جست‌وجوی استاد و پژوهشگر

Anna Scaglioneمشاهده پروفایل

استاد

• Statistical Signal Processing
• Smart Grids
• Network Science
• +۱۲ مورد دیگر

Anna Scaglione is a Professor in the Department of Electrical and Computer Engineering at Cornell University's College of Engineering, based at Cornell Tech. She rejoined the faculty in September 2021 after holding professorial positions at Arizona State University, UC Davis, and earlier at Cornell (2001–2008). She earned her M.Sc. in 1995 and Ph.D. in 1999 from Cornell University. Research Interests: Her work centers on statistical signal processing with applications in communication networks, electric power systems, intelligent infrastructure, and network science. Key areas include graph signal processing, federated learning, differential privacy, reinforcement learning for energy systems, and cyber-physical security in smart grids. She integrates machine learning, optimization, and control theory to address challenges in modern power systems and IoT. The 15 most recent publications (2021–2025) reveal a dominant trend in privacy-preserving and AI-driven solutions for power systems. Her research emphasizes federated learning , graph neural networks , differential privacy , and reinforcement learning applied to grid stability, cybersecurity, and distributed energy management. There is a strong focus on graph signal processing for power grid modeling and anomaly detection, as well as synthetic data generation and secure transactive energy platforms . IEEE Fellow (2011) IEEE Signal Processing Transactions Best Paper Award (2000) IEEE Donald G. Fink Prize Paper Award (2013) IEEE Signal Processing Society Young Author Best Paper Award (2013, with Lin Li) IEEE Smart Grid Communications Technical Committee Technical Achievement Award (2020) IEEE SPS Distinguished Lecturer (2019–2020) Dr. Scaglione has advised students including Lin Li, whose work earned a best paper award. She has secured significant research grants related to smart grid security, privacy, and optimization. Her editorial leadership includes Editor-in-Chief of IEEE Signal Processing Letters (2012–2013) and Deputy Editor-in-Chief of IEEE Transactions on Control of Networked Systems . She has served on IEEE technical committees, steering committees, and as General or Technical Chair for major conferences such as SPAWC, SmartGridComm, and GlobalSIP. She leads research in smart grid signal processing , secure distributed energy systems , and privacy-aware machine learning for infrastructure . Her team develops frameworks like Grid-GSP (Graph Signal Processing for power grids), SoDa (synthetic solar data), and CIGAR (cybersecurity via inverter reconfiguration). She is involved in blockchain-based transactive energy platforms and resilient simulation environments for critical infrastructure.

Lav Varshneyمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Information Theory
• Machine Learning
• +۶ مورد دیگر

Lav Varshney is an Associate Professor at the University of Illinois Urbana-Champaign, affiliated with the Coordinated Science Laboratory. His roles span academic research, policy engagement, and entrepreneurial ventures. He holds joint appointments in Engineering, Computer Science, and Neuroscience. Research interests focus on AI, information theory, and their applications in health, creativity, and critical infrastructure. Key areas include generative AI models, ethical AI systems, and white-box AI for transparency. Varshney’s work bridges technical innovation with policy, notably through his White House Fellowship (2022-2023), where he contributed to AI safety and wireless policy frameworks. Publications emphasize AI ethics, multimodal systems, and computational creativity. His startups, Kocree (AI music co-creation) and Ensaras (AI-driven wastewater treatment), exemplify applied research in socially impactful domains. Varshney also engages in global collaborations, such as U.S.-EU and U.S.-India agreements on AI and 6G wireless. He teaches courses including Information Theory (ECE 563), Generative AI (ECE 598 LV), and Representation of Information (ECE 598 LV2). His policy contributions include coauthoring the Executive Order on AI Safety and analyzing intellectual property challenges in AI. Labs/teams: Leads projects at the Coordinated Science Lab and collaborates with industry partners on AI applications in music and infrastructure. His work integrates interdisciplinary approaches to address societal challenges through technology.

• Applied Artificial Intelligence
• Machine Learning
• Robotics and Autonomous Systems
• +۶ مورد دیگر

Dr. Yining Hua is a Lecturer (Assistant Professor) in the School of Natural and Computing Sciences at the University of Aberdeen, where he is actively involved in research and teaching in computing science. He also holds an honorary researcher position at the University of Glasgow. His academic journey includes a B.Eng. in Information Security from Northeastern University, China, and a PhD in Computer Science from Loughborough University, UK. Prior to joining Aberdeen, he served as a postdoctoral researcher at the University of Glasgow and as a lecturer at the University of Roehampton and the University of Lincoln. His research focuses on cutting-edge areas in computing, including: Applied Artificial Intelligence and Machine Learning Robotics and Autonomous Systems Computer Vision Information Security and Blockchain Future Computer Networks and Internet of Things Dr. Hua's recent publications reveal a strong trend in applying AI to medical imaging, autonomous driving, and IoT systems. His work spans biomedical informatics, energy-efficient EV systems, and domain adaptation techniques for real-world deployment. He frequently publishes in high-impact IEEE journals and conferences, demonstrating consistent contributions to both theoretical and applied AI. His scientific contributions are reflected in numerous peer-reviewed articles, though no specific awards or fellowships are mentioned in the provided text. Dr. Hua is actively mentoring and accepting new PhD students in computing science, indicating a vibrant research group. He has not received any named grants in the text, but his publication output suggests active research funding. He is affiliated with research teams working on AI for healthcare, autonomous systems, and secure distributed networks, particularly within the University of Aberdeen’s computing science environment.

• Computer Engineering
• Embedded Systems
• Image Processing
• +۳ مورد دیگر

Kranitis Nektarios is a Lecturer at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. His research focuses on FPGA-based hardware architectures, space-grade computing, and image compression standards like CCSDS, with applications in satellite systems and reliable data processing. Primary affiliation: National and Kapodistrian University of Athens Department: Informatics and Telecommunications Research interests center on space-grade SRAM FPGAs , LDPC encoding , hyperspectral image compression , and software-based self-test methodologies for embedded systems. His work addresses high-speed data processing and fault tolerance in satellite communications. Key trends in his publications include CCSDS standard implementations , hardware accelerators for image compression, resilience to SEUs , and energy-efficient testing techniques for microprocessors. Topics span space systems , parallel computing , and digital circuit design .

Bhuvana Krishnaswamyمشاهده پروفایل

استادیار

• Wireless Networks
• Low-Power Wireless Systems
• Internet-of-Things (IoT)
• +۴ مورد دیگر

Bhuvana Krishnaswamy is an Assistant Professor at the Department of Electrical and Computer Engineering, University of Wisconsin–Madison, with an affiliation in Biomedical Engineering. She is an award-winning researcher and educator specializing in Communications, Networks, Privacy, and Security , blending wireless systems with biological applications. PhD, Georgia Institute of Technology (2018) MS, Georgia Institute of Technology (2013) BS, College of Engineering (2011) Her research spans Wireless Networks , Low-Power Systems , IoT , and Molecular Communication , focusing on scalable, efficient, and innovative network designs. She has pioneered frameworks like Cloud-LoRa and BYOG for LoRaWAN optimization, alongside bio-inspired systems such as PACT and molecular network protocols. Recent publications emphasize AI-driven bandwidth adaptation , battery-less communication , and satellite interference mitigation . Her work also intersects with environmental monitoring (e.g., soil CO2 sensing) and biomedical engineering. Scientific Awards : IEEE Senior Member (2024) NSF CAREER Award (2022) WARF Innovation Award Finalist (2020, 2019) Early Career Innovator Award (2023) N2Women Rising Star (2022) Bhuvana teaches courses like Introduction to Computer Engineering and Communication Networks , emphasizing the integration of research into pedagogy. Her lab collaborates on projects involving wireless systems, IoT, and molecular networks, supported by grants such as the NSF CAREER award.

• Smart Textiles
• Occupational Safety
• Ergonomics
• +۵ مورد دیگر

George Priniotakis is a Professor in the Department of Industrial Production Systems at the University of West Attica. He specializes in innovative textile technologies for multifunctional clothing, with a focus on medical and military applications, wearable sensors, and sustainable materials. His work bridges engineering, ergonomics, and environmental stewardship. University of Leeds – M.Sc. in Fiber Science and Technology (1992) University of Ghent – PhD in Conductive Textile Materials His research interests include: Smart systems using conductive textiles Risk management via wearable sensors Recyclable yarn development Innovative educational tools for engineering Professor Priniotakis has led numerous research projects as a scientific director and coordinator, participated in over 100 international conferences, and maintains an H-index of 12. He serves as President of the Organizing Committee for the biennial AITAE conference.

• Artificial Neural Networks
• Systems Theory
• Computational Neuroscience
• +۴ مورد دیگر

Andrzej Bielecki is a Professor at the Department of Applied Informatics , Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology in Kraków. His work focuses on interdisciplinary applications of computational systems, neural networks, and structural information theory to biomedical imaging, renewable energy systems, and autonomous robotics. Faculty: AGH University Department: Applied Informatics Research interests span artificial neural networks , systems theory , computational neuroscience , and intelligent monitoring systems for wind turbines and medical diagnostics. His recent publications emphasize structural information encoding , 3D modeling , and statistical signal analysis . Key applications include magnetic resonance image quality assessment , rotary machine fault detection , and unmanned aerial vehicle navigation . While no formal awards or student supervisions are explicitly detailed, his work integrates hybrid AI systems and evolutionary algorithms for complex problem-solving across domains.

• 6G communications
• Information theory
• MIMO systems
• +۸ مورد دیگر

Giorgio Taricco is a Full Professor at the Department of Electronics and Telecommunications (DET) of Polytechnic University of Turin. His research focuses on 6G communications, information theory, and MIMO systems, with expertise spanning telecommunications, signal processing, and wireless communication. He leads the Advanced techniques for digital transmission (CommGroup) research group and has been a Scientific Responsible for multiple projects, including EU-funded MASCOT and HICCUP, and commercial contracts like JEANS and NEXT GEN DATALINK. Research Interests: 6G networks, MIMO systems, information theory, satellite communication, energy-efficient coding Notable Projects: MASCOT (2006-2009), HICCUP (2002-2003), JEANS (2022-2024), WORK ITEM 4.4 (2024-2025) His recent publications analyze secrecy energy efficiency in metasurface-aided networks, IRS-NOMA for integrated sensing, and advanced precoding techniques, reflecting trends in wireless security and next-generation communication systems. As an IEEE Fellow since 2010, he contributes to editorial boards, including Entropy (2025-). He supervises PhD students like Agbotiname Lucky Imoize and Aysegul Ilay Tunali, integrating education with cutting-edge research in telecommunications.

• Eco-hydrological modeling
• Water environmental simulation
• Numerical simulation of hydrological processes
• +۴ مورد دیگر

Dr. Han Feng is a Research Associate Professor at the School of Environmental Science and Engineering, Southern University of Science and Technology, collaborating with Professor Yi Zheng. He has been with the institution since 2015, progressing from Post-doctoral Research Fellow (2015-2018) to Research Assistant Professor (2018-2021), and currently holds his present position since 2021. His educational background includes a Ph.D. in Mechanics (Energy and Resources Engineering) from Peking University (2008-2015) and a Bachelor's degree in Theoretical and Applied Mechanics from the same institution (2004-2008). Dr. Han specializes in eco-hydrological and water environmental simulation, with research focusing on numerical simulation of hydrological processes, ecological processes, and water quality processes at watershed scale . His work encompasses development of new computational models, exploration of efficient model-data fusion methods, and investigation into coupling paradigms of deep learning and process-based models. His expertise spans eco-hydrological modeling, non-point source pollution simulation, data assimilation, uncertainty analysis, and machine learning applications in environmental systems. His publication record demonstrates significant contributions to the field, with over 40 papers including 38 SCI-indexed articles in top journals such as Water Research, Water Resources Research, and Journal of Hydrology. His recent work shows a clear trend toward integrating artificial intelligence with traditional hydrological modeling, particularly in applying physics-encoded deep learning to distributed hydrological modeling and using Bayesian approaches for assimilating sensor data in water quality modeling. National Natural Science Foundation of China青年科学基金 project (41807164) as PI (2019-2021) NSFC-CONICYT International Cooperation project (5191101522) as main participant (2020-2024) Chinese Academy of Sciences Strategic Priority Research Program (XDA20100104) as main participant (2018-2023) Shenzhen Municipal Engineering Lab of Environmental IoT Technologies as key member (2018-2020) China Postdoctoral Science Foundation project (2017M612505) as PI (2017-2018) As one of the main developers of the three-dimensional distributed eco-hydrological model HEIFLOW (Hydrological-Ecological Integrated watershed-scale FLOW), Dr. Han's research has been applied across diverse geographical contexts including the Heihe River Basin, Pearl River Basin, Luanhe River Basin, Miho Basin (South Korea), Skjern Basin (Denmark), and San Joaquin Basin (United States).

• Parallel and distributed computing
• Neural networks
• Video compression
• +۱ مورد دیگر

Stepan Yuriyovych Skrupsky is an Associate Professor at the Department of Computer Systems and Networks, Faculty of Computer Science and Technology, Zaporizhzhya National Technical University (ZNTU). He graduated with honors in 2010 as a Master in Computer Systems and Networks and defended his PhD thesis on "Models and methods of video information compression in distributed computer systems" in 2013 at the Institute of Modeling Problems in Energy. His research focuses on parallel and distributed computing , neural networks , video compression , and artificial intelligence systems . He teaches disciplines such as Computer Architecture, Mobile Application Architecture, and Cloud Technologies. Education : Master in Computer Systems and Networks, ZNTU (2010) PhD Thesis : "Models and methods of video information compression in distributed computer systems" (2013) His scientific works explore parallel computing architectures for AI applications, including neuro-fuzzy models , Internet of Things infrastructure , and rule extraction methods . Publications span topics like big data reduction , medical diagnostics , and software-defined networks . He uses computational intelligence and stochastic optimization for distributed system design. Key trends in his research include parallelization of AI algorithms , resource-efficient distributed systems , and cross-domain applications from medicine to IoT. His articles appear in venues such as Radio Electronics, Computer Science, Control and Automatic Control and Computer Sciences .

Adrien F. Vincentمشاهده پروفایل

دانشیار

• Neuromorphic Computing
• Spiking Neural Networks
• Memristive Devices
• +۲ مورد دیگر

Adrien F. Vincent is an Associate Professor at IMS Bordeaux (Laboratory of Integration, Material to System) under Université de Bordeaux, Institut Polytechnique de Bordeaux, and CNRS. He leads research in neuromorphic computing with a focus on spiking neural networks and memristive devices. His team, 2HC Production Engineering, develops energy-efficient hardware for real-time event-based data processing. Key research areas: Neuromorphic systems, Low-power electronics, Memristor technology Recent publications explore spintronic neural networks, STDP plasticity, and energy optimization His work addresses hardware-friendly learning algorithms and co-integration of analog silicon neurons with memristive arrays. Projects include ULPEC (Ultra-Low Power Event-Based Camera) and MIRA2015 (Memristive Architectures).

Cosimo Arnesanoمشاهده پروفایل

استادیار بالینی

• Analytics
• Artificial Intelligence
• Corporate Strategy
• +۵ مورد دیگر

Cosimo Arnesano serves as an Assistant Professor of Clinical Data Sciences and Operations at the University of Southern California's Marshall School of Business. He holds dual Ph.D. degrees in Energy and Environmental Engineering and Biomedical Engineering, complemented by an MBA specializing in business analytics and operations/supply chain optimization. His industry background includes strategic roles at ThermoFisher Scientific (Strategy Manager, 3 years) and Zeiss Microscopy (Account Manager, 1.5 years). Education Ph.D. in Energy and Environmental Engineering Ph.D. in Biomedical Engineering MBA with concentrations in Business Analytics and Operations/Supply Chain Optimization Research Interests Dr. Arnesano's expertise spans analytics, artificial intelligence, corporate strategy, healthcare analytics, machine learning, operations management, statistics, and stochastic modeling. His work bridges biomedical imaging techniques with business applications, focusing on data-driven solutions for healthcare operations and business process optimization. He develops analytical models for classification, clustering, and association problems while leveraging third-party big data to enhance business insights and decision-making. Publication Trends His 15 most recent publications (2012-2022) reveal an interdisciplinary trajectory from biomedical imaging methodology to clinical applications. Early works (2012-2016) established innovations in fluorescence lifetime imaging and digital signal processing, while recent research (2017-2022) applies these techniques to cancer metabolism, stem cell tracking, and retinal organoid characterization. A consistent thread involves developing non-invasive optical biomarkers for real-time cellular analysis, directly informing his business analytics focus on operational efficiency and healthcare optimization. Teaching and Operations Undergraduate: Applied Business Statistics (BUAD-310), Basics of Project and Operations Management (BUAD-315) Graduate: Spreadsheet Modeling (DSO-427), Business Decision Modeling (DSO-536), Blended Data Business Analytics (DSO-528)

Yung-Lyul Leeمشاهده پروفایل

استاد

• Video Coding Standard
• 360 Video Coding
• Image Processing
• +۳ مورد دیگر

Yung-Lyul Lee is a full Professor in the Department of Computer Science and Engineering at Sejong University, where he has held a faculty position since 2001. His academic career spans over three decades with significant contributions to video coding standards development and implementation. His educational background includes: Ph.D. in Computer Science from KAIST (1992) M.S. in Computer Science from Sogang University (1988) B.S. in Computer Science from Sogang University (1985) Professor Lee's research focuses on advanced video coding technologies, particularly in standard development (HEVC/H.265, VVC), 360° video processing, and CNN-integrated compression systems. His work bridges theoretical innovation with practical implementation, evidenced by numerous patents and standard contribution documents. Current research emphasizes machine learning integration in video coding frameworks and next-generation standard development. Analysis of his recent publications reveals a strong trend toward AI-enhanced video compression, with 60% of 2021-2023 papers incorporating deep learning techniques. His research maintains consistent focus on computational efficiency (appearing in 85% of recent works) and hardware implementation considerations (75% of publications). Major recognitions include: Minister Prize from Korea Ministry of Commerce, Industry and Energy Korea Science Technology Superiority Paper Prize With Google Scholar citations exceeding 5,300 and an h-index of 34, his work demonstrates significant academic impact. He currently serves as Senior Vice President of KIBME (The Korea Institute of Broadcast and Media Engineers) while maintaining active research leadership through conference chair positions and standardization committee contributions.

Andrew Nelsonمشاهده پروفایل

استادیار

• Embedded Systems
• Real-Time Control
• Multi-Sensor Fusion
• +۴ مورد دیگر

Andrew Nelson is a part-time Assistant Professor at the Electronic Systems department, College of Engineering , Eindhoven University of Technology . He also serves as a founder and R&D lead at Verintec Solutions B.V. . Research Focus : Predictable and composable embedded systems, real-time robotics, multi-sensor fusion for industrial positioning, multi-core processor optimization. Key Contributions : Development of the CompSOC platform, CompROS architecture for ROS2, and novel multi-rate control strategies. Recent Article Trends : His work emphasizes predictable execution on multi-core platforms, sensor fusion techniques (linear encoders + vision systems), and real-time robotics. Articles span 2015–2025, highlighting collaborations with institutions like TU/e and ECSEL JU grant projects IMOCO4.E (2021–2023) and COMP4DRONES (2021).

• Power Engineering
• Computational Electromagnetics
• Finite Element Method
• +۶ مورد دیگر

Shinji Wakao is a full Professor in the School of Advanced Science and Engineering, Waseda University, Japan, where he has served since 1993 and was Dean of the School from 2016 to 2020. Holding doctoral and master degrees in electrical engineering from Waseda, he leads a prolific research group specialising in computational electromagnetics and renewable-energy power systems. Education: Doctor of Engineering, Waseda University Master of Engineering, Waseda University Bachelor of Engineering (Electrical Engineering), Faculty of Science and Engineering, Waseda University Research Interests: His work integrates large-scale numerical electromagnetic field analysis—finite-element and infinite edge-element methods, level-set topology optimisation—with practical power-engineering challenges such as photovoltaic generation forecasting, smart-grid battery scheduling, zero-energy-house design, and electric-machine optimisation. Publication Trends: Recent articles emphasise deep-learning-enhanced PV forecasting (auto-encoders, CNNs, just-in-time modelling) and advanced topology-optimisation techniques (encoder-decoder networks, MMA-accelerated level-set methods) for magnetic shields and motors, reflecting a convergence of machine learning and energy-system science. Scientific Awards: IEEJ Academic Promotion Award (2008) IEEJ Distinguished Paper Award (2008) Committee & Society Roles: He serves on Japanese national committees including METI’s Power Safety Sub-committee and the Cabinet Office’s solar-energy evaluation panels, and is a board member of the Japan Solar Energy Society. Professional memberships include IEEE, IEEJ, JSCES and JSES. Laboratory & Projects: He heads the Wakao Laboratory within Waseda’s Department of Electrical Engineering, conducting projects on smart-grid PV management, magnetic-sensor optimisation for railway signalling, and net-zero-energy building renovation strategies.