopathology, memory development, and social-emotional development. His work focuses on trauma interventions, refugee youth mental health, and disaster response. Legerski received his Ph.D. in Clinical Child Psychology from the University of Kansas (2010), with prior degrees from Brigham Young University (B.S., 2004) and a pre-doctoral internship at Boys Town (2010). His research explores topics including post-traumatic stress in disaster survivors, cultural adaptations of evidence-based therapies, and burnout among behavioral health professionals. Notable contributions include studies on Bosnian war-affected youth and Hurricane Katrina evacuees. He teaches courses such as Clinical Practice, Child Psychopathology, and Advanced Individual Research. Legerski's publications span academic journals and edited volumes, with emphasis on trauma treatment, diagnostic frameworks, and developmental psychology. His clinical work integrates mindfulness practices and cognitive-behavioral approaches. He currently oversees graduate student training in UND's Clinical Psychology PhD Program.

• Developmental Biology
• Systems Biology
• Cell Signaling
• +۵ مورد دیگر

Nate Lord, PhD, is an Assistant Professor at the University of Pittsburgh. He holds a PhD in Systems Biology from Harvard University. His research focuses on understanding the robustness of embryonic development, particularly how embryos maintain precise patterning and correct errors despite genetic and environmental challenges. Lord employs optogenetic manipulation, quantitative microscopy, computational modeling, and classical embryology to explore signaling dynamics and cell behavior across scales. His work bridges molecular mechanisms and tissue-level morphogenesis, aiming to uncover principles of developmental reliability. Education: PhD in Systems Biology, Harvard University Research Interests: Lord’s lab investigates how developing systems achieve robustness through multi-scale mechanisms. Key themes include Nodal signaling dynamics, optogenetic control of developmental processes, and stochastic regulation of cell fate decisions. By combining synthetic biology tools with quantitative analysis, his work addresses fundamental questions about error tolerance in embryogenesis, such as how signaling gradients stabilize patterns and how morphogenetic movements are coordinated despite perturbations. Publications: Recent work (2025–2013) highlights Lord’s focus on Nodal signaling pathways, optogenetic regulation of developmental processes, and stochastic mechanisms in microbial systems. His studies on human retinal organoids (e.g., DIO3’s role in photoreceptor development) demonstrate translational potential in modeling human tissue formation. Older studies (e.g., bacterial cell fate switching) reveal foundational insights into stochastic processes in cellular decision-making. Lab & Future Work: Lord’s lab integrates experimental and computational approaches to engineer synthetic systems that mimic developmental reliability. Ongoing projects aim to map feedback mechanisms that stabilize morphogen gradients and to design optogenetic tools for real-time embryonic signaling control.

Sanket Tavarageriمشاهده پروفایل

استادیار

• Big Data
• Machine Learning
• High Performance Computing
• +۲ مورد دیگر

Sanket Tavarageri is an Assistant Professor in the Computer Engineering Department at San José State University. He holds a Ph.D. in Computer Science and Engineering from The Ohio State University and a B.Tech from National Institute of Technology Karnataka. His research encompasses big data systems, machine learning infrastructure, and high-performance compiler technologies. Research areas include: Polyhedral compilation techniques for deep learning workloads Automatic parallelization and optimization frameworks Hardware-software co-design for computational efficiency Scalable machine learning systems His publication portfolio demonstrates consistent innovation in compiler architecture and parallel systems from 2013-2021. Recent work focuses on AI-enhanced compilation, automatic parallelism for deep learning models, and hardware-aware optimizations. Publications appear in ACM TACO, IEEE Big Data, IPDPS, and PLDI conferences. Tavarageri maintains an active industry connection as a researcher at Google. Tools developed through his BRIGHT laboratory are available on GitHub and Bitbucket. His teaching covers compiler technology, parallel computing, and systems design with emphasis on practical implementation.

• Composite Materials
• Topology Optimization
• Additive Manufacturing
• +۳ مورد دیگر

Professor Liyong Tong is a Professor in the School of Aerospace, Mechanical and Mechatronic Engineering at the University of Sydney. He holds BSc and MEngSc degrees from Dalian University of Technology and a PhD from Beihang University. A Fellow of Engineers Australia (FIEAust) and Member of AIAA, his research focuses on advanced composite materials and computational optimization methods. Research Focus: Multiscale topology optimization of composite structures and metamaterials Additive manufacturing of functional composites and smart structures Machine learning applications in structural optimization Analysis and design of advanced composite materials and structures His recent publications demonstrate integration of machine learning with structural optimization, novel approaches for metamaterial design, and experimental validation of 3D-printed structures. Research trends show emphasis on computational efficiency in optimization algorithms and multifunctional material systems. Current Projects: Microcrack suppression in thermoplastic composites for cryogenic hydrogen storage (NSW Space Research Network) Mitigating micro-vibration in satellite devices using multiscale shear damping Development of novel algorithms for topology optimization of laminated composites

Michael Huangمشاهده پروفایل

استاد

• High-Performance Computer Architecture
• Processor Microarchitecture
• Non-Von Neumann Computing
• +۴ مورد دیگر

Michael Huang is a Professor in the Department of Electrical and Computer Engineering and Computer Science at the University of Rochester's Hajim School of Engineering & Applied Sciences. He holds a PhD from the University of Illinois at Urbana-Champaign (2002) and has been faculty since 2002. His research focuses on high-performance computing, including processor microarchitecture, energy-efficient design, and non-von Neumann systems like Ising machines. He has received the NSF CAREER Award and is a member of ISCA/HPCA Hall of Fame. Education: BS (Tsinghua University, 1994), MS/PhD (UIUC, 1999/2002). Research emphasizes co-design of device, circuit, and system technologies. Notable projects include multi-chip Ising machine architectures and optical interconnects. Collaborates with IBM Research on future processor concepts. Active in top conferences like ISCA, HPCA, and ICLR. Key achievements include pioneering work on Ising machines for combinatorial optimization, energy-efficient architectures, and secure branch predictors. His work bridges traditional computer architecture with emerging technologies like optics and mixed-signal circuits.

Yuhao Zhuمشاهده پروفایل

دانشیار

• Visual Computing
• Imaging
• Human Perception
• +۵ مورد دیگر

Yuhao Zhu is an Associate Professor at the University of Rochester, holding joint appointments in the Department of Computer Science and the Department of Brain and Cognitive Sciences. He is affiliated with the Center for Visual Science and the Goergen Institute for Data Science. His research focuses on visual computing, spanning imaging (optics, image sensors), human perception/cognition, and computer systems (architecture, programming models). He earned his Ph.D. from The University of Texas at Austin and has held visiting positions at Harvard University and Arm Research. Education: Ph.D. in Electrical and Computer Engineering, The University of Texas at Austin, 2017 M.S.E. in Electrical and Computer Engineering, The University of Texas at Austin, 2015 B.S. in Computer Science and Engineering, Beihang University, 2010 Research Interests: Dr. Zhu explores interdisciplinary areas such as computational imaging, human visual perception modeling, and energy-efficient computing systems. His work bridges optics, cognition, and computer architecture, with applications in AR/VR, autonomous systems, and neural rendering. Key themes include optimizing systems using biological principles and developing tools for efficient visual computing. Awards: NSF CAREER Award (2020) University Research Award (2023, 2019) ISCA Hall of Fame (2023) ACM SIGARCH Dissertation Award (2018, Honorable Mention) Teaching and Service: He teaches courses on computer imaging, graphics, and systems architecture. He serves on program committees for top conferences (ISCA, ASPLOS, MICRO) and chairs university-wide committees on AI ethics and international collaborations. His lab, Horizon Lab, actively mentors students and fosters interdisciplinary research. Labs/Teams: The Horizon Lab at URCS focuses on visual computing, with projects in computational art, AR/VR optimization, and robotic computing systems. Collaborations span academia and industry (e.g., Meta, Arm).

• Computer Engineering
• Embedded Systems
• Radiation-Hardened Electronics
• +۳ مورد دیگر

Andrea Portaluri is a Ph.D. candidate in Computer Engineering (37th cycle, 2021-2024) at Politecnico di Torino's Department of Control and Computer Engineering (DAUIN). He also serves as an external lecturer and teaching collaborator for the course 'Operating Systems' in the 2024/25 academic year. His research focuses on radiation-hardened FPGA design, real-time embedded systems reliability, and fault tolerance in software-hardware co-design. Research interests include radiation effects on electronics, FPGA-based acceleration, real-time operating systems (RTOS), and mitigation techniques for soft errors in embedded systems. He collaborates with researchers like Luca Sterpone, Sarah Azimi, and Corrado De Sio on projects addressing reliability in space and safety-critical applications. His recent publications analyze radiation-induced errors in FPGAs, GPU-enhanced CAD tools for radiation-hardened designs, and multi-core neural network accelerators. Work trends emphasize improving embedded system reliability through hardware-software co-design, domain isolation, and fault-tolerant architectures. Portaluri actively participates in international conferences, presenting at venues like ACM Computing Frontiers, IEEE IOLTS, and IEEE Parallel and Distributed Computing. He has contributed to over 15 peer-reviewed articles and conference proceedings since 2021, focusing on radiation-hardening techniques and embedded system resilience.

Daniel Holcombمشاهده پروفایل

دانشیار

• Embedded Systems Security
• Hardware Security
• Formal Verification
• +۳ مورد دیگر

Daniel Holcomb is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Amherst, part of the College of Engineering. He specializes in secure and reliable embedded systems, with research interests spanning embedded systems security, hardware security, formal verification, VLSI design, and cryptography. Education : B.S. Electrical & Computer Engineering, University of Massachusetts (2005) M.S. Electrical & Computer Engineering, University of Massachusetts (2007) Ph.D. Electrical Engineering and Computer Sciences, University of California, Berkeley (2013) His research focuses on mitigating hardware vulnerabilities in FPGAs and chiplet-based systems, including countermeasures against side-channel attacks, voltage attacks, and IP theft via reverse engineering. He has contributed to the development of secure design methodologies, PUF-based authentication, and fault-resistant cryptographic implementations. His publications highlight advancements in FPGA security, such as defeating memory protection units (MPUs), preventing trojan insertion during fabrication, and mitigating power distribution attacks in multi-tenant environments. Holcomb is an active member of IEEE and Tau Beta Pi Honor Society. Labs & Teams : His work aligns with the College of Engineering’s research strengths in hardware security, including collaborations with labs focused on embedded systems and VLSI design.

Rouzbeh Abbassiمشاهده پروفایل

استاد

• Environmental Engineering
• Risk Analysis
• Corrosion Science
• +۴ مورد دیگر

Rouzbeh Abbassi is a Professor in Environmental Engineering at Macquarie University's School of Engineering. He holds adjunct roles at Memorial University of Newfoundland and the University of Tasmania. His expertise spans risk analysis, corrosion science, and renewable energy systems. Abbassi completed his PhD in Civil (Environmental) Engineering at Memorial University of Newfoundland, followed by postdoctoral research at Princeton University and Memorial University. He has led roles as Discipline Leader for Civil Engineering and Director of Research at the University of Tasmania. His research focuses on pipeline safety, hydrogen infrastructure, offshore energy systems, and corrosion management, leveraging machine learning and Bayesian networks. Key collaborations include projects on offshore renewable energy integration and hydrogen safety in maritime environments. Abbassi has received awards such as the Academic Excellence Award (2022) and the David Dunsiger Award (2010). His work emphasizes interdisciplinary approaches, addressing challenges in energy transition, environmental sustainability, and industrial safety. Notable contributions include frameworks for offshore wind farm site selection and risk assessment methodologies for microbiologically influenced corrosion (MIC) in subsea pipelines. Education: PhD in Civil (Environmental) Engineering, Memorial University of Newfoundland (2010) Affiliations: Adjunct Professor at Memorial University of Newfoundland, Adjunct Senior Lecturer at University of Tasmania Research Centers: Centre for Energy and Natural Resources Innovation and Transformation (CENRIT), Data Horizons Research Centre

Daniel Lohmannمشاهده پروفایل

استاد

• Operating Systems
• Embedded Systems
• Real-Time Systems
• +۴ مورد دیگر

Professor Daniel Lohmann leads the Systems Research and Architecture (SRA) group at Leibniz Universität Hannover since 2017, following his role as Associate Professor (Privatdozent) at Friedrich-Alexander-Universität Erlangen-Nürnberg (2009–2016). His work focuses on configurable system software, emphasizing embedded systems, real-time systems, and dependable architectures. Key projects include danceOS (dependability in embedded OS), Sloth (minimal-effort kernels), and AspectC++ (aspect-oriented C++ extension). His research is funded by DFG and addresses challenges in variability management, fault tolerance, and real-time scheduling. Research interests span operating systems design, software product lines, generative programming, and aspect-oriented development. Notable contributions include the CiAO OS family and the Sloth RTOS, leveraging aspect-oriented techniques for flexibility and efficiency. His work on configuration analysis (e.g., static variability in Linux) and fault mitigation (e.g., AN-Codes for soft errors) highlights his expertise in system reliability and optimization. Publications emphasize real-time systems, embedded software, and software engineering, with awards including the Best Paper Award at SPLC 2010 and an Outstanding Paper at RTAS 2017. He supervises theses on system software tailoring, fault tolerance, and industrial software ecosystems. His groups collaborate on invasive computing (SFB/TRR 89) and hardware-aware OS design.

Paolo Romanoمشاهده پروفایل

دانشیار

• Distributed Systems
• AI/ML Systems
• Autonomic Computing
• +۵ مورد دیگر

Paolo Romano is an Associate Professor at the Department of Computer Engineering, Instituto Superior Técnico (IST), Universidade Técnica de Lisboa. He is also a Senior Researcher at the Distributed Systems Group (INESC-ID). His research focuses on distributed systems, AI/ML systems, transactional memory, autonomic computing, and high-performance computing. He holds a PhD in Computer Engineering from Sapienza University of Rome (2007) and a Master's Degree (Summa Cum Laude) from the University of Rome Tor Vergata (2002). PhD Thesis: 'Protocols for End-to-End Reliability in Multi-Tier Systems' Supervised over 10 PhD and MSc students Research Interests : Dependable Distributed Systems Transactional Memory (Hardware/Software) Autonomic and Self-Optimizing Systems Performance Modelling & Evaluation Key achievements include leading the Cloud-TM EU-funded project (€1.7M), coordinating the Euro-TM COST Action, and winning multiple best-paper awards at ICAC, NETYS, and NCA conferences. He actively participates in technical committees for conferences like EuroPar, ICDCS, and Middleware. Grants & Projects : Coordinator of 'Euro-TM' (€400K) Coordinator of 'Cloud-TM' (€1.7M EC funding) Principal Investigator in national projects like 'specSTM' (€127K) He advises students in areas such as self-tuning distributed systems, transactional data stores, and hardware-software co-design for concurrency control. His work bridges theory and practice, with contributions to frameworks like D2STM and Cloud-TM.

• Renewable Energy
• Hydrogen Systems
• Electrical Power Engineering
• +۳ مورد دیگر

Mikias Hailu Kebede is a PhD candidate at the Department of Electric Energy, NTNU, affiliated with the Electricity Market and Energy System Planning (EMESP) research group. His research focuses on integrating large-scale green hydrogen production systems into power grids, addressing technical and economic challenges related to grid interaction, energy storage, and system reliability. He holds membership in NTNU Energy Team Solar and NTNU Energy Team Hydrogen. Education: PhD Candidate (ongoing) at NTNU. Research interests include renewable energy, hydrogen systems, energy efficiency auditing, and power quality. His doctoral work explores green microgrid alternatives for hydrogen production facilities, grid flexibility through hydrogen systems, techno-economic analysis of grid services, and evaluating energy storage technologies in Norway's power grid context. Teaching: He instructs courses such as GEng 1031 (Introduction to Engineering Profession), ECEg 6052 (Renewable Power Generation Technologies), and ECEg 6017 (Electric Power Quality and Reliability), demonstrating expertise in both foundational and advanced electrical engineering topics. Collaborations: Active in NTNU Energy Teams focused on solar and hydrogen technologies, contributing to interdisciplinary energy solutions.

Paul Gratzمشاهده پروفایل

استاد

• Computer Architecture
• Multicore Processors
• Network-on-Chip (NoC)
• +۳ مورد دیگر

Paul Gratz is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, leading the Computer Engineering and Systems Group. He is also affiliated with the Computer Science & Engineering department. His research focuses on secure, efficient, and reliable design in high-performance computer architectures, including processor memory systems and on-chip interconnection networks. He holds a Ph.D. from the University of Texas at Austin (2008), and previously worked as a design engineer at Intel Corporation (1997–2002). Gratz has received prestigious awards, including the Best Paper Award at ASPLOS 2009 and teaching accolades from Texas A&M. Education Background: B.S. and M.S. in Electrical Engineering, University of Florida (1994, 1997) Ph.D. in Electrical & Computer Engineering, University of Texas at Austin (2008) Research Interests: Security in multicore/distributed architectures Processor memory systems and on-chip networks Hardware prefetching and cache coherence Energy-efficient design and reliability optimization Publications highlight innovations in network-on-chip (NoC) design, hardware security, and performance optimization. His work includes seminal contributions like the TRIPS processor evaluation (ASPLOS 2009) and pioneering research on branch prediction-driven prefetching (MICRO 2014). Awards also include the 2016 Distinguished Teaching Award and 2017 College of Engineering Excellence Award. Gratz's lab, the Computer Engineering and Systems Group (CESG), explores cutting-edge topics in computer architecture and system design, emphasizing practical applications and interdisciplinary collaboration.