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Professor Aruna Prasad Seneviratne serves as the Foundation Professor of Telecommunications at the University of New South Wales (Australia), where he holds the prestigious Mahanakorn Chair of Telecommunications. He is currently the Research Director for the Cyber Physical Systems Research Program within Data61, following the merger of NICTA with CSIRO. Previously, he directed the Australian Technology Park Laboratory of NICTA and led their Networked Systems research activities. Professor Seneviratne's research focuses on physical analytics - technologies enabling applications to interact intelligently and securely with their environment in real time. His recent work includes behavioral biometrics, wearable device optimization, and IoT system verification. His extensive publication record spans cybersecurity, artificial intelligence, communications engineering, and mobile technologies, with particular emphasis on integrated communications and sensing systems. His scholarly contributions include over 180 refereed technical papers and book chapters, reflecting his leadership in telecommunications and networked systems research. Professor Seneviratne's work demonstrates a consistent trajectory toward developing practical solutions for next-generation digital services and security frameworks. His scientific recognition includes prestigious fellowships at British Telecom and Telecom Australia Research Labs, underscoring his industry impact alongside academic contributions. Professor Seneviratne has supervised 30 PhD dissertations throughout his career, mentoring the next generation of telecommunications researchers. His leadership extends to directing major research initiatives at NICTA and Data61, where he has guided the development of new technologies for establishing trust, energy-efficient content storage, search, and distribution within digital economies. His laboratory work centers on the Cyber Physical Systems Research Program at Data61, where his team develops innovative approaches to secure and intelligent interaction between digital systems and physical environments.

• Cybersecurity and privacy
• Artificial intelligence
• Communications engineering
• +5 more

Steven Chamberland is a Full Professor at the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He serves as Director of Academic Affairs and Student Life at the institution. With a Ph.D. from Polytechnique Montréal, an MBA from HEC Montréal, and an MIR from Queen's University, his expertise combines engineering rigor with strategic academic leadership. Ph.D. (Polytechnique Montréal) MBA (HEC Montréal) MIR (Queen's University) B.Eng. (Polytechnique Montréal) Dr. Chamberland specializes in network design and optimization , particularly for wireless and vehicular communication systems. His work addresses critical challenges in network reliability, congestion management, and resource allocation across emerging technologies like SDN, IoT, and 5G/6G systems. He actively explores machine learning applications for network optimization and quality-of-service improvements. His recent publications focus on heterogeneous vehicular networks , with contributions to congestion avoidance mechanisms using neural networks, routing protocols for intelligent transportation systems, and network slicing techniques with generative adversarial networks. These works align with his broader research in mobile computing and smart city infrastructure . Dr. Chamberland has supervised over 8 Ph.D. and 12 Master's students , including notable graduates like Falahatraftar, El Garoui, and Jaramillo Herrera. His leadership extends to the LARIM Laboratory , where he contributes to mobile computing research. Despite extensive publications (>110), no specific scientific awards were mentioned in the provided texts.

• Operations Research and Management Science
• Optimization
• Mathematical Modeling
• +4 more

Mahsa Yarmohammadi is an Assistant Research Scientist at the Center for Language and Speech Processing (CLSP), where she leads research in cross-lingual language and speech applications. Her work focuses on deep learning techniques for multilingual data representation, automatic speech recognition (ASR), and speech translation. She develops novel algorithms for ASR decoders and explores interfaces between ASR and machine translation systems. Education: PhD in Computer Science and Engineering, Oregon Health & Science University (2016) Master’s in Computer (Software) Engineering, Shahid Beheshti University, Iran (2007) Undergraduate degree in Computer (Software) Engineering, Amirkabir University of Technology (Tehran Polytechnic) (2004) Research Interests: Her expertise spans Natural Language Processing (syntactic/semantic parsing, information extraction), multilingual systems, deep learning, and ASR. She specializes in resource transfer across languages, neural lattice embeddings, and end-to-end system integration for speech and language technologies. Publication Trends: Recent articles (2019–2025) emphasize multilingual challenges: cross-lingual retrieval, contextual ASR, coreference resolution in dialogue, and zero-shot information extraction. Her work frequently addresses low-resource settings, neural algorithm design, and real-world dataset creation (e.g., earnings call analysis, COVID-19 QA pairs). Grants & Teams: As a prime researcher in multi-site programs, she collaborates on large-scale multilingual projects. She was mentored by Benjamin Van Durme (Johns Hopkins CS) and Sanjeev Khudanpur (Johns Hopkins ECE) during her CLSP post-doctoral fellowship (2021).

• Natural Language Processing
• Deep Learning
• Automatic Speech Recognition
• +4 more

Bengt Jonsson is a Professor at the Division of Computer Systems, Department of Information Technology, Uppsala University. His research focuses on formal methods, real-time and distributed systems, semantics and verification of concurrent systems, and IoT security. Current Projects: UPMARC (Software Technology for Multicore Programming), aSSIsT (Secure Software for IoT), and Designed for UPDATE (Safe Embedded Software Updates) Past Projects: CoDeR-MP (Multicore Real-Time Applications), ProFun (Wireless Sensor Networks), CONNECT (Networked Component Synthesis) His work includes automated verification, model checking, and symbolic execution for concurrent systems. Recent publications address dynamic partial order reduction, IoT protocol testing, and lock-free data structures. Scientific Awards : CAV Award 2017 He advises PhD students and teaches courses like Model-Based Development of Embedded Systems and graduate-level symbolic execution. Personal interests include piano playing and orienteering.

• Formal Methods
• Real-Time Systems
• Distributed Systems
• +6 more

Petteri Kaski is an Associate Professor at the Department of Computer Science, School of Science, Aalto University , and a member of the Helsinki Institute for Information Technology (HIIT) . His research focuses on theoretical computer science, particularly in algorithm design, exact and parameterized algorithms, algebraic algorithms, and combinatorics. Doctoral Degree in Engineering and Technology, Helsinki University of Technology (2005) Licentiate Degree in Engineering and Technology, Helsinki University of Technology (2002) Master's Degree in Engineering and Technology, Helsinki University of Technology (2001) His recent work explores tensor scaling, Johnson-Lindenstrauss transforms, Hamiltonian cycles, and computational complexity, with contributions to polynomial-time algorithms, finite field computations, and combinatorial optimization. Notable awards include the Best Paper Award at ICALP 2017 , an ERC Starting Grant (2014) , and the Kirkman Medal (2007) . He has served on scientific committees for conferences like STACS 2025 and ICALP 2024 , and collaborated with institutions such as the IT University of Copenhagen and Universität Regensburg . Key Research Areas : Theoretical Computer Science, Algorithm Design, Exact Algorithms, Algebraic Computation, Graph Theory, Combinatorics

• Theoretical Computer Science
• Algorithm Design
• Exact and Parameterized Algorithms
• +5 more

Dr. Kevin Austin is a Research Fellow at the School of Mechanical and Mining Engineering, Faculty of Engineering, Architecture and Information Technology at the University of Queensland. His work is affiliated with the Future Autonomous Systems and Technologies research group, where he focuses on automation and robotics applications in mining engineering. Dr. Austin received his academic qualifications from the University of Queensland, including a Bachelor (Honours) of Engineering and a Doctor of Philosophy. Dr. Austin's research centers on mining automation and autonomous systems for heavy machinery operations. His work spans several key areas including dragline operation planning, excavation sequencing, terrain mapping for autonomous bulldozers, and hyperspectral imaging for ore grade discrimination. He has made significant contributions to the development of algorithms for mining equipment automation, particularly in the areas of Monte-Carlo Tree Search for dragline operation planning and Iterative Closest Point variants for terrain scan matching. His research bridges theoretical advances in robotics and artificial intelligence with practical applications in the mining industry, focusing on improving efficiency, safety, and productivity. Dr. Austin has been involved in numerous research projects funded by industry partners including Caterpillar Inc and the Australian Coal Association Research Program (ACARP). His current research focuses on coal stockpile management for remote bulldozers, semi-autonomous bulldozers for mine site rehabilitation, and articulated truck automated systems. His work demonstrates a strong industry connection with practical applications in mining operations. As an academic supervisor, Dr. Austin has served as an Associate Advisor for multiple PhD and Master's students at the University of Queensland. His supervision portfolio includes research on real-time terrain mapping for autonomous bulldozers, mission planning for autonomous excavation, dragline excavation sequencing, and scan matching for terrain mapping in open-pit mining. His collaborative approach is evident in his work with other faculty members, particularly Professor Ross McAree. Dr. Austin's laboratory and research team work closely with industry partners through the Future Autonomous Systems and Technologies group. They maintain strong connections with mining equipment manufacturers and coal mining operations, ensuring their research addresses real-world challenges in the mining sector. Their facilities likely include simulation environments for mining equipment operation, testbeds for autonomous systems, and data analysis platforms for mining process optimization.

• Mining Automation
• Autonomous Systems
• Robotics
• +5 more

Dr. Gul N. Khan is a Professor in the Department of Electrical, Computer and Biomedical Engineering at Toronto Metropolitan University (formerly Ryerson University). He has held academic positions at the University of Saskatchewan, Nanyang Technological University, RMIT University, and Quaid-i-Azam University. His career spans over three decades with a focus on embedded systems , network-on-chip (NoC) , and heterogeneous computing . Education: Ph.D. (Imperial College, 1989), M.Sc. (Syracuse University, 1982), B.Sc. (UET Lahore, 1979) Dr. Khan’s research interests include hardware-software co-design , CPU-GPU systems , fault-tolerant computing , and smart RFID systems . His work has led to over 125 refereed publications and three US patents. His recent publications highlight advancements in GPU auto-tuning , NoC synthesis , and digital time interpolators . Despite being listed in a Google Scholar block with unrelated public health topics, these appear to be errors, as his core expertise remains in computer engineering. Dr. Khan has supervised numerous graduate projects in embedded systems and SoC design . He served as Program Director for Computer Engineering from 2004–2015 and leads the Microsystems Research Lab at Toronto Metropolitan University.

• Embedded Systems
• Hardware/Software Co-design
• Network-on-Chip (NoC)
• +7 more

Lei Chen is a Chair Professor and Director of HKUST Big Data Institute at the Hong Kong University of Science and Technology , where he has served since 2005. His research spans data-driven machine learning , crowdsourcing systems , and uncertain database processing , with notable contributions in privacy-preserving spatial queries and graph neural networks . Ph.D. in Computer Science, University of Waterloo (2004) MS in Computer Science, Asian Institute of Technology (1997) BS in Computer Science, Tianjin University (1994) His work focuses on: Spatial Crowdsourcing - Efficient task assignment and privacy frameworks Graph Processing - Novel indexing for heterogeneous networks Explainable AI - Human-centric model interpretation techniques Uncertain Data - Probabilistic query processing with crowdsourcing Recent publications cluster around secure data federation , distributed graph training , and privacy-preserving mobility systems , reflecting his leadership in ACM and IEEE communities. Students include 15 active Ph.D. candidates and 20+ graduated researchers now at institutions like BeiHang University and Huawei Noah's Ark Lab . Awards: ACM Fellow (2024), VLDB Best Paper (2022), SIGMOD Test-of-Time Award (2015).

• Data-Driven Machine Learning
• Crowdsourcing-Based Data Processing
• Uncertain and Probabilistic Databases
• +3 more

Yasser Mohamed is a Professor in the Civil and Environmental Engineering Department at the University of Alberta . His academic and professional focus revolves around construction engineering, discrete-event simulation, and process optimization for industrial and tunneling operations. He has also explored knowledge engineering techniques and the application of TRIZ (Theory of Inventive Problem Solving) to construction processes. Email: yaly@ualberta.ca Location: 7-269 Donadeo Innovation Centre For Engineering, Edmonton, AB Courses Taught: CIV E 603 (Construction Informatics), CIV E 606 (Design and Analysis of Construction Operations) His research emphasizes modeling construction processes using discrete-event simulation to optimize performance and develop synthetic environments for construction operations. Recent publications, however, indicate a shift toward power systems, focusing on DC microgrids , grid-forming converters , and renewable energy integration . Scientific Awards: None explicitly mentioned in the provided data. Advising and Grants: No formal advisees listed. A co-applicant on a CRD grant (2007–present) for synthetic environments in construction simulation.

• Construction Engineering
• Discrete-Event Simulation
• Industrial Construction
• +6 more

Marco Antonio Casanova is a Full Professor at the Department of Informatics and Coordinator of the Central Planning and Evaluation Office of the Pontifical Catholic University of Rio de Janeiro (PUC-Rio). He has held significant leadership positions at PUC-Rio including Graduate Program Coordinator (2005-2007) and Director of the Department of Informatics (2007-2011). His research interests concentrate on database conceptual modeling, construction of database management systems, and applications of Large Language Models. Dr. Casanova's work focuses on technologies that facilitate the dissemination and interpretation of data on the Web, with particular emphasis on techniques for designing databases to facilitate interoperability. His academic journey began with a degree in Electronic Engineering from the Military Institute of Engineering (1974), followed by an M.Sc. in Informatics from PUC-Rio (1976), and culminated with an M.Sc. (1978) and Ph.D. (1979) in Applied Mathematics from Harvard University. His recent publications (2023-2025) demonstrate a strong focus on the intersection of Large Language Models with database technologies, particularly in developing advanced Text-to-SQL and Text-to-SPARQL systems. His research spans both theoretical database concepts and practical applications across various domains including engineering, healthcare, and cultural heritage. Dr. Casanova has been particularly active in exploring how LLMs can enhance traditional database query interfaces while addressing real-world challenges in complex database environments. Scientific Recognition: Recipient of the Scientific Merit Award from the Brazilian Computer Society (2012) CNPq Level 1B Productivity Grant recipient Dr. Casanova maintains an active research program with numerous collaborations across Brazil and internationally. His work bridges theoretical database research with practical applications, particularly in the evolving landscape of AI-enhanced database systems. He has contributed significantly to the field of semantic technologies, knowledge graphs, and natural language interfaces to databases, with a recent emphasis on leveraging Large Language Models to solve longstanding database interoperability challenges. His laboratory and research team at PUC-Rio focus on developing innovative approaches to database management that incorporate cutting-edge AI techniques while maintaining strong theoretical foundations in database systems.

• Database Systems
• Conceptual Modeling
• Semantic Web
• +5 more

Zhiqiang Tao is an Assistant Professor in the School of Information at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT). Prior to RIT, he served as an Assistant Professor at Santa Clara University (2020–2022). He holds a Ph.D. from Northeastern University (ECE Department) and B.E. and M.E. degrees from Tianjin University. Research Focus: His research group develops smart, scalable, and secure machine learning algorithms for healthcare, AI education, computational imaging, and data science. Key areas include large vision-language models (LVLMs), calibrated uncertainties in low-level vision, ranking fairness, and multimodal understanding. Recent works explore LLM fairness, medical question-answering systems, and diffusion models for text-video retrieval. Awards & Honors: RIT GCCIS Emerging Scholar Award (2025) Teaching: IDAI-780: Capstone Project ISTE-780: Data-Driven Knowledge Discovery Advising & Team: Actively seeking motivated graduate/undergraduate students to join his research group. His team has produced impactful work in NeurIPS, CVPR, ECCV, and IEEE TPAMI/TKDE. Labs & Collaboration: Collaborates across disciplines to advance AI applications in healthcare and education, leveraging computational imaging and fair ML principles.

• Machine Learning
• Computer Vision
• Computational Imaging
• +7 more

Nicholas Port is a Professor at the Indiana University School of Optometry, where he has served since 2005. He holds a Ph.D. in Neuroscience from the University of Minnesota (1997) and completed postdoctoral training at the National Eye Institute's Laboratory for Sensorimotor Research. His research focuses on concussion/mTBI effects, athlete concussion epidemiology, neurobiology of brain injury, and human eye movement development. He is a key contributor to the NCAA-DoD CARE Consortium, advancing concussion assessment tools like VOMS and SCAT3. Education: Ph.D. in Neuroscience, University of Minnesota, 1997 Postdoctoral Training: National Eye Institute, 1997–2005 Research Interests: Concussion diagnosis and management in athletes Neurobiological mechanisms of TBI Developmental oculomotor systems Epidemiology of sports-related injuries His publications emphasize clinical and neuroimaging approaches to understanding concussion outcomes and athlete brain health. He teaches courses on ocular motility, biomedical systems, and neurobiology of concussion. His work bridges optometry, neuroscience, and sports medicine to address pressing health challenges in athletics. Advising & Grants: As a graduate faculty member with PhD committee leadership, Dr. Port mentors students in optometry and neuroscience. His research leverages interdisciplinary collaborations, such as the CARE Consortium’s large-scale athlete studies. Labs/Teams: Active in IU School of Optometry’s research programs, contributing to sensorimotor and vision science initiatives.

• Neuroscience
• Optometry
• Concussion Research
• +7 more

Raiford Guins is Professor and Director of Cinema and Media Studies at Indiana University's Media School, with an additional appointment as Adjunct Professor in the Luddy School of Informatics, Computing, and Engineering. His interdisciplinary scholarship bridges design history, game studies, and material culture studies. His research interests include: Design History & Culture Industrial Design & Graphic Design Game History & Preservation Life Histories of Objects & Material Culture History & Philosophy of Technology Cultural History & Museums Guins approaches technological artifacts through multiple disciplinary lenses, examining their material lives and cultural significance. His work demonstrates a consistent trajectory from early explorations of media control to contemporary investigations of game history and material culture. His publications reveal deep engagement with how objects circulate through culture and acquire meaning over time. Notable achievements: Named IU Presidential Arts and Humanities Fellow Co-editor of the MIT Press Game Histories Book Series with Henry Lowood Guins maintains an active research agenda with several current projects including 'King PONG: How Atari Bounced Across Markets to Make Millions' (expected Spring 2026) and 'Museum Games: Journeys Across the Globe in Search of Playable Media.' His writings appear in journals including American Journal of Play, Cabinet, Design Issues, Game Studies, and Journal of Design History.

• Design History & Culture
• Industrial Design & Graphic Design
• Game History & Preservation
• +3 more

Thang Hoang is an Assistant Professor in the Department of Computer Science at Virginia Tech and a CCI Researcher. He holds a PhD from the University of South Florida (2020), an M.S. from Chonnam National University (2014), and a B.S. from University of Science, VNU-HCMC (2010). His research focuses on applied cryptography, cybersecurity, privacy-preserving technologies, and secure machine learning. Notable contributions include work on encrypted databases (Hermes), certificate revocation systems (AccuRevoke), and privacy-preserving federated learning. He has received grants such as the NSF SaTC Core Medium Grant (2024) and has advised multiple students including Tung Le, Arman Riasi, and Hoang-Dung Nguyen. His work has been published in top venues like IEEE S&P, PETS, and ACM CCS. He is actively involved in program committees for conferences like PETS and IEEE MASS.

• Applied Cryptography
• Cybersecurity
• Privacy-Preserving Technologies
• +5 more

Ihudiya Finda Williams is an Assistant Professor in the Department of Computer Science at Virginia Tech’s College of Engineering. Her research focuses on human-computer interaction (HCI), digital literacy, and sociotechnical systems with an emphasis on marginalized communities and anti-racist design. She holds a Ph.D. in Information Science from the University of Michigan, an M.Ed. in Technology, Innovation, and Education from Harvard University, and a B.S. in Information Technology from Rochester Institute of Technology. Her work explores technology’s role in reentry programs, digital inclusion for formerly incarcerated individuals, and intersectional perspectives in HCI. She has conducted studies on technology use in Black churches and developed interventions to support equitable job search practices. Key themes include critical race theory applications, feminist digital practices, and ethical technology design. Her publications (2016-2024) reflect a trajectory toward addressing systemic inequities through technology research. Recent work emphasizes speculative design for conference reform (SIGCHI) and networked activism frameworks. Though no explicit awards are cited in the text, her focus on marginalized communities suggests potential alignment with social impact grants or ethics-focused funding opportunities.

• Human-Computer Interaction
• Digital Literacy
• Sociotechnical Systems
• +3 more