جستجوی استاد راهنمای دانشگاهی

Dr. Jason D. Bakos is a Professor in the Department of Computer Science and Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing. His research focuses on high-performance domain-specific architectures, including reconfigurable computing, embedded systems, and machine learning acceleration. He has held academic positions since 2005, progressing from Assistant to Associate Professor before becoming a full Professor in 2017. Education : Ph.D., Computer Science, University of Pittsburgh (2005) B.S., Computer Science, Youngstown State University (1999) Research Interests : Dr. Bakos specializes in computer architecture at multiple levels (circuit, micro-architectural, and system) with a focus on VLSI design, reconfigurable computing, high-performance computing, and applications in embedded systems. His recent work includes FPGA acceleration of machine learning algorithms, structural health monitoring systems, and real-time signal processing. Awards : 2018 Teaching Award in Computer Science and Engineering 2009 NSF CAREER Award Multiple design competition awards for innovative chip and circuit designs Grants & Funding : He leads and co-leads projects funded by NSF, Savannah River National Laboratory, and industry partners like Texas Instruments. Recent grants focus on edge computing for real-time machine learning, FPGA-based accelerators, and corrosion analysis of nuclear materials. Labs & Teams : His research group collaborates on projects involving embedded systems, FPGA design, and interdisciplinary applications in structural engineering and bioinformatics. He advises a dynamic team of graduate students and post-doctoral researchers.

• Computer Architecture
• Reconfigurable Computing
• High-Performance Computing
• +۴ مورد دیگر

Yiorgos Makris is a Professor in the Department of Electrical and Computer Engineering at the Erik Jonsson School of Engineering & Computer Science, The University of Texas at Dallas, since July 2011. Previously, he was a faculty member at Yale University for over a decade. He holds a Ph.D. in Computer Engineering from the University of California, San Diego, and a Diploma in Computer Engineering from the University of Patras, Greece. Education: Ph.D. in Computer Engineering, University of California, San Diego (2001) M.S. in Computer Engineering, University of California, San Diego (1997) Diploma in Computer Engineering and Informatics, University of Patras, Greece (1995) Research Interests: Hardware Security and Trustworthiness Statistical Side-Channel Fingerprinting Machine Learning in Semiconductor Manufacturing Trusted and Reliable Integrated Circuits Hardware Trojans in Wireless Cryptographic ICs On-Die Learning and Emergent Technologies His work focuses on enhancing hardware security through statistical methods, machine learning, and formal verification, with applications in analog/RF ICs, post-production calibration, and secure IC design. Key Contributions: Co-Founder and Site-PI of NSF CHEST I/UCRC (Hardware and Embedded System Security and Trust) Leader of the Safety, Security, and Healthcare Thrust at TxACE (Texas Analog Center of Excellence) Director of the Trusted and RELiable Architectures (TRELA) Lab Grants and Funding: NSF, NIH, SRC, ARO, AFRL, AFWERX, DARPA, DOE, and industry partnerships with Boeing, Northrop Grumman, IBM, Intel, Qualcomm, etc. Recent grants include projects on DNA storage security, analog neural networks, and malicious hardware detection. Awards and Recognition: IEEE Fellow (2025) Best Paper Awards at DATE'13, VTS'15, DCAS'22 Best Hardware Demonstration Awards at HOST'16 and HOST'18 Erik Jonsson School Faculty Research Award (2020) Labs and Teams: TRELA Lab (Focus: Secure Hardware Design, Trusted Architectures) CHEST I/UCRC (Industry-University Collaboration)

• Hardware Security
• Trusted Systems
• Machine Learning in Semiconductor Manufacturing
• +۹ مورد دیگر

Kimia Zamiri Azar serves as an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Central Florida, focusing on hardware security and verification methodologies. Her research bridges theoretical formal methods with practical security implementations in semiconductor design and testing. Her educational background includes: Ph.D. in Electrical and Computer Engineering, George Mason University (2021) Postdoctoral Research, University of Florida Dr. Azar's research spans hardware security with emphasis on system-level verification, VLSI design-for-trust, and advanced IC testing. She pioneers techniques in logic locking, secure heterogeneous integration, and IC supply chain security, developing frameworks for authenticated encryption in Systems-in-Package and runtime security monitoring. Her work integrates formal verification with innovative testing methodologies to address hardware trust challenges across the semiconductor lifecycle. Analysis of her recent publications reveals two dominant trends: (1) Application of large language models (LLMs) to hardware design tasks including high-level synthesis code generation and RTL optimization, and (2) Advancement of secure heterogeneous integration techniques for System-in-Package architectures with focus on counterfeit prevention and split-test security protocols. These directions address critical gaps in hardware trustworthiness amid increasingly complex semiconductor supply chains. Her scientific contributions have earned significant recognition: Best Paper Award at ICCAD 2019 Best Paper Award at ISVLSI 2020 Best Paper Award at ICCAD 2020 Best Paper Award at IEEE DCAS 2020 Best Paper Award at HOST 2022 Best Paper Award at DATE 2023 Dr. Azar secures substantial research funding from premier agencies including NSF, SRC, DARPA, AFRL, DoD (NG), and Microsemi. Her grants support projects spanning hardware security validation frameworks, secure heterogeneous integration, and AI-augmented verification methodologies. She actively mentors students in her research group, guiding publications in top venues like IEEE D&T, IEEE TC, and DAC while fostering industry-academic collaborations. Her work directly impacts semiconductor security standards through patented innovations and open-source verification tools. As an active IEEE and ACM member, she contributes to community advancement through conference organization (HOST, DATE), journal editorial roles, and workshop leadership on hardware security standards. Her research group collaborates with semiconductor industry leaders to translate theoretical security frameworks into practical design-for-trust methodologies for next-generation integrated circuits.

• Hardware Security
• Verification Formal Methods
• Advanced VLSI Testing
• +۳ مورد دیگر

J. Haadi Jafarian is an Assistant Professor in the Department of Computer Science and Engineering at the University of Colorado Denver, where he leads the Active Cyber and Infrastructure Defense (ACID) Lab. He earned his Ph.D. from the University of North Carolina Charlotte in 2017. Research Interests: Active Cyber Defense (Moving Target Defense, Cyber Deception) Big Data Analytics for Cyber Threat Intelligence Security for Cyber-Physical Systems & Critical Infrastructures Cyber Resilience and Automation Recent Publications highlight innovations in traffic obfuscation, adversarial machine learning, and deception-based threat detection. His work spans network security, cybersecurity analytics, and scalable defense frameworks. Teaching includes: CSCI 4743/5743: Cyber and Infrastructure Defense (Fall 2023) CSCI 4742/5742: Cyber Programming and Analysis (Spring 2023) CSCI 4741: Cybersecurity Principles (Spring 2022) CSCI 4800: Web Application Development (Spring 2021) CSCI 3761: Computer Networks (Spring 2020) Labs & Teams: The ACID Lab focuses on developing proactive cyber defense strategies, including moving target defense, deception techniques, and security analytics for critical infrastructure.

• Security and Privacy
• Active Cyber Defense
• Security Analytics
• +۶ مورد دیگر

Dr. Esam Abdel-Raheem is a Professor in the Department of Electrical and Computer Engineering at the University of Windsor, Faculty of Engineering. His research focuses on digital signal processing, biomedical engineering, cognitive radio networks, and VLSI design. He holds a Ph.D. from the University of Victoria (1995) and is a Professional Engineer (P.Eng.) in Ontario and a Senior Member of IEEE. Education: B.Sc. Electrical Engineering, Ain Shams University (1984) M.Sc. Electrical Engineering, Ain Shams University (1989) Ph.D. Electrical Engineering, University of Victoria (1995) Research Interests: Dr. Abdel-Raheem’s work spans signal processing for communications, biomedical signal processing, and VLSI implementations. He has pioneered algorithms for cognitive radio networks and adaptive filtering. His recent studies leverage deep learning for medical diagnostics (e.g., lung nodule detection, Parkinson’s disease voice analysis) and cognitive radio spectrum sensing. Publications Trends: Recent work emphasizes biomedical applications (e.g., CT scan analysis, diabetic retinopathy detection) and machine learning integration in communications (e.g., federated learning for traffic crowdsourcing). His articles often bridge theoretical signal processing with practical implementations in hardware (e.g., FPGA-based filters). Awards/Grants: Not explicitly listed in the text, though his senior IEEE membership and prolific publications suggest sustained professional recognition. Lab/Teams: While not detailed, his research themes imply involvement in interdisciplinary teams focusing on biomedical engineering, telecommunications, and VLSI design.

• Digital Signal Processing
• Biomedical Engineering
• Cognitive Radio Networks
• +۳ مورد دیگر

Dr. Tingkai Wang is a Senior Lecturer in the School of Computing and Digital Media at London Metropolitan University. His research focuses on mobile robots, intelligent systems, artificial intelligence, control systems, image/signal processing, and virtual reality. He teaches the Programming for Computer Science module and has led projects like the Virtual Environment and Simulation System (2000-2002) and Navigation and Control of Mobile Robots (1995-1998). His work emphasizes interdisciplinary approaches, combining expert systems, neural networks, and fuzzy logic to address challenges in autonomous systems. Notable contributions include AGV navigation algorithms, hybrid control systems, and predictive modeling. Over 30 publications span robotics, control engineering, and AI applications. He collaborates internationally and has presented at venues like the International Conference on Intelligent Systems Engineering and the IEEE Conference on Engineering in Medicine and Biology. Dr. Wang’s expertise bridges theoretical modeling and practical implementation, with applications in manufacturing automation, environmental monitoring, and industrial management systems. His current research continues exploring adaptive control mechanisms and AI-driven robotics solutions.

• Mobile Robots
• Intelligent Systems
• Artificial Intelligence (AI)
• +۵ مورد دیگر

Jian Liu is an Assistant Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. He leads the Mobile Sensing and Intelligence Security (MoSIS) Lab, focusing on robust AI, mobile security, computational sensing, and smart healthcare. His research has been published in top-tier venues like S&P/Oakland, CVPR, and IEEE journals, with over 6,400 citations. He holds seven U.S. patents, including two licensed to industry. Education: PhD in Electrical and Computer Engineering from Rutgers University (2019), ME and BE in Communication Engineering from Wuhan University of Technology (China). Research interests include trustworthy AI, federated learning, privacy-preserving technologies, and adversarial machine learning. His recent work includes HarmonyCloak (music copyright protection against generative AI), 3D facial authentication systems, and robust backdoor attack defenses. Notable awards include the University of Tennessee’s Professional Promise in Research Award (2025), Stanford’s World’s Top 2% Cited Scientists, and multiple best paper awards. He teaches Mobile and Embedded System Security (ECE 469/569) and has supervised projects funded by UT Grand Challenges grants. Labs/Teams: MoSIS Lab focuses on AI-driven security solutions, wearable sensing, and healthcare applications. Collaborations involve interdisciplinary projects with UT’s engineering and medical schools.

• Robust and Trustworthy AI
• Machine Learning
• Human-Computer Interaction
• +۶ مورد دیگر

Jason Trelewicz is a Professor at Stony Brook University’s Department of Chemical & Molecular Engineering and holds joint faculty status at Oak Ridge National Laboratory. His research focuses on interface-engineered materials for extreme environments, leveraging advanced processing, characterization tools, and multiscale modeling. He received his Ph.D. in Materials Science from MIT (2008) and previously served as Research Director at MesoScribe Technologies. His work emphasizes fusion materials, nanocrystalline alloys, additive manufacturing, and radiation effects. Awards include the DOE Early Career Award (2017), NSF CAREER Award (2016), and multiple best paper awards (2022). His lab, the Engineered Microstructures and Radiation Effects Laboratory, explores topics like ceramic composite moderators and plasma-facing materials. Education: Ph.D., Materials Science & Engineering, MIT (2008) Affiliations: Oak Ridge National Laboratory (Joint Faculty) Key research areas include thermal-mechanical evaluation of fusion reactor components, alloy design for additive manufacturing, and radiation tolerance of nanocrystalline materials. He has pioneered studies on helium bubble dynamics in tungsten and stability of doped nanocrystalline alloys. Awards: DOE Early Career Award, NSF CAREER Award, 2022 Best Paper Awards in Nuclear Materials and Asian Ceramics. Grants/Projects: Supported by DOE, NSF, and collaborative initiatives with Japan (FRONTIER). His group investigates corrosion behavior in 3D-printed steels and develops novel composite moderators for high-temperature reactors. Ongoing work includes multiscale modeling for fusion materials and in-situ TEM studies of irradiation effects.

• Materials Science
• Nuclear Engineering
• Advanced Materials for Extreme Environments
• +۴ مورد دیگر

Justin Gottschlich is an Adjunct Lecturer in the Computer Science Department at Stanford University, where he teaches the graduate course Machine Programming (CS 329M) . He also serves as Founder, CEO, and Chief Scientist of Merly Inc., a startup focused on machine programming systems to improve software development efficiency and quality. Previously, he led the Machine Programming Research group at Intel Labs, pioneering advancements in automating software development through a fusion of machine learning, programming languages, and systems research. His academic roles include prior positions as Adjunct Professor at University of Colorado-Boulder and Adjunct Assistant Professor at University of Pennsylvania. He has advised numerous graduate students across institutions, contributing to their research in machine programming and related fields. Gottschlich has authored dozens of research papers and holds multiple patents, with his work highlighted by prominent outlets like the Wall Street Journal and Communications of the ACM. He actively contributes to academic committees, including serving as Steering Committee Chair for the ACM SIGPLAN Machine Programming Symposium (MAPS). His research interests span machine programming, autonomous software development, formal methods, and the integration of AI into software engineering practices. Education: PhD in Computer Science from University of Colorado-Boulder Keynote Engagements: LADSIOS (2021), MIT DSAIL (2021), Penn PRECISE (2019) Labs/Teams: Intel Labs Machine Programming Research Group, Merly's R&D Team Grants & Funding: Extensive industry and academic research funding through Intel Labs and Merly

• Machine Programming
• Artificial Intelligence
• Software Engineering
• +۳ مورد دیگر

Prof. Dr. Roland Zengerle serves as Full Professor for Application Development at the Institute of Microsystems Technology within the Faculty of Engineering at Albert Ludwigs University of Freiburg, concurrently holding the position of Director at Hahn-Schickard Institute for Microanalysis Systems in Freiburg. His academic leadership spans microsystems engineering with a focus on translational research bridging fundamental science and clinical applications. Zengerle's research expertise centers on Microfluidics, Lab-on-a-Chip systems, Bio-MEMS, Electrochemical Energy Systems, and Tomographic Reconstruction of Mesoporous Materials. He pioneers hybrid manufacturing techniques integrating molten metal printing with polymer processing to develop point-of-care diagnostic platforms and advanced energy systems. Current projects include UTI-Diag for urinary tract infection diagnosis and PhotonMed, a 32-million-euro medical technology initiative where his MEMS Applications Laboratory develops centrifugal microfluidic solutions. Analysis of his recent publications reveals a dominant trend toward multi-technology integration: centrifugal microfluidics combined with 3D bioprinting for organoid-based drug testing, molten metal printing for flexible electronics, and bead-based immunoassays for infectious disease detection. The work demonstrates strong clinical translation focus, particularly in cancer diagnostics (circulating tumor cell isolation), infectious disease testing (TB diagnostics), and regenerative medicine (spheroid/organoid handling). His laboratory has secured significant funding for high-impact projects including: UTI-Diag: Molecular diagnostics for urinary tract infections PhotonMed: Medical technology innovation consortium livMatS: Living, Adaptive and Energy-autonomous Materials Systems Zengerle actively mentors researchers through Freiburg's Master Lab program and Writer's Studio initiative while promoting young talent via Bootcamp training. His group maintains strategic alliances with Hahn-Schickard spin-offs and industry partners, leveraging university cleanroom facilities and specialized service centers for microfabrication. The MEMS Applications Laboratory operates as a hub for interdisciplinary innovation, combining microfabrication expertise with clinical insights to develop commercializable diagnostic solutions. Current infrastructure supports centrifugal microfluidic cartridge development, 3D-bioprinting of tissue models, and electrochemical sensor integration, with ongoing work focused on automating complex biological workflows for point-of-care applications.

• Microfluidics
• Lab-on-a-Chip
• Bio-MEMS
• +۶ مورد دیگر

Ambrus Pal is a Reader in Pure Mathematics at the Department of Mathematics, Imperial College London, within the Faculty of Natural Sciences. His research interests span Pure Mathematics, Mathematical Physics, and Applied Mathematics, with a focus on algebraic geometry, number theory, and cohomology theories. He is affiliated with the CNRS-Imperial Abraham de Moivre UMI and contributes to both research and teaching in mathematics. His work often addresses cohomological pairings, arithmetic geometry, and the Brauer-Manin obstruction. Notable projects include studies on p-adic cohomology, motivic Euler characteristics, and the arithmetic Yau-Zaslow formula. Pal has collaborated on international conferences and schools, such as the LMS-CMI Research School on Homotopy Theory and Arithmetic Geometry. His publications reflect a deep engagement with abstract algebraic structures, including quasi-Boolean groups, Grothendieck-Witt rings, and real projective geometry. While his research emphasizes theoretical advances, applications to arithmetic applications and global function fields are recurrent themes. No scientific awards or advising records are explicitly mentioned.

• Pure Mathematics
• Mathematical Physics
• Applied Mathematics
• +۳ مورد دیگر

Andreas Peter Burg is a Tenured Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Telecommunications Circuits Laboratory (TCL) within the School of Engineering. He holds multiple academic and administrative roles at EPFL including Associate Professor in Teaching (SEL, EDMI, EDEE), Director of SEL Management, and Member of the Doctoral Program Committee for Electrical Engineering. Dr. Burg received his Dipl.-Ing. degree in 2000 and Dr. sc. techn. degree in 2006 from ETH Zurich. His academic career includes positions as SNF Assistant Professor at ETH Zurich (2009-2011) before joining EPFL in January 2011 as a Tenure Track Assistant Professor, where he was promoted to Tenured Associate Professor in June 2018. His research focuses on circuits and systems for telecommunications , with particular expertise in silicon implementation of communication technologies, communication algorithms optimization for hardware, low-power VLSI signal processing, and digital integrated circuits. His work bridges theoretical communication concepts with practical circuit implementations, addressing challenges in wireless and wired communication systems. His recent publications (2024-2025) demonstrate a strong focus on next-generation communication technologies including 6G systems, advanced error correction coding, wireless sensing applications, and ultra-low power circuit design. These works span multiple subfields from LDPC and polar code decoding to RF signal processing and machine learning applications in wireless systems. Willi Studer Award (2000) ETH Medal for diploma thesis (2000) ETH Medal for Ph.D. dissertation (2006) Swiss National Science Foundation Assistant Professorship grant (2008) Dr. Burg has been involved in the development of more than 25 ASICs throughout his career and co-founded Celestrius, an ETH spinoff in MIMO wireless communication. His laboratory work focuses on practical implementations of communication algorithms with emphasis on power efficiency and hardware optimization. Current research directions include 6G technologies, wireless sensing applications, and novel error correction techniques for next-generation communication systems.

• Circuits and systems for telecommunications
• Silicon implementation of communication technologies
• Communication algorithms optimization
• +۵ مورد دیگر

Long Nguyen is a Professor of Statistics at the University of Michigan, Ann Arbor, with a courtesy appointment in Electrical Engineering and Computer Science. He is affiliated with the Michigan Institute for Data Science (MIDAS) and the Vietnam Institute for Advanced Study in Mathematics (VIASM). His research focuses on Bayesian nonparametrics, optimal transport, machine learning, and spatiotemporal data analysis. Nguyen holds a PhD in Computer Science from UC Berkeley and has held postdoctoral positions at Duke University and the Statistical and Applied Mathematical Institute. Education: B.Sc. in Computer Science from Pohang University of Science and Technology; M.Sc. in Mathematics from Arizona State University; Ph.D. in Computer Science from UC Berkeley (2007). Research Interests: Bayesian nonparametric methods, optimal transport theory, statistical inference for complex models, and applications in spatiotemporal data, functional data analysis, and hierarchical modeling. He emphasizes developing scalable algorithms and geometric approaches for statistical learning. Editorial Roles : Annals of Statistics Journal of Machine Learning Research SIAM Journal on Mathematics of Data Science Bayesian Analysis Awards : IMS Fellow, ASA Fellow NSF CAREER Award IEEE Signal Processing Young Author Award L. J. Savage Dissertation Award (via student Aritra Guha) Advising & Collaborations : Guided over 20 PhD students and postdocs, many now in academia and industry. Collaborates on projects in AI ethics, music theory, and environmental data science. Active in organizing summer schools in Vietnam on Bayesian statistics and machine learning. Labs & Teams : Co-leads the Statistical Machine Learning reading group at U-M and collaborates with the VIASM on advanced mathematical research in Hanoi.

• Nonparametric Bayesian Statistics
• Optimal Transport
• Machine Learning
• +۴ مورد دیگر

Dr. Maxime Cordeil is a Senior Lecturer in Human Centred Computing at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on Virtual and Augmented Reality technologies for data interaction, interactive visualization systems, and AR interfaces for industry applications. He has authored over 60 publications in top-tier venues such as ACM CHI and IEEE VR, and was nominated as Australia's Field Leader in Computer Graphics in 2021 and 2022. Research Interests: Data visualization, immersive analytics, medical imaging, collaborative systems, and human-computer interaction. Current Projects: Includes embedded visualizations for sports performance, interactive machine learning in 3D environments, and mixed-reality applications in forensic science. PhD Supervision: Actively guiding students in topics like immersive gesture exploration, AR for digital health, and collaborative VR systems. His work bridges theory and practice, with tools like IATK (Immersive Analytics Toolkit) and the MADE-Axes hardware system. He collaborates with industry partners like Raytracer and CSIRO, focusing on applications in space exploration, underwater training, and remote operations. Awards: Multiple best paper recognitions at ISS and CHI, plus industry-driven research grants. Labs: Leads the Immersive Analytics research group at UQ, specializing in embodied interaction and spatial computing.

• Data Visualisation
• Immersive Analytics
• Virtual Reality
• +۵ مورد دیگر

Yongyi Mao is a Professor at the School of Electrical Engineering and Computer Science, University of Ottawa. He holds a Ph.D. in Electrical Engineering from the University of Toronto and has a multidisciplinary background in medical biophysics and engineering. His research focuses on communications and machine learning, with notable contributions to federated learning, information theory, and adversarial robustness. Professor Mao has held academic roles since 2003, advancing from Assistant to Full Professor by 2012. Education: B.Eng., Southeast University, 1992 M.D., Nanjing Medical University, 1995 M.S., University of Toronto (Medical Biophysics), 1998 Ph.D., University of Toronto (Electrical Engineering), 2003 Research interests span machine learning frameworks, federated learning, adversarial attacks, and domain adaptation. His work often bridges theoretical foundations (e.g., generalization bounds) with practical applications in text classification and watermarking. Publications reflect a strong emphasis on machine learning theory and NLP applications, with recent trends toward improving model robustness and generalization. No scientific awards are explicitly listed, though his prolific output suggests significant recognition in the field. Advising and grants: While specific student names or grant details are not provided, his position as a Full Professor indicates active research supervision and likely grant involvement. His lab focuses on advancing AI and communication technologies through interdisciplinary approaches.

• Communications
• Machine Learning