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Badri NathView profile

Professor

• Machine Learning
• Distributed Systems
• Computer Networks
• +3 more

Badri Nath is a Professor in the Department of Computer Science at Rutgers University, where he serves as the Director of the Master of Science Program. He holds a Ph.D. in Computer Science from the University of Massachusetts, Amherst. His research focuses on applying machine learning to monitor and predict the health of distributed systems, with a particular emphasis on network performance and energy efficiency. He has contributed significantly to areas such as congestion control, routing algorithms, and mobile sensing systems. Professor Nath has been recognized with prestigious awards, including the ACM SIGMOBILE and IEEE INFOCOM Test of Time Awards (2016 and 2015), and the VLDB 10-Year Best Paper Award (2002). He is also an award-winning educator, having received the Excellence in Teaching Award (2010) and the Professor of the Year Award (1995). He teaches advanced courses such as CS 552 (Computer Networks) and CS 416 (Operating Systems), emphasizing cutting-edge topics like data center networking, SDN, and energy-efficient protocols. His work often bridges theoretical research with practical applications, such as real-time air quality monitoring via mobile sensors and balanced traffic routing systems. Key projects include the development of ML models for distributed systems health prediction, the InsideOut CO measurement model, and the DoppelDriver travel-time prediction system. His research has been highlighted in outlets like Forbes and Rutgers Focus, underscoring its societal impact in transportation and environmental monitoring.

Pablo Pavón MariñoView profile

Professor

• Communication Networks
• Optimization of Computer Networks
• Optical Networks
• +5 more

Pablo Pavón Mariño is a Full Professor at the Polytechnic University of Cartagena (UPCT), leading the Telecomunications Network Engineering Group (GIRTEL) and co-founder of E-ligthouse Network Solutions. His research focuses on optimizing communication networks, with contributions to tools like Net2Plan, JOM, and MatPlanWDM. He holds a Telecommunications Engineering degree from the University of Vigo (1999), a PhD from UPCT (2004), and a Mathematics degree from UNED (2010). Research Interests: Network planning, optical transport systems, SDN/NFV integration, vehicular networks, and smart city infrastructure. His work bridges academia and industry through open-source tools like Net2Plan, used in teaching and research. Projects & Contributions: Authored over 50 journal papers and 80+ conference papers, and served as General Chair for IEEE HPSR 2011, ICTON 2013, and ONDM 2016. His tools are applied in 5G backhaul planning, optical metro networks, and multi-domain service provisioning. Patents: Includes methods for message control in communication networks (NEC Europe) and RFID systems (Digital Home). Labs & Teams: Directs the GIRTEL group, emphasizing network optimization and open-source software development.

Esteban Egea LópezView profile

Associate Professor

• Vehicular Networks
• Ad-hoc Networks
• Wireless Sensor Networks
• +7 more

Esteban Egea López is an Associate Professor in the Department of Information Technologies and Communications at the Universidad Politécnica de Cartagena (UPCT), Spain. He holds a Telecommunications Engineering degree from the Polytechnic University of Valencia (2000), a Master's in Electronics from the University of Gävle (2001), and a Ph.D. in Telecommunications from UPCT (2006). His research focuses on vehicular networks, wireless sensor networks, RFID, and biomedical applications of machine learning. Key areas of expertise include vehicular communication protocols, reinforcement learning for diabetes management, electromagnetic field analysis, and distributed computing. He leads projects like VENERIS (3D vehicular network simulator) and ORIENTATE (clinical machine learning tool). His work has been published in top journals like IEEE Transactions on Intelligent Transportation Systems and IEEE Access. He has secured funding from European Commission projects such as BREATHE and ASSIST-IoT, and collaborates on national initiatives like ONOFRE-4 for connected mobility. His contributions span 80+ peer-reviewed articles and multiple software frameworks including PRAIOS and MINOS for network optimization.

Edward LamView profile

Research Fellow

• Combinatorial Optimization
• Neuro-Symbolic AI
• Operations Research
• +3 more

Edward Lam is a Research Fellow in the Department of Data Science and Artificial Intelligence at Monash University. His research focuses on the intersection of combinatorial optimization and neuro-symbolic AI, developing algorithms for routing, scheduling, control, and analytics, primarily applied to automated decision-making in transportation and logistics. He holds a PhD in Computer Science (2019) and a BSc(Hons) in Mathematics and Statistics (2010), both from the University of Melbourne. Lam is affiliated with CSIRO Data61 and actively supervises PhD students in optimization and machine learning. **Education**: PhD in Computer Science, University of Melbourne (2019) BSc(Hons) in Mathematics and Statistics, University of Melbourne (2010) **Research Interests**: Combinatorial optimization, neuro-symbolic AI, multi-agent systems, and applications in autonomous logistics. His work addresses challenges in routing, scheduling, and decision-making under constraints. **Article Trends**: Recent publications emphasize branch-and-cut-and-price algorithms for multi-agent pathfinding, electric vehicle routing, and hybrid optimization methods. His work bridges theoretical algorithm design with real-world applications in transportation and warehousing. **Awards**: Received the Doctoral Research Award (2019) for outstanding contributions to optimization algorithms. **Advising & Grants**: Currently accepting PhD students focused on combinatorial optimization and machine learning, with available scholarships. Collaborates on projects funded by industry and governmental entities like CSIRO. **Affiliations**: Maintains an external position at CSIRO Data61 and has contributed to patents in optimization algorithms (2018).

• Computer Networks
• Internet Video Distribution
• Network Synthesis
• +6 more

Sanjay Rao is a Professor in the School of Electrical and Computer Engineering at Purdue University, leading the Internet Systems Laboratory. His research focuses on network synthesis/design/verification and Internet video distribution. He holds a B.Tech from IIT Madras and a Ph.D from Carnegie Mellon University. He has held visiting roles at Google, AT&T Research, and Princeton University. Research & Awards: His work includes groundbreaking contributions like End System Multicast (winner of the ACM SIGMETRICS Test of Time Award), Oboe (ABR auto-tuning), and Veritas (causal video streaming analysis). He received the NSF CAREER Award (2010) and is an ACM Distinguished Member (2021). Teaching: Courses include Computer Networking (ECE 463), Object-Oriented Programming (ECE 39595), and Computer Network Systems (ECE 595). Service: Chair, ACM Sigcomm Doctoral Dissertation Award Committee (2022); Associate Editor, IEEE/ACM Transactions on Networking (2016–2020). Labs & Teams: Directs the Internet Systems Lab (ISL) with active projects on video streaming, network resilience, and intent-based design. Graduate students collaborate on these initiatives.

• Computer Architecture
• GPU Optimization
• Machine Learning Acceleration
• +4 more

Dr. T Vijaykumar is a Professor in the Department of Electrical and Computer Engineering at Purdue University. His research focuses on computer architecture, GPU optimization, machine learning acceleration, and high-performance computing. He leads projects involving hardware-software co-design for energy-efficient systems and secure speculative execution techniques. His work spans innovations in GPU concurrency, sparse matrix processing, and distributed training frameworks for neural networks. He has contributed to advancements in memory systems, including PIM architectures and address translation optimizations. Recent projects include Proteus (model confidentiality) and Disorf (mobile 3D reconstruction). Key research themes include accelerating irregular computational graphs, secure hardware design, and efficient data movement in disaggregated systems. His publications reflect a strong emphasis on practical implementations with real-world applications in robotics, genomics, and cloud computing. Dr. Vijaykumar has received institutional awards for his contributions to computer architecture and holds patents in areas like microfluidics and data center congestion control. He collaborates with industry on GPU kernel design and memory consistency verification frameworks like QED.

• Product Management
• Supply Chain Management
• Artificial Intelligence
• +3 more

Amin Kaboli is a Lecturer at the Swiss Federal Institute of Technology in Lausanne (EPFL), holding positions in the School of Mechanical and Process Engineering and the School of Computer and Communication Sciences . As Co-Director of the AI Product Management program , he specializes in teaching students to apply AI/ML principles to real-world product development. His academic roles include designing curricula that blend technical engineering with business leadership, such as courses on Sustainable Products and Production Management. He earned a Ph.D. in Manufacturing Systems & Robotics from EPFL and holds a graduate degree in Industrial Engineering. His leadership training at IMD Business School complements his engineering background, enabling him to advise Fortune 500 corporations like Philip Morris International, Nespresso, and Rolex, as well as Swiss startups. His professional experience includes roles as an executive and COO of a startup, alongside work as an executive coach (PCC-certified). Kaboli’s research interests span AI-driven innovation , supply chain optimization , and sustainability . He explores solutions for energy management in smart grids, waste reduction in construction, and fault detection in industrial machinery. His work integrates fuzzy logic, multi-criteria decision-making (MCDM), and hybrid machine learning frameworks to address complex challenges. He teaches courses such as AI Product Management (focus on practical AI/ML applications), Sustainable Products and Supply Chains (sustainability principles and logistics optimization), Production Management (cost-effective manufacturing strategies), and Continuous Improvement of Manufacturing Systems (process optimization and team leadership). No scientific awards or grants are explicitly listed. His contributions include developing frameworks for eco-city solutions and waste management strategies. Kaboli’s office is located at ME A2 408 , EPFL’s Lausanne campus, where he also engages in administrative and educational leadership roles.

Lynda ZITOUNEView profile

Associate Professor

• Systems and Control
• Telecommunications and Networks
• Signal Processing
• +5 more

Lynda ZITOUNE is an Associate Professor at the Laboratory of Signals and Systems (L2S) within CentraleSupélec, located in Gif-sur-Yvette, France. Her research focuses on systems and control, telecommunications, and signal processing, with applications in energy-efficient networks, Industry 4.0, and health-related technologies. She is affiliated with the COMEDY, MODESTY, and Modelling and Estimation teams, contributing to projects like SYCOMORE. Her work emphasizes innovative solutions for network optimization, including load balancing in WiFi networks, congestion control in IoT systems, and energy efficiency in cellular and mesh architectures. She employs advanced techniques such as deep reinforcement learning, fluid modeling, and hybrid algorithms (e.g., Q-learning with ant systems) to address complex networking challenges. ZITOUNE’s recent publications (2021–2025) highlight contributions to intelligent association protocols, distributed control in large-scale systems, and AI-driven approaches to network management. Her research bridges theoretical modeling with practical applications, aiming to enhance network scalability, reliability, and sustainability. She collaborates actively with international teams and participates in key conferences like ICC, MASCOTS, and GLOBECOM. Her research interests span telecommunications, signal processing, and control systems, with a focus on interdisciplinary applications in energy and healthcare. She leads initiatives in fluid-based modeling for cellular networks and develops protocols for delay-tolerant wireless sensor networks using aerial mobile sinks.

Torsten HoeflerView profile

Professor

• High-Performance Computing
• Parallel Computing
• Computer Networks
• +3 more

Prof. Torsten Hoefler is a Full Professor at the Department of Computer Science, ETH Zürich. His research focuses on High-Performance Computing (HPC), parallel systems, networking, and AI-infrastructure. He leads projects on scalable interconnects, network topology design, and cloud computing benchmarks like SeBS. His work bridges theoretical foundations with practical implementations in distributed systems. Research Interests : High-Performance Computing & Networking Parallel Algorithms & Architectures AI Infrastructure & Distributed Systems Chiplet Interconnects & Topology Optimization Key Contributions : Developed tools like ATLAHS (AI/HPC network simulation) Advanced RDMA-based communication protocols (SDR-RDMA) Benchmarks for serverless computing (SeBS-Flow) His recent articles (2025) emphasize adaptive networks, low-precision AI models, and energy-efficient HPC systems. He also explores ethical computing and sustainability in supercomputing through initiatives like Core Hours & Carbon Credits.

• Networking
• Communications
• Cybersecurity
• +5 more

Martin Reisslein is a Professor at Arizona State University's School of Electrical, Computer and Energy Engineering. His research focuses on advanced networking technologies, cybersecurity, and edge computing, with a specialization in Time-Sensitive Networking (TSN), 5G, and network coding. He collaborates closely with institutions like the National Research Data Infrastructure (NFDI) and has published extensively in top-tier journals and conferences. His work spans theoretical and applied domains, including hardware-software co-design for low-latency systems, cybersecurity for satellite and IoT networks, and AI-driven resource management in transportation networks. Recent projects highlight contributions to TSN testbeds, molecular communication simulations, and unified threat management systems for home networks. Reisslein has co-authored over 270 publications, with notable contributions in IEEE Transactions on Networking, Communications Surveys & Tutorials, and the Journal of Signal Processing Systems. His research emphasizes practical implementations, such as hardware-aligned TSN simulations and flexible measurement frameworks for edge computing.

• Transportation Engineering
• Traffic Flow Analysis
• Urban Mobility
• +4 more

Mónica Menéndez is a Professor at New York University Abu Dhabi, UAE, specializing in transportation engineering and intelligent mobility systems. Her research focuses on urban traffic flow modeling, signal control optimization, and machine learning applications for transportation networks. Key Affiliations: New York University Abu Dhabi Coauthors: Alexander Genser, Saif Eddin Jabari, Kaidi Yang, Lukas Ambühl Her research interests include traffic flow analysis, connected vehicle technologies, and sensitivity analysis of transportation models. Recent work explores modular vehicle deployment for congestion mitigation, kinematic wave theory integration with deep learning, and network-level fundamental diagram modeling. Selected publications (2022-2025) demonstrate trends in applying supervised learning to traffic signal control, synthetic trip generation, and perimeter control strategies. She has contributed to journals like IEEE Transactions on Intelligent Transportation Systems and CoRR , with peer-reviewed conference papers at ITSC and VEHITS. As an advisor, she has mentored researchers working on autonomous vehicle impacts, traffic data imputation, and modular transit systems. Her lab collaborates on global multi-source traffic data analysis and sustainable urban mobility solutions.

• Wireless Communication
• Internet of Things (IoT)
• Network Protocols
• +4 more

Jong-Deok Kim is an active researcher in wireless communication and IoT systems, with frequent collaborations on publications spanning dynamic channel bonding, network optimization, and low-power protocols. His work addresses challenges in Wi-Fi, LoRa, and millimeter-wave networks, focusing on throughput, latency, and reliability. Research Focus: Wireless networks, edge computing, and blockchain for IoT. Key Topics: Channel allocation, federated learning, and error compensation methods. His recent publications highlight trends in adaptive algorithms for dense networks, hybrid positioning systems, and federated learning applications. Awards and grants are not explicitly mentioned in the provided data.

Il-Min KimView profile

Professor

• Wireless Artificial Intelligence
• Machine Learning
• Deep Learning
• +9 more

Professor Il-Min Kim is a Full Professor in the Department of Electrical and Computer Engineering at Queen's University (Smith Engineering). He leads the Wireless Artificial Intelligence Laboratory (WAI Lab), focusing on AI-driven wireless systems, IoT/IoE/IIoT, federated learning, edge computing, and 6G/V2X communications. His work integrates machine learning, signal processing, and cybersecurity with wireless infrastructure. Education: B.S. (Yonsei University, 1996), M.S./Ph.D. (KAIST, 2001). Postdoctoral research at MIT (2001-2002) and Harvard (2002-2003) preceded his faculty role at Queen's since 2003. Promoted to Associate Professor (2009) and Full Professor (2014). Research emphasizes Wireless AI , including on-device AI, federated learning, geoscience AI (Geo-AI), and secure communication protocols. Key areas: AI for 6G/V2X, energy-efficient edge computing, and robust spectrum sensing. His lab develops frameworks for distributed systems, compressive sensing, and nonlinear energy harvesting. Notable contributions include hybrid replay mechanisms for incremental learning, diffusion models for industrial IoT, and deep learning-based channel estimation. His work addresses challenges in data security, model robustness, and real-time inference under resource constraints.

Krstevski PetarView profile

Assistant Professor

• Power Systems
• Renewable Energy Integration
• Demand-Side Management
• +3 more

Krstevski Petar is an Assistant Professor at the Faculty of Electrical Engineering and Information Technologies, University "Sts. Cyril and Methodius" in Skopje. He holds a PhD (2018), MSc (2012), and BSc (2010) from the same institution. His research focuses on power systems resilience, renewable energy integration, demand-side management, and legislative alignment with EU energy policies. He is affiliated with the Transmission Power Systems Institute and has contributed to advancements in grid flexibility, energy markets, and cybersecurity in critical infrastructure. Education: PhD/MSc/BSc in Electrical Engineering from Ss. Cyril and Methodius University His technical interests emphasize optimizing customer and cross-border flexibility in distribution grids, analyzing legislative gaps in energy frameworks, and enhancing regional market integration. Recent work includes studies on virtual power plants, decentralized charging solutions for EVs, and cybersecurity protocols for transmission systems. Publications highlight innovative approaches to energy balancing markets, RES integration, and policy compliance in Southeast Europe. His research bridges technical and regulatory challenges in modernizing grid infrastructure.

• Heart Failure
• Cardiovascular Disease
• Diabetes
• +3 more

Professor John J McMurray is a Professor of Cardiac Diseases and honorary Consultant Cardiologist at the University of Glasgow, affiliated with the School of Cardiovascular and Metabolic Health and BHF Glasgow Cardiovascular Research Centre. He holds international roles, including serving as Past-President of the Heart Failure Association of the European Society of Cardiology (ESC) and Congress Programme Committee Chair for ESC 2023/2024. His research focuses on heart failure, diabetes-cardiovascular interactions, and chronic kidney disease, leading major clinical trials (e.g., PARADIGM-HF, DAPA-HF, DELIVER). He is a globally recognized researcher with a H-index exceeding 200 and has received prestigious awards like the ESC Gold Medal and honorary OBE. Education: BSc (Hons), MB ChB (Hons), MD. Professional Roles: Editorial board member of NEJM, European Journal of Heart Failure, and Circulation. Member of ESC Task Forces and guideline committees. Research: Over 1,600 publications, emphasizing clinical trials, guideline development, and SGLT2 inhibitor efficacy in heart failure. Leads international trials like FINEARTS-HF and FINALITY. Awards: Stokes Medal, Mackenzie Medal, ESC Gold Medal, and honorary OBE. Key Contributions: Co-developed global heart failure guidelines, advanced understanding of cardiorenal-metabolic overlap, and pioneered treatments for heart failure across ejection fraction spectrums. His work bridges clinical practice and research, influencing global cardiovascular care and policy.