جستجوی استاد راهنمای دانشگاهی

Giorgos Bouritsasمشاهده پروفایل

استادیار مدعو

• Graph neural networks
• Geometric deep learning
• Weight space learning
• +۴ مورد دیگر

Giorgos Bouritsas is a machine learning scientist serving as a postdoctoral fellow at the Archimedes AI unit and the University of Athens, while also holding an adjunct lecturer position at NCSR Demokritos. He received his PhD in computer science from Imperial College London and his MEng in electrical and computer engineering from the National Technical University of Athens. His academic journey includes research stints at Google DeepMind, École Polytechnique Fédérale de Lausanne, KU Leuven, NCSR Demokritos, and Universitat Politècnica de Catalunya. Dr. Bouritsas specializes in geometric and graph deep learning, with research focusing on neural network architectures for geometric data, weight space learning, and applications in biology and chemistry. His work bridges theoretical analysis with practical implementations, particularly in developing methodologies for complex networks, physical systems, 3D objects, and neural network weight spaces. He has made significant contributions to graph neural networks, including novel approaches to improving expressivity through subgraph isomorphism counting and developing spiral convolutional networks for 3D shape representation. His recent publications demonstrate a strong trajectory in geometric deep learning, with papers accepted at top conferences including NeurIPS (with an oral presentation in 2024), ICML, CVPR, and ICCV. His 2024 workshop proposal on Neural Network Weights as a New Data Modality was accepted for ICLR 2025. He regularly serves as a reviewer for major machine learning conferences, earning outstanding reviewer distinctions at NeurIPS, ICML, and LoG. Outstanding Reviewer at NeurIPS 2021, 2024 Outstanding Reviewer at ICML 2022, 2024 Outstanding Reviewer at LoG 2022, 2023 Dr. Bouritsas teaches Deep Learning in the MSc in AI program at NCSR Demokritos, demonstrating his commitment to academic service and education. His research has practical applications spanning cryo-EM image analysis, 3D facial recognition for medical diagnostics, and theoretical foundations of contrastive learning frameworks.

• Neuroscience
• Memory
• Hippocampus
• +۷ مورد دیگر

Robert Sutherland is a Professor and Director at the Canadian Centre for Behavioural Neuroscience (CCBN), University of Lethbridge. His research focuses on neural mechanisms of memory, hippocampal function, and cognitive recovery in conditions like stroke and fetal alcohol spectrum disorders. He holds a B.Sc., M.A., and Ph.D. in Psychology. Research interests span: Hippocampal neurogenesis and memory consolidation Impact of stress on stroke recovery Episodic memory systems in rodents Neurodegeneration and regeneration models Cognitive deficits in fetal alcohol disorders His publications (2011-2014) emphasize hippocampal-dependent memory, stress-pathology interactions, and innovative behavioral paradigms. Trends include rodent models of cognitive deficits, temporal coding in neural circuits, and critiques of experimental methodologies. As Principal Investigator, he secured major grants: CIHR ($683,550, 2010-2015): Hippocampal dentate gyrus repair CFI ($8,030,000, 2009-2010): Epigenetics infrastructure NSERC ($338,335, 2007-2012): Hippocampus and memory NIH ($346,000, 2003-2008): Adult neurogenesis He directs the CCBN, overseeing advanced neuroimaging facilities and collaborative projects on neural plasticity.

Daniel Lemireمشاهده پروفایل

استاد

• Software Performance
• Vectorization (SIMD)
• Web Technologies
• +۸ مورد دیگر

Daniel Lemire is a full professor of computer science at the Université du Québec (TELUQ), recognized as one of the top 2% most cited scientists globally according to Stanford University's 2024 rankings. He ranks among the 0.0006% most followed programmers on GitHub, with his work adopted by major technology companies including Google, Facebook, Intel, and Shopify. Education: Ph.D. in Engineering Mathematics (University of Montreal and Polytechnique Montréal), Master's in Mathematics (University of Toronto), Bachelor's in Mathematics with High Distinction (University of Toronto) Current Role: Editor of Software: Practice and Experience journal since 2020 Professional Recognition: Co-chair of NSERC's Computer Science Discovery Grants Committee (2020-2021) Professor Lemire's research focuses on software performance optimization and data indexing techniques. His work bridges theoretical computer science with practical applications, particularly in areas where performance bottlenecks exist in real-world systems. He specializes in leveraging hardware capabilities through vectorization (SIMD instructions) to dramatically improve processing speeds for fundamental operations that have remained inefficient for decades. His approach combines deep theoretical understanding with practical implementation, resulting in algorithms that are both mathematically sound and immediately applicable in production systems. Lemire's research portfolio demonstrates a consistent pattern of identifying critical performance bottlenecks in widely used software operations and developing innovative solutions that achieve order-of-magnitude improvements. His work spans multiple domains including JSON parsing, Unicode string processing, URL parsing, base64 encoding, and bitmap indexing. A common thread through his publications is the application of hardware-specific optimizations, particularly SIMD instructions, to accelerate operations that were previously considered near-optimal. His research has evolved from foundational algorithm development to influencing major software ecosystems, with his libraries becoming integral components of industry-standard tools. Among the 2% most cited scientists globally (Stanford University, 2024) Université du Québec's Prix d'excellence 2020 for research success Most read articles at Software: Practice and Experience (2024, 2025) Best voted talk at QCon San Francisco 2019 Editor of Software: Practice and Experience journal since 2020 Numerous citations in patents held by Microsoft, LinkedIn, Oracle, and Fujitsu Professor Lemire maintains an active research group that has graduated numerous PhD students, many of whom now hold key positions at leading technology companies. He offers automatic scholarships for all students making progress on M.Sc. theses and Ph.D. programs in his lab, with tuition waivers for international Ph.D. students. His laboratory is equipped with a diverse server farm featuring multiple processor architectures (Intel Xeon, Core i7, Xeon Phi, POWER9, ARMv8) specifically designed for software performance experiments. The lab also explores virtual reality applications in data science. Lemire actively recruits students who are passionate about high-performance programming and open-source development, with special programs for Canadian undergraduate and graduate students through NSERC funding mechanisms.

Yifan Wuمشاهده پروفایل

پژوهشگر

• AIOps
• Log Analysis
• Software Engineering
• +۳ مورد دیگر

Yifan Wu is a Postdoctoral Researcher at the School of Computer Science, Peking University. With a Ph.D. in Software Engineering from Peking University (2019-2024), Wu focuses on applying artificial intelligence techniques to software engineering problems, particularly in the domain of AIOps. Wu's educational background includes: Ph.D. in Software Engineering, Peking University (2019-2024), supervised by Prof. Ying Li B.E. in Software Engineering, University of Electronic Science and Technology of China (2015-2019) Research interests center around AIOps, with particular emphasis on log analysis, anomaly detection, and root cause analysis. Wu's work bridges the gap between artificial intelligence and practical software engineering challenges, developing novel approaches for log-based fault diagnosis, software reliability maintenance, and incident management. Recent research has increasingly incorporated large language models for various software engineering tasks, demonstrating innovative applications of cutting-edge AI techniques to longstanding software engineering problems. Analysis of Wu's recent publications reveals a strong focus on log analysis techniques, particularly log parsing and fault diagnosis. There's a clear trend toward integrating large language models with traditional software engineering practices, as well as a growing interest in change risk detection and incident management. Wu's work spans both theoretical advancements and practical applications in real-world software systems, with numerous publications in top-tier conferences including ASE, ICSE, and ESEC/FSE. Scientific contributions include: Multiple publications in top-tier software engineering conferences (ASE, ICSE, ESEC/FSE) Research on log analysis, anomaly detection, and root cause localization Studies on change-induced incidents and software reliability Applications of large language models to software engineering tasks Development of novel frameworks like CSLParser and OCRCL Wu actively contributes to the academic community as a committee member for conferences including ICSE 2026, ACL ARR 2025, ISSRE 2025, ISSTA 2025, and as a journal reviewer for TOSEM, TSE, and TCC. This demonstrates strong engagement with the research community and recognition of expertise in the field. Current research directions focus on leveraging advancements in artificial intelligence, particularly large language models, to address longstanding challenges in software engineering and operations, with potential applications across various domains of software development and maintenance.

• Electrical Engineering
• Spintronics
• Nanomagnetism
• +۴ مورد دیگر

Giovanni Finocchio is an Associate Professor of Electrical Engineering at the Department of Mathematics and Computer Science, Physical Sciences, and Earth Sciences at the University of Messina. He maintains significant research affiliations as an associate researcher at the Istituto Nazionale di Geofisica e Vulcanologia (Roma2 section) since 2014 and serves as co-director of the Joint Laboratory UNIME-SINANO of PETAscale computing and SPINtronics (PETASPIN) since 2018. His international experience includes visiting scholar positions at Northwestern University's Department of Electrical and Computer Engineering in 2019 and 2022. Finocchio earned his PhD (XVIII cycle) in "Advanced Technologies for Optoelectronics, Photonics, and Electromagnetic Modeling." His academic journey has positioned him at the forefront of spintronics research, with expertise spanning nanomagnetism, unconventional computing, and micromagnetic modeling. His research interests focus on cutting-edge areas of spintronics, including antiferromagnetic spintronic devices, skyrmion-based memory systems, magnetic solitons, and unconventional computing approaches. His work bridges fundamental physics with practical applications in next-generation computing and memory technologies. He has made significant contributions to understanding magnetic vortices, skyrmion dynamics, and the development of energy-efficient spintronic devices for both conventional and emerging computing paradigms. Analysis of his recent publications reveals a strong focus on advancing spintronic memory technologies, particularly through the investigation of skyrmions and magnetic solitons for high-density storage applications. His research also explores novel computing architectures based on Ising machines and spintronic oscillators, demonstrating a strategic shift toward energy-efficient unconventional computing paradigms. The interdisciplinary nature of his work connects materials science, electrical engineering, and computational physics to address fundamental challenges in next-generation information technologies. American Physical Society (APS) Outstanding Referee (2020) Outstanding Reviewer Awards 2016 (New Journal of Physics) "Research Highlights" from Applied Physics Letters (American Institute of Physics) (2013) Finocchio serves as Principal Investigator for significant projects including the EU-funded "Low Power Spintronics Wireless Autonomous Node (SWAN)" (SWAN-on-chip code 101070287) and the MUR-funded national project "The Italian factory of micromagnetic modeling and spintronics" (PRIN 2020LWPKH7). He has co-authored over 180 publications in prestigious journals including IEEE, Nature group, APS, IOP, AIP, and Wiley publications, and holds 5 patents while having co-founded two spin-off companies. He directs the SpintronicFactory at the University of Messina and serves as president of the petaspin.com association. As one of the leading coordinators of Transverse Theme 2: Design and Modelling, he plays a pivotal role in shaping research directions in spintronics and magnetic computing at both institutional and national levels.

Yanyan Jiangمشاهده پروفایل

دانشیار

• Software Engineering
• Software Testing
• Software Verification
• +۲ مورد دیگر

Dr. Yanyan Jiang is an Associate Professor at Nanjing University, China, specializing in software systems with a focus on validation, verification, and synthesis. His research spans software testing, analysis, and program synthesis, with significant contributions to the field of software engineering. Dr. Jiang's research interests include: Software Testing - particularly code coverage validation and Android application testing Software Verification - with focus on compiler validation and system correctness Program Synthesis - especially for dynamic software updates and state transformation Operating Systems - including educational approaches and system design principles His recent work shows a strong trend toward practical applications of software engineering research, with focus on Android systems, compiler validation, and self-adaptive software. His publications demonstrate expertise in both theoretical approaches to software validation and practical tools for real-world systems, particularly in the areas of code coverage validation, dynamic software updating, and compiler verification. Dr. Jiang has received numerous prestigious awards: SOSP'23 Best Paper Award ACM SIGSOFT Distinguished Paper Awards (ICSE'21 and ASE'18) ACM Europe Council Best Paper Award Microsoft Research Asia Fellowship Award CCF Outstanding Doctoral Dissertation Award Dr. Jiang actively contributes to the software engineering community through program committee memberships at major conferences including ASE, ICSE, and ESEC/FSE. He also serves on the Steering Committee for regional Computing Olympiad contests and as a Technical Committee member for the Asia-Pacific Informatics Olympiad (APIO) 2022-2024, demonstrating his commitment to both research excellence and educational outreach in computer science.

• Power Systems Engineering
• Control Systems
• Automation
• +۳ مورد دیگر

Prof. Dr. Jochen Wendiggensen serves as a Professor in the Department of Energy and Environmental Management within the Faculty of Energy and Biotechnology at Flensburg University of Applied Sciences, where he has been employed since September 1994. Educational Background: Jochen Wendiggensen studied electrical engineering at Ruhr University Bochum followed by industry experience in process control system development at ASEA BROWN BOVERY. He earned his doctorate in 1993 as a research associate at the Institute for Automation Technology, Helmut-Schmidt-Universität of the Federal Armed Forces in Hamburg. Research Interests: His work focuses on power systems engineering with emphasis on grid modeling, battery storage control strategies, and renewable integration. Key contributions include open-data power flow simulations, predictive control for frequency regulation, and analysis of European network codes impacting German balancing markets. His research bridges theoretical control engineering with practical energy automation applications. Teaching: Professor Wendiggensen teaches Energy Automation, Control Theory (modeling/simulation), Process Control Systems (communication/automation), and Programming projects, emphasizing industrial application contexts. Professional Background: Prior to academia, he developed process control systems at ASEA BROWN BOVERY, providing industry-relevant perspective to his university role since 1994. No scientific awards, student advising records, or laboratory affiliations were documented in the source materials.

Peter Van Duijsenمشاهده پروفایل

استاد

• Power Electronics
• Renewable Energy Technologies
• DC Grids
• +۵ مورد دیگر

Peter Van Duijsen serves as a Prof. of Practice at The Hague University of Applied Sciences, with nearly 30 years of experience in computer simulation and power electronics. He heads the DC Lab in The Hague, Netherlands, and maintains a close cooperation with the Technical University of Delft regarding DC grids and storage. Since 2008, he has also been a guest lecturer at the Korean University of Technology. His educational background includes a Masters in Electrical Engineering followed by a PhD in 2003 in the field of Modeling and Simulation of Power Electronic Systems from the Technical University of Delft. After completing his studies, he founded Simulation Research where he developed the simulation program CASPOC. Dr. Van Duijsen's research spans power electronics, renewable energy technologies, and electrical drive systems, with a growing focus on DC grid applications. His expertise extends to power converters, inverters, transformers, and electrical power engineering with emphasis on modeling and simulation techniques. His recent work demonstrates an increasing emphasis on practical educational applications and real-world implementations of power electronics in sustainable energy systems. Analysis of his recent publications reveals a strong trend toward DC grid technology, educational tools for power electronics, and renewable energy integration. His work bridges theoretical concepts with practical implementations, particularly in nano-grids for tiny houses, electric vehicle charging, and rural electrification projects. The publications show consistent development of hardware trainers and educational setups that combine theoretical knowledge with hands-on experience. Dr. Van Duijsen has co-authored several textbooks in the fields of Power Electronics, Drives, Solar, Wind and eVehicles. His research has accumulated 442 citations and 23,620 reads across his 73 publications, demonstrating significant impact in his field. His work with students focuses on practical implementation of theoretical concepts, as evidenced by numerous publications on educational laboratory setups and course development. He has been involved in bachelor course development on electrical energy transition and eVehicle motor control, emphasizing the direct application of theory to improve student understanding and progress. As head of the DC Lab at The Hague University of Applied Sciences, Dr. Van Duijsen leads research in DC grid technology, power electronics education, and renewable energy integration. His lab develops practical hardware solutions like the universal power electronics hardware trainer and educational droop control laboratory setups, focusing on making complex power electronics concepts accessible through hands-on learning experiences.

Cui Yuمشاهده پروفایل

دانشیار

• Database Management
• Information Retrieval
• High-Dimensional Indexing
• +۲ مورد دیگر

Dr. Cui Yu serves as an Associate Professor in the Department of Computer Science and Software Engineering at Monmouth University, where she maintains an active research program focused on database systems and information retrieval. With over two decades of scholarly contributions, her work bridges theoretical foundations and practical applications in data management. Her academic foundation includes a Ph.D. from the National University of Singapore, establishing her expertise in advanced database methodologies. This background enabled her pioneering work in high-dimensional indexing techniques that address the 'curse of dimensionality' in modern data systems. Dr. Yu's research program centers on innovative solutions for database indexing, query optimization, and information retrieval. She developed the influential iDistance method and FB+-tree indexing structures that transform high-dimensional data into manageable single-dimensional spaces. Her recent work demonstrates significant evolution from core database theory toward applied domains including privacy-preserving social networks, smart city infrastructure (notably CNN-based parking systems), and mobile applications for skill development. This trajectory reflects her ability to adapt database principles to emerging technological challenges while maintaining rigorous theoretical grounding. Analysis of her publication history reveals consistent leadership in database indexing innovation, with foundational work in the early 2000s maturing into diverse applications by the late 2010s. Her research maintains strong connections between theoretical indexing advances and real-world implementations across domains including multimedia retrieval, geospatial systems, and security applications. Her scientific recognition includes two significant U.S. patents: Apparatus and method for performing transformation-based indexing of high-dimensional data (US patent: 7117217, 2006) Transformation-based method for indexing high-dimensional data for nearest neighbor queries (US patent: 6,834,278, 2004) As an educator, Dr. Yu actively mentors students through research projects and application development initiatives. She teaches core curriculum courses including Computer Architecture, Database Systems at multiple levels (undergraduate CS 432, graduate CS 533), and Senior Capstone Projects. Her teaching load demonstrates substantial commitment to both foundational computer science education and advanced specialized topics, with consistent course offerings through 2025. She maintains active professional engagement through IEEE membership and continuous scholarly output.

Neda Aboutorabمشاهده پروفایل

دانشیار

• Network Coding
• Wireless Communications
• Physical-Layer Security
• +۸ مورد دیگر

Associate Professor Neda Aboutorab is affiliated with the University of New South Wales (UNSW) Canberra, where she holds the position of Deputy Head of School at the School of Engineering and Technology. She completed her PhD in Electrical Engineering at the University of Sydney in 2012 and served as a Postdoctoral Research Fellow at the Australian National University from 2012 to 2015 before joining UNSW. Her research focuses on network coding, wireless communications, physical-layer security, industrial IoT, fog computing, and machine learning applications in networking, with additional contributions to information theory and adaptive modulation. PhD: Electrical Engineering, University of Sydney (2012) Her research interests encompass a wide array of topics including secure network coding, distributed storage systems, and the integration of machine learning into communication networks. She explores theoretical foundations of coding schemes alongside practical applications such as energy-efficient offloading in fog radio access networks and intrusion detection in industrial IoT environments. Her work often bridges mathematical rigor with real-world system optimization, addressing challenges in wireless communication reliability and security. In publication trends, her recent work emphasizes physical-layer security enhancements through jamming techniques and explores the boundaries of linear/nonlinear coding in index coding problems. She also investigates fog computing architectures and cybersecurity solutions for industrial IoT, highlighting a shift toward practical machine learning-driven systems. While no scientific awards are explicitly listed, her contributions to network coding and wireless security are notable within her field. Her advising and grants narrative remains unspecified in the provided information, though her role as Deputy Head of School suggests active involvement in academic leadership and research coordination. No dedicated lab or team affiliations are mentioned beyond her institutional roles.

Nancy Lynchمشاهده پروفایل

استاد

• Distributed Algorithms
• Real-Time Computing
• Formal Modeling and Verification
• +۴ مورد دیگر

Nancy Lynch is the NEC Professor of Software Science and Engineering in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT). She leads the Theory of Distributed Systems (TDS) research group at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), part of the broader Theory of Computation (TOC) group. Her research focuses on distributed computing, real-time systems, algorithms, formal modeling, and biological distributed systems, including brain networks and insect colony algorithms. Lynch earned her academic training in mathematics at Brooklyn College and MIT. Before joining MIT in 1982, she held positions at universities like the University of Southern California and Georgia Tech. Her work bridges theoretical computer science with applications in distributed systems, wireless networks, and computational biology. Key research interests include distributed algorithms, impossibility results, timed I/O automata, and algorithmic theories of biological systems. She has authored influential textbooks such as Distributed Algorithms and contributed to foundational theories in distributed computing. Affiliations: NEC Professor of Software Science and Engineering, MIT EECS Leader, Theory of Distributed Systems Group (TDS), CSAIL Member, MIT Theory of Computation (TOC) group Awards: Member, National Academy of Engineering Member, National Academy of Sciences ACM Fellow Recipient of major prizes in distributed computing Grants & Projects: Funding for research on distributed algorithms, biological systems, and neural networks Collaborations in robotics, wireless networks, and computational biology Her lab's current projects explore algorithms for wireless networks, brain network modeling, and swarm intelligence, with applications in robotics and healthcare.

Gregory Gangerمشاهده پروفایل

استاد

• Computer Systems
• Cloud Computing
• Distributed Systems
• +۶ مورد دیگر

Gregory Ganger is the Stephen J. Jatras Professor at Carnegie Mellon University's College of Engineering, holding joint affiliations with the Computer Science Department and CyLab. He has served as Director of the Parallel Data Lab (PDL) since 2000 and teaches Storage Systems (Fall) and Advanced Cloud Computing (Spring) courses. Education: Ph.D. in Computer Science & Engineering (1995, University of Michigan) M.S. in Computer Science & Engineering (1993, University of Michigan) B.S. in Computer Science (1991, University of Michigan) Dr. Ganger's research spans computer systems, focusing on: cloud computing, distributed systems, storage architectures, and machine learning infrastructure. His work addresses challenges in resource scheduling, non-volatile memory optimization, and scalable infrastructure design through projects like BigLearning, CILES, HeART, and Zoned Storage systems. Recent publications demonstrate expertise in: DNN training optimization (GraphPipe, Nonuniform-Tensor-Parallelism), sustainable storage (Storage Emissions, FairyWREN), and cloud efficiency (MACARON Cache). These works intersect machine learning, storage systems, and hardware-software co-design. Scientific Awards: 2021 OSDI Best Paper 2021 SOSP Best Paper 2021 SoCC Test of Time Award 2021 R&D 100 Award As advisor to 8 graduate students, he contributes to training the next generation of systems researchers. His lab (PDL) collaborates with industry partners on cutting-edge storage and cloud infrastructure problems.

• Ventilation Systems
• Thermal Comfort
• Indoor Environmental Quality
• +۷ مورد دیگر

Beat Frei is an Associate Lecturer at the Lucerne School of Engineering and Architecture, part of the Institute of Building Technology and Energy. His academic role focuses on teaching and research in ventilation systems, thermal comfort, and building energy efficiency. Professional Competencies: Dipl. HLK-Ingenieur (HTL/ASHRAE/VDI/ISIAQ/IBPSA), Smoke Control Engineering, Indoor Environmental Quality, and Tracer Gas Methods. He serves as a reviewer for the Elsevier Journal Energy and Buildings and is a Swiss Expert for CEN TC 156 (Ventilation in Buildings) working group 19. Research Focus : Natural ventilation strategies, adaptive thermal comfort, mechanical ventilation systems in residential/non-residential buildings, and performance evaluation of building systems. His work bridges HVAC technology with regulatory standards like SIA norms. Key Projects : Coordinated the development of comfort requirements for SIA 180 and SIA 382/1 standards. His research emphasizes practical applications of ventilation effectiveness measurements and energy-efficient building systems. Publications : Recent work includes studies on enthalpy recovery systems (2024), ventilation effectiveness during pandemic conditions (2022), and building standards for mechanical ventilation (2021). Earlier contributions address stairwell airflow measurements (2016) and hybrid heating systems (2002). Labs/Teams : Active within the Institute of Building Technology and Energy (IGE), collaborating on interdisciplinary projects combining thermal engineering and sustainable building practices.

• Mobile Communications
• 5G/6G Systems
• Machine-to-Machine (M2M) Communications
• +۷ مورد دیگر

Dr. Omar Alani is a Senior Lecturer and Head of the Networking and Mathematics Group at the School of Science, Engineering & Environment, University of Salford, UK. He holds a PhD in Telecommunication Engineering from De Montfort University and has held academic and research roles at Swansea University, the University of Leeds, and De Montfort University. His expertise spans Mobile Communications, 5G/6G systems, IoT, and Green ICT. Education: PhD (Telecommunication Engineering, De Montfort University, 2005), MSc (Advanced Control Systems), BEng (Electrical Engineering) Affiliations: Informatics Research Centre, University of Salford Research Interests: Alani focuses on advancing communication systems through projects in next-generation networks, including 6G/5G innovations, cognitive radio, and energy-efficient telecommunication solutions. His work intersects with renewable energy monitoring via ICT and smart grid technologies. He has authored over 90 publications and supervised multiple theses in these areas. Teaching: He teaches courses on Mobile Communication Networks, IoT, and Control Systems Engineering, emphasizing practical applications in postgraduate project management. Labs/Teams: Leads the Networking and Mathematics Group, collaborating on projects related to routing protocols, antenna design, and network security.

Hua Sunمشاهده پروفایل

استادیار

• Information Theory
• Secure Communication
• Coding Theory
• +۱ مورد دیگر

Hua Sun is an Assistant Professor in Electrical Engineering at the University of North Texas. His research focuses on information theory, secure communication, coding theory, and private information retrieval. Recent publications demonstrate strong emphasis on secure aggregation methods, information-theoretic privacy guarantees, and coded systems optimization. Key trends in his work include: 1) Fundamental limits of secure computation under collusion and dropout constraints, 2) Quantum and classical code optimizations, 3) Private information retrieval architectures. His research consistently addresses information-theoretic security in distributed systems.