جستجوی استاد راهنمای دانشگاهی

Fabian E. Bustamanteمشاهده پروفایل

استاد

• Internet-scale Computer Networks
• Distributed Systems
• Network Measurement
• +۳ مورد دیگر

Fabian E. Bustamante is a Professor of Computer Science at the McCormick School of Engineering, Northwestern University. His research focuses on experimental analysis of large-scale Internet networks and distributed systems, emphasizing network measurement, infrastructure characterization, and system design improvements. He leads the AquaLab research group. Education: Ph.D. Computer Science, Georgia Institute of Technology (200?) M.S. Computer Science, Georgia Institute of Technology Licenciado en Ciencias de la Computacion, Universidad Nacional de la Patagonia San Juan Bosco, Argentina Analista Programador Universitario, same university Research Interests: Professor Bustamante's work spans network survivability, crisis-driven network analysis (e.g., Venezuela's internet), and re-architecting internet infrastructure. His lab develops tools to improve network visibility and system resilience. Recent Work Trends: 2023-2024 publications emphasize geopolitical network impacts, infrastructure interdependencies, and latency optimization across global networks. His team collaborates with institutions like ACM and Springer on measurement-driven system redesign. Lab/Team: AquaLab focuses on applied networking research with real-world deployment implications. Current projects include crisis network analysis and internet topology optimization.

Clyde Kruskalمشاهده پروفایل

دانشیار

• Parallel Computing
• Algorithms
• Computer Architecture
• +۳ مورد دیگر

Clyde Kruskal is an Associate Professor in the Department of Computer Science at the University of Maryland, College Park. His research focuses on parallel architectures, models, and algorithms. He earned a Ph.D. from New York University in 1981 and a bachelor’s degree from Brandeis University in 1976. His work includes foundational contributions to parallel computing, such as the read–modify–write concept in distributed systems. Kruskal’s research spans topics like interconnection networks, synchronization mechanisms, and algorithm design for parallel systems. Education: Bachelor’s Degree: Brandeis University, 1976 Master’s Degree: New York University (Courant Institute), 1978 Ph.D.: New York University (Courant Institute), 1981 Research Interests: Parallel computing architectures, parallel algorithms design, multiprocessor synchronization, interconnection networks, and computational geometry problems like graph coloring and visibility analysis. His work emphasizes theoretical foundations and practical implementations in parallel systems. Notable Contributions: Kruskal co-authored the book Problems With A Point: Exploring Math And Computer Science (2019), and his research includes foundational papers on parallel prefix operations, sparse matrix algorithms, and synchronization protocols. His publications span over three decades, reflecting sustained contributions to parallel computing theory and practice. Advising & Outreach: He has mentored students through programs like the Summer Combinatorial Algorithms REU at UMD, fostering undergraduate research in algorithm design and parallel computing.

Dimitris Gizopoulosمشاهده پروفایل

استاد

• Computer Architecture
• Computer Systems
• Fault Tolerance
• +۵ مورد دیگر

Prof. Dimitris Gizopoulos is a Professor at the Department of Informatics & Telecommunications, University of Athens, leading the Computer Architecture Laboratory. His research focuses on fault tolerance, design validation, performance, and energy efficiency in microprocessors, GPUs, and AI accelerators. He is an IEEE Fellow (2013) and ACM Distinguished Member (2022). His work is supported by Horizon Europe projects like DARE, Neuropuls, and Vitamin-V, alongside industry grants from AMD, Cisco, and Meta. He participates in networks like HiPEAC and Eurolab4HPC and serves on editorial boards of journals including ACM Computing Surveys and IEEE Transactions on Computers . Prof. Gizopoulos teaches Computer Architecture courses at both undergraduate and graduate levels. His research spans cross-layer reliability analysis, voltage scaling effects, and secure hardware design. Notable contributions include frameworks for GPU reliability assessment (GUFI, GPUI-4) and tools like MerLIN for microarchitecture-level analysis. His lab’s work has been funded by the EuroHPC Joint Undertaking and the Greek-China Research Collaboration program. Key Projects: DARE (RISC-V Europe), Neuropuls (neuromorphic accelerators), Vitamin-V (RISC-V cloud environments). Awards: IEEE Fellow (2013), ACM Distinguished Member (2022), IEEE Golden Core (since 2002). Industry Partnerships: AMD, Cisco, Bosch, NVIDIA, Intel, IBM Research. Labs: Leads the Computer Architecture Lab, focusing on fault tolerance and energy-efficient computing. His work emphasizes bridging hardware-software co-design challenges, with publications in top venues like IEEE Transactions on Computers and ACM Computing Surveys . Recent efforts include analyzing silent data corruptions (SDCs) in CPUs and GPUs, and developing validation frameworks for cloud-native architectures.

Amro Awadمشاهده پروفایل

دانشیار

• Computer Architecture
• Secure Hardware Architectures
• Memory Systems
• +۳ مورد دیگر

Amro Awad is an Associate Professor in the Department of Electrical and Computer Engineering (ECE) at North Carolina State University's College of Engineering. He previously served as an Assistant Professor at the University of Central Florida and as a Senior Member of Technical Staff at Sandia National Laboratories. Dr. Awad earned his Ph.D. and Master's in Computer Engineering from NC State and a Bachelor's from Jordan University of Science and Technology. Research Focus His research spans computer architecture and security, emphasizing secure hardware systems , memory security , and integration of emerging technologies . Key contributions include novel approaches to secure and efficient GPU memory management, FPGA resource scheduling, and DRAM simulation. Publications & Awards Dr. Awad's work appears in top-tier venues like ISCA, MICRO, ASPLOS, and HPCA. He holds six U.S. patents and received the prestigious R. Ray Bennett Faculty Fellow Award and recognition as a Goodnight Early Career Innovator . Funding & Collaborations His research group has been supported by DARPA , Sandia National Laboratories , NSF , Naval Surface Warfare Center , and Air Force Research Lab . Collaborations include AMD Research, Los Alamos National Lab, HP Labs, and Air Force Research Laboratory.

Michael Gastparمشاهده پروفایل

استاد

• Information Theory
• Signal Processing
• Communications
• +۳ مورد دیگر

Michael Gastpar is a full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, where he leads the Laboratory for Information in Networked Systems (LINX). He previously held faculty positions at the University of California, Berkeley (2003-2011, earning tenure in 2008) and Delft University of Technology. His research spans information theory, signal processing, communications, and systems neuroscience. His research interests focus on network information theory and related coding and signal processing techniques, with applications to sensor networks and neuroscience. Recent work demonstrates a strong shift toward exploring the theoretical foundations of modern machine learning, particularly investigating transformer architectures from an information-theoretic perspective. His research group at EPFL explores how information theory principles can provide fundamental limits and novel approaches for contemporary machine learning problems. His recent publications reveal a clear trend toward bridging classical information theory with modern machine learning. The 15 most recent papers show increasing focus on theoretical analysis of transformers, rate-distortion frameworks for language models, universal prediction methods, and applications of information measures to machine learning theory. This represents a strategic evolution from his earlier work on sensor networks and physical-layer network coding toward foundational questions in artificial intelligence. Scientific Awards: IEEE Fellow 2013 Communications Society & Information Theory Society Joint Paper Award Information Theory Society Distinguished Lecturer (2009-2011) ERC Starting Grant (2010) Okawa Foundation Research Grant (2008) NSF CAREER award (2004) 2002 EPFL Best Thesis Award Professor Gastpar has advised over 20 PhD students who have gone on to successful careers in both academia and industry. His research has been generously supported by major grants including an ERC Starting Grant "ComCom" (2011-2016) and ongoing support from the Swiss National Science Foundation. He has served in significant editorial roles, including as Associate Editor for Shannon Theory for the IEEE Transactions on Information Theory (2008-11) and as Technical Program Committee Co-Chair for the IEEE International Symposium on Information Theory in 2010 and 2021. He leads the Laboratory for Information in Networked Systems (LINX) at EPFL, which brings together researchers working at the intersection of information theory, machine learning, and networked systems. The lab maintains strong connections with both theoretical research communities and practical applications in communications and neuroscience.

• Neuromorphic Computing
• Hardware Accelerators
• VLSI Circuits
• +۵ مورد دیگر

Prof. Dr. Tobias Gemmeke is a University Professor at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, leading the Chair of Integrated Digital Systems and Circuit Design. His work focuses on neuromorphic computing, hardware accelerators, and energy-efficient electronics. He has pioneered advancements in FPGA-based computational neuroscience simulators, neuromorphic processor architectures, and sensor integration for industrial and medical applications. Research interests include time-domain computing, ReRAM reliability, and co-optimization of neural networks with hardware. Notable contributions include the neuroAIx framework for accelerated neuroscience simulations and energy-efficient ASIC designs for post-quantum cryptography. He actively explores memristive devices and domain generalization techniques for edge computing. Recent publications highlight innovations in spiking neural networks, sensor systems for plain bearings, and time-domain compute-in-memory engines. His work bridges theoretical neuroscience with practical hardware implementations, emphasizing scalability and real-time performance.

Rayan Saabمشاهده پروفایل

استادیار

• Compressed Sensing
• Signal Processing
• Applied and Computational Harmonic Analysis
• +۵ مورد دیگر

Rayan Saab is an Assistant Professor in the Department of Mathematics at the University of California, San Diego. His research focuses on the mathematics of information, data science, and signal processing, with an emphasis on quantization, compressed sensing, and machine learning algorithms. Education: Ph.D. in Electrical and Computer Engineering from the University of British Columbia (2010). His work bridges theoretical mathematics and practical applications in data acquisition, digitization, and processing. Recent publications analyze quantization methods for neural networks and compressed sensing systems. He has taught graduate and undergraduate courses in numerical analysis, optimization, and mathematical methods in data science. Research trends include: Developing quantization algorithms with provable guarantees. Applications in high-dimensional data and machine learning. Stochastic frameworks for neural network compression. Scientific awards: Banting Postdoctoral Fellowship (2011-2013). Hellman Fellowship. Advising and grants: Advised students in thesis projects related to signal processing and data science. Received funding for research in mathematical data acquisition. Contact: rsaab@ucsd.edu | Office: AP&M 5157.

Behnaam Aazhangمشاهده پروفایل

استاد

• Signal and Data Processing
• Information Theory
• Neuroengineering
• +۴ مورد دیگر

Behnaam Aazhang is the J.S. Abercrombie Professor of Electrical and Computer Engineering at Rice University and Director of the Rice Neuroengineering Initiative (NEI). He holds a B.S., M.S., and Ph.D. from the University of Illinois at Urbana-Champaign. His roles include leading the multi-university Rice Neuroengineering Initiative and directing the Center for Neuroengineering. He has held an Academy of Finland Distinguished Visiting Professorship (FiDiPro) at the University of Oulu (2006-2014) and received an Honorary Doctorate from the University of Oulu in 2017. Education: Ph.D. in Electrical Engineering, University of Illinois at Urbana-Champaign (1986) M.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1983) B.S. in Electrical Engineering, University of Illinois at Urbana-Champaign (1981) Research Interests: Dr. Aazhang’s work focuses on signal/data processing, information theory, and neuroengineering applications. Key areas include: Neuronal circuit connectivity and learning impacts Real-time closed-loop neuromodulation for neurological disorders (epilepsy, Parkinson’s, depression) Patient-specific cardiac pacing systems Cybersecurity in cloud computing Awards & Honors: 2022 Rice Outstanding Doctoral Thesis Advisor Award 2019 SIGMOBILE Test of Time Award 2017 Honorary Doctorate (University of Oulu) 2013 IEEE Communication Society Advances in Communication Award AAAS and IEEE Fellowships (2012 and 1999) Grants & Advising: His research is supported by multi-university collaborations and grants. He has advised numerous graduate students in electrical engineering and neuroengineering, though specific names are not listed here. Labs & Teams: Leads the Aazhang Lab and the Rice Neuroengineering Initiative, focusing on translational technologies for neurological and cardiac disorders, including non-invasive neuromodulation and cloud security systems.

Monica Lamمشاهده پروفایل

استاد

• Artificial Intelligence
• Privacy-Preserving Technologies
• Compiler Design
• +۴ مورد دیگر

Monica Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in Stanford University's School of Engineering and holds a courtesy professorship in Electrical Engineering. She leads the Stanford Open Virtual Assistant Laboratory and has pioneered work in virtual assistants, privacy protection, and compiler design. Her research includes the Almond virtual assistant, privacy-preserving IoT systems, and the ThingTalk programming language. She co-authored the seminal 'dragon book' on compilers and co-founded Tensilica (now part of Cadence). Education: Bachelor of Science (Honors), Computer Science, University of British Columbia, 1980 Master of Science, Computer Science, Carnegie Mellon University, 1982 Doctor of Philosophy (PhD), Computer Science, Carnegie Mellon University, 1987 Research Interests: Dr. Lam's work focuses on conversational AI with privacy guarantees, compiler optimization for parallel computing, and open-source virtual assistant ecosystems. She is a leader in decentralized systems, having developed frameworks like SociaLite for large-scale graph analysis and Musubi for mobile social networking without centralized platforms. Article Trends: Recent publications emphasize multimodal interactions, multilingual dialogue systems, and LLM-driven applications in areas like question answering, persuasive chatbots, and adaptive assistants. Her work bridges foundational AI research (e.g., semantic parsing) with real-world deployments (e.g., privacy-compliant IoT). Awards & Honors: Member of the National Academy of Engineering ACM Fellow Popular Science's Best of What's New Award (Security, 2019) Advising & Grants: Lam oversees the Open Virtual Assistant Initiative, a collaborative project to build open-source semantic models. Her NSF CNS grant (CNS Core) focuses on federated privacy systems. She has advised over 50 students in AI and systems research, though specific names are not listed in the provided texts. Labs & Teams: Directs the Stanford Open Virtual Assistant Laboratory, collaborates with the Stanford NLP group, and maintains ties to industry through former startup Tensilica's legacy in embedded processors.

Ivan Martinovicمشاهده پروفایل

استاد

• Cyber-Physical Systems Security
• Wireless Network Security
• Authentication Systems
• +۳ مورد دیگر

Prof. Ivan Martinovic is a Professor of Computer Science at the University of Oxford's Department of Computer Science, holding a Fellowship at the institution. He specializes in cyber-physical systems security, wireless network security, and behavioral biometrics. His research focuses on authentication mechanisms, intrusion detection, and trade-offs between security and performance in systems like satellite communications, EV charging infrastructure, and aviation networks. Education: PhD (TU Kaiserslautern); MSc (TU Darmstadt, Germany). Prior roles include postdoctoral research at UC Berkeley (2011) and UC Irvine (2009–2010), supported by a Carl-Zeiss Foundation Fellowship (2009–2011). He has also been an associate lecturer at TU Kaiserslautern. Research Interests: Cyber-physical systems security, wireless networks, behavioral biometrics (e.g., continuous authentication via physiological signals), robust communication protocols, and analysis of security vs. performance trade-offs in distributed systems. Technologies include software-defined radios, NFC/RFIDs, and EEG/eye-tracking devices. Scientific Awards: Carl-Zeiss Foundation Fellowship (2009–2011). Advising & Grants: No explicit student listings; however, his work involves interdisciplinary collaborations and has been funded by government and industrial grants. His research spans labs and teams focused on satellite cybersecurity, smart infrastructure security, and biometric systems. Labs/Teams: Engages with groups exploring satellite communication security, EV charging infrastructure vulnerabilities, and behavioral authentication systems, though specific lab names are not disclosed in the text.

Mohammed Aledhariمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Data Science
• +۳ مورد دیگر

Mohammed Aledhari is an Assistant Professor at the University of North Texas, specializing in cybersecurity, machine learning, and data science. His research focuses on applications in computational medicine, bioinformatics, and autonomous systems. He holds a Ph.D. from Western Michigan University and degrees from the University of Basrah and the University of Anbar. His research interests include social cybersecurity techniques, federated learning in IoT, and AI-driven solutions for healthcare and transportation. Recent work explores blockchain-enabled digital twins, DDoS attack detection, and equitable ASD diagnostics using machine learning. His publications span cybersecurity frameworks, autonomous vehicle communication protocols, and biomedical IoT innovations. Notable contributions include optimizing intrusion detection in IoMT networks and developing interpretable machine learning models for healthcare. While no formal awards or grants are listed, his work emphasizes interdisciplinary applications of AI in healthcare, transportation, and energy markets. His email is Mohammed.Aledhari@unt.edu .

Ellie Pavlickمشاهده پروفایل

استادیار

• Language Acquisition
• Grounded Language Learning
• Neural Language Models
• +۷ مورد دیگر

Ellie Pavlick is an Assistant Professor of Computer Science and Linguistics at Brown University, where she leads the Language Understanding and Representation (LUNAR) Lab . Her work bridges computational linguistics and cognitive science, focusing on grounded language learning and the structural dynamics of neural language models. PhD from University of Pennsylvania (2017), specializing in paraphrasing and lexical semantics Collaborates with Brown's Robotics, Visual Computing labs, and Cognitive, Linguistic, and Psychological Sciences department Research interests center on cognitively-inspired approaches to language acquisition, analyzing how LLMs and humans encode abstract reasoning and compositional structures. Her lab explores: Grounded language learning in multimodal systems Emergence of compositional behavior in neural networks Interpretability frameworks for black-box AI Key publication areas include: Transformer mechanisms and cognitive modeling Formality and complexity in language systems Relational reasoning and multimodal integration Ellie teaches courses on computational linguistics and human-machine language processing, with a focus on synergies between AI and psychological science.

Kenneth Rossمشاهده پروفایل

استاد

• Database Systems
• Query Processing
• Query Language Design
• +۷ مورد دیگر

Kenneth Ross is a Professor in the Computer Science Department at Columbia University in New York City. His primary appointment is within the Department of Computer Science, with affiliations including the Foundations of Data Science Committee. His work bridges theoretical database research and practical system implementation. His research focuses on database systems with particular expertise in query processing, query language design, data warehousing, and architecture-sensitive database system design. Additional research spans computational biology, especially analysis of large genomic data sets. Current projects include Linear Algebra Operators in Databases for machine learning workloads and Repeats and Somatic Mutation analysis in genomics. His work consistently addresses the intersection of hardware capabilities and database system design. Ross leads the Database Research Lab at Columbia, which has produced significant work on query optimization, GPU database processing, and hardware-conscious database systems. His recent publications demonstrate strong focus on adapting database systems to modern hardware including GPUs, SIMD processors, and persistent memory. His scientific recognition includes: Packard Foundation Fellowship Sloan Foundation Fellowship NSF Young Investigator Award Distinguished Faculty Teaching Award (2008) Ross actively advises undergraduate engineering students (juniors with last names P-Z) and has taught foundational courses including Introduction to Databases and Programming and Problem Solving for over two decades. His teaching portfolio shows consistent engagement with both theoretical concepts and practical implementation challenges in computer science education.

Shungeng Zhangمشاهده پروفایل

استادیار

• Distributed Systems
• Cloud Computing
• Cybersecurity
• +۴ مورد دیگر

Shungeng Zhang serves as an Assistant Professor in the Department of Computer & Cyber Sciences at Augusta University's School of Computer and Cyber Sciences. He holds a Ph.D. in Computer Science from Louisiana State University (2021) and a B.E. in Computer Engineering from Huazhong University of Science and Technology (2014). His educational background includes: Ph.D. in Computer Science, Louisiana State University, 2021 B.E. in Computer Engineering, Huazhong University of Science and Technology, 2014 Dr. Zhang's research spans distributed systems, cloud computing, and cybersecurity with a focus on enhancing performance and scalability of web applications and IoT stream processing in cloud environments. He employs sophisticated timeline analysis and fine-grained monitoring to identify transient bottlenecks causing long-tail latency problems, addressing propagation effects in complex dependency chains among application components. His publication record (2017-2022) reveals consistent contributions to cloud systems performance, particularly in n-tier architectures, concurrency control, and latency optimization. Key themes include stream processing synchronization, fanout query performance, adaptive concurrency for SLO compliance, and mitigation of transient resource contention attacks across distributed systems. No scientific awards were mentioned in the provided information. Dr. Zhang actively seeks graduate students with strong computer systems backgrounds for research collaboration. His departmental service includes Faculty Assembly participation (2021-2022) and faculty interviewing duties. Professionally, he serves as a reviewer for ACM SoCC'23, ACM TOIT, SmartCom 2023, The Journal of Supercomputing, and ACM SoCC'22. Teaching responsibilities include AIST 4720 (Enterprise System Architectures), CSCI 1200 (Introduction to Computers and Programming), and graduate courses such as CSCI 8940 (Dissertation Research) and AIST 3310 (Advanced Networking). No dedicated research labs or teams were specified in the available documentation.

• Software Engineering
• Data Analytics
• Mining Software Repositories
• +۵ مورد دیگر

Ahmed E. Hassan is a Professor and Canada Research Chair in Software Analytics at the School of Computing, Queen's University. He serves as NSERC RIM Industrial Research Chair and leads the Software Analysis and Intelligence Lab (SAIL). Dr. Hassan pioneered the Mining Software Repositories (MSR) conference and co-edited special issues in the IEEE Transactions on Software Engineering and the Journal of Empirical Software Engineering on MSR topics. Education Ph.D. in Computer Science, University of Waterloo (2005) MMath in Computer Science, University of Waterloo BMath in Computer Science, University of Waterloo His research focuses on software analytics, mining software repositories, and systems engineering. Projects at SAIL include analyzing version control systems, predicting software defects, and improving software quality through empirical methods. The lab's work spans software evolution, architecture analysis, and debugging of distributed systems. Dr. Hassan has taught courses like CISC 322: Software Architecture and CISC 880: Mining Software Engineering Data at Queen's University, University of Victoria, and University of Waterloo. His teaching philosophy emphasizes hands-on learning with tools like WEKA and R, and includes project-based assignments using MSR Challenge datasets. Scientific Awards NSERC RIM Industrial Research Chair in Software Engineering Canada Research Chair in Software Analytics He has industrial experience from RIM (Blackberry platform), IBM Research (Almaden Lab), and Nortel Networks. Dr. Hassan is a named inventor on patents in the US, Europe, Canada, Japan, and India. SAIL lab is funded by NSERC, ORF, CFI, and industry partners.