جست‌وجوی استاد و پژوهشگر

Lindi Liaoمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۹ مورد دیگر

Dr. Lindi Liao is an Associate Professor in the School of Computing at George Mason University. She specializes in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and has over 20 years of R&D experience. Her research includes multimodal AI, generative AI, edge AI, self-driving cars, robotics, and geospatial computing. She holds a PhD in Computer Science from George Washington University and an MS from Purdue University. Education: PhD in Computer Science, George Washington University MS in Computer Science, Purdue University Research Interests: Dr. Liao's work spans Artificial Intelligence , Machine Learning , Natural Language Processing , Computer Vision , and Big Data Computing . She explores cutting-edge areas like Generative AI , Agentic AI , Human-Robot Interaction , and AI for Arts & Music . Her contributions include frameworks for multimodal knowledge retrieval , automated defect detection , and music generation algorithms . Advising & Grants: Dr. Liao oversees rigorous GTA/GRA selection processes with stringent academic and technical criteria. She prioritizes candidates with top-tier academic performance, research passion, and collaborative skills. Her industry collaborations include Fortune Global 30 companies and government agencies like the U.S. Department of Transportation. Labs/Teams: While specific lab names are not mentioned, Dr. Liao's work bridges academia and industry through collaborative R&D initiatives in AI, robotics, and Big Data.

José R. Herreroمشاهده پروفایل

دانشیار

• High Performance Computing
• Parallel Computing
• Dense Linear Algebra Algorithms
• +۳ مورد دیگر

José R. Herrero is an Associate Professor in the Computer Architecture Department (DAC) at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB). His primary research area is High Performance Computing (HPC), focusing on parallel algorithms, dense linear algebra, and multicore processor optimization. He has held administrative roles including Vice-Dean for Academic Studies (2010–2013) and Vice-Dean for Institutional and International Relations (2013–2015) at FIB. Education: PhD in Computer Science from UPC (2006), Postgraduate in Training of University Professors (1997), and a Computer Science degree (1993). Teaching experience includes over 15 courses in Computer Architecture, Operating Systems, and Parallel Programming at both undergraduate and graduate levels. Research interests span HPC, parallel computing, and algorithm design. His work emphasizes optimizing matrix computations and linear algebra libraries for modern architectures. Notable contributions include advancements in sparse Cholesky factorization, task-parallel algorithms, and energy-efficient computing. Recognition includes UPC's Teaching Mention for Outstanding Quality (2009, 2014, 2019) and a Certificate of Merit for contributions to scientific computing workshops. He advises numerous PhD students and collaborates internationally, including visiting roles at GeorgiaTech and École Polytechnique de Montréal.

Ishfaq Hussainمشاهده پروفایل

مدرس

• Energy Optimization
• Reconfigurable MPSoC Architectures
• Embedded Systems
• +۳ مورد دیگر

Ishfaq Hussain is a Lecturer at the University of Sargodha and a visiting researcher at Emwi-Tech. He holds a Master's degree in Electrical and Electronics Engineering from HITEC University, Pakistan, with over 4.5 years of experience in embedded systems and artificial intelligence development. His research focuses on energy/performance optimization in reconfigurable MPSoC architectures, mixed-criticality systems, and real-time scheduling algorithms. Education: Masters in Electrical and Electronics Engineering, HITEC University, Pakistan Research Interests: Energy-efficient embedded systems, reconfigurable computing, real-time systems, and mixed-criticality scheduling. He has contributed to frameworks for WCRT analysis in multiframe systems and energy-aware MPSoC modeling using fuzzy logic. Grants & Awards: Recipient of a research grant from the National ICT R&D Fund Outstanding Paper Award at RTNS 2019 Events & Talks: Speaker at CISTER's Seminar Series (2018, 2020), presenting on multicore processor platforms and mixed-criticality systems Participant at WFCS 2022, ICESS 2021, RTNS 2019 Affiliations: Active in the CISTER Research Centre and associated with Emwi-Tech through visiting research collaborations.

• Computer Architecture
• GPU Architecture
• Power and Performance Modeling
• +۵ مورد دیگر

Sohan Lal is a postdoctoral researcher at the Technical University of Berlin (TU Berlin), focusing on advanced modeling and runtime support for large-scale HPC clusters under a DFG-funded project. His PhD in Computer Engineering from TU Berlin (2019) explored power modeling and architectural techniques for energy-efficient GPUs. He contributed to EU-funded LPGPU projects on low-power GPU computing, leading tasks and collaborating across consortium members. Previously, he lectured at Shri Mata Vaishno Devi University and worked as an IT specialist in the Government of India. Education: PhD in Computer Engineering, TU Berlin (2019) Masters in Computer Science, IIT Delhi (2011) Bachelor in Computer Science and Engineering, GCET Jammu (2003) His research interests span GPU architecture, power/performance modeling, memory systems, and applied machine learning. Notable contributions include techniques like Selective Lossy Compression (SLC) for GPUs and entropy encoding-based memory compression (E²MC). He received HiPEAC travel/grants and was an ACM SRC semifinalist (2018). Grants & Collaborations: HiPEAC Collaboration Grant for joint work with TU/e DFG-funded postdoctoral research He actively teaches advanced computer architectures and multicore systems at TU Berlin, reflecting his passion for education developed during his early teaching career.

John C. Bowmanمشاهده پروفایل

استاد

• Statistical closures in turbulence
• Spectral Reduction
• 3D vector graphics
• +۱۰ مورد دیگر

John C. Bowman is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. He has maintained an active teaching career with courses spanning undergraduate to graduate levels, including Honours Calculus (Math 117/118), Real Variables (Math 417), Coding Theory (Math 422), Linear Algebra II (Math 225), and Statistical Theories of Turbulence (Math 655). His office is located in CAB 521, and he has consistently provided regular office hours for student consultation across multiple courses. Dr. Bowman earned his BS in Engineering from the University of Alberta, followed by an MA and PhD from Princeton University. His educational background has informed his diverse research trajectory spanning mathematical physics, computational mathematics, and scientific visualization. His teaching philosophy emphasizes deep conceptual understanding, as evidenced by his development of comprehensive hyperlinked 3D lecture notes for Honours Calculus. His primary research focuses on statistical theories of turbulence, particularly his development of Spectral Reduction as a reduced statistical description of turbulence. His work shows remarkable agreement with full numerical simulations, even in flows containing long-lived coherent structures. Additional research interests include 3D vector graphics (notably the Asymptote language he developed), implicit dealiasing of convolutions, exponential integrators, and exactly conservative integrators for numerical simulations. He has been involved with the University of Alberta's Geophysical Fluid Dynamics Research Group, where his work bridges theoretical mathematics with practical computational applications. Dr. Bowman's publication record demonstrates consistent contributions to computational mathematics and fluid dynamics over several decades. His recent work (2015-2024) shows continued innovation in computational methods for turbulence simulation, with particular attention to dealiasing techniques, efficient convolution algorithms, and novel approaches to spectral methods. His research has practical applications in assessing dissipation mechanisms in large-eddy simulations and modeling high-Reynolds number turbulence. As an educator, Dr. Bowman has developed comprehensive teaching materials and maintains an active engagement with students through office hours and detailed course websites. His commitment to mathematical education extends to high school outreach through contributions to the "Pi in the Sky" mathematics magazine. His work on the Asymptote vector graphics language has created a valuable tool for mathematical visualization and scientific publishing that integrates seamlessly with TeX.

Fred Chongمشاهده پروفایل

استاد

• Quantum Computing
• Computer Architecture
• Computer Security
• +۴ مورد دیگر

Fred Chong is the Seymour Goodman Professor of Computer Science at the University of Chicago and Chief Scientist for Quantum Software at Infleqtion. He leads the NSF-funded EPiQC Project, aiming to bridge theoretical quantum algorithms with practical hardware. His research spans quantum computing, computer architecture, security, and sustainable computing. Chong holds a PhD from MIT (1996) and previously served at UC Davis and UCSB. He has been awarded the NSF CAREER Award, IEEE Fellow distinction, and over a dozen best paper awards. His work includes co-founding Super.tech (acquired by ColdQuanta) and advising on the National Quantum Initiative. Education: PhD in Computer Science from MIT (1996). Past roles include Chancellor’s Fellow at UC Davis (1997-2005) and Professor/Director at UCSB (2005-2015). Research focuses on quantum software/hardware co-design, quantum algorithms, and practical quantum systems. His team develops tools like WESTPA for weighted ensemble simulations and collaborates on projects like the Greenscale Center for Energy-Efficient Computing. Awards include the Quantrell Award (teaching) and University of Chicago’s Graduate Teaching and Mentoring Award. Key grants total over $80M led/co-led. Labs/groups include the EPiQC Consortium, Systems Group, and CERES Center for Unstoppable Computing. Recent work includes scaling quantum networks, improving qubit reliability, and applying quantum computing to drug discovery and oncology.

Ali Hursonمشاهده پروفایل

استاد

• High-performance computing
• Pervasive computing
• Mobile databases
• +۱۱ مورد دیگر

Dr. Ali Hurson is a Professor in the Department of Electrical and Computer Engineering at Missouri University of Science and Technology. His research spans high-performance computing, pervasive computing, mobile databases, personalized education, intelligent transportation systems, and cyber-physical systems. PhD, Computer Science, University of Central Florida MS, Computer Science, University of Iowa BS, Physics, University of Tehran Dr. Hurson’s research focuses on mobile data access systems, cybersecurity for critical infrastructure, and educational technologies. His work addresses challenges in data dissemination, power management, and fault propagation in heterogeneous systems. His recent publications emphasize agent-based modeling for cyber attacks, predictive analytics in education, and fault tolerance in cyber-physical systems. Trends include integration of machine learning, security frameworks, and sustainable computing. Editor-in-Chief, Advances in Computers Editor-in-Chief, Journal of Sustainable Computing and Communication Dr. Hurson has secured over $3 million in grants from NSF, DOE, DOT, and industry partners. He has held academic roles at Penn State University and the University of Oklahoma, transitioning to Missouri S&T in 2007.

Yaonan Houمشاهده پروفایل

مدرس ارشد

• Semiconductor Photonic Devices
• Photonic Integrated Systems
• Quantum Dots
• +۳ مورد دیگر

Dr Yaonan Hou is a Senior Lecturer in the Department of Electronic and Electrical Engineering at Swansea University. His primary research focuses on semiconductor photonic devices and photonic integrated systems, emphasizing energy-efficient design, fabrication, and integration. Key areas include semiconductor laser diodes, light-emitting diodes (LEDs), photodetectors, and metamaterial-enabled devices. Professional roles include teaching modules like EG-142 Instrumentation and Control and EG-143 Digital Design. Hou holds grants such as Fast-speed Ge detectors on Si integrated with waveguides (2023–2024), supported by the National Epitaxy Facility, and Waveguide integrated Ge-on-Si photodetectors (2023–2023), funded via the Zepler Institute. His research spans metamaterial applications, semiconductor growth, and hybrid integration. Notable projects include surface/interface engineering for III-V/SiN photonic integration and investigations into InAs quantum dots. Hou has supervised a current PhD student on compound semiconductor photoelectrochemical cells for solar hydrogen generation. Publications highlight advancements in molecular beam epitaxy, optical fiber sensors, and photonic integration. His work bridges fundamental semiconductor physics with practical optoelectronic applications, contributing to next-generation photonic systems.

Håkon Kvale Stenslandمشاهده پروفایل

دانشیار

• Machine Learning
• Multimedia Systems
• Real-Time Processing
• +۵ مورد دیگر

Håkon Kvale Stensland is an Associate Professor in the Department of Networks and Distributed Systems at the University of Oslo (UiO), affiliated with Simula Research Laboratory. His work focuses on multimedia systems, machine learning applications in medical imaging and sports analytics, distributed computing, and GPU optimization. He has contributed to projects like HyperKvasir (a gastrointestinal dataset) and SmartIO (PCIe networking for device sharing). Key research areas include real-time video processing, 3D convolutional neural networks for event detection in soccer, and energy-efficient multimedia workloads. He has co-authored over 50 publications in venues like ACM Multimedia, IEEE Transactions, and Nature Communications. His tools and datasets, such as Saga and Bagadus, emphasize collaborative machine learning and real-time sports analytics. Recent work (2024-2025) explores prompt generation for medical segmentation and multi-host device sharing in high-performance clusters. His research bridges theoretical computer science with practical applications in healthcare, sports, and distributed systems.

• Operating systems
• Distributed systems
• Computer Science
• +۳ مورد دیگر

Professor Willy Zwaenepoel is a distinguished academic and Dean of the Faculty of Engineering at the University of Sydney. He holds Fellowships from ACM, IEEE, and ATSE. His research focuses on distributed systems, operating systems, and experimental computer science. Previously, he spent two decades at Rice University and nine years as Dean of EPFL's School of Computer and Communication Sciences before joining Sydney in 2018. Education: BS/MS, Ghent University (1979) MS/PhD, Stanford University (1980/1984) Research Interests: His work emphasizes distributed systems and operating systems, with contributions to key-value stores, transactional systems, and large-scale graph processing. Recent projects include optimizing geo-replicated systems and improving datacenter scheduling efficiency. Articles Trends: Recent publications address OS scheduling (Nest), distributed graph mining (Tesseract), and transactional systems' performance limits. He explores hardware-software co-design for multicore systems and energy-efficient data centers. Awards: Fellow of ACM (2021) Fellow of IEEE (2020) Fellow of ATSE (2019) Grants & Advising: Active grants include adaptive key-value store research (2021) and large-graph processing systems (2018). He advises PhD students and postdocs on distributed systems and storage challenges. Labs/Teams: Leads the Sydney systems research group focusing on scalable distributed systems and cloud infrastructure.

• Converged optical access networks
• Network planning and resilience
• Dependable virtualized networks
• +۳ مورد دیگر

Carmen Mas Machuca is an Adjunct Teaching Professor at the Chair of Communication Networks at Technical University of Munich (TUM), Germany. She holds a Dipl.-Ing. from Universitat Politècnica de Catalunya (1996), a Dr.-Ing. from École polytechnique fédérale de Lausanne (2000), and a Habilitation from TUM (2017). As an IEEE Senior member, her research focuses on converged optical access networks, network resilience, quantum communication systems, and techno-economic analysis. Affiliations: Chair of Communication Networks (Prof. Kellerer), TUM Munich Current Projects: BMBF OptiCON, DFG Software Dependability, AI-NET ANTILLAS, 6G Future Lab Bavaria Her research emphasizes dependable virtualized networks and has contributed to projects like EU 5GPPP VirtuWind, HARMONICS, and OASE. She co-chairs major conferences (ICIN 2024, ONDM 2023) and serves as guest editor for IEEE Transactions on Network and Service Management. Key research areas include quantum key distribution networks, railway communication systems, and network sovereignty metrics. She advises PhD candidates on topics like optical access planning and network reliability, and leads labs such as the FlexComNetsLab. Her work bridges theoretical network design with practical implementation, including tools like SDR-MDNet for multi-domain routing and PyRBD for reliability analysis.

• Fiber Optic Telecommunications Systems
• Optical Networks
• Multicore Fiber Technology
• +۷ مورد دیگر

Adolfo da Visitação Tregeira Cartaxo is a Full Professor at ISCTE – University Institute of Lisbon, affiliated with the Department of Information Science and Technology within the School of Technologies and Architecture. He serves as Director and Integrated Researcher at the Institute of Telecommunications – IUL (IT-IUL), where he leads research in the Optical Communications and Photonics Group (OCPG). His academic career began at Instituto Superior Técnico (IST), Technical University of Lisbon, where he earned his BSc, MSc, PhD, and Habilitation in Electrical and Computer Engineering. Bachelor's Degree in Electrical and Computer Engineering, IST, 1985 Master's Degree in Electrical and Computer Engineering, IST, 1989 PhD in Electrical and Computer Engineering, IST, 1992 Habilitation in Electrical and Computer Engineering, IST, 2005 His primary research interests lie in fiber optic telecommunications systems and optical networks, with a strong focus on multicore fiber technology, signal processing, and next-generation optical networks. He has contributed significantly to the development of digital mitigation techniques for inter-core crosstalk and machine learning applications in optical systems. The available articles highlight his expertise in photonic signal processing and radio-over-fiber systems, particularly in OFDM-UWB transmission over PONs and photonic analog-to-digital conversion. These works exemplify his work at the intersection of microwave and optical technologies for advanced communication systems. Scientific Awards: Best paper award at the 2013 OPTICS Conference, Reykjavik Best paper award at the 2011 OPTICS Conference, Seville UTL/Santander Totta Scientific Award (2008) for highest scientific impact in Electrical Engineering and related fields (2003–2007) He has supervised numerous PhD and Master’s theses and is currently guiding multiple doctoral candidates. He leads major research initiatives, including the EU-funded MATCH project (Multicore Fiber Applications and Technology), which trains 13 doctoral candidates across Europe. He has also led national and international projects such as DigCORE, RETIOT, AMEN, and EUIMWP. He serves on the editorial boards of journals including Photonics (Q2), Telecom , and International Journal of Optics (Q3), and is a Senior Member of IEEE. He is actively involved in academic leadership, having served as Deputy Director of the School of Technologies and Architecture (2018–2021) and in various roles within the Scientific Council and Scientific Committee at ISCTE-IUL. His research lab, the Optical Communications and Photonics Group (OCPG), is part of the Institute of Telecommunications, a leading research unit in Portugal.

Ramon Canal Corretgerمشاهده پروفایل

استاد

• Microarchitecture
• Processor Design
• Memory Design
• +۱۵ مورد دیگر

Ramon Canal Corretger is a Full Professor in the Department of Computer Architecture at the Faculty of Informatics of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC). He previously served as Vice Dean of Postgraduate Studies at FIB and leads the VirtuOS (Virtualization and Operating Systems) research group. His work bridges computer architecture, hardware security, and system-level reliability. Doctorate from UPC, co-supervised at the University of Wisconsin-Madison Sabbaticals at Harvard University (2006–2007) and the University of Cyprus (2019–2020) Active leadership in EU-funded projects such as Vitamin-V (Horizon Europe) His research focuses on microarchitecture, processor and memory design, reliability under variability, and security at the hardware-software interface. He explores low-power multicore architectures, virtualization optimizations, and secure RISC-V-based systems. His recent work integrates AI for intrusion detection and privacy-preserving federated learning in fog computing environments. The most recent publications demonstrate a strong trend toward security, reliability, and trustworthy computing , particularly in RISC-V ecosystems and cloud/edge infrastructures. There is increasing emphasis on hardware-software co-design , attack detection via performance monitoring , and energy-efficient secure accelerators using emerging technologies like neuromorphic and photonic computing. Scientific Awards: HiPEAC Paper Awards (2017, 2010) IEEE Senior Member (2016) Fulbright Award (2006) IBM Faculty Award (2000) Best Student Paper at HPCA-6 (2000) Multiple teaching excellence recognitions from UPC and AQU Catalunya First Prize in Epson Foundation Rosina Ribalta Award (2001) He has advised several PhD students including Manish Rana, Zoran Jaksic, and Shrikanth Ganapathy, many of whom received honors such as the Intel Doctoral Student Programme recognition. His research is supported by competitive grants from the Spanish government, EU Horizon programs, and industry collaborations. He is actively involved in the design of secure, reliable, and efficient computing systems for future cloud and embedded applications. He leads the VirtuOS research group, which focuses on virtualization, operating systems, and hardware-software interface optimization. The group contributes to open-source RISC-V initiatives and participates in large-scale European R&D projects targeting trustworthy computing infrastructures.

Steven Bellمشاهده پروفایل

استاد آموزشی

• Engineering Education
• Embedded Systems
• Camera Systems
• +۳ مورد دیگر

Steven Bell is an Associate Teaching Professor in the Department of Electrical and Computer Engineering at Tufts University's School of Engineering. He has held roles including Assistant Teaching Professor (2019–2024) and Lecturer (2018–2019). His research focuses on engineering education, embedded systems, camera systems, and computational photography. He actively develops educational tools like VHDLweb , an online platform for learning VHDL, and advocates for affordable lab solutions using low-cost FPGAs. Bell has received the Teaching with Technology Awards Honorable Mention (2022) and the Tufts Teaching with Technology Award (2019) for his contributions to pedagogy. He teaches courses such as Embedded Systems, Intro to Computing in Engineering, and Special Topics in Advanced Embedded Systems. His professional activities include roles on the SoE Academic Standing Committee and First-Year Experience planning subcommittee. He has also secured grants, including the Affordable Course Materials grant (2022–2023). Bell's GitHub repositories showcase projects like VHDLweb , UPduino , and FPGA tools for educational and research use. His work emphasizes bridging theoretical concepts with hands-on, accessible engineering practices.

Tobias Grosserمشاهده پروفایل

دانشیار

• Compilers
• Programming Languages
• Performance Programming
• +۷ مورد دیگر

Tobias Grosser is an Associate Professor in the Department of Computer Science and Technology at the University of Cambridge. His research focuses on rethinking performance programming by bridging the gap between developers and compilers. He holds a PhD from École Normale Supérieure Paris and has held positions including Reader at the University of Edinburgh and Ambizione Fellow at ETH Zurich. His research interests span compilers, programming language design, static/dynamic analysis, and the integration of machine learning into compiler development. He emphasizes making compilation more modular, automatic, and trustworthy, with applications in quantum computing, climate science, and open-source hardware. Key projects include xDSL (a Python-native compiler framework), LoopOpt, and the Open Earth Compiler for climate simulations. Recent publications highlight advancements in multi-level intermediate representations (IR), formal verification in MLIR, and performance optimization for GPUs and FPGAs. His work often addresses barriers between programmers and compilers, aiming for intuitive collaboration between developers and automated systems. Tobias mentors a dynamic team of PhD students, postdocs, and researchers, including notable contributors like Siddharth Bhat, Arjun Pitchanathan, and Mathieu Fehr. His lab focuses on compiler toolchains for domain-specific hardware accelerators, quantum computing ecosystems, and verified compilation techniques.