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Anthony D. JosephView profile

Professor

• Computer and Network Security
• Distributed Systems
• Mobile Computing
• +6 more

Anthony D. Joseph is a Chancellor's Professor in the Department of Computer Science at the University of California, Berkeley, within the College of Engineering. He is a faculty member in the Computer Science Division and part of the RISE Lab and AMP Lab at UC Berkeley. His research spans multiple domains in computer science with a focus on security, distributed systems, and networking. Education: 1998, Ph.D., Computer Science, MIT 1988, S.M./S.B., Electrical Engineering and Computer Science/Computer Science and Engineering, MIT Professor Joseph's primary research interests include Computer and Network Security, Distributed Systems, Mobile Computing, Wireless Networking, Software Engineering, Operating Systems, Genomics, Secure Machine Learning, and Datacenters. His work has significant implications for both theoretical computer science and practical applications in industry. He leads multiple research projects including Mesos, SecML (Secure Machine Learning), D-Trigger, DETER, and Tapestry/Brochure. His research has been instrumental in advancing the fields of distributed systems and security, particularly in the context of machine learning applications. His publications reflect a strong focus on the intersection of security and distributed systems, with recent work emphasizing secure machine learning techniques and resource management in data centers. Professor Joseph's research has evolved from foundational work in networking and distributed systems to addressing contemporary challenges in cloud computing and AI security. Scientific Awards: Diane S. McEntyre Award for Excellence in Teaching Computer Science (2007) NSF Faculty Early Career Development Award (CAREER) (2000) Okawa Research Grant (1999) Professor Joseph has advised numerous graduate and undergraduate students, many of whom have gone on to make significant contributions in academia and industry. His research has been supported by various grants, including the NSF CAREER award. He has been actively involved in teaching core computer science courses including CS162: Operating Systems and Systems Programming and CS262: Advanced Topics in Computer Systems. He leads several research groups including the AMP Lab (which focuses on data analytics) and has been instrumental in projects like Mesos (for resource sharing in data centers) and SecML (focusing on the security of machine learning systems). His labs work on cutting-edge problems at the intersection of systems, networking, and security, with applications ranging from cloud computing to critical infrastructure protection.

• Cloud Computing
• Green Computing
• High-Performance Computing
• +7 more

Jordi Guitart Fernández is a Professor at the Department of Computer Architecture, Barcelona School of Informatics (FIB), Universitat Politècnica de Catalunya (UPC). He is also affiliated with the Barcelona Supercomputing Center (BSC-CNS), a leading national supercomputing facility. He leads the CROMAI research group, focusing on Computing Resources Orchestration and Management for AI. His work bridges high-performance computing, cloud systems, and artificial intelligence. Research Interests: Cloud Computing and Edge Computing Green and Energy-Efficient Computing Containerization and Virtualization for HPC Resource Orchestration and Management Autonomic and Self-Adaptive Systems Machine Learning Workflow Management AI-Driven System Optimization His recent publications reveal a strong focus on intelligent management of computing resources across cloud, edge, and HPC environments using machine learning and agent-based frameworks. He investigates performance, efficiency, and reliability in containerized AI and HPC workloads, particularly within Kubernetes and distributed infrastructures. His work increasingly integrates human-in-the-loop and trustworthiness aspects into AI systems. Scientific Awards: CLOUD Conference 2025 Best Paper Award VISIGRAPP 2025 Best Student Paper Award Premi Extraordinari de Doctorat 2025 - Àmbit d'Enginyeria de les TIC Test of Time Award Honorable Mention (e-Energy) Reconeixement als Mèrits Docents d'Especial Qualitat Top reviewers for Polytechnic University of Catalonia (Computer Science) - September 2017 Advising and Grants: He has advised doctoral students, including Peini Liu. He leads and participates in numerous competitive R+D+i projects, such as CROMAI and DALEST, funded by national and European programs like HORIZON 2020 and the Spanish State Research Plans. His work is supported by grants focused on knowledge generation and industrial leadership in computing technologies. Labs and Teams: He is the leader of the CROMAI - Computing Resources Orchestration and Management for AI research group at UPC. He also collaborates closely with the Barcelona Supercomputing Center (BSC-CNS), contributing to large-scale computing initiatives and strategic research agendas in Europe.

Alexandros DaglisView profile

Associate Professor

• Rack-Scale Computing
• Network-Compute Integration
• Low-Latency Datacenter Architecture
• +5 more

Alexandros Daglis is an Associate Professor of Computer Science at the Georgia Institute of Technology, with an adjunct appointment in the School of Electrical and Computer Engineering. His research focuses on blurring boundaries between network and compute for high-performance, scalable microsecond-scale services in datacenters, particularly through network endpoints and memory-centric computing. Primary Affiliation: Georgia Tech College of Computing, School of Computer Science Adjunct Affiliation: School of Electrical and Computer Engineering Key research areas include: Rack-scale computing and network-compute co-design CXL-based memory systems Low-latency datacenter architectures Transactional memory and concurrency control Edge-cloud continuum and geo-distributed infrastructures He has received prestigious awards including the NSF CAREER Award, Google Faculty Research Award, and Georgia Tech's Outstanding Junior Faculty Teaching Award. His students include Marina Vemmou (network-compute co-design), Albert Cho (memory system design), and Peidi Song (microsecond-scale scheduling). Grants: NSF, IARPA, Intel, Samsung Teaching: High Performance Computer Architecture, Systems and Networks, Datacenter Design

Yuanyuan ShiView profile

Assistant Professor

• Energy Systems
• Cyber-Physical Systems
• Machine Learning for Energy Management
• +6 more

Yuanyuan Shi is an Assistant Professor in the Electrical and Computer Engineering Department at the University of California, San Diego (UCSD), with affiliations at the Center for Energy Research and the MICS. Her research integrates machine learning with control theory, focusing on energy systems, cyber-physical systems, and PDE-governed systems, aiming to provide reliable and efficient decision-making in complex environments like power grids and buildings. Assistant Professor, UCSD (2021–present) Postdoctoral Fellow, Caltech (2020–2021) Ph.D., Electrical and Computer Engineering, University of Washington (2020) M.Sc., Electrical Engineering and Statistics, University of Washington B.Eng., Nanjing University, China Her work spans machine learning, optimization, and control theory, with applications in power systems, PDEs, and intelligent systems. She develops algorithms that combine learning with control guarantees, enabling robust solutions for energy management and grid stability. Recent publications highlight her focus on neural operators for PDE and delay systems, stability-constrained reinforcement learning, and multi-agent control in sustainability contexts. These works advance physics-informed models, grid frequency regulation, and commercialized energy storage integration. She has received prestigious awards, including: NSF CAREER Award (2025) Schmidt Sciences AI2050 Early Career Fellowship (2025) Hellman Fellowship (2023) Jacobs School Early-Career Faculty Acceleration Award (2024) MIT Rising Star in EECS (2018) Clean Energy Institute Scientific Achievement Award (2020) At UCSD, her lab collaborates on projects like FedNeMO (federated neural operators) and BEAR-Data (multi-zone building dataset). She co-organized Control Meets Learning seminars and serves as guest co-editor for the Applied Energy special issue on Trustworthy Machine Learning.

Mohamed HefeedaView profile

Professor

• Multimedia Networking
• Peer-to-Peer Systems
• Internet Protocols
• +6 more

Mohamed Hefeeda is a Professor in the School of Computing Science at Simon Fraser University (SFU), Canada. He leads the Network and Multimedia Systems Lab (NMSL) and previously served as Director of the School from 2018 to 2023. His research focuses on multimedia networking, mobile computing, cloud systems, and hyperspectral imaging. He holds an ACM Distinguished Member designation and has received prestigious awards including the NSERC Discovery Accelerator Supplements (2011) and multiple best paper awards at top conferences like ACM MM and IEEE Infocom. Education: Ph.D., Purdue University, 2004 M.Sc., University of Connecticut, 2001 B.Sc., Mansoura University, Egypt, 1994 Research Interests: Design of efficient multimedia systems and protocols for wired/wireless networks Cloud gaming optimization and video encoding techniques Hyperspectral imaging for healthcare and mobile applications AI-driven multimedia systems and mobile computing innovations Grants & Industry Collaborations: Funded by NSERC, CFI, and companies like AMD, Huawei, and CBC Co-founded Video Semantics (acquired by tech firm) Partnered with CBC on peer-assisted content distribution systems Awards Highlights: 2025: ACM Distinguished Member 2019: Best Student Paper Award at ACM MMSys 2015: NSERC Discovery Accelerator Supplements Labs & Leadership: Network and Multimedia Systems Lab (NMSL) at SFU Contributed to creation of Qatar Computing Research Institute (QCRI)

Elsa A. OlivettiView profile

Professor

• Sustainable Materials
• Environmental Engineering
• Materials Science
• +3 more

Elsa A. Olivetti is the Jerry McAfee (1940) Professor in Engineering and Professor of Materials Science and Engineering at MIT, and a MacVicar Faculty Fellow. She leads the Olivetti Group, focusing on sustainable materials design, recycling strategies, and computational models for environmental and economic impact assessment. Her work bridges materials science with sustainability, emphasizing circular economy principles and decarbonization. Education: B.S. in Engineering Science from University of Virginia (2000); Ph.D. in Materials Science and Engineering from MIT (2007). Her doctoral research centered on lithium-ion battery electrode materials. She joined MIT’s Department of Materials Science and Engineering (DMSE) in 2014 as an Assistant Professor, later advancing to full Professor. She co-directs the MIT Climate & Sustainability Consortium and chairs the MIT Climate Nucleus. Research interests include: sustainable materials systems, recycling-friendly material design, waste mining, and AI-driven materials discovery. She develops models for cost prediction, environmental impact analysis, and policy-relevant supply chain dynamics. Notable contributions include high-throughput zeolite design and battery recycling frameworks. Awards include the Bose Teaching Award (2021), NSF Early Career Award (2018), and Minerals, Metals & Materials Society Early Career Fellowship (2019). Her work emphasizes education and curriculum development, including courses for MIT’s Climate Scholars program. Labs/Teams: Olivetti Group (MIT), MIT Climate & Sustainability Consortium. Active in global sustainability initiatives, focusing on materials for energy transition and climate resilience.

Monowar BhuyanView profile

Associate Professor

• Machine Learning
• Anomaly Detection
• Systems and AI Security
• +6 more

Monowar Bhuyan is an Associate Professor in the Department of Computing Science at Umeå University, Sweden, leading the Cyber Analytics and Learning Group within ADSLab. He holds a Ph.D. in Computer Science from Tezpur University and has held academic positions at Assam Kaziranga University and Umeå University. His research focuses on machine learning, anomaly detection, edge AI, and distributed systems security. He has secured over 35 MSEK in grants from WASP, STINT, and EU Horizon programs. Education Ph.D. in Computer Science and Engineering, Tezpur University (2014) M.Tech. in Information Technology, Tezpur University (2009) B.E. in Computer Science and Engineering, IETE (2007) Research Interests Distributed/Federated/Responsible Machine Learning Cybersecurity and Anomaly Detection in Edge Clouds AI for DDoS Defense and Cyber Resilience Edge AI and Serverless Computing Recent Contributions His recent work addresses secure federated learning, DDoS attack detection in cloud-edge systems, and responsible AI. Key publications include novel frameworks for VSI-DDoS detection and federated learning optimizations. Awards & Grants Best Paper Awards at ICONIP 2023 and ACM ICACCI 2012 WASP NEST Grant (AIR2 Project, 5 MSEK) EU Horizon Europe Grant (SovereignEdge.Cognit, 8.27 MSEK) Lab & Collaborations He leads the Cyber Analytics and Learning Group (ADSlab), collaborating with institutions like KTH, Linköping University, and Nara Institute of Science and Technology (NAIST). The lab focuses on AI-driven security solutions for distributed systems.

• Optical Packet/Burst Switching
• High-Speed Optical Signal Processing
• Wireless Optical Networks – Radio-over-fiber access networks
• +11 more

Pleros Nikos is a Professor in the Department of Informatics at Aristotle University of Thessaloniki (AUTH), leading the Photonics Systems and Networks (Phos-Net) research group and co-founding the interdisciplinary Wireless and Photonic Systems and Networks (WinPhoS) research group in 2016 at AUTH’s Center for Interdisciplinary Research and Innovation (C.I.R.I.). His office is located in Kalamaria, Office 15, with office hours Tuesday/Wednesday 10:00-12:00 and Thursday 16:00-17:00. His research spans optical and photonic systems, specializing in Optical Packet/Burst Switching, High-Speed Optical Signal Processing, Wireless Optical Networks (including Radio-over-fiber access networks), and High-Speed WDM/OTDM Optical Transmitters. The WinPhoS group, which integrates departments of Physics, Informatics, and Electrical Engineering, advances applications in integrated photonics, plasmonics, optical interconnects for DataCenters, neuromorphic photonics, and 5G mmWave fiber-wireless systems, supported by state-of-the-art laboratories for optical interconnects and mmWave testing. WinPhoS has participated in over 30 FP7 and Horizon EU research projects during the last 15 years, frequently serving as project coordinators, and maintains global collaborations with institutions and industry across Greece, Europe, the USA, and Japan. Professor Nikos actively mentors PhD students, with current openings in “Photonic Neural Networks and Photonic AI processors”, and oversees research grants focused on next-generation optical networking technologies.

H. Jonathan ChaoView profile

Professor

• Networking
• Data Center Networks
• High-Speed Packet Processing
• +3 more

H. Jonathan Chao is a Professor in the Department of Electrical and Computer Engineering at New York University (NYU Tandon School of Engineering). He is the Director of the High-Speed Networking Lab, leading a team of 6 PhD students and 10 Master’s students. His research focuses on software-defined networking, network function virtualization, datacenter networks, and high-speed packet processing. Chao has held significant roles, including Head of the ECE Department (2004–2014) and former CTO of Coree Networks. He has authored over 200 publications and holds 58 patents. His awards include IEEE Fellow and National Academy of Inventors (NAI) Fellow. Education: B.S. and M.S. from National Chiao Tung University (Taiwan), Ph.D. from Ohio State University. Research Highlights Developing solutions for data center networks, network security, and quality of service control. Pioneering work in programmable packet schedulers, reinforcement learning for traffic engineering, and SDN security frameworks like SDNShield. Contributions to hybrid SDN networks, bufferless switch architectures, and energy-efficient data center designs. Awards Fellow of National Academy of Inventors (NAI) Fellow of IEEE Telcordia Excellence Award (1987) IEEE Best Paper Award (2001) IEEE New Jersey Coast Section Speaker of the Year (2003) Advisees & Labs Supervises 6 PhD and 10 Master’s students in the High-Speed Networking Lab. Collaborates with the Center for Advanced Technology in Telecommunications (CATT) to advance telecom innovations. Labs & Teams Directs the High-Speed Networking Lab, focusing on cutting-edge networking solutions, and contributes to CATT’s mission of technology transfer and entrepreneurship.

Baris KasikciView profile

Associate Professor

• Computer Systems
• Reliability
• Security
• +8 more

Baris Kasikci is an Associate Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Previously (2017-2023), he was a Morris Wellman Assistant Professor in the Electrical Engineering and Computer Science Department at the University of Michigan. His research focuses on building efficient and trustworthy computer systems through innovative combinations of approaches from systems, computer architecture, and programming languages. Dr. Kasikci received his PhD in Computer Science at EPFL and has held research positions at Microsoft Research Cambridge, Google, Intel, and VMware. His work addresses critical challenges in system reliability, security, and performance in increasingly complex software ecosystems. His research interests center on improving the efficiency of datacenter applications and machine learning systems, analyzing and fixing failures, and enhancing hardware security. His lab develops techniques for automated bug detection, formal verification of distributed systems, and building systems support for heterogeneous hardware architectures. Recent projects include Whisper (profile-guided branch misprediction elimination), Huron (taming false sharing), and Agamotto (automatic detection and repair of bugs in persistent memory applications). Analysis of his recent publications shows a strong trend toward optimizing large language model serving, hardware security, and performance optimization for modern heterogeneous architectures. His work bridges traditional systems research with emerging AI infrastructure needs, particularly in efficient LLM serving, security vulnerabilities in modern hardware, and performance optimization for heterogeneous computing environments. NSF CAREER award Microsoft Research Faculty Fellowship Intel Rising Star Award VMware Early Career Faculty Grant Google Faculty Award Roger Needham PhD Award (best PhD thesis in computer systems in Europe) Patrick Denantes Memorial Prize (best PhD thesis at EPFL) Best Paper Award at OSDI'18 Best Paper Award at MICRO'22 Dr. Kasikci has advised numerous PhD students who have gone on to prestigious positions in academia and industry, including Tanvir Ahmed Khan (Assistant Professor at Columbia University), Akshitha Sriraman (Assistant Professor at CMU), and Jiacheng Ma (AMD). His research has been supported by significant grants from NSF, DARPA, Intel, Google, Microsoft, VMware, and Amazon. His lab, the EfesLab, focuses on building tools and techniques that make computer systems more reliable, secure, and efficient. The EfesLab, led by Dr. Kasikci, brings together postdocs, PhD students, and undergraduate researchers to tackle fundamental challenges in systems reliability and performance. The lab has developed numerous influential tools including Whisper, Huron, and Agamotto that address critical performance and reliability issues in modern computing systems. Current research directions include efficient LLM serving, security of emerging hardware technologies, and automated debugging techniques.

Pedro FonsecaView profile

Assistant Professor

• Computer Security
• Operating Systems
• Distributed Systems
• +4 more

Pedro Fonseca is an Assistant Professor at the Department of Computer Science, Purdue University. He leads the Reliable and Secure Systems Lab, focusing on building reliable and secure core software systems such as operating systems, hypervisors, and distributed systems. His research has been recognized with awards including the NSF CAREER Award and Google Faculty Research Awards. Before Purdue, he completed a postdoc at the University of Washington, working with Arvind Krishnamurthy, Hank Levy, and Xi Wang. He earned his PhD from MPI-SWS and the University of Saarland under Rodrigo Rodrigues. His academic contributions span over 30 peer-reviewed publications in top-tier conferences like SOSP, OSDI, EuroSys, and ASPLOS. He teaches courses including CS503 (Operating Systems), CS592 (Reliable and Secure Systems), and CS408 (Software Testing). He actively serves on program committees for major systems conferences including SOSP, OSDI, EuroSys, and ASPLOS.

• Computer Architecture
• Interconnection Networks
• High-Performance Computing
• +4 more

Francisco J. Quiles is a Full Professor of Computer Architecture and Technology at the University of Castilla-La Mancha (UCLM), where he has been a faculty member since 1987. He currently serves as Director of the Department of Computer Systems and leads the High Performance Networks and Architectures (RAAP) research group, which he founded 33 years ago. His leadership extends to national and international organizations, including his current role as President of the Spanish Scientific Society of Computer Science (SCIE) since 2024. Dr. Quiles earned his Degree and PhD in Physics (Electronics and Computer Science) from the University of Valencia in 1986 and 1993, respectively. His academic journey began at UCLM in 1987, where he has held numerous leadership positions including Dean of the Department of Computer Science (1991-1994), first Dean of the Computer Science Engineering Faculty (1994-2000), and Vice-Rector of Research (2000-2011). Dr. Quiles' research focuses on high-performance interconnection networks for multiprocessor systems, clusters, and datacenters, along with parallel algorithms for video compression and transmission. His work bridges theoretical computer architecture with practical applications in high-performance computing environments. His research group has established significant collaborations with CERN's ATLAS project since 2019, demonstrating the real-world impact of his work in large-scale scientific computing. His publication record shows a consistent focus on congestion control, routing algorithms, and network performance optimization across various topologies including Dragonfly, Fat-Tree, and Slim Fly networks. Recent work (2023-2025) has emphasized practical implementations in InfiniBand hardware and solutions for modern datacenter challenges, reflecting the evolving nature of high-performance computing infrastructure. Dr. Quiles has received recognition through his leadership roles in professional organizations including SARTECO (Spanish Society of Computer Architecture and Technology) and IEEE Computer Society, where he is a Senior Member. His contributions to the field have shaped research directions in computer architecture both nationally and internationally. With over 270 peer-reviewed publications, supervision of 9 doctoral theses, and participation in more than 128 research projects supported by various national and international funding bodies, Dr. Quiles has made substantial contributions to both academic research and industry collaboration. His work with companies like Huawei, Bull, and Mellanox demonstrates the practical applicability of his research. The RAAP research group under Dr. Quiles' leadership has established itself as a significant player in computer architecture research, with particular expertise in interconnection networks. Their association with CERN's ATLAS project since 2019 represents a major achievement in applying academic research to fundamental scientific discovery.

Ahmed SaeedView profile

Assistant Professor

• Scalable Computer Networks
• Congestion Control
• Operating Systems
• +5 more

Ahmed Saeed is an Assistant Professor in the School of Computer Science at Georgia Institute of Technology, specializing in scalable computer networks and systems. His research spans congestion control, operating systems, LEO satellite networks, and formal methods, with a strong record of publications and active mentorship. Education: PhD in Computer Science, Georgia Institute of Technology (2019) Bachelor's in Computer and Systems Engineering, Alexandria University (2010) Postdoctoral Associate, MIT (with Prof. Mohammad Alizadeh) Research Interests: Ahmed's work focuses on the theory, design, and implementation of scalable networked systems. Key themes include: Congestion control algorithms for datacenter and WAN traffic Overload control mechanisms for microsecond-scale RPCs Performance debugging tools for datacenter applications LEO satellite network modeling and policy analysis Formal verification of network protocols and resource schedulers Recent Publications Trend: His 2024-2025 papers emphasize LEO satellite resilience and datacenter performance , with contributions to emergency failover modeling, latency debugging tools, and congestion control protocols. These works combine empirical measurement, formal modeling, and policy recommendations. Awards & Funding: NSF CAREER Award (2024) – LEO satellite variability ($600k) NSF CNS Core Awards (2022) – Edge server stacks & formal verification (total $2.38M) Google Research Award (2022) – Scalable edge systems ($80k) DARPA Risers Top 5 Poster (2022) Spec Tech Award (2023) – Nanomodular electronics routing ($40k) Teaching & Service: He regularly teaches Computer Networking I (CS 3251) and Datacenter Networks & Systems (CS 8803) . Service includes PC roles for SIGCOMM, NSDI, CoNEXT, and Networking area co-chair for JSys. Lab & Students: Ahmed leads an active research group with PhD students Peidi Song, Bhaskar Pardeshi, Sherif Abdelrazek; MS students Dhyey Thummar, Pratyush Sahu, Sammy Kapoor; and undergraduate Demi Lei. Alumni have joined industry leaders like Juniper, Microsoft, and Snowflake.

• Computer Architecture
• VLSI Design
• Machine Learning Acceleration
• +4 more

T. N. Vijaykumar is a Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering. His research focuses on computer architecture, VLSI design, and hardware acceleration for machine learning and datacenter systems. He holds a B.E. (Hons) in Electrical and Electronics Engineering and M.Sc.(Tech) in Computer Science from Birla Institute of Technology and Science, followed by M.S. and Ph.D. in Computer Science from the University of Wisconsin. His work spans GPU architecture optimization, memory systems, network security, and energy-efficient computing. Notable contributions include sparse tensor accelerators, disaggregated datacenter architectures, and secure speculative execution techniques. He has been actively involved in developing accelerators for machine learning inference and frameworks for distributed training of neural radiance fields. His publications address challenges in parallel computing, hardware-software co-design, and real-time systems, with applications in robotics, genomics, and microfluidics. He leads research initiatives funded by NSF and industry partnerships, emphasizing cross-layer optimizations across hardware, software, and networking layers.

Changcheng HuangView profile

Professor

• Machine Learning
• Network Architecture
• Optical Networks
• +3 more

Changcheng Huang is a Professor at Carleton University's Department of Systems and Computer Engineering, part of the Faculty of Engineering and Design. He holds a Ph.D. from Carleton University and is licensed as P.Eng. His research focuses on Machine Learning, Network Architecture, and Optical Networks, emphasizing resource optimization and protocol design. Dr. Huang leads the Advanced Optical Network Laboratory (AONL), funded by CFI and OIT, which explores optical network technologies and interworking with electronic networks. His lab includes state-of-the-art equipment like Nortel switches and photonic switches. Recently, he advised PhD students Qiao Lu, Khoa Nguyen, and others, and completed postdoc Eslam G. AbdAllah. RA positions are available at both master's and PhD levels. His work spans publications in journals like IEEE Transactions and conferences such as Globecom and ICC. Research areas include intelligent network control mechanisms, wireless networks, and network protocol implementation. Education: Ph.D. (Carleton University). Research interests also include modeling/simulation techniques and reliability mechanisms for optical networks. He teaches courses like SYSC 5108 (Deep Learning) and SYSC 4602 (Computer Communications). Grants funded by CFI and OIT support his lab's optical networking projects. Over 150+ publications highlight his contributions to virtual network embedding, edge computing, and optical data center networks. Lab facilities include OMM photonic switches, Nortel routers, and Dell servers. Collaborative projects involve industry and academic partnerships, advancing interworking technologies between optical and electronic networks. His work bridges theoretical research with practical implementations, addressing challenges in network scalability, energy efficiency, and reliability.