Lingjia Liu is a Professor and Bradley Senior Faculty Fellow at Virginia Tech's Bradley Department of Electrical and Computer Engineering. Her research focuses on enabling technologies for 5G/6G networks, including massive MIMO systems, dynamic spectrum access, and AI-driven communication networks. She holds a Ph.D. from Texas A&M University (2008). Research Interests : 5G/6G Network Architectures (3D MIMO, cloud-RAN, ultra-low latency) AI in Communications (Reservoir Computing, federated learning) IoT & Cyber-Physical Systems (energy harvesting, privacy protection) Non-Terrestrial Networks (satellite-based connectivity) Recent work emphasizes generative AI for network simulation, explainable AI in communication systems, and secure dynamic spectrum sharing. Her research spans theoretical foundations (e.g., OTFS modulation analysis) and practical implementations (e.g., FPGA-based reservoir computing). Awards : Bradley Senior Faculty Fellow (Virginia Tech). Her contributions bridge communication theory and AI, addressing 6G challenges through innovative algorithmic and architectural solutions. Current projects explore agentic protocol learning, federated multi-agent RL for spectrum access, and resilient ML under adversarial conditions.
Haining Wang is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His research focuses on cybersecurity, networking systems, cloud computing, and cyber-physical systems. He holds a Ph.D. from the University of Michigan (2003). His work addresses critical challenges in network security, IoT device fingerprinting, drone navigation security, and 5G/6G infrastructure vulnerabilities. Notable contributions include developing frameworks for detecting deceptive reviews, securing industrial IoT devices, and enhancing geofencing systems with 6G technologies. Wang's IEEE Fellow award (2020) recognizes his contributions to network and cloud security. His research also explores cloud gaming security, data center thermal vulnerabilities, and DNS privacy risks. He actively publishes on topics like container registry typosquatting, acoustic indoor localization, and encrypted DNS censorship analysis. Education: Ph.D., University of Michigan, 2003 Awards: IEEE Fellow (2020) Key Research Areas: Cybersecurity, Network Measurement, IoT Security, 5G/6G Systems Wang's recent work emphasizes securing emerging technologies like drone navigation systems and optimizing sensor placements in indoor environments. His projects often bridge theoretical frameworks with practical implementations in real-world networks and cloud infrastructures.
Professor Minh N. Do is the Thomas and Margaret Huang Endowed Professor in Signal Processing & Data Science at the University of Illinois at Urbana-Champaign (UIUC), with primary appointment in the Department of Electrical and Computer Engineering. He holds multiple affiliate appointments across campus including with the Coordinated Science Laboratory, Beckman Institute for Advanced Science and Technology, Department of Bioengineering, Department of Computer Science, Institute for Genomic Biology, College of Medicine, and School of Computing and Data Science. Additionally, he serves as Director of the joint VinUni-Illinois Smart Health Center and holds an Honorary Vice-Provost position at VinUniversity. Professor Do received his B.Eng. in Computer Engineering (First Class Honors) from the University of Canberra, Australia in 1997, followed by his Dr.Sci. in Communication Systems from the Swiss Federal Institute of Technology Lausanne (EPFL) in 2001. His educational journey was marked by exceptional achievement, earning the University Medal from the University of Canberra and a Silver Medal from the 32nd International Mathematical Olympiad. Professor Do's research focuses on developing new multidimensional signal processing tools with applications across several domains. His primary research interests include smart health, data science, computational imaging, and signal processing. His work spans biomedical imaging, machine learning, computer vision, and robotics, with particular emphasis on geometric image representations, integrating image formation and processing, and image processing from multiple sensors. His research bridges theoretical investigations with practical applications, creating impactful solutions in healthcare, diagnostics, and AI systems. His recent publications demonstrate a consistent trajectory toward multimodal AI systems, robust learning frameworks, and healthcare applications. Professor Do's work increasingly integrates signal processing with deep learning approaches to address challenges in medical imaging, cross-modal transfer, and real-world deployment of AI systems. His research shows strong emphasis on practical applications with societal impact, particularly in healthcare diagnostics and smart health technologies. Professor Do's scientific achievements have been recognized with numerous prestigious awards: Member of the National Academy of Artificial Intelligence (2025) Fellow of Asia-Pacific Artificial Intelligence Association (2023) Thomas and Margaret Huang Endowed Professor, UIUC (2020-present) Fellow of IEEE (2014) Young Author Best Paper Award, IEEE Signal Processing Society (2008) CAREER award from the National Science Foundation (2003) Best Doctoral Thesis Award from EPFL (2001) As an educator, Professor Do has taught numerous courses spanning digital signal processing, probability, data science, and image processing. His teaching excellence has been recognized with multiple "Teachers Ranked as Excellent" awards at UIUC. He also maintains active industry connections through tech-transfer efforts, having co-founded Personify and served as Chief Scientist of Misfit. His leadership extends to administrative roles, having served as Vice-Provost for VinUniversity during 2020-2021. Professor Do leads research initiatives at the intersection of signal processing and healthcare applications, with particular focus on the Smart Health Center collaboration between UIUC and VinUniversity. His lab develops innovative solutions for medical diagnostics, point-of-care testing, and neurological assessment using advanced signal processing and AI techniques.
Prashant Shenoy is a Distinguished Professor and Associate Dean in the College of Information and Computer Sciences at the University of Massachusetts Amherst. He has been on the faculty since 1998 and heads the Laboratory for Advanced Systems Software while directing the Center for Smart and Connected Society. His research focuses on systems issues for distributed systems ranging from large server clusters to networks of small sensors. Shenoy received his PhD in Computer Science from the University of Texas at Austin in 1998, following an MS from the same institution in 1994. He earned his BTech in Computer Science and Engineering from the Indian Institute of Technology, Bombay in 1993. His academic progression at UMass Amherst has been from Assistant Professor (1998-2004) to Associate Professor (2004-2009) to Professor (2009-2020) to Distinguished Professor (2020-present). His primary research interests include distributed systems, networking, cloud and edge computing, mobile computing and Internet of Things, and energy and sustainability. Over the past decade, his work has increasingly focused on computational decarbonization, as evidenced by his recent $12 million NSF Expedition award in this area. His research group maintains several important resources including the UMass Trace Repository, UMass CS Weather Station, BenchLab, and Smart* Dataset. Shenoy's publications reflect a progression from foundational distributed systems work to increasingly sustainability-focused research. His recent work centers on carbon-aware computing, energy optimization, and computational decarbonization across various computing domains including cloud, edge, and IoT systems. ACM Fellow (2019) AAAS Fellow (2018) IEEE Fellow (2013) ACM Sigmetrics Test of Time Award (2016) NSF Career Award recipient Conti Research Fellowship recipient Lilly Foundation Teaching Fellow As an educator, Shenoy has consistently taught Distributed and Operating Systems (Compsci 677) and has mentored numerous PhD students who have received awards and gone on to successful careers. He serves as the founding Chair of the ACM Special Interest Group on Energy (SIGEnergy) and has organized numerous conferences including serving as PC chairs for the ACM Symposium on Edge Computing in 2025. His research has secured significant funding, including a recent $12 million NSF Expedition in Computational Decarbonization awarded in May 2024. Shenoy leads the Laboratory for Advanced Systems Software at UMass Amherst and directs the Center for Smart and Connected Society. He serves on editorial boards of several journals including ACM Transactions on IOT (TIOT), ACM Modeling and Performance Evaluation of Computing Systems (TOMPECS), and ACM Transactions on the Web (TWEB).
Bettina Kemme is a Professor in the School of Computer Science at McGill University, Montreal, Canada. She leads the Distributed Information Systems Lab (DISL) and specializes in large-scale data management, distributed systems, and cloud computing. Her academic roles include teaching COMP 512 (Distributed Systems) and COMP 421 (Database Systems). Education: Diplom (M.Sc. equivalent) in Computer Science, Friedrich-Alexander University, Erlangen, Germany (1996) PhD in Computer Science, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (2000) Research Interests: Distributed systems, cloud-native data management, in-database analytics (AIDA project), monitoring-as-a-service frameworks, and scalable pub/sub systems for online games. Current projects focus on integrating machine learning with databases, cloud performance monitoring using SDN, and sustainable data systems for data science. Lab & Collaborations: Leads the Distributed Information Systems Lab (DISL) with active projects in distributed databases, cloud computing, and game systems. Collaborates on EU-Canada initiatives like the SustainSys program for sustainable data infrastructure. Advising: Supervises PhD and M.Sc. students in topics like monitoring frameworks (Mona ElSaadawy), in-database ML (Winnie He), and distributed systems (Maximilian Schiedermeier). Alumni include over 50 researchers from PhD candidates to undergraduate researchers.
Christina Delimitrou is an Associate Professor at MIT's Department of Electrical Engineering and Computer Science (EECS) and a Principal Investigator at the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on optimizing cloud computing systems, with a strong emphasis on resource management, sustainability, and machine learning-driven solutions. Delimitrou leads projects on carbon-aware scheduling, efficient datacenter operations, and serverless computing frameworks like Ursa and Ditto. Her work bridges theoretical system design with practical deployment challenges, addressing topics such as microservices orchestration, approximation techniques for resource efficiency, and security implications of multi-tenancy in shared cloud environments. Notably, she received the Presidential Early Career Award for her contributions to improving datacenter efficiency through innovative scheduling and resource allocation strategies. Delimitrou's research group develops tools like Sage (ML-driven performance debugging) and Seer (big data analytics for cloud systems), emphasizing reproducibility and scalability. Her lab also explores edge computing, swarm robotics coordination (e.g., Hivemind), and hardware-software co-design for next-generation systems. Her academic affiliations include MIT CSAIL's Systems Community of Research, where she collaborates on large-scale software systems. Key themes in her work include QoS-aware resource management, sustainable computing practices, and leveraging approximation to enhance cloud resource utilization.
Dr. Suranga Seneviratne is a Senior Lecturer in Security at the School of Computer Science, University of Sydney. He holds a PhD from the University of New South Wales (2015) and a Bachelor's degree from the University of Moratuwa, Sri Lanka (2005). Before academia, he worked in telecommunications for six years. His research focuses on cybersecurity, particularly privacy and security in mobile systems, AI applications in security, and behavioral biometrics. He has developed tools like an app security rating system and intrusion-free authentication methods. Key awards include the ACM Mobicom 2015 Gold Prize, NASSCOM Technical Innovation Award, and IESL NSW Engineering Excellence Award (all 2015). Current research students include Pasindu Marasinghe (Multi-Objective Optimization in Flat Glass Cutting Production), Braylon SHU (Efficient Parameter Tuning for Large Language Models), and Gaurav VERMA (Threats and Defenses in IoT Wireless Protocols). Grants include funding from the Australian Research Council, NSW Network for Cyber Security, and Google Research. His work spans collaborations with the NSW Smart Sensing Network and the University of Technology Sydney. Labs/Teams: Collaborates with the Centre for Distributed and High-Performance Computing and the NSW Smart Sensing Network (NSSN).
Nashid Shahriar is an Assistant Professor in the Department of Computer Science at the University of Regina, Faculty of Science. His research addresses resource allocation challenges in next-generation networks including 5G, elastic optical networks, cloud infrastructures, and IoT systems. He holds a Ph.D. in Computer Science from the University of Waterloo, an M.Sc. from Bangladesh University of Engineering and Technology (BUET), and a B.Sc. from BUET. His work leverages optimization, machine learning, and AI for network management. Recent publications focus on 5G network slicing, intrusion detection, and NFV security. Research emphasizes practical AI-driven solutions for telecommunications and cloud systems.
Dr. Raman Adaikkalavan is a Professor in the Department of Computer and Information Sciences at Indiana University South Bend (IUSB), and serves as Associate Vice Chancellor for Enrollment Management. He holds a Ph.D. in Computer Science and Engineering from the University of Texas at Arlington (2006), and has extensive academic leadership experience. His research focuses on information security (particularly IoT and Android), data streaming, and computer science education. Notable contributions include developing the IU Test web-based assessment tool and advancing secure data stream processing architectures. Education: B.E. (1999) from Bharathidasan University, M.S. and Ph.D. (2002/2006) from University of Texas at Arlington, with additional certificates in online teaching (2013). Research emphasizes practical applications like secure stream processing in cloud environments and improving pedagogical methods through active learning. His work has been supported by NSF grants and institutional funding. Awards include the IU Trustees' Teaching Award (2011) and recognition as a University Scholar (UT Arlington). He advises students on topics like secure data stream processing and software engineering. Collaborations include projects with Dr. Indrakshi Ray (Colorado State) and Dr. Sharma Chakravarthy (UT Arlington). His IU Test system aids in program assessment and accreditation reporting for ABET.
Aftab Ahmad is a Professor in the Department of Computer Science at the City University of New York (CUNY), specializing in cybersecurity and machine learning applications. He holds a Doctor of Science from George Washington University. His research focuses on developing machine learning algorithms for cyber threat intelligence (CTI) and public health prediction, along with designing secure generative deep learning models resistant to reverse-engineering. Key research areas include: Cybersecurity frameworks and privacy-preserving architectures Generative adversarial networks (GANs) with embedded security features Biomedical signal analysis and human body channel modeling Secure wireless protocols for critical infrastructure His publication trends emphasize: Privacy metrics and data protection mechanisms Smart grid and IoT security Neuroscience-inspired machine learning models Wireless network vulnerability assessments No scientific awards or grants were explicitly mentioned in the provided texts. He teaches advanced courses in computer security and network forensics at undergraduate and graduate levels. No advising relationships or lab affiliations were detailed in the available information.
Shweta Jain is a Professor in the Department of Mathematics and Computer Science at John Jay College of Criminal Justice, part of the City University of New York (CUNY). She holds dual roles as Graduate Faculty in the Digital Forensics and Cyber Security program and Doctoral Faculty in Computer Science at CUNY's Graduate Center. With a Ph.D. in Computer Science from Stony Brook University (2007), her expertise spans Cybersecurity, Blockchain, Wireless Networks, and Software Development. Education Background: Ph.D. Computer Science, Stony Brook University, 2007 M.S. Computer Science, Stony Brook University, 2005 B.E. Electronics and Telecommunication Engineering, Indian Institute of Engineering Science and Technology (IIEST) Shibpur, 2005 Research Interests: Cybersecurity frameworks and digital forensics Blockchain applications in social systems Wireless network protocols and security Perceptual hashing for image authentication Network vulnerability analysis Notable Achievements: Recipient of 2014 IEEE Region-1 Award for Outstanding Teaching Senior Member of IEEE Over 30 peer-reviewed publications and patents in networks, forensics, and distributed systems Advising & Grants: Guided multiple student research projects in network security and forensics Developed innovative tools like E-Witness for digital evidence preservation Contributed to NSF-funded projects on wireless simulation realism Labs & Teams: Director of the Cybersecurity Research Lab at John Jay College Collaborates with WINLAB at Rutgers University on wireless protocols
Dr. Khandaker Mamun Ahmed is an Assistant Professor at The Beacom College of Computer & Cyber Sciences, Dakota State University. He teaches undergraduate and graduate courses in artificial intelligence, algorithms, and data structures. He holds a Ph.D. in Computer Science from Florida International University (2024), an M.Sc. from the same institution (2023), and a B.Sc. in Software Engineering from the University of Dhaka (2016). His research focuses on computer vision, federated learning, cybersecurity, explainable AI, vision-language models, and optimization algorithms. He has contributed to peer-reviewed publications and conference presentations, with notable work in federated learning for IoT, anomaly detection in videos, and AI applications in healthcare and agriculture. Recent articles highlight advancements in federated learning frameworks, AI-driven healthcare systems, and real-time object detection using neural networks. His work also addresses cybersecurity challenges in DevOps pipelines and generative AI for educational datasets. Recipient of the 'Best graduate student in research award' (2022), Dr. Ahmed advises on AI ethics and mentors students through academic-industry collaborations. His research bridges theoretical computer science with practical applications in agriculture, healthcare, and infrastructure monitoring.
Emil Salib is a Professor in the Department of Computer Science and Information Technology Program at James Madison University (JMU), part of the College of Integrated Science & Engineering. His expertise spans networking, cybersecurity, and cloud computing. He holds a Ph.D. in Solid State Physics from the University of Wollongong and dual bachelor's degrees in Electrical Engineering and Physics from Cairo University. Education: Ph.D. in Solid State Physics, University of Wollongong, Australia M.S. in Telecommunications Networks, Cairo University B.S. in Electrical Engineering (Electronics and Communications), Cairo University B.S. in Physics, Cairo University Research Interests: Focuses on DevOps tools (Git, Ansible), cloud platforms (OpenStack), SDN/SD-WAN, wireless security algorithms, and blockchain applications. His work bridges theoretical physics and practical network engineering, emphasizing automation and infrastructure orchestration. Experience: Executive Director at Ericsson/Telcordia Technologies Director at Bellcore for network software systems Postdoctoral Researcher at University of Hull's Magneto-Optics Group Courses Taught: Includes advanced networking, cybersecurity, telecommunications, and capstone project courses. Employs industry-relevant tools like Docker, Kubernetes, and OpenStack in curriculum. Labs/Teams: Leads initiatives in JMU's Information Technology program integrating industry standards with academic rigor.
Riccardo Lancellotti is an Associate Professor at the Department of Engineering 'Enzo Ferrari' of the University of Modena and Reggio Emilia. His research focuses on Edge/Fog/Cloud Computing, Cyber Security, and Resource Management in distributed systems. He has extensive contributions in optimizing infrastructure performance, load balancing, and energy efficiency in cloud and fog environments. His work often combines theoretical models with practical simulations, addressing challenges like stale information in edge systems and heterogeneous resource allocation in smart cities. Key research areas include: Fog/Edge computing infrastructure design and optimization Cloud resource provisioning and SLA compliance Security for Industry 4.0 and automotive systems Genetic algorithms for service placement Scalable VM clustering and resource allocation Publications highlight trends in cloud/fog integration, robust game theory for microservices, and distributed load balancing under dynamic conditions. His work emphasizes practical applications, such as pharmaceutical distribution routing and smart city sensor management. No awards are explicitly listed, but his extensive publication record reflects recognition in the field.
Professor Piyushimita (Vonu) Thakuriah is a Visiting Professor of Urban Studies in the School of Social & Political Sciences and Ch2m Chair of Transport at the University of Glasgow. She directs the UK ESRC Urban Big Data Centre (UBDC), a national initiative fostering innovations in sustainable urban development through Big Data. Her roles include affiliated professorship in the School of Engineering and directorship of the UBDC, leading a 7-university consortium. Her research focuses on smart, socially-just urban transport systems, Urban Informatics, and Big Data applications in city planning. Key interests include transport equity, mobility technologies, and the societal impacts of AI/automation. She has authored over 170 publications, including books on transportation technology and urban data analytics. Thakuriah has secured £25M+ in grants as PI, addressing topics like low-wage worker mobility, disability access, and data-driven urban policies. She co-convenes the MSc in Urban Transport and teaches transport planning. Her international collaborations span Malaysia, China, India, and Europe. She advises governments, including testimony to US Congress and UK Parliament. Awards: European Commission Marie Curie Fellowship, Ch2m Chair, NSF Fellowships. Grants: ESRC UBDC funding, NSF, USDOT, and EU grants totaling £63M+ across roles. Labs/Teams: Leads UBDC with initiatives like iMCD (Integrated Multimedia City Data) and SUDS (Spatial Urban Data System).
