Marco Aiello is affiliated with the Vienna University of Technology (TU Wien), Department of Distributed Systems within the Faculty of Informatics. His work focuses on distributed systems, service-oriented computing, and cloud computing, with contributions to edge computing and IoT. He has edited multiple conference proceedings, including the 2023 SummerSOC conference and the 2016 Service-Oriented and Cloud Computing volume. His research includes projects like TEADAL (2022–2025) and SM4ALL (2008–2011), exploring middleware for pervasive environments and home automation. Aiello has authored influential papers on web service indexing, QoS composition, and embedded systems. He received the 2006 Web Service Challenge award for his indexing work. Education details: PhD in Informatics (not explicitly listed but inferred from role). His research interests span distributed systems' theoretical foundations and practical implementations, emphasizing accessibility and scalability. Recent publications highlight trends in serverless architectures and edge-based IoT processes. He is actively involved in academic publishing, serving as an editor and conference organizer. Key Projects : TEADAL (2022–2025), SM4ALL (2008–2011) Awards : 2006 Web Service Challenge (2nd place) Grants : FFG-funded project (2009–2011) Labs/Teams: Part of TU Wien's Distributed Systems research group, collaborating on middleware and service-oriented technologies.
Dwight Makaroff is a Professor in the Department of Computer Science at the University of Saskatchewan . He leads the DISCUS research group , focusing on distributed systems, networking, and performance analysis. Makaroff holds a Ph.D. from the University of British Columbia (1998), an M.Sc. (1988), and a B.Comm. (1985) from the University of Saskatchewan. Research Interests: Distributed Data Processing & Hadoop Network Support for Multiplayer Games Information-Centric Networking Energy Efficiency in Mobile Devices Multicore Architectures Wireless Network Security Sensor Networks & Data Aggregation Teaching: Courses include Operating Systems Principles , Topics in Parallel & Distributed Systems , and advanced systems courses. He coordinated the ACM ICPC programming contest teams for over a decade. Committees: Graduate Committee Chair (2013-2015) University Council Member (2006-2014) Program Committee roles at IEEE/ACM conferences (IPCCC, CASCON, etc.) Recent Research Highlights: IoT security via blockchain Wearable device communication challenges Caching strategies for information-centric networks
Abdelhakim HAFID is a Full Professor at the University of Montreal , affiliated with the Faculty of Arts and Science and the Department of Computer Science and Operations Research . He leads the LRC — Laboratoire de recherche en réseaux de communication and is a member of several research centers including CIRRELT (interuniversity research center on enterprise networks, logistics, and transportation), the Cyberjustice Lab , and talents (AI for cybersecurity lab). His research focuses on Blockchain security , IoT , edge and fog computing , intelligent transport systems , and network resource management . He has supervised over 30 graduate students and led/co-led numerous research projects funded by agencies like NSERC , FRQNT , and MITACS . Notable projects include Blockchain Evolution: Quantum-Resistant Security (2025–2031) and Design and Management of Fog Networks (2019–2026). His work spans vehicular networks , mobile cloud computing , and machine learning for network optimization . He actively contributes to cybersecurity initiatives, including IoT authentication frameworks and AI-driven solutions for judicial systems (e.g., Cyberjustice ). Key Projects : Quantum-Resistant Blockchain Security (2025–2031) IoT Authentication via Blockchain and Intelligent SIM (2022–2026) Fog Network Design (2019–2026) Grants : NSERC Discovery Grants MITACS Acceleration Fellowships FRQNT Team Research Grants His lab collaborations include CIRRELT (logistics & transport) and Cyberjustice (digital law), reflecting interdisciplinary strengths in both technical and societal domains.
Professor Sang-Woo Jun is a leading researcher in systems and software for big data analytics, focusing on FPGA-based hardware acceleration and non-volatile memory (NVM) storage. His work spans applications such as graph analytics and bioinformatics, with a strong emphasis on cost-effective, high-performance computing architectures. He advises PhD students like Shengquan Ni and Yicong Huang, both of whom have achieved notable milestones (e.g., thesis defense, fellowship awards). Research Interests: Hardware Acceleration for Big Data FPGA-Based System Architectures Non-Volatile Memory Systems Graph Analytics and Bioinformatics Edge Computing and Low-Power Systems Recent Contributions: His articles highlight innovations in edge accelerators (e.g., IceSpy, Eciton), genomics acceleration (Bancroft), and scalable graph processing (Durin, Sting). These works emphasize reconfigurable systems, privacy-preserving techniques, and energy-efficient designs. Lab & Team: As part of the Intelligent Systems Group (ISG), he collaborates on events like the Southern California Database Day. His research bridges hardware-software co-design with real-world applications in IoT, environmental monitoring, and genomics.
Jonathan Balkind is an Assistant Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB). His research focuses on the intersection of computer architecture, programming languages, and operating systems, with an emphasis on pragmatic system design and open-source hardware. He leads the ArchLab at UCSB and is affiliated with the OpenPiton project, an open-source manycore research framework. Education includes a PhD and MA in Computer Science from Princeton University (adviser: Prof. David Wentzlaff), an MSci in Computing Science from the University of Glasgow (advisers: Prof. Joseph Sventek and Dr. John O'Donnell), and exchange studies at UCSB. His work has been supported by awards such as the NSF Early CAREER Award (2023) and the Open Hardware Trailblazer Fellowship (2022). Research interests span heterogeneous computing, cache-coherent systems, FPGA integration, and domain-specific architectures. Notable projects include the 25-core Piton chip, the CIFER SoC with embedded FPGA, and the DECADES manycore processor. Recent publications address fused-kernel operating systems (Stramash), control logic synthesis, and hyperloop data-center architectures. His awards reflect contributions to open-source hardware and academic mentorship, including Siebel Scholarship (2018), Gordon Y.S. Wu Fellowship (2013–2017), and multiple teaching/research recognitions. He actively collaborates with industry (e.g., Microsoft Research, ARM) and advises on open-source projects.
Hady W. LAUW is an Associate Professor in the School of Computing and Information Systems (SCIS) at Singapore Management University (SMU), serving as Director of the BSc (Computer Science) Programme and a Lee Kong Chian Fellow. He holds a PhD from Nanyang Technological University (2008). His research focuses on artificial intelligence, data science, machine learning, and recommender systems, with notable contributions to multimodal recommendation frameworks like Cornac and collaborative filtering techniques. He teaches advanced courses including IS712 Machine Learning for postgraduate students and CS608 Recommender Systems for MITB programme participants. His work emphasizes practical applications, such as designing explainable recommendation systems and integrating A/B testing into frameworks. Key research interests include web mining, preference learning, representation learning, and decision support systems. He has advised multiple students on topics like neural networks, collaborative filtering, and comparative analysis of reviews. Scientific achievements include the Lee Kong Chian Fellowship and over 100 publications in top venues like ACM WWW and IEEE TKDE. He actively contributes to conferences as a PC member and chairs events like PAKDD. His Cornac framework supports reproducible research in multimodal recommendations. He leads the Preferred.AI research group, fostering undergraduate and postgraduate research in computing. His work bridges theoretical advancements with industry applications, particularly in data-driven decision making and automated evaluation metrics.
Bowen Xu is an Assistant Professor in the Department of Computer Science at North Carolina State University (NC State), College of Engineering. His research focuses on software engineering, machine learning, and program analysis, particularly in securing AI models and improving code quality. He holds a PhD from Singapore Management University (SMU), where he also conducted postdoctoral research. Education: PhD in Computer Science, Singapore Management University (SMU) Postdoctoral Researcher, SMU School of Computing and Information Systems Research Interests: AI for Code, Backdoor Attacks on Code Models, Vulnerability Detection Code Representation Learning, Model Compression, Safety of AI Systems Chatbot Development for Developers, Automatic Code Review Key Contributions: Developed PTM4Tag+, a Stack Overflow tag recommendation system using pre-trained models Explored stealthy backdoor attacks in code and reinforcement learning systems Pioneered work on automatic vulnerability repair using LLMs and broader input analysis Awards: 2022: Honorable Mention Award (ACSAC) 2018: Highly Commended Full Paper Award (ESEM) Service Roles: Editorial Board Member, Empirical Software Engineering Journal Program Committee Co-chair for ICSE/FSE Research Tracks Organized workshops like FORGE, MaLTeSQuE, and SEA4DQ Labs & Teams: Leads the Softmax Lab at NC State, advising 12+ students across PhD, Master's, and undergraduate levels. Alumni include industry professionals at Microsoft, Barclays, and Marvell Semiconductor.
Kyle Montague is an Associate Professor at Northumbria University's Computer and Information Sciences Department. His research focuses on leveraging digital technologies to address social challenges, particularly for marginalized communities, aligning with UN Sustainable Development Goals like Education. He holds a PhD in Computing from the University of Dundee (2014) and a BSc (Hons) in Computer Science (2009). Montague's work spans Human-Computer Interaction, Accessibility, and Participatory Design. He develops tools like GitUI for democratizing user interfaces and KaraokAI for enhancing academic engagement. His research explores AI assistants, wearable technologies, and smartphone accessibility solutions. His recent publications (15 most cited) emphasize real-time web accessibility, community-led ethical frameworks, and collaborative futures in embedded research. He actively contributes to academic discourse through 82 research outputs and has received 11 prizes, including multiple Best Paper Awards (2020, 2022) and Diversity & Inclusion recognitions (2019, 2020). Education : PhD (Computing, University of Dundee), BSc (Computer Science, University of Dundee) Research : Human-Computer Interaction, Accessibility, Participatory Design Awards : Best Paper Awards (2020, 2022), Diversity & Inclusion recognitions (2019, 2020)
Wenwu Zhu is a Professor and Vice Chair of the Department of Computer Science and Technology at Tsinghua University. He has held prominent positions at Microsoft Research Asia, Intel Research China, and Bell Labs, establishing himself as a leading figure in multimedia computing and networking with international recognition as a FOREIGN member of the Academy of Europe (elected 2018). His educational background includes: Ph.D. in Electrical and Computer Engineering from New York University (1996) Professor Zhu's research focuses on the intersection of multimedia systems, networking, and big data. His work has pioneered advancements in internet video streaming, multimedia cloud computing, and social-aware content distribution. He has made significant contributions to understanding how multimedia content can be efficiently delivered across diverse network environments, from traditional wired networks to modern mobile and social platforms. His research bridges theoretical computer science with practical applications, with his work on social-aware video content distribution being transferred to Tencent company. His publication record shows a clear evolution from foundational work on internet video streaming in the early 2000s, through multimedia cloud computing in the early 2010s, to more recent work on social-aware multimedia and network embedding using deep learning approaches. This progression reflects the changing landscape of multimedia computing from infrastructure-focused to socially-aware and AI-driven systems. Professor Zhu has received numerous prestigious honors: AAAS Fellow (2016) SPIE Fellow (2013) IEEE Fellow (2010) Minister of Education's Natural Science Award, 1st prize (2017) Chinese Institution of Electronics's Natural Science Award, 1st prize (2015, 2012) National Natural Science Award, 2nd prize (2012) Chief Scientist for NSFC Major Project (2016) Chief Scientist for Ministry of Science and Technology's 973 Project (2014) Multiple Best Paper Awards including ACM Multimedia 2012 As Editor-in-Chief of IEEE Transactions on Multimedia since 2017 and through leadership roles as General Co-Chair for ACM CIKM 2019 and ACM Multimedia 2018, Professor Zhu has significantly shaped the multimedia research community. His research has been supported by major grants including NSFC Major Projects and Ministry of Science and Technology's 973 Projects, demonstrating both academic and national strategic importance. He has published over 300 referred papers with an H-Index of 55, including 6 Best Paper Awards and 7 books or book chapters. Professor Zhu leads a research group at Tsinghua University focused on multimedia big data computing, with strong industry connections. His team has made pioneering contributions to structural network embedding using deep learning and social contextual recommendation systems, bridging theoretical advances with practical applications in social media platforms.
Professor Arunabha Sen is a faculty member at Arizona State University (ASU), affiliated with the School of Computing and Augmented Intelligence and the College of Health Solutions as a Health Solutions Ambassador. He joined ASU in 1987 and holds a Ph.D. in Computer Science from the University of South Carolina (1987). His research focuses on resource optimization in telecommunication networks, VLSI circuits, hardware-software co-design, and network security. Key areas include algorithm design, combinatorial optimization, and network processor systems. His work spans wireless, optical, and sensor networks, with contributions to video transmission over mobile ad-hoc networks and interference-aware channel assignment. Notable projects include robust network design against WMD attacks and tools for resilient communication networks. He has served on multiple technical committees for conferences like IEEE and IFIP, and contributed to academic initiatives such as capstone courses on network processors. Grants include NSF, DOD-DTRA, and Motorola Labs funding, emphasizing interdisciplinary research in network science and communications. Teaching responsibilities include courses on algorithms, game theory, and network design. His service roles include Associate Editor for IEEE Transactions on Mobile Computing and leadership in graduate program committees. Research outputs include over 30 peer-reviewed publications, with recent work in algorithmic network design and social computing data mining.
Giulia Pedrielli is an Associate Professor at Arizona State University's School of Computing and Augmented Intelligence, with affiliations as a Senior Global Futures Scientist. She holds a Ph.D., M.Sc., and B.S. in Mechanical and Industrial Engineering from Politecnico di Milano, Italy. Her research focuses on stochastic simulation, optimization, and machine learning applications in biomanufacturing, digital twins, and cyber-physical systems. She has contributed to frameworks for pandemic modeling (PySIRTEM), RNA design, and high-dimensional optimization. Pedrielli has collaborated with institutions like NUS and UC Berkeley, and her work spans biomedical engineering, supply chain resilience, and security of critical infrastructure. Recent articles emphasize co-simulation tools, AI-driven logic translation, and falsification methods for CPS security. Her teaching includes advanced stochastic simulation courses and thesis supervision across multiple disciplines. Education: Ph.D. Mechanical Engineering, Politecnico di Milano, Italy M.Sc. Industrial Engineering, Politecnico di Milano, Italy B.S. Industrial Engineering, Politecnico di Milano, Italy Research interests integrate simulation-based optimization with machine learning, particularly in safety-critical systems like healthcare and smart infrastructure. She develops digital twin methodologies for real-time control under uncertainty, leveraging Gaussian processes and Bayesian approaches. Pedrielli's work bridges theoretical advancements (e.g., BASSO optimization algorithm) with practical applications in biomanufacturing and pandemic response.
Stephen Taylor is a Professor of Computer Engineering at Dartmouth College's Thayer School of Engineering. His research focuses on cybersecurity, distributed computing, and embedded systems security. He has held leadership roles including DARPA Program Manager and Air Force Research Laboratory IPA. Taylor's work includes foundational contributions to the National Cyber Range and Air Force Cyber Experimentation Environment. Education: BSc in Computer Systems from Essex University (1982), MSc in Computer Science from Columbia University (1985), and PhD in Computer Science from the Weizmann Institute (1989). Research emphasizes resilient operating systems for cloud computing, security mechanisms for embedded systems, and large-scale experimentation infrastructure. Awards include the USAF Exemplary Civilian Service Medal and Secretary of Defense Medal for Public Service. Teaches courses on microprocessors, software design, and cyberspace technology. Advises on multi-disciplinary projects and has authored 4 books and over 25 journal articles. His lab explores hardware-software co-design for cyber resilience and soil-based sustainable computing.
Shuvra S. Bhattacharyya is a Professor at the University of Maryland, College Park (UMD), Department of Electrical and Computer Engineering, with a joint appointment in UMIACS. He is also a part-time visiting professor at Tampere University (Finland) and INSA Rennes (France). His research focuses on signal processing systems, embedded systems, biomedical engineering, and machine learning. He has authored 7 books and over 300 papers, and directs the Maryland DSPCAD Research Group. He holds an NSF Career Award and is an IEEE Fellow. His honors include the Fulbright Senior Specialist and FiDiPro Professorship. He advises graduate students and leads projects in neural decoding, cyber-physical systems, and criminal justice AI. Education: B.S., University of Wisconsin-Madison Ph.D., University of California, Berkeley Research Interests: Signal processing architectures, biomedical circuits, embedded software, hardware/software co-design, and AI for healthcare and criminal justice. His work emphasizes real-time systems, energy efficiency, and interdisciplinary applications. Grants & Awards: NIH Grant for real-time neural decoding (2023) IEEE Fellow (2011) Jimmy Lin Entrepreneurship Award (2012) Labs & Teams: Maryland DSPCAD Research Group, collaborating on dynamic data-driven systems, neural interface technologies, and AI ethics in criminal justice. His work integrates academic and industrial partnerships, including roles at Hitachi America and Kuck & Associates.
Mohamed Atia is an Associate Professor in the Department of Systems and Computer Engineering at Carleton University's Faculty of Engineering and Design. He conducts research in sensor fusion for autonomous systems, including robotics, autonomous vehicles, and the Internet of Things, with an emphasis on real-time embedded implementation. PhD, Electrical and Computer Engineering, Queen’s University (2013) M.Sc., Computer Systems, Ain Shams University (2006) B.S., Computer Systems, Ain Shams University (2000) His research focuses on sensor fusion , integrating data from heterogeneous sensors such as GNSS, IMU, Vision, Radar, and LiDAR under real-time constraints on embedded platforms like FPGAs and microcontrollers. He applies advanced signal processing, estimation theory, machine learning, and AI to solve challenges in state estimation, observability, fault tolerance, and concurrency management. Dr. Atia’s work has applications in intelligent vehicles, indoor navigation, mobile robotics, smart buildings, medical devices, and remote sensing . He leads a research lab focused on embedded multi-sensor systems, where students develop practical solutions for autonomous navigation and SLAM. His teaching emphasizes hands-on learning through labs and projects. He teaches undergraduate courses in computer systems, embedded systems, and signal processing, and a graduate course (SYSC 5807) on Advanced Topics in Computer Systems, specifically Sensor Fusion Systems . Scientific Awards: Alberta Innovate Association Award (2011) IEEE Excellence in PhD Research (2013) Mitacs Elevate Industrial Postdoc Award (2014) NSERC PDF Award (2015) Queen’s University Teaching Award (2016) Dr. Atia has advised graduate students such as Hamza Sadruddin and Alan Zhang , who have presented at IEEE Sensors and ION GNSS+ conferences, with Zhang winning Best Presentation. He has no listed grants in the provided text but has received prestigious fellowships. His research lab supports innovation in real-time embedded sensor fusion with links to industry and open-source tools.
Kang Zhao is the Leonard A. Hadley Professor in Business Analytics and Senior Associate Dean at the Tippie College of Business, University of Iowa. He holds a faculty appointment in the Department of Business Analytics and is affiliated with the Interdisciplinary Graduate Program in Informatics. He previously served as Department Executive Officer (2023–2025) and Faculty Director of the Master of Business Analytics program (2020–2023). His educational background includes: PhD in Information Sciences and Technology, Pennsylvania State University MS in Computer Science, Eastern Michigan University BE in Electrical and Information Engineering, Beijing Institute of Technology Dr. Zhao's research lies at the intersection of data science and network analytics, focusing on social and business networks. His work explores social influence, engagement, and health outcomes in online health communities; develops advanced recommendation systems for social media and live streaming platforms; applies 'people analytics' to scholarly and sports data; models information and behavior diffusion; and analyzes supply chain network resilience. His research has been featured in prominent media outlets such as MIT Technology Review, Forbes, BBC, and The Washington Post. The 15 most recent publications reflect a strong trend in applying graph neural networks, dynamic representation learning, and deep learning to model complex networked systems in health, finance, and social media. Key themes include user engagement prediction, misinformation diffusion, stock movement forecasting, and personalized recommendations using temporal and structural network data. His scientific achievements have been recognized with numerous awards: Top Cited Article, Journal of the Association for Information Science and Technology (2024, 2023) Journal of Operations Management Ambassador Award (2024) Charles A. Taff Research Excellence Award, Tippie College of Business (2023) Best Paper Award, INFORMS Workshop on Data Science (2017) Early Career Faculty Research Award, Tippie College of Business (2016) Dr. Zhao has successfully led multiple research grants as Principal or Co-Principal Investigator from the National Science Foundation (NSF), National Institutes of Health (NIH), and the Institute of Museum and Library Services. His projects span topics including gender gaps in academic science, smoking cessation networks, misinformation in health communities, and disaster resilience in public libraries. He actively mentors a cohort of PhD and master’s students, many of whom have secured positions in academia and leading industry firms. He also contributes to the academic community through editorial roles at top journals such as Decision Support Systems , Electronic Commerce Research and Applications , and Journal of the Association for Information Science and Technology . He leads the Data and Network Analytics Research Group at the University of Iowa, which focuses on cutting-edge research in network science, machine learning, and data mining applied to real-world social and business problems.
