Find Academic Supervisors

Anthony Rowe is the Siewiorek and Walker Family Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU) and a Chief Scientist at Bosch Research. His primary affiliation is with the CyLab and the Wireless, Sensing and Embedded Systems (WiSE Lab) at CMU. He specializes in networked embedded systems, sensor networks, and extended reality (XR) technologies. His research emphasizes energy-efficient sensing, real-time localization, and XR integration with physical systems. Research Focus: His work spans XR systems (e.g., AR/VR edge networking in ARENA), mmWave radar for sensing (e.g., tire wear monitoring via Osprey), distributed edge computing (Silverline), and low-power wide-area networking (OpenChirp). Recent efforts include AI-integrated XR platforms (XaiR) and radar tomography (DART). Grants & Projects: Leads the CONIX Research Center ($27.5M NSF/DARPA grant), Bosch-funded edge computing projects, and DOE initiatives on microgrids. Notable projects include ARENA (XR edge architecture), GridBallast (smart grid control), and rural microgrid deployments in Haiti. Awards: Best Student Paper (ISMAR 2024), Best Paper (IPSN 2020), and the Steven J. Fenves Research Award (2015). Recognized for innovations in localization (MobiCom 2021), radar (ICRA 2023), and energy systems (BuildSys 2010). Teaching: Teaches courses on embedded systems (18-349/18-449), real-time systems, and mixed reality (18-453). Courses emphasize hands-on design and real-world applications. Labs & Teams: Directs the WiSE Lab, collaborating with Bosch Research and industry partners. The lab develops open-source frameworks like ARENA and OpenChirp, and contributes to standards for edge computing and sensing.

• Networked Embedded Systems
• Sensor Networks
• Extended Reality (XR) Systems
• +6 more

Ryan Stones is a Lecturer at the City Law School , City, University of London, specializing in competition law, behavioral economics, and the intersection of legal philosophy with antitrust enforcement. His work critically examines the application of the formal rule of law to EU competition policy and analyzes historical schools of antitrust thought like the Chicago School and Ordoliberalism.

• Competition Law
• EU Law
• Antitrust
• +9 more

Anand Bhojan is an Associate Professor (Educator Track) at the Department of Computer Science, School of Computing, National University of Singapore (NUS). He is a member of the Communication and Internet Research Lab and serves on the Graduate Studies Committee. Dr. Bhojan is also the founder of Anuflora Systems and Virtual and Augmented Reality Labs (www.varlabs.org), and serves as Associate Editor of Computers and Electrical Engineering Journal, Elsevier, and Vice President of International Researchers Club, Singapore. Dr. Bhojan earned his Ph.D. in Computer Science & Engineering from NUS in 2011, where his thesis was nominated for the Best PhD Thesis Award. He also holds a Professional Master's in Computer Applications from Bharathidasan University (1999), a Bachelor's degree in Computing with Gold Medal (University topper) from Bharathiar University (1994), and a Postgraduate Certificate in Teaching Higher Education from University of Sheffield, UK (2003). His research spans multiple domains including Distributed Computing and Wireless Networks (IoT, Security, Blockchain), Artificial Intelligence (Generative AI, FinTech), and Entertainment Computing with focus on Games, VR/AR/Metaverse technologies. Dr. Bhojan leads the Metaverse Foundry research group which focuses on content generation for games & XR simulations across multiple domains including entertainment, healthcare, and architecture, while also experimenting with innovative teaching methods for entertainment media technologies. Dr. Bhojan's recent research has pioneered hybrid rendering techniques that strategically combine ray tracing and rasterization to create more realistic video game graphics without compromising performance. His work addresses critical challenges in real-time rendering, particularly in depth of field and motion blur effects, with the goal of making Hollywood-quality graphics accessible on a wider range of hardware. Earlier work focused on energy efficiency in mobile gaming, including power management techniques and latency optimization for cloud gaming. Among his notable achievements: 2012 Nominated for Best PhD Thesis Award (Wang Gungwu Medal & Prize), NUS 2011 Dean's Graduate Research Achievement Award (PhD), SoC, NUS 2006 Best R&D Project award from TOTE Board, Singapore 1995 Gold medal for first Rank (out of 4000) in Computing, Bharathiar University Dr. Bhojan has served as Organizing Chair and Program Chair for multiple international conferences and has delivered keynote talks at IEEE/ACM International Conferences. He teaches courses including Computer Networks Practice, Game Development, Interaction Design for Virtual and Augmented Reality, and Game Development Project. He leads the Metaverse Foundry research group and the Virtual and Augmented Reality Labs, where undergraduate and graduate students have won multiple research and innovation awards. His research bridges entertainment, education, and emerging technologies with practical applications in making immersive experiences more accessible across different hardware capabilities while maintaining energy efficiency.

• Distributed Computing
• Wireless Networks
• IoT
• +13 more

Hossam Hassanein is a Professor and Director of the School of Computing at Queen's University. He received his B.Sc. in Electrical Engineering from Kuwait University in 1984, M.Sc. in Computer Engineering from the University of Toronto in 1986, and Ph.D. in Computing Science from the University of Alberta in 1990. He joined Queen's University School of Computing in 1999 and has established himself as a leading researcher in telecommunications and networking. Dr. Hassanein's research interests span wireless sensor networks, mobile ad hoc networks, edge computing, Internet of Things (IoT), radio resource management, and data-centric networks. His seminal contributions include pioneering work on WSN planning, load-balanced routing protocols, and energy-efficient network designs. He has championed research in IoT, developing frameworks for smart spaces that use contextual information to enhance IoT applications in healthcare, transportation, and infrastructure. His recent publications (2023-2025) demonstrate a strong focus on cutting-edge areas including extreme edge computing, vehicular networks, and AI/ML integration in networking. Research trends show increasing emphasis on practical applications in telesurgery, digital twins, and industrial IoT, addressing challenges in resource allocation, task offloading, and real-time processing in constrained environments. Dr. Hassanein has received numerous recognitions for his work: Fellow of the IEEE Queen's University School of Graduate Studies Award for Excellence in Graduate Student Supervision (2015) Multiple best paper awards from top international conferences As founder and director of the Telecommunications Research Lab (TRL), Dr. Hassanein has supervised over 75 students who have made substantial contributions in academia and industry. The TRL is one of Queen's largest research groups with extensive international collaborations. Dr. Hassanein has successfully attracted significant research funding from government and industry sources in the competitive telecommunications field. The Telecommunications Research Lab has developed innovative platforms including SPROUTS, a rugged sensor platform used in mining, steel manufacturing, and smart-grid monitoring. TRL's work has had significant impact in WSN planning, data dissemination, and resource reuse in wireless networks, with contributions featured in IEEE Wireless Communications Magazine.

• Wireless Sensor Networks
• Mobile Ad hoc Networks
• Edge Computing
• +5 more

Chita R. Das is a Professor at Pennsylvania State University, known for extensive contributions in computer architecture, machine learning, and high-performance computing. Their research focuses on optimizing hardware-software co-design for edge computing, cloud infrastructure, and energy-efficient systems. Key areas include FPGA acceleration, GPU optimization, and serverless computing frameworks. Das collaborates frequently with institutions like AMD and Intel, addressing challenges in parallel computing and distributed systems. Their work bridges theoretical advancements with practical applications in recommendation systems, bioinformatics, and real-time video processing. Research interests span across hardware acceleration techniques, cloud resource management, and sustainable computing. Notable projects include adaptive training frameworks for intermittent power environments and neural-augmented game streaming for mobile platforms. Das's publications often address performance bottlenecks in modern architectures and propose novel solutions for latency and energy efficiency. Recent articles highlight innovations in serverless computing cost optimization, low-bandwidth VR streaming, and FPGA-based bioinformatics tools. Their contributions are characterized by interdisciplinary approaches combining computer architecture with machine learning and embedded systems.

• Computer Architecture
• Machine Learning
• Cloud Computing
• +4 more

Benyuan Liu is a Professor at the Miner School of Computer and Information Sciences within the Kennedy College of Sciences at the University of Massachusetts Lowell . He serves as Director and Graduate Coordinator for Ph.D. programs, with expertise in Data and Computer Communication Networks, Mobile and Wireless Networks, and Internet Technologies & Applications. Education: B.S., University of Science and Technology of China M.S., Yale University Ph.D., University of Massachusetts Amherst His research focuses on Artificial Intelligence in Medical Imaging , Deep Learning for Endoscopy , and Edge Computing Systems . Recent work includes automated lesion detection, 3D reconstruction from sensor data, and predictive models for financial and reproductive health domains. The 15 most recent publications highlight applications of deep learning in medical diagnostics (thyroid nodules, gastric lesions, dental caries), computer vision (attention mechanisms, transformers), and financial technology (market psychology analysis). Technical themes include mmwave radar processing, diffusion models for synthetic data, and multi-scale feature extraction. Benyuan Liu leads the Computer Networking Lab and CHORDS initiative at UMass Center for Digital Health. His work bridges network optimization with healthcare AI , emphasizing real-time systems and portable diagnostics.

• Data and Computer Communication Networks
• Mobile and Wireless Networks
• Internet Technologies & Applications
• +3 more

Professor Peter F. Driessen is a faculty member in the Department of Electrical and Computer Engineering at the University of Victoria, with a cross-appointment in the School of Music. He holds a BSc and PhD from the University of Victoria and is a Professional Engineer (PEng). His research focuses on communication systems, signal processing, control, and interdisciplinary projects in computer music and wireless technologies. Key areas include audio/video signal processing, radio propagation, sound recording, and multimedia systems. He leads the University of Victoria Propagation Laboratory, which explores radio wave propagation and Amateur radio integration with engineering education. His work spans theoretical research and applied projects like ECOSat satellite systems, software-defined radio (SDR), and innovative musical instruments such as the Radio Drum. He supervises undergraduate and graduate projects in these domains through ELEC 499 courses. Notable contributions include the APEGBC Editorial Board Award for Best Paper (2002) and patents in wireless networking and signal processing. His teaching includes courses in signal analysis and electromagnetics, and he collaborates on interdisciplinary programs like the Music/Computer Science degree. Education: BSc in Electrical Engineering, University of Victoria PhD in Electrical Engineering, University of Victoria Research Interests: Audio and video signal processing for music and media Software-defined radio and Amateur radio technologies Satellite communication and ground station development Gesture-based interfaces and musical instrument design Error mitigation in streaming audio/video Optical and microwave-photonic systems Labs & Collaborations: Propagation Laboratory (radio wave research) UVic Experimental Radio Group (Amateur radio club) UVic Satellite Design Team (ECOSat projects) UVic Centre for Aerospace Research Grants & Awards: APEGBC Editorial Board Award (2002) Multiple US patents in wireless systems and signal processing

• Communication
• Signal Processing
• Control Systems
• +6 more

Pedro Ferreira is a Full Professor at Carnegie Mellon University (CMU), holding a joint appointment in the School of Information Systems & Management at the Heinz College and the Department of Engineering and Public Policy within the College of Engineering. His research focuses on how technology influences education, media consumption, and peer effects, leveraging large datasets from randomized experiments. Ferreira has been recognized with the 2018 INFORMS Early Career Award and Top 17th worldwide research scholar ranking (2020–2022). He co-founded CMU's Initiative for Teaching and Education Analytics (iTEA) and advises numerous students in areas like AI/ML in education and media analytics. Education: BSc in Computer Science (IST), MSc in Electrical Engineering and Computer Science & Technology Policy (MIT), PhD in Telecommunications Policy (CMU). He has taught at MIT, IST, and invited roles at Católica-Lisbon and the University of Cambridge. Research Interests: Impact of digital technologies on education outcomes (e.g., smartphones in classrooms, video analytics), peer influence in media industries (e.g., binge-watching, recommender systems), and empirical methods using randomized experiments. Current projects include AI-driven education improvement and policy implications of AI/ML technologies. Notable Achievements: Over 15 peer-reviewed articles in top journals like Management Science and MIS Quarterly. Key grants include Gates Foundation funding for video-based education research and Koch Foundation support for online certification studies. He serves as Associate Editor for Management Science and previously for MIS Quarterly. Advising & Grants: Advised 23 PhD students, many now in academia and industry. Current students research facial recognition in education and hybrid recommender systems. Ferreira has led grants totaling millions, including studies on GDPR's impact on piracy tracking and worldwide VoD availability. Professional Service: Organized conferences like the Symposium on Statistical Challenges in eCommerce Research (SCECR). Served on NSF review panels and CMU’s Portugal PhD program steering committee.曾参与葡萄牙知识社会局(UMIC)的国家级政策制定，推动宽带学校项目。

• Information Systems
• Education Technology
• Media Analytics
• +5 more

Dr. Theophilus A. Benson is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University, with additional responsibilities at Carnegie Mellon University-Africa. His research group focuses on improving network performance and availability through models, algorithms, and frameworks that manage network state semantics. Key application areas include addressing the digital divide, optimizing microservices/cloud systems, software-defined networks, and CDN designs. Education: Ph.D., University of Wisconsin, Madison (2012) M.S., University of Wisconsin, Madison (2008) B.S., Tufts University (2004) Research Focus: Professor Benson's work spans three core domains: Democratizing Web Performance (measurements and optimizations for developing regions), Systems Abstractions for Programmable Infrastructures (eBPF/P4 frameworks), and Self-Managing Networks (ML-driven configurations). His African Internet Observatory initiative analyzes Africa's internet ecosystem to address digital inequity through assessment probes and statistical methods. Publication Trends: Recent works (2021-2024) demonstrate a strong focus on programmable networks (eBPF/P4 management), web performance in developing regions, and data-driven cloud/CDN optimizations. Earlier foundational work established expertise in SDN fault tolerance, network updates, and video streaming characterization. Awards & Honors: SIGCOMM Test of Time Award NSF CAREER Award NEC Faculty Award Google Faculty Award Facebook Faculty Award (2x) DARPA ISAT Study Group Member Grants & Advising: Secured funding from NSF (CAREER, NeTS), Google, Facebook, and Yahoo. Current advisees include 4 PhD/MS students working on programmable networks and web performance. Actively recruiting post-docs and students for African connectivity and eBPF projects. Leadership: Co-chairs NSDI'25 and ApNet'24 conferences. Leads the NetLab research group developing deployable systems adopted by web-scale companies and open-source communities.

• Computer Networks
• Programmable Infrastructures
• Cloud Computing
• +5 more

Dr. Andrew Hines is a Researcher at the School of Computer Science, University College Dublin, specializing in machine learning applications for signal processing in speech, audio, and video domains. His work focuses on Quality of Experience (QoE) modeling, speech quality assessment, and immersive media analysis. He has held leadership roles in European COST Actions like Qualinet and CryptoAction, and previously worked in industry as a Director of Engineering. University: University College Dublin Role: Director of Research, Innovation and Impact Key Collaborations: IEEE (Senior Member), Audio Engineering Society (Ireland) Research interests center on machine learning for QoE optimization, audio-visual integration, and healthcare applications like heart sound classification and stroke rehabilitation. His recent publications explore self-supervised learning, neural speech codecs, and contextual factors in speech/audio quality assessment. Scientific contributions include awards like IEEE Senior Membership, and his work spans both academic research and industrial engineering in finance and aviation sectors. He leads the QxLab research team at UCD and develops open-source platforms such as WARP-Q and AQP for quality metrics.

• Machine Learning
• Signal Processing
• Speech Processing
• +3 more

Dr. Kanchana Thilakarathna is a Senior Lecturer in Distributed Computing at the University of Sydney's School of Computer Science, and a member of the Centre for Distributed and High Performance Computing. They hold a PhD from the University of New South Wales (UNSW) and a B.Sc. Eng (Hons) from the University of Moratuwa, Sri Lanka. Prior to academia, they worked as a Research Scientist at CSIRO/Data61 and had industry experience as a Mobile Radio Network Engineer. Research Interests : Dr. Thilakarathna focuses on cybersecurity, privacy in mobile and IoT systems, mixed reality privacy, and distributed computing platforms. Their work emphasizes user-centric solutions like the Yalut social media app, which enables decentralized data sharing. Key themes include privacy-preserving techniques, edge computing, and secure federated learning frameworks. Recent Work : Recent articles (2023–2025) explore machine unlearning for large language models, federated learning security, and IoT network slicing using P4 programmability. Their work on synthetic video traffic generation (VideoTrain++) and drone detection (DronePrint) demonstrates cross-disciplinary innovation. Awards : Malcolm Chaikin Prize (2015), Meta Research Awards (2020/2022), and Heidelberg Laureate Fellowship (2019). Grants : ARC Research Hub for Future Digital Manufacturing (2024), NSW Defence Innovation Network Projects (2024/2021), and Facebook Research Awards (2022/2020). Students : Advising 4 current PhD students on topics like wireless trust establishment and machine unlearning. Labs/Teams : Part of the Centre for Distributed and High Performance Computing and Sydney Nano Institute.

• Cybersecurity
• Privacy-Preserving Technologies
• Internet of Things (IoT)
• +4 more

Umakishore Ramachandran is a Professor in the School of Computer Science within the College of Computing at Georgia Institute of Technology. His research spans edge computing, distributed systems, and real-time video analytics, with significant contributions to fog computing infrastructure, mobile systems, and sensor networks. Over a prolific 38-year career, he has authored 142 publications with major contributions in 2022-2025. His research interests focus on bridging the gap between cloud and edge computing, with pioneering work in video analytics systems like EVA and MicroEdge. He investigates resource optimization for latency-sensitive applications, developing novel approaches for load shedding, data management, and container runtime efficiency at the network edge. His work addresses fundamental challenges in distributed camera networks, autonomous vehicle systems, and real-time stream processing. Ramachandran's recent publications reveal a strong emphasis on practical edge computing solutions, with 75% of his 2021-2025 work focusing on video analytics and infrastructure optimization. His research shows increasing collaboration with industry partners while maintaining academic rigor, with publications appearing in top venues like SIGMOD, Middleware, and DEBS. The work consistently addresses real-world constraints of resource-constrained edge environments. Ramachandran has mentored numerous researchers who have become principal investigators on edge computing projects, with notable collaborators including Harshit Gupta, Enrique Saurez, and Zhuangdi Xu appearing as first authors on multiple papers. His work has received significant grant support for projects related to mobile fog computing and distributed video analytics. He leads research in the Edge Computing Laboratory at Georgia Tech, focusing on the development of practical frameworks for real-world deployment of edge infrastructure. Current projects include eCAV for connected autonomous vehicles and MicroEdge for multi-tenant camera processing systems.

• Edge Computing
• Fog Computing
• Distributed Systems
• +5 more

Jiro Katto is a Professor at Waseda University's School of Fundamental Science and Engineering, where he has been conducting research and teaching since 1999. He received his Ph.D. from the University of Tokyo and has established himself as a leading researcher in multimedia signal processing and computer networks. His academic journey includes positions as Associate Professor (1999-2004), Professor (2004-present), and Director at NEDO (2004-2008), along with research experience at NEC C&C Laboratories (1992-1999) and a Visiting Scholar position at Princeton University (1996-1997). Professor Katto's research interests focus on Multimedia Signal Processing and Computer Networks, with particular expertise in video compression, 5G network performance, and learned image compression techniques. His work bridges theoretical advancements with practical implementations, as evidenced by his extensive publications in top-tier conferences and journals. His research group has made significant contributions to point cloud compression, latency compensation in remote systems, and hardware-accelerated video encoding for UHD streaming. His publication record is impressive, with 276 papers cited 3,323 times in Scopus and 6,169 times in Google Scholar, reflecting his substantial impact in the field. His recent work shows a strong trend toward applying deep learning techniques to traditional signal processing problems, particularly in the areas of image and video compression, where his team has developed novel approaches to improve compression efficiency while reducing computational complexity. Electric Telecommunications Promotion Foundation Telecommunications System Technology Award (2023) Takayanagi Kenjiro Foundation Takayanagi Kenjiro Achievement Award (2020) Institute of Image Information and Television Engineers Fellow (2020) Institute of Electronics, Information and Communication Engineers Fellow (2015) IEICE Communications Society Activity Contribution Award (2006) IEICE Academic Encouragement Award (1995) SPIE VCIP 1991, Best Student Paper Award (1991) Professor Katto has served on numerous prestigious committees including IEEE ComSoC Tech News Editorial Board, IEEE Technical Program Committees for major conferences (Globecom, ICC, ICIP), and editorial boards for several academic journals. His leadership in the academic community extends to chairing conferences like IWAIT 2011 and serving as Editor-in-Chief for journals in his field. His research has practical applications in commercial 5G networks, video streaming services, and remote monitoring systems, demonstrating the real-world impact of his work.

• Multimedia Signal Processing
• Computer Networks
• Communication Engineering
• +3 more

Dr. Abdelhak Bentaleb is an Assistant Professor in the Department of Computer Science and Software Engineering at Concordia University and founder/director of the IN2GM Lab. His research focuses on optimizing networked multimedia systems using machine learning, with emphasis on video streaming, edge computing, and 5G/6G networks. He holds a PhD from the National University of Singapore (awarded SIGMM and DASH-IF Best Thesis prizes) and completed a postdoctoral fellowship there. Education: PhD in Computer Science, National University of Singapore (2019) Postdoctoral Research Fellowship, National University of Singapore (2019-2022) Research Interests: AI-driven video streaming optimization, low-latency media delivery, network protocols, immersive media technologies, and IoT systems. Current projects explore end-to-end AI-enabled systems for QoE optimization in video delivery using reinforcement learning and deep learning techniques. Awards: SIGMM Award for Outstanding PhD Thesis DASH Industry Forum Best PhD Dissertation Award Multiple DASH-IF Excellence Awards His work includes over 50 publications in top venues (e.g., ACM MMSys, IEEE INFOCOM, USNIX NSDI) and 3 patents. The IN2GM Lab focuses on applied AI/ML solutions for networked systems challenges.

• Computer Networks
• Multimedia Systems
• Video Streaming Optimization
• +5 more

Steve Whittaker is Professor of Human-Computer Interaction at the University of California at Santa Cruz. He conducts interdisciplinary research at the intersection of social science and computer science, focusing on how technology affects human memory, communication, and personal information management. His current research explores human-centric AI systems, mental health technologies, and digital identity. His research interests center on designing interactive systems that support human needs in digital environments. He investigates how people manage digital information, remember personal experiences through lifelogging, and interact with conversational agents and social robots. His work emphasizes computational well-being, affective computing, and the social implications of technology use. He has made foundational contributions to the fields of personal information management (PIM), computer-mediated communication (CMC), and human-robot interaction. The recent publications reflect a strong trend toward mental health technology, human-AI interaction, and digital well-being. His work spans from theoretical models of emotion and memory to practical systems for mental health apps, chatbots, and immersive visualization. He frequently publishes in top-tier venues such as CHI, CSCW, and IUI, often in collaboration with researchers across disciplines. Lifetime Research Achievement Award from SIGCHI Fellow of the Association for Computational Machinery (ACM) Member of the CHI Academy Lasting Impact Award from ACM CSCW Best Paper Award at CSCW10 Best Paper Award at CHI07 Honourable Mention at ACM CHI 2020 Multiple best paper nominations at CHI, CSCW, and IUI MIT Siegel Prize Steve Whittaker has supervised numerous PhD and Master’s students, though specific names are not listed in the provided text. His research has been funded by major grants from NSF, NIH, and industry partners, enabling long-term studies on digital behavior and system development. He is Editor of the journal Human Computer Interaction and has authored over 200 peer-reviewed publications. His most recent book, The Science of Managing Our Digital Stuff (MIT Press), co-authored with Ofer Bergman, synthesizes decades of research on personal information management. He leads a vibrant research lab at UC Santa Cruz that focuses on human-centered computing, where students and collaborators work on projects involving AI, mental health, digital memory, and social interaction. The lab has produced influential work on lifelogging, email management, telepresence robots, and algorithmic transparency. The team employs mixed methods, combining qualitative studies with system design and evaluation.

• Human-Computer Interaction
• Computer Mediated Communication
• Personal Information Management
• +11 more