Christian Timmerer is a Professor at the Institute of Information Technology, Alpen-Adria-Universität Klagenfurt. His research focuses on adaptive video streaming , energy efficiency , MPEG standardization , and quality of experience (QoE) , with significant contributions to HTTP Adaptive Streaming (HAS), multi-codec optimization, and immersive media systems. Email: christian.timmerer@aau.at Office Hours: Monday 3:00-4:00 PM (by appointment) Projects: CD-Labor ATHENA, GAIA, SPIRIT His research integrates machine learning and generative AI to enhance video encoding, super-resolution, and voice dubbing, while prioritizing sustainability through energy-aware algorithms and open-source tools like GREEM and VEED. Current work emphasizes latency reduction and dynamic bitrate adaptation in live streaming environments. Recent publications address VVC optimization , multi-resolution encoding , and perceptual quality modeling , reflecting interdisciplinary efforts in networking , computer vision , and human-computer interaction . Awards include leading funded projects on adaptive streaming and green video systems.
Kshirasagar Naik is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Ontario. He is actively involved in graduate research supervision and has been a member of IEEE since 1994. His academic career spans decades, with a focus on wireless communication, energy efficiency, and cybersecurity. 1992, Doctorate in Computer Engineering from Concordia University, Ontario 1988, Master of Mathematics in Computer Science from University of Waterloo, Ontario 1983, MTech in Computer Engineering from Indian Institute of Technology, Kharagpur, India 1981, BScEng in Electronics and Telecommunication from Sambalpur University, India His research interests include Mobile and Ad Hoc Networks , Cybersecurity , Internet of Things (IoT) , and Intelligent Transportation Systems . He has published extensively on energy optimization in wireless devices, delay-tolerant networks, and security protocols for vehicular systems. Recent publications highlight the integration of Machine Learning and IoT in environmental monitoring, particularly forest fire detection and prediction. Other works focus on cybersecurity , vehicular networks , and energy optimization in data centers and handheld devices. Professor Naik is currently accepting graduate students for research in mobile systems, network protocols, and green computing at the University of Waterloo.
Prof. Akash Kumar is a Professor at the Chair of Embedded Systems at Ruhr University Bochum, Germany. He previously held professorships at TU Dresden (2015–2024) and the National University of Singapore (NUS; 2011–2015). His research focuses on design automation of embedded systems, reliability optimization, and approximate computing, with a strong emphasis on FPGA and emerging technologies. He leads projects such as Lean-MICS (DFG-funded) and SecuREFET-II, addressing cross-layer reliability and secure circuits. Education: PhD in Multimedia Multiprocessor Systems from Eindhoven University of Technology (TUe) and NUS (2005–2009), Master of Technological Design (Embedded Systems) from NUS (2003–2004), and B.Eng (Computer Engineering) from NUS (1999–2002, First Class Honours). Research interests span embedded systems, reconfigurable architectures, and hardware-software co-design. His work includes optimizing energy efficiency, fault tolerance, and cross-layer approximation techniques. Recent publications highlight advancements in FPGA-based accelerators, machine learning optimizations, and mixed-criticality systems. Active in grants and leadership, Kumar is Principal Investigator on multiple DFG and industry-funded projects, emphasizing collaborative research in distributed computing and approximate architectures. His contributions bridge theory and practice, with applications in edge AI, IoT, and cybersecurity.
Ashish Khisti is an Associate Professor at the University of Toronto's Department of Electrical and Computer Engineering (ECE), where he directs the Signals, Multimedia and Algorithms Laboratory (SMA Lab). He holds the Canada Research Chair (Tier II) and maintains affiliations with the Vector Institute for Artificial Intelligence. His research bridges communication systems, information-theoretic security, and machine learning, with a focus on real-time streaming and privacy-preserving algorithms. Research Trends: Recent publications emphasize streaming codes for latency-sensitive networks , machine learning-driven compression , and privacy mechanisms in federated learning . Scientific Recognition: Canada Research Chair (Tier II), 2012 and 2017 renewal Cisco Research Center Award, 2017 Ontario Early Researcher Award, 2012 Best Paper at NeurIPS 2021 Deep Generative Models Workshop Academic Contributions: Supervised PhD students Ahmed Badr, Farrokh Etezadi, and Si-Hyeon Lee. Served as Associate Editor for IEEE Transactions on Communications (2012-2015) and IEEE Transactions on Information Theory (2015-2018). Labs & Collaborations: Leads the Signals, Multimedia and Algorithms Laboratory, collaborating with institutions like KAUST, Texas A&M University (Qatar), and the Vector Institute. Organized workshops at BIRS and IEEE conferences.
Dr. Wei Song is a Professor and the Coordinator of Software Engineering at the Faculty of Computer Science, University of New Brunswick (UNB) in Fredericton, New Brunswick, Canada. She has been with UNB since 2009, after completing her postdoctoral studies at UC Berkeley, and has established herself as a leading researcher in mobile networking and wireless communications. Her office is located in room ID419 and she can be reached at wsong@unb.ca. Education Ph.D. in Electrical and Computer Engineering, University of Waterloo (2003-2007) Postdoctoral Fellow, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (2008-2009) Research Focus Dr. Song's research spans multiple cutting-edge areas in mobile and wireless networking, with a strong emphasis on integrating artificial intelligence and machine learning techniques. Her work addresses fundamental problems in mobile social networks, Internet of Things, vehicular networks, and mobile cloud computing. She explores how cooperative intelligence and distributed AI can enhance network performance while addressing practical constraints such as energy efficiency and user incentives. Her recent work particularly focuses on intelligent edge computing, mobile crowdsensing with deep reinforcement learning, and social-aware data dissemination through device-to-device communications. She investigates how to turn decentralized mobile "crowds" into coherent working groups and how social connections can be leveraged to improve data dissemination efficiency. Publication Trends Dr. Song's recent publications (2016-2023) demonstrate a clear evolution from traditional wireless networking to AI-driven approaches. While her earlier work focused on fundamental problems in device-to-device communications and resource allocation, her recent publications increasingly incorporate deep reinforcement learning, graph neural networks, and other AI techniques to solve complex optimization problems in mobile crowdsensing and edge computing. This shift reflects broader trends in the field toward intelligent, adaptive networking solutions. Scientific Recognition Best Paper Award from IEEE ICC (2018) UNB Merit Award (2014) Best Student Paper Award from IEEE CCNC (2013) Top 10% Award from IEEE MMSP (2009) NSERC postdoctoral fellowship (2008) Best Paper Award from IEEE WCNC (2007) Professional Service and Mentoring Dr. Song serves as Senior Member of IEEE and has held significant leadership roles, including Chair of the Joint Computer and Communications Chapter of IEEE New Brunswick Section (2014-2020). She has chaired symposia at major conferences including IEEE VTC Fall 2023, 2017, and 2016. As a supervisor, she mentors graduate students in areas including intelligent edge computing and deep learning for networking, and is currently recruiting students for Winter 2024 and Fall 2025.
Kuljeet Kaur is a Professor in the Department of Electrical Engineering at École de technologie supérieure (ÉTS) in Montreal, Canada. Her research is conducted through the LACIME (Communications and Microelectronic Integration Laboratory), a renowned research unit focusing on communications and microelectronic integration. She maintains an active research program with numerous publications and student supervision activities. Professor Kaur's research spans multiple interconnected domains focused on next-generation computing and communication systems. Her primary research axes include Sensors, Networks and Connectivity; Intelligent and Autonomous Systems; and Software Systems, Multimedia and Cybersecurity. Within these broad areas, she specializes in Cloud Computing, Edge/Fog Computing, Internet of Things (IoT), Cybersecurity, Privacy, Federated Learning, and Energy Management. Her work bridges theoretical foundations with practical implementations in intelligent transportation systems, healthcare applications, and smart grid technologies. Analysis of Professor Kaur's recent publications reveals a strong focus on security and privacy challenges in emerging computing paradigms. A significant portion of her work addresses federated learning approaches that maintain data privacy while enabling collaborative AI model training. Her research also demonstrates expertise in edge computing architectures, particularly for IoT applications, with emphasis on energy efficiency and security. The publications show consistent interdisciplinary collaboration across computer science, electrical engineering, and transportation domains. Professor Kaur actively supervises multiple graduate students at various levels. Her supervision portfolio includes doctoral candidates working on topics like decentralized AI networks and secure federated learning, as well as master's students focusing on edge AI for IoT applications, sensor drift compensation, and zero trust architecture for IoT. She also guides project students working on practical implementations of AI for smart grid optimization and secure IoT protocols. Her research is conducted within the LACIME laboratory, which brings together researchers working on everything from micro- and nanofabrication processes to communication protocols and signal processing. The lab provides a transdisciplinary environment where Professor Kaur's work on cyber-physical systems and secure communications benefits from complementary expertise in integrated circuit design and microsystems.
Professor Mohammed Salamah is a distinguished faculty member in the Computer Engineering Department at Eastern Mediterranean University's Faculty of Engineering. He maintains an office in room 114 and can be contacted at +90 392 630 1149/1334 or via email at muhammed.salamah@emu.edu.tr. His academic website provides additional resources for students and colleagues. Dr. Salamah earned his BS, MS, and PhD degrees in Electrical and Electronics Engineering from Middle East Technical University in 1988, 1990, and 1996 respectively, establishing a strong foundation for his career in network communications and wireless systems. His research interests span multiple critical areas in modern networking, with particular expertise in Wireless Sensor Networks, Internet of Things (IoT) security, Mobile Communications, and Energy Efficiency in network protocols. Professor Salamah has made significant contributions to the understanding of network security mechanisms, trust management systems, and optimization of wireless communication protocols. An analysis of his recent scholarly output reveals a strong focus on security challenges in IoT communication systems, controller placement optimization in software-defined wireless sensor networks, and trust-based malicious node detection schemes. His work demonstrates consistent attention to practical network performance issues while addressing emerging challenges in next-generation communication technologies. Throughout his academic career, Professor Salamah has demonstrated exceptional commitment to student mentorship, supervising numerous graduate students through their research journey. His administrative contributions include service as an associate editor, reviewer, and session chair for academic conferences. His laboratory work focuses on practical implementations of wireless communication protocols, with emphasis on energy efficiency, security mechanisms, and performance optimization for various network architectures including cellular networks, cognitive radio systems, and wireless sensor networks.
Li Wei is a distinguished academic affiliated with Tsinghua University, with a focus on interdisciplinary research spanning artificial intelligence, machine learning, and computer vision. His work often intersects with medical informatics, remote sensing, and signal processing, demonstrating a commitment to advancing technological solutions in healthcare, environmental monitoring, and engineering systems. Research interests include deep learning applications in clinical diagnostics, satellite data analysis for climate modeling, and optimization of energy storage systems. He has contributed to innovative solutions in areas such as UAV-enabled edge computing, privacy-preserving blockchain protocols, and thermal-based surveillance systems. His collaborative projects often involve multidisciplinary teams across institutions. Publications reflect a strong emphasis on practical applications, such as mobile health tools for tumor recognition, transformer-based super-resolution techniques for oceanography, and AI-driven risk classification models for respiratory diseases. While no specific awards or grants are listed, his prolific output across top-tier journals indicates sustained research impact. Professional activities include contributions to conferences like RecSys, MICCAI, and AAAI, and editorial roles are implied through his extensive publication record. Collaborations with industry partners (e.g., in energy systems and medical imaging) suggest engagement with real-world problem-solving.
Dr. Mohammed Elamassie is an Assistant Professor at Özyeğin University's Graduate School of Science and Engineering, Department of Electrical and Electronics Engineering. He co-directs the Centre of Excellence in Optical Wireless Communication Technologies (OKATEM) and holds senior memberships in IEEE and Optica. PhD in Electrical and Electronics Engineering (Özyeğin University, 2020) MSc in Electrical and Electronics Engineering (Islamic University of Gaza, 2011) BSc in Electrical and Electronics Engineering (Islamic University of Gaza, 2006) Dr. Elamassie's research focuses on optical wireless communication systems, with specific expertise in underwater visible light communication (UVLC), vehicular visible light communication (V2V), airborne free space optical (FSO) networks, and turbulence mitigation techniques. His work addresses atmospheric channel modeling, diversity techniques, and MIMO communication challenges across multiple mediums. Analysis of his 15 most recent publications reveals critical trends in UVLC turbulence modeling, FSO UAV optimization, RIS-aided systems, and vehicular communication reliability. These works demonstrate his leadership in developing practical solutions for channel degradation and mobility-induced challenges. Best Paper Award, IEEE Black Sea Conference (2019) IEEE Turkey PhD Thesis Award (2020) Senior Member, IEEE Senior Member, Optica Optica Traveling Lecturer/Speaker Dr. Elamassie serves as Review Editor for Frontiers in Communications and Networks, covering 'Non-Conventional Communications' and 'Wireless Communications' sections. He contributes to OKATEM's research on optical wireless technologies, focusing on practical implementations across underwater, vehicular, and airborne domains.
Dr. Faisal Mohd-Yasin is a Senior Lecturer in the School of Engineering and Built Environment at Griffith University, specializing in Electrical and Electronic Engineering. He has been with Griffith University since 2010, initially as a Lecturer (2010-2016) and promoted to Senior Lecturer in 2017. He is also a member of the Queensland Quantum and Advanced Technologies Research Institute (QUATRI) since 2025. His research spans microelectronics, MEMS technology, compound semiconductors, and electronic sensors/instrumentation, with particular expertise in silicon carbide-based devices for harsh environments. Dr. Mohd-Yasin holds dual PhD qualifications: a Doctor of Philosophy (Engineering) from Multimedia University, Cyberjaya, Malaysia (2014) and a PhD in Engineering from Ibaraki University, Hitachi, Japan (2009). His educational background provides a strong foundation for his interdisciplinary research that bridges semiconductor physics, sensor technology, and electronic circuit design. His research interests focus on Microelectromechanical systems (MEMS), compound semiconductors (particularly $$ ext{SiC}$$), electronic sensors, and electronic instrumentation. He has made significant contributions to the development of silicon carbide MEMS devices for harsh environments, piezoelectric energy harvesters, and noise analysis in microelectronic systems. His work has important applications in sustainable cities (SDG 11), health and well-being (SDG 3), and clean energy (SDG 7). Analysis of his recent publications reveals a strong trend toward MEMS sensor technology, particularly silicon carbide-based devices for harsh environments, and noise analysis in piezoelectric sensors. His research also demonstrates a growing interest in engineering education, with several publications on practical electronics teaching methods. The publications span electrical engineering, sensor technology, energy harvesting, and engineering education, reflecting his interdisciplinary approach to research and teaching. Dr. Mohd-Yasin has successfully supervised multiple doctoral and masters students through completion, including Utkarsh Jadli (PhD on Parasitic Capacitances of Power Transistors), Siti Aisyah Zawawi (PhD on MEMS capacitive microphone), Mei Kum Khaw (PhD on magnetically actuated droplets), Abid Iqbal (PhD on AlN thin films), Noraini Marsi (PhD on MEMS pressure sensors), and Kai Meng Mui (Masters on Power management IC). He has also secured numerous research grants totaling over $1.5 million from various sources including Griffith University, Innovative Manufacturing CRC, IRU, and Malaysian research councils. He is actively involved in professional service as a peer reviewer for the IEEE Sensors Conference series (2015-2025), Micro and Nano Engineering Conference series (2009-2018), and the International Conference on Solid-State Sensors, Actuators and Microsystems (2018-2019). He is also a member of IEEE (Institute of Electrical and Electronics Engineers) since 1997. Dr. Mohd-Yasin's research is primarily conducted through the Queensland Quantum and Advanced Technologies Research Institute (QUATRI), where he collaborates with researchers working on advanced semiconductor technologies and quantum applications. His laboratory work focuses on MEMS fabrication, sensor characterization, and circuit design for harsh environment applications.
Prof. Hermann Hellwagner is a Full Professor at the Department of Information Technology, University of Klagenfurt. He has held roles such as Vice President (Natural and Technical Sciences) at the Austrian Science Fund (FWF) and Vice Dean of the Faculty of Technical Sciences. His research focuses on multimedia communication, network engineering, and future internet architectures. Notable projects include work on adaptive streaming, edge computing, and drone networks. He holds a Ph.D. in Systolic Architectures from the University of Linz (1988). Research interests span distributed multimedia systems, information-centric networking (ICN), and optimizing video streaming quality-of-experience (QoE). Recent work emphasizes edge computing solutions for low-latency streaming and dynamic codec adaptation. His contributions include frameworks like ALPHAS and MEDUSA for bitrate optimization, and studies on point cloud streaming in augmented reality. Publications (2021–2025) highlight advancements in edge-assisted streaming, hybrid P2P-CDN architectures, and transcoding techniques. His work often bridges theoretical models with real-world implementations, addressing challenges in latency, cost, and device adaptability. Current projects involve 6DoF video streaming and multi-robot system optimization. Labs/Teams: Part of the Institute of Information Technology (ITEC), Klagenfurt. Collaborates on EU-funded projects and industry partnerships in 5G edge computing and drone networks. Active in standards groups for HTTP adaptive streaming protocols.
Dr. Yao Liu is an Assistant Professor in the Department of Electrical and Computer Engineering at Rutgers University, New Brunswick, since Fall 2021. Previously, she held an Associate Professor (tenured) position at Binghamton University, SUNY. Her research focuses on immersive streaming technologies, including 360-degree and volumetric video delivery, edge/cloud computing, and distributed systems. She has led projects such as SGSS for 6-DoF navigation in 3DGS scenes and EVASR for edge-based video enhancement. Her work has been recognized with awards like the NSF CAREER Award and Best Paper Awards at MMSys (2017, 2020). Research interests include immersive video streaming, virtual/augmented reality, mobile systems, and network optimization. Notable contributions include the 👁️NavGS dataset for VR navigation and the Dynamic 6-DoF Volumetric Video toolkit. She advises PhD students like Mufeng Zhu and Na Li, with past advisees receiving accolades such as the Binghamton Distinguished Dissertation Award. Publications span conferences like ACM Multimedia Systems (MMSys), IEEE ICME, and AAAI. Her work emphasizes practical solutions for bandwidth efficiency, real-time streaming, and energy optimization in immersive media. Grants include NSF CAREER funding for immersive streaming research. Labs and collaborations involve open-source projects hosted on GitHub (e.g., symmru repositories), emphasizing reproducibility and accessibility.
Per Gunnar Kjeldsberg is a Professor at the Department of Electronic Systems, Norwegian University of Science and Technology (NTNU), and currently serves as acting head of the institute. His research focuses on embedded heterogeneous multi-processor systems , particularly in multimedia and digital signal processing applications . He has led and participated in numerous national and international projects, including EU Horizon 2020 initiatives like READEX (as work package leader) and Tulipp (as principal researcher), and supervises the MSCA-IF project Palmera . Kjeldsberg is a Senior Member of IEEE and part of the European Network of Excellence HiPEAC . Education : Sivilingeniør (MSc) in Electrical Engineering (1992), PhD (2001) from Norwegian Institute of Technology (NTH)/NTNU His work spans energy-efficient computing , radiation-hardened memory design for space applications, and dynamic hardware management . Publications include co-authoring three books and over 150 peer-reviewed articles in journals and conferences. He leads the Circuit and Radio Systems group and drives a strategic NTNU initiative on Energy Efficient Computing Systems . Kjeldsberg has held visiting researcher roles at imec (Belgium), University of California, Irvine, imec Netherlands (Holst Centre), and University of New South Wales (Australia). Scientific Awards : Senior Member of IEEE Mikroelektronikkprisen (2006–2015)
Dr. Eve M. Schooler is a Visiting Professor of Sustainable Computing at the University of Oxford , sponsored by the Royal Academy of Engineering. She is an IEEE Fellow and co-recipient of the IEEE Internet Award (2020), with expertise in Networking , Distributed Systems , and Carbon-aware Networking . Her work bridges industry-academia partnerships, focusing on edge-cloud infrastructure and AI for cybersecurity . BS, MS, PhD in Computer Science (Yale, UCLA, Caltech) Board of Directors, Computing Research Association (US) Advisory Council, University of Delaware College of Engineering Her research spans IoT security , smart grids , reverse CDNs , and data-centric networking . She co-founded the IETF's SUSTAIN research group on sustainability and chairs standards initiatives in fog computing and open footprints. Recent trends in her publications include carbon-aware networking , edge-cloud convergence , and AI-driven cybersecurity , with over 100 papers and 35 patents. IEEE Fellow (2021) IEEE Internet Award (2020) N2Women Stars in Networking (2023) Dr. Schooler champions STEM outreach , serving organizations like Grace Hopper Conference and Sally Ride Science. She leads industry-academia collaborations through projects like EU H2020 SPATIAL and NSF-Intel ICN-WEN.
Professor Ai-Chun Pang is affiliated with the National Taiwan University , serving in both the Department of Computer Science and Information Engineering and the Graduate Institute of Networking and Multimedia . He held leadership roles including Associate Dean (2018-2022) and Director (2013-2016) within the College of Electrical Engineering and Computer Science. His research spans Fog/Edge Computing , Wireless Networking , Mobile Computing , and AIoT Systems , with recent advancements in federated learning security, energy-efficient network design, and 5G/6G optimization. Collaborative work includes applications in vehicular networks, industrial control systems, and non-terrestrial connectivity. Key publication themes: Edge Intelligence and Privacy (2024) Federated Learning for Heterogeneous Devices (2023-2024) 5G Backhaul Optimization (2017-2021) Wireless Energy Transfer (2022) Awarded IEEE Fellow 2021 for contributions to mobile edge networks, he has received multiple IEEE Vehicular Technology Society awards, the CES 2019 Innovation Award , and teaching accolades including National Taiwan University Distinguished Teaching Award (2010) . His lab has produced 16 PhD students now in academia and industry. As Editor-in-Chief of IEEE Wireless Communications Letters and active in conference organization, he shapes global research directions. Current projects focus on GenAI for Networking and Non-Terrestrial Networks , with recent 2024 admissions for new students.
