Octavia A. Dobre is a full Professor and Canada Research Chair Tier-1 in Ubiquitous Connectivity at Memorial University's Faculty of Engineering and Applied Science. Her research focuses on wireless, optical, and underwater communications, integrated sensing and communication systems, and AI-driven network innovations. She leads over 500+ journal/conference publications and holds positions like VP Publications of IEEE Communications Society. Education: Dipl.-Ing. and PhD from Politehnica University of Bucharest Roles: Editor-in-Chief of IEEE Open Journal of Communications Society, former Editor of IEEE Communications Letters Her work spans IEEE standards development, conference leadership (e.g., General Chair, Technical Chair), and industry partnerships with entities like Huawei and DRDC. Recognized globally with awards like IEEE Fellow (2020), Fellow of the Canadian Academy of Engineering (2021), and Top 2% Global Scientist (Stanford, 2020-2024). Her lab explores cutting-edge topics including quantum networks, reconfigurable surfaces, and 6G innovations.
David Mould is a Professor in the School of Computer Science at Carleton University. His research focuses on computer graphics, procedural modeling of natural phenomena, non-photorealistic rendering, and computer games. He holds a PhD from the University of Toronto (2002), MSc from the University of Saskatchewan (1996), and BSc from the University of British Columbia (1994). PhD: University of Toronto (2002) MSc: University of Saskatchewan (1996) BSc: University of British Columbia (1994) Research interests include procedural modeling of trees, lightning, and terrain; image stylization techniques such as stained glass transformation and wax crayon simulation; and nonlinear storytelling in games. His work emphasizes algorithmic innovation and perceptual quality in graphics. Recent publications span topics like texture synthesis, real-time video stylization, and fluid animation techniques. He leads the Graphics, Imaging, and Games (GIGL) research group at Carleton. Teaching responsibilities include courses in game development (COMP 1501–4501), technical writing (COMP 3301), and graduate courses on game design and image processing.
Alexandru G. Bardas is an Associate Professor at the University of Kansas in the Department of Electrical Engineering & Computer Science (EECS) and the Institute for Information Sciences (I2S) . He received his PhD from Kansas State University under advisors Xinming (Simon) Ou and Scott A. DeLoach. His research focuses on cybersecurity from a systems perspective , including moving target defenses, security operations center (SOC) metrics, DevOps security, power grid cybersecurity, and defensive technologies for political activists. He explores UDP-based DDoS detection, DNS traffic analysis, and the intersection of AI with cybersecurity, emphasizing foundational knowledge over tool-specific training. Key research areas: Cybersecurity, Systems Security, Moving Target Defenses, SOC Metrics, DevOps Security Recent publications in ACSAC 2024 , USENIX Security 2024/2023 , and IEEE Security & Privacy 2022 Dr. Bardas has received significant recognition including: NSF CAREER Award (2022) for SOC automation Bellows Scholar (2021) at KU NSA SoS Honorable Mention (2023) He actively advises students across disciplines, with graduates now at Sandia National Laboratories , Blue Cross Blue Shield , and Pacific Northwest National Laboratory . Dr. Bardas participates in NSF grant reviews , serves on program committees for SOUPS and MILCOM , and leads outreach initiatives like the GenCyber Summer Camp .
Emily Whiting is an Associate Professor of Computer Science at Boston University and Director of the Shape Design & Computation Lab. She also serves as Director of PhD Admissions and Co-Director of the BU Computer Graphics Lab. Her research focuses on computational fabrication, architectural geometry, and computer-aided design, bridging digital geometry processing, engineering mechanics, and rapid prototyping. She holds a PhD from MIT (2012), an SM in Design & Computation from MIT (2006), and a BASc in Engineering Science from the University of Toronto (2004). Previously, she was faculty at Dartmouth and a Marie Curie Postdoctoral Fellow at ETH Zurich. Her research interests include 3D printing optimization, structural design for fabrication, and tools for functionally-valid object creation. Notable projects include work on elastic garments, climbing experience replication, and print-wind instrument design. Her work has been featured on TEDx and PBS NOVA, and she has received awards such as the NSF CAREER Award and Sloan Research Fellowship. Education: PhD (MIT), SM (MIT), BASc (University of Toronto) Labs: Shape Design & Computation Lab, BU Computer Graphics Lab Key Projects: Knitting 4D garments, Environment-Scale Fabrication, Thermal-comfort casts Recent professional activities include program committee roles at SIGGRAPH 2025 and UIST 2024, and serving as Program Co-Chair for Pacific Graphics 2024. She advises a team of PhD and MS students, with alumni now in academia and tech industries.
Dr. Amir Sanati Nezhad is a Full Professor in the Department of Biomedical Engineering and Mechanical and Manufacturing Engineering at the University of Calgary's Schulich School of Engineering. He leads the BioMEMS and Bioinspired Microfluidic Laboratory and holds memberships in the Hotchkiss Brain Institute, Snyder Institute for Chronic Diseases, and Arnie Charbonneau Cancer Institute. With a PhD in Mechanical Engineering from Concordia University (2013) and postdoctoral training at Harvard and McGill, he specializes in bioinspired microfluidics, tissue engineering, biosensors, and organ-on-chip technologies. His research focuses on developing point-of-care devices for cancer, brain injury, and infectious disease diagnostics, alongside bioinspired microdevices for disease modeling. He has published over 350 peer-reviewed works and holds prestigious awards like the Canada Research Chair and Governor General’s Gold Medal. His educational background includes degrees from Isfahan University of Technology (B.S., 2006), Amirkabir University (M.S., 2009), and Concordia University (PhD, 2013). Research interests include biosensing, microfluidics, and digital health technologies. Recent articles highlight innovations in wearable biosensors, molecularly imprinted polymers, and self-powered microfluidic systems. His awards reflect contributions to both research and teaching excellence. Grants and collaborations include licensing technologies to diagnostic companies. His lab emphasizes translational research, integrating microfluidics with AI for healthcare applications. He teaches advanced biomedical engineering courses and oversees interdisciplinary projects in organ-on-chip and biomaterials.
Chen Wei Wayne is an Assistant Professor in the Department of Mechanical Engineering at Texas A&M University. His research focuses on generative design AI, machine learning, uncertainty quantification, and advanced manufacturing. He leads the DIGIT Lab, which develops AI methods for design innovation, automation, and manufacturing integration. Education: Ph.D., Mechanical Engineering, University of Maryland, College Park (2019) M.S., Mechanical Engineering, Chongqing University, China (2015) B.S., Mechanical Engineering, Chongqing University, China (2012) Research Interests: Generative adversarial networks (GANs) for design synthesis Data-driven metamaterials and multiscale systems Uncertainty quantification in engineering design AI-driven design automation Awards & Honors: ASME Journal of Mechanical Design Reviewer of the Year Award (2023) ASME DAC Best Paper Award (2022) Journal of Mechanical Design Editors’ Choice Honorable Mention (2021) Lab Activities: Recent lab milestones include successful completion of TAMUQ Summer Research Programs (2024) Hosts undergraduate researchers like Wisam Gadam and Eddie Guerrero
Dr. Nicolas Francois is an Associate Professor in the Department of Materials Physics at Australian National University (ANU), specializing in experimental geomaterials physics, soft matter, and fluid hydrodynamics. He leads the X-ray Tomography and Applications Research Group, combining curiosity-driven and applied research in out-of-equilibrium systems. ARC Industry Fellow (2024-2030): Improving Australian iron ore comminution for green steel production ARC DECRA Fellow (2016-2018): Biofilms in two-dimensional turbulent flows His research spans fundamental questions in: Fragmentation of solid materials Autonomous devices powered by chaotic flows Hydrodynamic waves Stochastic thermodynamics Granular matter Polymer rheology and applied areas in: Comminution of geomaterials Mechanics of fractured rocks Wave-energy conversion Environmental fluid mechanics Publications reveal a trajectory focused on X-ray tomography applications, granular dynamics, and turbulence-driven systems. He utilizes advanced imaging techniques to study material failure mechanisms and fluid-structure interactions, contributing to fields ranging from green steel production to biofilm dynamics. Current student projects and grants emphasize sustainable resource processing and fundamental fluid physics.
Laurent Caraffa is a Researcher at Université Gustave Eiffel, working at the LaSTIG laboratory of IGN (National Institute of Geographic and Forest Information). His research focuses on large-scale 3D data processing, including surface reconstruction from point clouds and images, leveraging triangulated structures and implicit methods. His work also covers indexing and searching within point clouds for large-scale place recognition, with applications in urban environments and navigation systems. Caraffa's research interests span 3D Data Processing, Surface Reconstruction, Point Cloud Processing, Large-scale Place Recognition, Indexing and Retrieval, Big Data, Cloud Computing, Mathematical Optimization, 3D Mapping, and Photogrammetry in degraded conditions. His work bridges theoretical computational geometry with practical applications in geographic information systems and autonomous navigation. His publication record demonstrates significant contributions to distributed 3D processing, particularly through advancements in Delaunay triangulation, watertight surface reconstruction, and neural radiance fields. Recent work shows a clear trajectory toward more efficient and scalable methods for processing massive 3D datasets, with growing emphasis on implicit representations and learning-based approaches for 3D reconstruction. Caraffa actively participates in the scientific community through organizing events like the Big Data Day 2023 at IGN and contributing to major research projects. His work has resulted in publications in top-tier conferences including ICLR, CVPR, ISPRS, and IEEE Big Data, establishing him as a significant contributor to the field of large-scale 3D data processing. As a research supervisor, Caraffa currently co-supervises four PhD students working on projects funded by AID, Criteo, and Huawei, focusing on large-scale place recognition, implicit representations for 3D reconstruction, and 3D reconstruction in degraded conditions. He is also the co-founder of ExtraLabs, a company developing distributed computing solutions for cooperative digital twins, demonstrating the practical impact of his research.
Dr. Carmen Cheh is a Research Scientist in the Department of Computer Science at the University of Illinois at Urbana-Champaign. She holds a Ph.D. in Computer Science from UIUC and a Bachelor of Computing (Hons) from the National University of Singapore. Her research focuses on cyber-physical system security, threat modeling, and critical infrastructure resilience. She has served as Co-Principal Investigator on two major grants: the 2023-2024 project on threat modeling for government systems and the 2022-2025 initiative on real-time fraud detection in e-commerce platforms. Her work bridges theoretical computer security with practical applications in critical infrastructure protection. Dr. Cheh has contributed to tools like CyberSAGE, which aids in automated security argument evaluation, and frameworks for detecting business logic flaws in software systems. She collaborates extensively with industry and government entities to advance cybersecurity practices in both digital and physical domains. Education: Ph.D., Computer Science, University of Illinois at Urbana-Champaign Bachelor of Computing (Hons), National University of Singapore Her research interests emphasize real-world security challenges in interconnected systems, including automated threat modeling, vulnerability discovery in business logic, and compliance frameworks for financial services. She has pioneered constraint-based methodologies for identifying security flaws in complex systems and developed novel approaches to insider threat detection using physical access data. Recent work explores the intersection of machine learning and cybersecurity in fraud detection systems. Her publications span top venues like ACM Transactions on Modeling and Computer Simulation, IEEE Secure Development Conference, and the IEEE International Conference on Computer Communications. Collaborations include projects with institutions like Argonne National Laboratory and the National Science Foundation.
Aaron Maxwell is an Associate Professor in the Department of Geology and Geography at West Virginia University (WVU). He serves as the principal investigator of West Virginia View, a member of AmericaView, and director of the WV GIS Technical Center. His research focuses on spatial predictive modeling, geohazard mapping, machine/deep learning applications in remote sensing, and thematic map accuracy assessment. He holds degrees from Alderson Broaddus University (B.S. in Biology, Chemistry, Environmental Science) and WVU (M.S. and Ph.D. in Geology), with a GIS Professional (GISP) certification. Education: Bachelor of Science in Biology, Chemistry, and Environmental Science – Alderson Broaddus University Master of Science in Geology – West Virginia University Doctor of Philosophy in Geology – West Virginia University Research Interests: Dr. Maxwell’s work emphasizes computational methods to extract insights from geospatial data. Key areas include: Deep learning for geomorphic feature extraction (e.g., LIDAR-based semantic segmentation) Machine learning applications in forest fuel load estimation and slope failure modeling Best practices for assessing deep learning outputs in remote sensing Synthetic data generation for predictive modeling Community flood resiliency and participatory GIS Publications: His recent work highlights advancements in geospatial deep learning (e.g., the geodl R package), accuracy assessment metrics for imbalanced datasets, and modeling post-mining landscape evolution. Articles often blend theoretical frameworks with applied case studies across environmental and geotechnical domains. Grants & Funding: Supported by NSF (CAREER Award) and AmericaView, his work trains students and develops open-source geospatial tools. Current projects include synthetic forest stand modeling and flood resiliency initiatives. Labs & Teams: Leads WV View, fostering remote sensing education and data sharing. Collaborates on geospatial workforce development and open-source software initiatives (e.g., GIScience courses, ArcGIS Pro labs).
Bhavin J. Shastri is an Assistant Professor in the Department of Physics, Engineering Physics and Astronomy at Queen's University in Canada. His research explores the physics of light for computing , pushing frontiers in information and signal processing through photonic computing and quantum/neuromorphic photonics . He is affiliated with the Centre for Nanophotonics and NUCLEUS , a pan-Canadian photonic computing program funded by NSERC CREATE, bridging artificial intelligence and quantum information . Canada Research Chair & Principal Investigator Faculty Affiliate at Vector Institute (2020-) Editorial Board Member of JPhys Photonics (2019-) Member of IEEE Photonics Society Technical Affairs Council (2019-) Visiting Researcher Scholar at Princeton University (2018-) Shastri Lab members have access to world-class shared facilities, including the Centre for Nanophotonics (CFI-Innovation Fund), Nanofabrication Kingston , the Centre for Advanced Computing , and the Digital Research Alliance of Canada . The lab takes an interdisciplinary approach combining nanophotonics with complex systems on emerging substrates. His research focuses on silicon photonics , nanophonic processors , and photonic integrated circuits with applications to deep learning , nonlinear programming , and quantum information science . His articles show consistent exploration of quantum photonic neural networks , photonic memory systems , and optical signal processing for machine learning and quantum technologies . 2020 IUPAP Young Scientist Prize in Optics 2014 Banting Postdoctoral Fellowship 2012 D. W. Ambridge Prize 2011 IEEE Photonics Society Graduate Student Fellowship 2011 NSERC Postdoctoral Fellowship Multiple Best Student Paper Awards Shastri's lab supervises Ph.D. candidates and postdoctoral fellows working on quantum photonics , neuromorphic computing , and photonic AI systems . His recent work includes photonic tensor cores for scientific computing , quantum photonic neural networks , and all-optical memory systems. Shastri Lab designs programmable nanophotonic processors with potential to outperform microelectronic processors in energy efficiency and computational speeds by seven and four orders of magnitude respectively. Their work spans from device design to system-level implementations in optical computing for machine learning and quantum information processing .
Dr. Weihua Zhuang is a University Professor and University Research Chair in the Department of Electrical and Computer Engineering at the University of Waterloo. She holds prestigious fellowships from IEEE, Royal Society of Canada, and other organizations. Her research focuses on future communication networks, including 6G, network virtualization, autonomous vehicles, and smart grids. She has led groundbreaking work on MAC protocols like VeMAC for vehicular networks and has contributed extensively to AI-driven network management. Education : Doctorate in Electrical Engineering, University of New Brunswick, Canada (1993) M.Sc. and B.Sc. in Electrical Engineering, Dalian Maritime University, China Research Interests : Dr. Zhuang's work spans wireless networking, IoT, autonomous systems, and smart infrastructure. She explores solutions for network architecture evolution, machine learning applications in communication systems, and service customization for dynamic environments. Her recent projects include digital twin-driven networks, cross-modal transmission strategies, and AI-native slicing for 6G. Awards : Women's Distinguished Career Award (IEEE VTS, 2021) R.A. Fessenden Award (IEEE Canada, 2021) Fellowships from IEEE, RSC, CAE, EIC Grants & Professional Activities : She led the Tier I Canada Research Chair in Wireless Communication Networks (2010–2024) and has held roles such as IEEE VTS President (2023–2024). Her grants include the NSERC Discovery Accelerator Supplements and PREA awards. She edits journals like IEEE Transactions on Vehicular Technology and co-chairs major conferences. Labs & Teams : Her research group focuses on network architecture, AI-driven protocols, and vehicular communication. Collaborations include projects on 6G, satellite-terrestrial integration, and edge computing for autonomous systems.
Hjalti H. Sigmarsson is an Assistant Professor at the University of Oklahoma's School of Electrical and Computer Engineering within the Gallogly College of Engineering. His research focuses on reconfigurable RF/microwave hardware, spectral management for cognitive radios, heterogeneous integration packaging, and nanomaterial-based device development. Education : B.S.E.C.E., University of Iceland (2003) M.S.E.C.E., Purdue University (2005) Ph.D., Electrical and Computer Engineering, Purdue University (2010) Research Interests : His work advances agile communication systems through tunable microwave components and explores novel packaging techniques for heterogeneous material integration. His nanomaterial research targets next-generation RF devices, while his radar systems development contributes to meteorological observations and mobile phased arrays. Scientific Contributions : He has pioneered liquid metal-tuned filters, substrate integrated waveguide technologies, and evanescent-mode cavity resonators. His publications demonstrate expertise in hybrid acoustic-electromagnetic filters, SAR imaging, and filter shape optimization. Awards : DARPA ASP program recognition (2008) Best paper awards at IMAPS (2008, 2009) Outstanding student paper, IMAPS (2009) Best paper, Microwave/Radio Applications session at IMAPS (2008, 2009) Labs & Centers : He leads research at the University of Oklahoma's Radar Innovations Lab and contributes to the Advanced Radar Research Center. His work includes the Horus All-Digital Phased Array Weather Radar project.
Adlen Ksentini is a Professor at EURECOM, a leading graduate school and research center in Sophia Antipolis, France, specializing in digital science and communication systems. His extensive research focuses on next-generation mobile networks (5G/6G), network management, and the integration of artificial intelligence with telecommunications infrastructure. Dr. Ksentini actively contributes to major EU research initiatives including 6G-BRICKS and AC3, serving as a key researcher and project leader in the development of future network architectures. Dr. Ksentini's research interests center around network slicing, intent-based networking, edge computing, and the application of machine learning to network management problems. His work bridges theoretical advancements with practical implementations in 5G/6G systems, with particular emphasis on zero-touch network management, energy efficiency optimization, quality of service assurance, and the integration of large language models with network operations. His research has significantly contributed to the development of O-RAN (Open Radio Access Network) frameworks and the evolution of network automation. His recent publication trends reveal a strategic shift toward AI-native network architectures, with increasing focus on integrating large language models (LLMs) with network management systems. His work demonstrates a clear progression from traditional network management approaches to more autonomous, AI-powered systems capable of intent-based configuration, self-optimization, and predictive maintenance. The publications show strong emphasis on practical implementations within the 6G research ecosystem, addressing critical challenges in network slicing, resource allocation, and energy efficiency. As a research supervisor, Dr. Ksentini mentors several PhD students including Abdelkader Mekrache, Karim Boutiba, Bouziane Brik, and Houda Hafi, who frequently appear as co-authors on his publications. His research is primarily funded through major EU research projects such as 6G-BRICKS (Building Reusable Testbed Infrastructures for Cloud-to-Device Breakthrough Technologies) and AC3 (which focuses on Cloud Edge Continuum). Dr. Ksentini is actively involved with the 6G-BRICKS project consortium and the AC3 project team, where he contributes to developing next-generation network architectures that integrate communication, computing, and sensing capabilities. His work within these projects focuses on creating reusable testbed infrastructures and addressing security and trust management challenges in the cloud-edge continuum.
David E. Breen is a Professor in the Department of Computer Science within the College of Computing & Informatics (CCI) at Drexel University. He leads the Geometric Biomedical Computing Group and is affiliated with the Metadata Research Center and the Center for Biological Discovery from Big Data. His research spans interdisciplinary domains including biomedical image informatics, geometric modeling, textile modeling, and bio-inspired self-organization algorithms. Education: PhD, Computer and Systems Engineering, Rensselaer Polytechnic Institute MS, Computer and Systems Engineering, Rensselaer Polytechnic Institute BA, Physics, Colgate University His research interests focus on computational methods for biomedical applications, including shape and image analysis for cancer diagnosis, 3D reconstruction of biological tissues, and video analysis of animal behavior. He also investigates geometric modeling techniques for textiles and self-organizing systems. His work integrates computer science with biology, medicine, and engineering to solve complex problems in biomedical computing. The recent publications highlight a strong trend in computational modeling of textiles, biomedical image informatics, and AI-driven data analysis. Key themes include geometric modeling of knitted fabrics, deep learning for medical image classification, agent-based modeling of cancer metastasis, and metadata generation for biological image collections. His work bridges fundamental geometric algorithms with practical applications in healthcare and digital archives. Scientific Awards: No specific awards mentioned in the provided text. Breen has advised numerous students and collaborators across multiple domains, particularly in biomedical computing and textile modeling. His research has been supported through affiliations with major centers and collaborations with institutions such as Johns Hopkins University and the Max Planck Institute. He has been involved in projects related to NSF Center for Visual & Decision Informatics and has contributed to over 100 technical publications. He leads the Geometric Biomedical Computing Group , which conducts research at the intersection of biology, medicine, engineering, and computer science. The group develops algorithms and software for geometry-related computing problems in biomedical applications. Collaborations include the Drexel Integrated Laboratory for Cellular Tissue Engineering, Dr. Dan Marenda's Lab, and Dr. Aleister Saunder's Lab in Drexel's Biology Department.
