جست‌وجوی استاد و پژوهشگر

Aakash Tyagiمشاهده پروفایل

استاد

• Hardware Verification
• Secure Computing
• High Performance Computing Architectures
• +۳ مورد دیگر

Aakash Tyagi is a Professor of Practice in Computer Science & Engineering at Texas A&M University, affiliated with the Multidisciplinary Engineering program. His roles include academic leadership, teaching, and industry collaboration. He holds a Ph.D. in Computer Engineering from the University of Louisiana (1993), an M.S. in Electrical and Computer Engineering (1989), and a B.S. in Electronics & Communication from Kamla Nehru Institute of Technology, India (1987). His research focuses on hardware verification, secure computing, and high-performance computing architectures. He has over two decades of industry experience at Intel, contributing to CPU design and managing projects like the Knights Landing processor. Teaching interests include computer architecture, data structures, and project management. His publications emphasize innovative applications of machine learning in hardware verification, formal methods for processor security, and optimization techniques for EDA tools. Notable works include HyPFuzz (2023), MinBLoG (2024), and Spec2Assertion (2025), advancing automated verification and vulnerability detection. Tyagi has received over 15 teaching and service awards from Texas A&M, including multiple Distinguished Achievement Awards (2015–2024) and grants for instructional innovation. He actively bridges academia and industry through research partnerships and mentorship.

Keith D. Cooperمشاهده پروفایل

استاد

• Compiler Construction
• Program Analysis and Optimization
• Parallel Computing
• +۵ مورد دیگر

Keith D. Cooper is the L. John and Ann H. Doerr Professor in Computational Engineering and Professor of Computer Science at Rice University. He holds a courtesy appointment in the Department of Electrical and Computer Engineering. His research focuses on program analysis, optimization, and compiler construction. He has authored influential textbooks like Engineering a Compiler and has produced 18 Ph.D. students. Cooper has served in key administrative roles, including Chair of Computational and Applied Mathematics (2019–2020), Associate Dean for Research in the Brown School of Engineering (2012–2018), and Co-Director of the Ken Kennedy Institute for Information Technology (2015–2019). Education: Ph.D. (1983), M.A. (1982), and B.S. (1978) in Mathematical Sciences and Electrical Engineering from Rice University. Research Interests: Program analysis and optimization, compiler design, parallel computing, adaptive compilation, and memory hierarchy optimization. His work includes foundational contributions to interprocedural analysis, register allocation (Chaitin-Briggs algorithm), and compiler frameworks like ParaScope. Awards: ACM Fellow, George R. Brown Award for Superior Teaching (2019), and the L. John and Ann H. Doerr Chair (2019). Grants & Leadership: Played a pivotal role in Rice’s Data Science Initiative and the design of Duncan Hall. Advised over 25 students and contributed to numerous grants in compiler research and high-performance computing. Labs & Teams: Key contributor to the Rice Compiler Group and the Ken Kennedy Institute, advancing research in compilers, parallel computing, and computational science.

• Static Program Analysis
• Abstract Interpretation
• Programming Languages
• +۹ مورد دیگر

Caterina Urban is a Research Scientist at INRIA, affiliated with École Normale Supérieure in Paris, where she is a member of the ANTIQUE research team. She holds a PhD in Computer Science from École Normale Supérieure and completed her undergraduate and graduate studies at Università degli Studi di Udine with summa cum laude honors. She previously conducted postdoctoral research at ETH Zurich. PhD in Computer Science, École Normale Supérieure, Paris, France (2015) MSc in Computer Science, Università degli Studi di Udine, Italy (2011) BSc in Computer Science, Università degli Studi di Udine, Italy (2009) Her research spans formal methods, abstract interpretation, and static analysis, with a strong emphasis on ensuring reliability, fairness, and robustness in machine learning and data science software. She leads projects such as Lyra (on data science software), Libra (fairness certification), ForML, and SAIF. Her work bridges theoretical foundations with practical tool development, targeting real-world software challenges in AI safety. Her recent publications reveal a consistent focus on applying abstract interpretation to machine learning, data leakage, fairness, and program termination. Key themes include certified robustness, input usage analysis, and perfectly parallel verification, demonstrating both theoretical depth and practical impact. She has developed open-source tools like Libra and FuncTion. Best Poster Award at SOFSEM 2011 Artifact Evaluation Badges at OOPSLA 2020 Mentored award-winning undergraduate research (Global Undergraduate Awards 2022) She advises multiple PhD and postdoctoral researchers and has supervised numerous master’s and bachelor’s students. She is actively involved in the academic community, serving on program committees for PLDI, OOPSLA, and CAV, and is the general chair of iFM 2025. She teaches advanced courses at MPRI and international summer schools on abstract interpretation and formal methods. She leads research on data science software analysis, fairness in ML, and static verification of neural networks, often in collaboration with institutions across Europe. Her lab, part of the ANTIQUE team, fosters interdisciplinary work combining formal methods with AI and software engineering.

Qingzhao Zhangمشاهده پروفایل

استادیار

• Computer Security
• Artificial Intelligence Security
• Cyber-Physical Systems Security
• +۳ مورد دیگر

Qingzhao Zhang is an incoming Assistant Professor in the Department of Electrical and Computer Engineering at the University of Arizona, starting in Fall 2025. Previously, he completed his Ph.D. at the University of Michigan under the supervision of Professor Z. Morley Mao. His academic journey bridges computer science, electrical engineering, and cybersecurity disciplines with a focus on practical security applications. Dr. Zhang's research spans computer security at the intersection of system security, artificial intelligence, and cyber-physical systems. His work addresses critical security challenges in safety-critical applications, particularly connected and autonomous vehicles. His research methodology combines formal verification, system building, and security analysis to develop practical solutions for real-world security problems. His expertise extends to blockchain security, industrial control systems, and AI security, demonstrating a broad yet cohesive research vision. Analysis of Zhang's recent publications reveals a consistent focus on security challenges in emerging technologies, with a particular emphasis on autonomous systems. His work demonstrates strong technical depth across multiple domains, from low-level network protocols to high-level AI systems. The trajectory of his research shows increasing sophistication in addressing complex security problems in cyber-physical systems, with recent work expanding into large language model security. Reviewer for ICLR 2025, ICRA 2025, ACM Multimedia 2024 Artifact Evaluation Reviewer for Usenix Security 2022 Pre-Review Task Force for NSDI 2025 Teaching experience in Multidisciplinary Design Program at University of Michigan (2022-2023) Dr. Zhang is actively recruiting PhD students for Fall 2026 and research interns. His research group focuses on developing security solutions for next-generation AI and cyber-physical systems, with particular emphasis on autonomous vehicles and critical infrastructure security. He maintains strong industry connections through his applied research in automotive security and blockchain technologies.

Andrea Benigniمشاهده پروفایل

استاد

• Energy Systems
• Power Grids
• Simulation Tools
• +۵ مورد دیگر

Prof. Andrea Benigni is a Professor and Director at the Research Center Jülich GmbH, leading the Energy System Technology (ICE-1) department within the Institute of Climate and Energy Systems (ICE). His work focuses on advanced simulation tools for multi-energy systems, power grid optimization, and real-time control solutions. Key research areas include: Multi-energy system integration (power, gas, thermal) High-performance simulation frameworks (e.g., GasNetSim, HeatNetSim) Quantum computing applications for grid partitioning Hydrogen blending in gas networks Hardware-in-the-loop testing for control architectures PMU-based fault detection and grid resilience Benigni has pioneered open-source tools like HeatNetSim and contributed to FIWARE-based ICT platforms for building/district-level energy systems. His recent work emphasizes digital twin applications for pseudo-measurement generation and parallel simulation techniques leveraging GPUs and FPGAs. Notable achievements include: Development of MGRIT-based parallel-in-time electromagnetic simulations Optimization methods for battery sizing in multi-vector systems Quantitative analysis of high PV penetration impacts in African grids Current projects involve: Hydrogen integration strategies for Southern Italy gas networks DC microgrid communication protocols via low-frequency injection Cloud-based multi-agent systems for grid flexibility management

• U.S. History
• Political Economy
• Capitalism
• +۱۰ مورد دیگر

Louis Hyman is the Dorothy Ross Professor of History and SNF Agora Professor at Johns Hopkins University, affiliated with both the Department of History and the SNF Agora Institute. He previously served as an assistant professor at Cornell University’s School of Industrial and Labor Relations (ILR), where he was also the Neufeld Professor. Education: PhD in American History, Harvard University, 2007 BA in History and Mathematics, Columbia University Louis Hyman is a leading historian of American capitalism, labor, and financial systems. His research explores the evolution of consumer debt, the transformation of work, and the rise of the gig economy. His interdisciplinary approach combines historical analysis with quantitative methods, offering critical insights into contemporary economic challenges. He is particularly known for demonstrating how modern economic insecurities are not new but rooted in mid-20th century corporate and policy decisions. His major publications, including Debtor Nation , Borrow , and Temp , reveal long-term trends in financial culture and labor instability. These works have shaped public and academic understanding of economic history, showing how consumer credit and temporary work became central to American life. His current research focuses on the history of e-commerce, extending his analysis into the digital economy. Scientific Awards and Recognition: William G. Bowen Prize Shortlisted for the 800-CEO-READ Business Book Award Named a 'Triumph' of 2018 by New York Times Book Critics Louis Hyman is actively involved in academic mentorship and public engagement. He is a founding editor of the Columbia Studies in the History of U.S. Capitalism book series and director of the History of Capitalism Summer Camp, which supports emerging scholars. He frequently advises on historical narratives and engages with book clubs, media, and policy discussions. His work bridges academia and public discourse, making complex economic histories accessible and relevant. Labs and Research Initiatives: Director, History of Capitalism Summer Camp Founding Editor, Columbia Studies in the History of U.S. Capitalism

• High Performance Computing
• Parallel Computing
• Heterogeneous Computing
• +۱۲ مورد دیگر

Anne C. Elster is a Professor in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. She is the founder and director of the HPC-Lab, a leading research group in heterogeneous and parallel computing. She also maintains a long-standing affiliation with the Oden Institute at the University of Texas at Austin as a Senior Visiting Scientist until Summer 2025. Research Interests: Her work spans high-performance computing (HPC), GPU computing, parallel algorithms, auto-tuning, performance optimization, and machine learning applications in scientific computing. She leads research in heterogeneous architectures and has contributed significantly to compiler and runtime systems for GPUs and accelerators. Publications Trends: Her recent publications (2021–2024) focus on GPU acceleration, auto-tuning frameworks (e.g., BAT, LS-CAT), performance modeling (Roofline), machine learning integration in HPC, and applications in geophysical and scientific computing. There is a strong emphasis on empirical evaluation, benchmarking, and practical optimization techniques. Scientific Awards and Recognition: IEEE Senior Member (2000) IEEE Computer Society Distinguished Contributor Charter member, NTNU's Board (2021) Distinguished Speaker, IEEE Computer Society (2019–2022) Advising and Grants: She has advised over 100 master’s students and several PhD students. She has led major funded projects including the RCN SFI Centre for Geophysical Forecasting, EU H2020 CloudLightning and TICOH, and NFR FRINATEK on Computational Microscopy. She has served on numerous international program committees and evaluation boards. Labs and Teams: She leads the HPC-Lab at NTNU, which includes postdocs, PhDs, and master’s students, and collaborates with international researchers. The lab is a hub for innovation in GPU computing, auto-tuning, and HPC applications.

Jinhua Guoمشاهده پروفایل

دانشیار

• Networking
• Vehicular Networking
• Data Center Networking
• +۵ مورد دیگر

Jinhua Guo is an Associate Professor in the Department of Computer and Information Science at the University of Michigan-Dearborn's College of Engineering and Computer Science. He holds a Ph.D. from the University of Georgia and B.E./M.E. degrees in Computer Science from Dalian University of Technology. His research spans vehicular networking , mobile/cloud computing , cybersecurity , and optimization algorithms . His recent work focuses on data center energy optimization using neural networks, cache robustness in named data networks , and real-time task scheduling on multi-core processors . Earlier contributions include MAC protocols for vehicular networks and context-aware routing in VANETs . Scientific awards and grants include: NSF Grant (co-PI): Enhancing Pervasive/Mobile Computing Security Education (2014-2016) Amazon AWS in Education Grant (PI): 2012-2015 NSF Grant (PI): Mobile Computing Research for Automotive Applications (2005-2010) He has advised 14 graduate students, including: John P. Baugh (Ph.D., 2018) Vishal Singh (Ph.D., 2016) Jun Liu (Ph.D., 2015) Ryan Bankston (Ph.D. in progress)

Lingkun Kongمشاهده پروفایل

پژوهشگر

• Computer Science
• Software Engineering
• Parallel Computing
• +۳ مورد دیگر

Lingkun Kong is a researcher at Rice University, United States, actively contributing to programming languages, compiler optimization, and high-performance computing. His work focuses on regular expression matching, bit-parallel algorithms, and hardware-software co-design. Recent Research Trends: Analysis of automata-based regex engines, GPU acceleration for pattern matching, and domain-specific languages for streaming data. Publications span venues like SPLASH, PLDI, and OOPSLA tracks.

• Machine Learning
• Natural Language Processing (NLP)
• Political Science
• +۴ مورد دیگر

Victor Kristof is a researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the INDY2 laboratory. His work spans interdisciplinary domains, combining Natural Language Processing (NLP) , Machine Learning , and Social Process Modeling to analyze legislative dynamics, vote prediction, and environmental perception. Research Focus: Kristof develops interpretable models for democratic transparency, including aligning interest group positions with parliamentary speeches. He pioneered methods for predicting legislative edit acceptance using matrix factorization and NLP. His work on Swiss referendum prediction integrates historical data with real-time analysis via the Predikon platform . Methodological Contributions: He applies Bayesian statistics , time-dynamic pairwise comparison models , and active learning algorithms to diverse problems, from carbon footprint perception to sports analytics. His War of Words framework reveals ideological patterns in EU law-making, while his Player Kernel model improves football match prediction. Labs & Collaborations: Based at EPFL's Laboratory of Dynamic Information and Networks (INDY2) , he collaborates with researchers like Matthias Grossglauser and Patrick Thiran. His datasets on legislative edits and carbon perception have advanced transparency studies.

Alfredo Pulvirentiمشاهده پروفایل

استاد

• Bioinformatics
• Biomedicine
• RNA Interference
• +۹ مورد دیگر

Alfredo Pulvirenti is a Full Professor of Computer Science at the University of Catania, Italy. He holds a joint appointment with the Department of Clinical and Experimental Medicine and the Department of Mathematics and Computer Science. Born in 1974, he earned his Laurea (1999, summa cum laude), PhD (2003), and post-doc (2004) in Computer Science from the University of Catania. His academic career includes roles as Assistant Professor (2005-2014), Associate Professor (2014-2023), and Full Professor since 2023. His research bridges Bioinformatics and Biomedicine , focusing on RNA interference (microRNAs, long non-coding RNAs) via stochastic and network-based inference methods. Key contributions include subgraph matching , motif finding , and multiple network alignment using Monte Carlo techniques for analyzing biological networks . He also applies time series analysis to seismic and infrasonic signals through collaborations with Italy's National Institute of Geophysics and Volcanology (INGV). Recent publications highlight advancements in graph algorithms (e.g., MultiGraphMatch, ArcMatch) and systems biology tools like MITHrIL and SPECifIC. He co-directs the Jacob T. Schawartz International School for Scientific Research and leads national/regional projects on Bioinformatics and Big Data in Cancer . His group includes 3 researchers, 1 post-doc, and 3 PhD students in Complex Systems. Awards and Leadership : Best Paper Award at CBS 2009 Guest Editor for BMC Bioinformatics, Briefings in Bioinformatics, Elsevier Program Committee Member for major international conferences International Collaborations span institutions like NYU, Ohio State University Wexner Medical Center, Brown University, Tel Aviv University, Toronto University, and Università di Ancona.

John Sartoriمشاهده پروفایل

دانشیار

• Ultra-low-power embedded systems
• Internet of Things (IoT)
• Wearable electronics
• +۵ مورد دیگر

John Sartori is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota , holding the Robert and Sydney Anderson Professorship. His research focuses on extending Moore's law through energy-efficient computing by addressing hardware bottlene | [truncated for display]

• Biomedical Informatics
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Dr. Yanqing Ji is a Professor in the Department of Electrical and Computer Engineering at Gonzaga University, specializing in biomedical informatics and high-performance computing. He serves as an editor for the Journal of Digital Signal Processing and a reviewer for 23 international journals. Education: Ph.D. in Computer Engineering, Wayne State University M.Sc. in Physical Electronics, University of Science & Technology of China B.E. in Industrial Automation, Qingdao University Dr. Ji's research focuses on biomedical association mining, adverse drug reaction detection, and parallel/cloud computing applications in healthcare. His work integrates machine learning, fuzzy logic, and multi-agent systems to enhance drug safety surveillance and biomedical literature search. His publications emphasize scalable MapReduce solutions for biomedical text mining, temporal association mining for drug interactions, and intelligent agent systems for pharmacovigilance. These span prestigious conferences like IEEE BIBM, ITNG, and NAFIPS, alongside journals such as BMC Bioinformatics and IEEE Transactions on Knowledge and Data Engineering . Scientific awards include NIH R21, AHRQ R18 grants, and NSF-funded projects (IGERT 9987598, MRI 9977815, ITR 0081696). He has led system design for distributed healthcare applications and optimized decision models using genetic algorithms.

Yajuan Guanمشاهده پروفایل

دانشیار

• Power Electronics
• Microgrids
• Renewable Energy Integration
• +۴ مورد دیگر

Yajuan Guan is an Associate Professor at Aalborg University's Faculty of Engineering and Science, specializing in Electric Power Systems and Microgrids. Her research focuses on advanced control strategies for renewable energy systems and grid-forming inverters. Education: PhD in Electrical Engineering (2016), Aalborg University, thesis: "Novel Control Strategies for Parallel-Connected Inverters in AC Microgrids" Research Interests: Microgrid stability and control Grid-forming wind power plants Smart grid technologies Renewable energy integration Power quality improvement Cyber-physical energy systems Article Trends: Recent publications emphasize large-scale wind power plant dynamics, fractional-order control for grid-forming converters, resilience in microgrid systems under climate crises, and thermal optimization for energy storage. Professional Activities: Conference speaker at Mission Innovation - Green Energy Community Webinar (2021) Chair of IEEE ECCE ASIA 2020 Special Session Editorial work for "IoT and Energy Internet" special issue (2018-2019)

Stefan Resmeritaمشاهده پروفایل

دانشیار

• Embedded Systems
• Real-Time Systems
• Software Timing Analysis
• +۴ مورد دیگر

Stefan Resmerita is an Associate Professor and Deputy Head of the Department of Computer Science at the University of Salzburg, Faculty of Digital and Analytical Sciences. His office is located at Jakob-Haringer-Str. 2, Room 2.32, 5020 Salzburg, Austria, with contact number +43 662 8044-6406. His research focuses on embedded and real-time systems, particularly in the areas of software timing analysis, logical execution time paradigms, and model-based design for automotive applications. His work addresses challenges in migrating legacy embedded software to modern architectures while maintaining timing predictability and verification. He has extensively researched the Timing Definition Language (TDL) and developed the Validator Tool Suite for bridging the gap between software-in-the-loop and hardware-in-the-loop simulation environments. His publication record shows consistent contributions to real-time systems from 2022 back to 1997, with recent work focusing on fine-grained timing in Simulink models, data consistency in automotive multi-core applications, and efficient realization of logical execution times in legacy systems. His research demonstrates a clear trajectory from foundational work on hybrid systems and air traffic management toward contemporary challenges in automotive and embedded software systems. As Deputy Head of Department, he plays a significant administrative role within the Computer Science department while maintaining an active research profile in embedded systems engineering.