جست‌وجوی استاد و پژوهشگر

• Cloud Computing
• Mobile Networks
• Security and Privacy in Networks
• +۷ مورد دیگر

Dr. Melody Moh is a Professor and Interim Chair in the Department of Computer Science at San Jose State University, where she has been a faculty member since 1993. Her research focuses on cloud computing, mobile networks, security/privacy in networks, and machine learning applications. She coordinates Cyber Security Certificates and has published over 130 refereed papers in journals, conferences, and book chapters. Moh holds an MS and PhD in Computer Science from the University of California, Davis. Research Interests: Cloud and Network Security, 5G Networks, IoT Security, Blockchain Applications, Machine Learning for Cybersecurity, Smart Grid Authentication, and Fog Computing. Her work bridges theoretical advancements with practical implementations, such as developing efficient cache management for Cloud-RAN systems and adversarial defense mechanisms for deep learning models. Recent Publications: Recent work includes blockchain-based public key infrastructure, adversarial attacks on deep learning models, and autonomous driving systems using LiDAR data. Her research often involves student collaborators, with many publications featuring *SJSU students. Awards: Best Paper Runner-Up (2019 ACM Southeast Conference) for randomized load balancing research, and Honorable/Best Paper Award (2018 ACM Southeast) for cache management in 5G networks. Grants & Labs: Secured over $500K in NSF and industry grants. Leads research teams focusing on cybersecurity, edge computing, and AI-driven network optimization. Active in collaborative projects with industry partners and government agencies.

Zubair Abdullah-Vetterمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Renewable Energy Engineering
• Solar Cell Technology
• +۳ مورد دیگر

Dr. Zubair Abdullah-Vetter is a Research Fellow at the University of New South Wales (UNSW) , affiliated with the College of Engineering and the Photovoltaic and Renewable Energy Engineering department. His work focuses on integrating advanced machine learning techniques with photovoltaic device analysis to enhance solar energy system quality and reliability. PhD in Photovoltaic Engineering Specializes in AI-driven solar cell diagnostics Active in both journal publications and conference presentations Research interests center on machine learning for renewable energy systems , particularly: Deep learning for defect analysis in solar cells Predictive modeling of photovoltaic degradation Accelerated testing protocols using AI Quantum efficiency measurement automation Luminescence image interpretation Time-series analysis of PV performance Recent publications demonstrate expertise in applying neural networks and regression techniques to photovoltaic diagnostics. The work has significant implications for climate change mitigation through improved solar technology efficiency. Scientific recognition includes: BT Imaging Photovoltaic Thesis Prize (2020) Best Poster Award at IEEE PVSC (2022) ACAP Best Student Paper Award (2024) Collaborates with leading researchers in photovoltaic materials and device reliability while contributing to practical applications in Australian solar infrastructure projects.

Robert Mullinsمشاهده پروفایل

استاد

• Computer Architecture
• Secure Machine Learning
• Hardware Accelerators
• +۳ مورد دیگر

Robert Mullins is a Professor in Computer Architecture at the University of Cambridge's Department of Computer Science and Technology. He is a Fellow of St. John's College and Director of Studies for Computer Science at St. John's and Peterhouse Colleges. His research focuses on secure machine learning, hardware accelerators, and open-source silicon design through initiatives like the lowRISC Foundation and the Raspberry Pi Foundation. He leads projects in efficient AI hardware, including the OpenTitan project and the ARIA 'Scaling Compute' programme targeting 1000x cost reduction in AI infrastructure. Key research themes include architectural security, FPGA optimization, and ethical AI deployment. Mullins contributes to interdisciplinary initiatives such as the CHIA Centre for Human-inspired AI and the ELIAS Alliance for European AI leadership. His work spans both theoretical advancements and practical applications, including open-source hardware development and educational outreach through the Raspberry Pi Foundation. Recent projects highlight trends in adversarial defense mechanisms, neural architecture search, and hardware-software co-design for energy efficiency. His involvement in global collaborations underscores a commitment to bridging academic research with industry impact.

• Structural Dynamics
• Wind Energy Systems
• Renewable Energy
• +۴ مورد دیگر

Dr. Breiffni Fitzgerald is an Associate Professor in the Department of Civil, Structural and Environmental Engineering at Trinity College Dublin (TCD), where he also serves as a Fellow. He holds the senior role of Director of Postgraduate Teaching and Learning for TCD's School of Engineering, overseeing doctoral education strategy and postgraduate policy development. His research focuses on structural dynamics, wind energy systems, and machine learning applications to enhance wind turbine performance and resilience. He has authored over 50 peer-reviewed publications and serves as an editor for Wind Energy . Dr. Fitzgerald's engineering credentials include Chartered Engineer status from both the Institution of Engineers of Ireland and ASCE. He has received awards such as the 2023 TCD Teaching Excellence Award and the 2024 IStructE Research into Practice Award. His projects emphasize advanced control systems for wind farms, structural health monitoring, and offshore wind energy innovation. His recent work includes developing TMDI (Tuned Mass Damper Inerter) systems for vibration control, machine learning models for blade deformation prediction, and analyzing wind-wave misalignment impacts in Irish coastal zones. Collaborative initiatives like the MeLodiC project aim to optimize wind farm performance using AI-driven control algorithms. Key Projects: Machine Learning for Wind Farm Aerodynamics and Control (MeLodiC) - funded by SFI Data-Driven Digital Twin of Wind Farms (TwinFarm) - SEAI-funded REMOTE-WIND: Drone-Based Monitoring - SEAI-funded Dr. Fitzgerald's research bridges theoretical advancements with practical industry applications, addressing challenges in offshore wind energy reliability and operational efficiency.

Dejan Kosticمشاهده پروفایل

استاد

• Distributed Systems
• Computer Networks
• Operating Systems
• +۶ مورد دیگر

Dejan Kostic is a Chair Professor of Internetworking at KTH Royal Institute of Technology, leading the Networked Systems Laboratory. He is affiliated with RISE Research Institutes of Sweden and the Wallenberg AI, Autonomous Systems and Software Program (WASP). His research focuses on distributed systems, computer networks, operating systems, and machine learning applications in networking. Education: Ph.D. in Computer Science, Duke University M.S. in Computer Science, University of Texas at Dallas B.S. in Computer Engineering, University of Belgrade Research Interests: His work spans distributed systems , high-performance networking , smartNIC offload , AI-driven systems , and network function virtualization (NFV) . Recent projects include energy-efficient AI inferencing (Wallenberg Scholar Project) and scalable federated learning for healthcare applications. Key contributions include the PacketMill framework, FAJITA stateful processing, and the LineFS distributed file system. Publications & Awards: ERC Starting (2010) and Consolidator (2018) Awards Wallenberg Scholar (2024) Best Paper at SOSP 2021 and Community Award at NSDI 2022 Advising & Grants: Supervised over 15 doctoral students, including notable alumni like Hamid Ghasemirahni and Alireza Farshin. Leads projects funded by ERC, WASP, and Swedish Research Council (VR). Active in grant review panels for ERC and Swedish Foundations. Labs & Teams: Heads the Network Systems Laboratory (NSLab) at KTH, collaborating with Ericsson and RISE on industrial projects. Key initiatives include the Networked Systems for Machine Learning course and the Connected Intelligence unit at RISE.

Rampi Ramprasadمشاهده پروفایل

استاد

• Materials Informatics
• Polymer Design
• Machine Learning
• +۳ مورد دیگر

Rampi Ramprasad is Professor and Michael E. Tennenbaum Family Chair at Georgia Tech, holding joint appointments as Georgia Research Alliance Eminent Scholar in Energy Sustainability. His research develops machine learning methods for accelerated materials discovery. Education: B.Tech. in Metallurgical Engineering, Indian Institute of Technology Madras M.S. in Materials Science & Engineering, Washington State University Ph.D. in Materials Science & Engineering, University of Illinois Urbana-Champaign Research Leadership: Leads multi-institution projects including ONR MURI programs on polymeric dielectrics. Research areas: Machine learning for polymer, metal, and dielectric design High-performance materials for energy storage applications Computational frameworks for predicting material properties Data-driven discovery of sustainable materials Recognition: Fellow of multiple scientific societies including American Physical Society and recipient of Max Planck Society Fellowship. Current research demonstrates strong focus on AI-augmented materials design, particularly for energy storage polymers and recyclable materials.

Stefan Reifمشاهده پروفایل

پژوهشگر

• Energy-Aware Systems
• Parallel Systems
• Operating Systems
• +۳ مورد دیگر

Dr.-Ing. Stefan Reif is a Researcher at the Department of Computer Science 4 (Systems Software) at Friedrich-Alexander-Universität Erlangen-Nürnberg. His research focuses on Energy-Aware Systems and efficient, scalable Parallel Systems, with a particular emphasis on Operating Systems, Real-Time Systems, and Network Latency optimization. He is involved in projects such as LARN (Latency- and Resilience-Aware Networking) and PAX (Power-Aware Critical Sections), funded by the German Research Foundation (DFG). His academic activities include reviewing for top conferences like EMSOFT, EuroSys, and ISORC. Teaching contributions include courses on Concurrent Systems. Supervised theses include work on energy implications of security mitigations (Meltdown/Spectre), system-call aggregation, and energy-demand estimation for neural networks. Publications highlight innovations in energy-efficient OS configurations, cross-layer pacing for edge systems, and synchronization algorithms for real-time multi-core systems. Key research tools include the NEON middleware and ANTILLAS benchmarking framework.

Ana Goulartمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Smart Grid Security
• VoIP and Signaling Protocols
• +۷ مورد دیگر

Ana Goulart is a Professor in the Department of Engineering Technology and Industrial Distribution at Texas A&M University's College of Engineering. Her primary research focuses on cyber-physical systems, smart grid security, and emergency communications protocols like NG-9-1-1. She holds a Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology (2005), alongside multiple master's and bachelor’s degrees in engineering and management. Her research interests span internet telephony (VoIP), IP-based emergency systems, session initiation protocol (SIP), and testbed-driven network performance analysis. Notable projects include developing scalable simulation tools for cyber-physical energy systems and enhancing grid resilience through AI-driven detection and reinforcement learning. Recent publications (2023–2025) emphasize cyber-physical security, anomaly detection via machine learning, and multi-stage attack mitigation. She has received prestigious awards such as the Corrie & Jim Furber Faculty Fellowship (2011–2012) and Eugene E. Webb Faculty Fellowship (2010). Her work integrates academic and industry collaboration, exemplified by projects like the AMAR autonomous agricultural robot and firewall optimization for smart grid networks. She also contributes to engineering education through active learning initiatives and capstone design curricula.

Tirthak Patelمشاهده پروفایل

استادیار

• Quantum Computing
• High-Performance Computing (HPC)
• Quantum Machine Learning
• +۳ مورد دیگر

Tirthak Patel is an Assistant Professor in the Department of Computer Science at Rice University and Director of the Positive Technology Lab. His research focuses on systems-level challenges at the intersection of quantum computing and high-performance computing (HPC), emphasizing reliability, performance, and energy efficiency trade-offs. He holds a PhD from Northeastern University (2023) and prior degrees from Northeastern and the University of Toronto. Education: PhD in Computer Engineering, Northeastern University (2023) MS in Electrical and Computer Engineering, Northeastern University (2019) Bachelor of Applied Science in Electrical Engineering, University of Toronto (2017) His research explores quantum system software, compiler design for neutral atom quantum architectures, and quantum machine learning. Notable contributions include frameworks like PARALLAX (compiler for neutral atom quantum computers) and GEYSER (compilation for neutral atom systems). He prioritizes open-source tools and reproducible research. Research Trends in Publications: Recent work emphasizes quantum noise utilization, quantum GANs, and compiler optimizations for NISQ-era hardware. He also addresses challenges in hybrid quantum-classical systems and quantum cloud security (e.g., OpaQue for program obfuscation). Awards: 2024 ACM SIGHPC Doctoral Dissertation Honorable Mention 2021 ACM-IEEE CS George Michael HPC Fellowship 2024 Rice Outstanding Undergraduate Research Mentor Award Advising & Grants: While specific grant details aren't listed, his work is supported by open-source contributions and industry collaborations. He advises students through Rice's Quantum Computing programs and teaches courses like COMP 448/558: Quantum Computing Algorithms. Labs & Teams: Directs the Positive Technology Lab, focused on advancing quantum computing systems through interdisciplinary approaches. Collaborates with industry partners and international conferences as a TPC member for venues like SC, ISCA, and ICCAD.

Joseph R. Cavallaroمشاهده پروفایل

استاد

• VLSI Signal Processing
• Wireless Communication Systems Architectures
• VLSI Systems Design and Prototyping
• +۳ مورد دیگر

Joseph R. Cavallaro is a Professor in the Department of Electrical and Computer Engineering and holds a courtesy appointment in the Department of Computer Science at Rice University. He serves as Director of the Center for Multimedia Communications. His research focuses on VLSI processor architectures, wireless communication systems, and high-speed computer arithmetic. Cavallaro joined Rice in 1988 and has contributed extensively to advancements in special-purpose VLSI systems for signal processing, robotics, and computer graphics. Education: B.S. Electrical Engineering, University of Pennsylvania (1981) M.S. Electrical Engineering, Princeton University (1982) Ph.D. Electrical Engineering, Cornell University (1988) Research Interests: Special-purpose VLSI architectures for signal processing Wireless communication systems design Low-power and high-throughput LDPC decoders Hardware acceleration for neural networks RF fingerprint identification for IoT security Key Achievements: IEEE Fellow (2015) for contributions to VLSI architectures and wireless communications Recipient of the NSF Research Initiation Award (1989-1992) IEEE Circuits and Systems Society Distinguished Lecturer (2012-2013) Advising & Grants: Principal Investigator for NeTS: BRICK grant (2017) Collaborative Research: BAMM grant (2014) Guided research in FPGA-based accelerators, massive MIMO systems, and biomedical signal processing Labs & Teams: Leads the Center for Multimedia Communications and collaborates on projects involving VLSI design, edge computing, and medical signal processing.

• Energy Harvesting
• Low-Power Integrated Circuit Design
• Miniature System Design
• +۳ مورد دیگر

Hee Lee is an Assistant Professor at the University of Pittsburgh's Department of Electrical and Computer Engineering. His research focuses on energy harvesting, low-power integrated circuits, miniature system design, and battery management. He holds a Ph.D. from the University of Michigan (2014) and M.S./B.S. degrees from Yonsei University (2007/2005). Research Interests: Design of ultra-low-power analog/mixed-signal circuits for IoT and biomedical applications Energy-autonomous systems using photovoltaic, piezoelectric, and biofuel-based harvesting Miniaturized sensor nodes with embedded neural network accelerators Recent work emphasizes hybrid timestamping circuits (2025), subthreshold voltage references (2024-2025), and mm-scale neural accelerators (2023). His publications span IEEE journals and conferences like ISSCC, JSSC, and VLSI Symposia. Patents include innovations in voltage references and energy harvesting interfaces. He has developed systems like the mSAIL platform for monarch butterfly tracking and implantable IOP monitors. Lab activities involve collaboration with Michigan's Blaauw/Sylvester group on projects like System-on-Mud oceanic sensors and millimeter-scale biofuel-powered devices. Current research explores high-temperature IoT systems and neuromorphic hardware accelerators.

• Materials Science
• Nanotechnology
• Neuromorphic Computing
• +۵ مورد دیگر

Feng Xiong is an Associate Professor at the Swanson School of Engineering, University of Pittsburgh. His research focuses on materials science, nanotechnology, and neuromorphic computing, with contributions to thermoelectrics, biomedical sensors, and semiconductor devices. He holds a postdoctoral fellowship from Stanford (2014–2016) and has received prestigious awards such as the Materials Research Society’s Gold Award (2014) and the Chinese Government Overseas Scholarship (2012). Education: Not explicitly listed in provided text, but includes notable fellowships at UIUC and National University of Singapore. Research Interests: Xiong’s work spans interdisciplinary areas including phase-change materials, bio-inspired neuromorphic systems, and energy-efficient electronics. His innovations in DNA-based doping and thermoelectric cooling aim to advance semiconductor and biomedical technologies. Awards and Honors: Nano- and Quantum Science Postdoctoral Fellowship, Stanford Materials Research Society Gold Award Hong, McCully, and Allen Fellowship, UIUC Multiple academic recognitions from China and Taiwan Advising & Grants: No student advisees listed. Research supported through institutional and industrial collaborations. Labs/Teams: Active in University of Pittsburgh’s nanotechnology and materials engineering research groups.

Tanya Vladimirovaمشاهده پروفایل

استاد

• Embedded Systems
• Artificial Intelligence
• Space Systems Engineering
• +۵ مورد دیگر

Professor Tanya Vladimirova is a Professor of Electronic and Computer Systems Engineering at the University of Leicester, affiliated with the School of Engineering's Mathematical Sciences department. Her research focuses on custom computing for edge intelligence, embedded systems design, and space-based applications. She pioneered innovations in satellite computing systems, including reconfigurable on-board computers, fault-tolerant designs, and FPGA-accelerated image processing. Her work integrates AI, digital signal processing, and hardware acceleration for applications in autonomous vehicles, spacecraft, and IoT. Key achievements include the first FPGA-based on-board hyperspectral image compression systems, fault-tolerant distributed computing frameworks for satellites, and self-healing distributed intra-satellite systems. Current projects explore machine learning for satellite reliability and autonomous vehicle vision systems. Prior to Leicester, she led a globally recognized group at the Surrey Space Centre, collaborating with Surrey Satellite Technology Limited (SSTL). Her research spans satellite miniaturization, wireless sensor networks for planetary exploration, and radiation-hardened computing. Her publications address challenges in satellite data processing, fault tolerance, and embedded AI. Notable contributions include novel algorithms for landmine detection, radiation mitigation strategies for FPGAs, and adaptive middleware for distributed space systems. Her work bridges theoretical computing with practical aerospace engineering, emphasizing resilience and efficiency in hostile environments.

Jorge Fernández-Berniمشاهده پروفایل

دانشیار

• Smart CMOS image sensors
• Vision chips
• Embedded systems
• +۱۰ مورد دیگر

Jorge Fernández-Berni is an Associate Professor at the University of Seville, affiliated with the Institute of Microelectronics of Seville (IMSE-CNM), a joint research center of CSIC and the university. He holds a PhD in Microelectronics (2011, with honors) and has been a faculty member since 2018, following a Juan de la Cierva Research Fellowship (2016–2017). Research Interests: Smart CMOS image sensors Vision chips Embedded systems and hardware-software co-design Distributed smart sensors Deep learning and edge AI Nature and biodiversity monitoring His recent publications reflect a strong trend toward integrating AI with low-power embedded vision systems for ecological applications, particularly in wildlife and environmental monitoring. The work emphasizes efficient, real-time processing on edge devices, focal-plane computation, and system-level optimization for IoT deployment. Topics include high dynamic range imaging, Gaussian pyramid extraction, thermal throttling impact, and performance prediction for deep learning models on embedded platforms. Scientific Awards: Best Paper Award, SPIE Electronic Imaging 2014 Third Prize, Student Paper Award, IEEE CNNA 2010 Winner, 'De Idea a Producto' contest 2022 HiPEAC 2023 Technology Transfer Award Winner, Startup Olé Marbella 2024 Student Pitch Competition Advising and Grants: Dr. Fernández-Berni has served as Principal Investigator (PI) in numerous research and tech transfer projects, including ULTIMATE (smart embedded platform for nature monitoring), SEMIoTICS (intelligent IoT components), and collaborations with SEO/BirdLife on automated bird identification. He has participated in approximately 25 research projects, with funding from national agencies (Ministerio de Ciencia e Innovación, Junta de Andalucía), the European Union (H2020), and the U.S. Office of Naval Research. He has also led the spin-off BiodAIverse (now biotfy), which has received recognition and acceleration support. Labs and Teams: He is a core researcher at the Institute of Microelectronics of Seville (IMSE-CNM), where he contributes to advanced sensor development and participates in the Doctoral Program on Physical Sciences and Technologies. He has collaborated extensively with the research group led by Á. Rodríguez-Vázquez and R. Carmona-Galán, focusing on smart vision systems and embedded processing.

• Energy-Efficient Computing
• High-Performance Computing
• Compiler Techniques
• +۴ مورد دیگر

Kanishkan Vadivel is a Researcher in the Electronic Systems group at Eindhoven University of Technology (TU/e), specializing in energy-efficient hardware architectures and compiler-based code-generation techniques. He holds a Master’s degree in Embedded Systems from TU/e (2017) and a Bachelor’s from Coimbatore Institute of Technology (India). Prior to academia, he worked in embedded systems at Tata Engineering and Arm Ltd. Research Interests : His work focuses on computation-in-memory architectures using resistive devices, optimal code generation for CGRA (Coarse-Grained Reconfigurable Architecture), and high-performance computing. Key projects include the MNEMOSENE initiative and development of the CIM-SIM simulator for computation-in-memory systems. Awards : HiPEAC collaboration grant (2019) Advising & Grants : His research is supported by grants focused on neuromorphic processors and edge-AI hardware. He collaborates on projects like NEUROKIT2E for embedded deep learning systems. Labs/Teams : Active member of TU/e’s Electronic Systems Center and Efficient Stream Processing Lab, contributing to neuromorphic and energy-efficient computing initiatives.