Dr. Ian Church is an Associate Professor in the Department of Geodesy and Geomatics Engineering within the Faculty of Engineering at the University of New Brunswick. He specializes in ocean mapping using acoustics, with primary research interests in integrating hydrodynamic numerical modeling with ocean mapping, marine habitat mapping, acoustic water column interpretation, and evaluating autonomous mapping platforms. His research aims to better understand the marine environment using acoustics and minimize uncertainty in ocean mapping products. Dr. Church received all his degrees (B.Sc.Eng, M.Sc.Eng, Ph.D.) from UNB while working with the Ocean Mapping Group. His professional experience includes operating and managing multibeam sonar systems onboard the coast guard ship Amundsen in the Canadian Arctic (2005-2013), and positions at the University of Southern Mississippi before returning to UNB in 2016. His publications demonstrate consistent focus on hydrographic surveying, ocean mapping technologies, and marine data processing. Recent trends show increased emphasis on AI applications in seabed mapping, advanced sensor technologies, collaborative training programs, and hydrodynamic modeling for coastal management. Dr. Church also contributes to educational initiatives like the COMREN International School on Hydrographic Surveying.
Professor Jinjun Shan is a Full Professor of Space Engineering and former Department Chair (2018-2023) in the Department of Earth and Space Science and Engineering at York University's Lassonde School of Engineering. An internationally recognized expert in dynamics, control and navigation, he joined York University as an Assistant Professor in 2006, was promoted to Associate Professor in 2011, and became a Full Professor in 2016. Dr. Shan received his B.Eng., M.Eng., and Ph.D. degrees from Harbin Institute of Technology, China, in 1997, 1999, and 2002, respectively. Before joining York, he was a Post-Doctoral Fellow at the University of Toronto Institute for Aerospace Studies (2003-2006) and a Research Assistant at City University of Hong Kong (2002-2003). His research focuses on dynamics, control and navigation, autonomous systems, multi-agent systems, smart materials and structures, space instrumentation, active vibration control, and orbit dynamics. Dr. Shan has made significant contributions to national and international space missions including NEOSSat and has attracted over $5 million in research funding from governmental agencies and industry partners. His laboratory, the Spacecraft Dynamics Control and Navigation Laboratory (SDCNLab), which he founded in 2006, conducts cutting-edge research in space engineering. Dr. Shan's extensive publication record includes over 200 peer-reviewed journal and conference papers, with his most recent work focusing on multi-agent formation control, autonomous vehicle decision-making, quadrotor control systems, and smart material applications. His research shows a clear progression from fundamental dynamics and control theory toward increasingly complex multi-agent systems and real-world applications in autonomous vehicles and space engineering. Fellow of Canadian Academy of Engineering (CAE) Fellow of Engineering Institute of Canada (EIC) Fellow of American Astronautical Society (AAS) Associate Fellow of AIAA Alexander von Humboldt Research Fellowship JSPS Fellowship Lassonde Educator of the Year Award (2022) Named in Stanford's list of world's top 2% researchers Dr. Shan has successfully mentored numerous graduate students and post-doctoral fellows, with current advisees working on cutting-edge projects in multi-agent systems, UAV control, and smart materials. His research is supported by substantial funding from NSERC, CSA, and industry partners. As the founding director of SDCNLab, he has built a comprehensive research facility for spacecraft dynamics, control, and navigation, recently expanding to include autonomous unmanned vehicle research through a CFI JELF award. His laboratory continues to make significant contributions to both theoretical advancements and practical applications in space engineering and autonomous systems.
Dr. Luigi Benedicenti is a Professor and Dean of the Faculty of Computer Science at the University of New Brunswick. He holds a Laurea in Electrical Engineering and a Ph.D. in Electrical and Computer Engineering from the University of Genoa, Italy. A licensed Professional Engineer in Saskatchewan and an Italian-licensed Engineer, he collaborates extensively with TRLabs and IEEE, engaging in global research partnerships across Europe, Southeast Asia, and North America. His research focuses on three core areas: Software Agents, Software Process Improvement, and New Media Technology. Current work emphasizes unifying distributed digital service platforms, optimizing delivery across devices and locations. Benedicenti's recent studies explore emotional contagion in open-source collaborations, agile methodologies in mission-critical systems, and decision-support frameworks for software development. Publications highlight themes like emotional dynamics in agile teams, optimization in manufacturing processes, and adaptive frameworks for defense software. His work bridges theoretical models with practical applications in security-critical systems and ubiquitous computing environments. Professional contributions include leadership roles in advancing software engineering practices and fostering interdisciplinary collaboration. His research integrates technical innovation with human-centric design principles to address complex challenges in modern software ecosystems.
Dr. Oliver Terna Iorhemen is an Assistant Professor of Environmental Engineering at the University of Northern British Columbia (UNBC), joining in August 2021. He holds a Ph.D. in Civil Engineering (Environmental Engineering) from the University of Calgary, an MSc from the University of Leeds, and a B.Eng. from Ahmadu Bello University, Nigeria. His research focuses on biological wastewater treatment, resource recovery from wastewater, and rural water supply solutions. He has participated in collaborative projects with municipalities, industries, and universities. Education Ph.D. in Civil Engineering (Environmental Engineering), University of Calgary (2019) MSc(Eng) in Environmental Engineering & Project Management, University of Leeds (2011) B.Eng. in Water Resources & Environmental Engineering, Ahmadu Bello University (2008) Research Interests Dr. Iorhemen’s work emphasizes aerobic granular sludge (AGS) technology for wastewater treatment and resource recovery, including phosphorus removal, contaminants of emerging concern (CEC), and biofilm processes. His current projects explore AGS applications in brewery wastewater treatment, biofilter systems for rural communities, and constructed wetlands in cold climates. He integrates machine learning and data-driven models to optimize bioreactor performance. Scientific Awards Recipient of prestigious awards such as the NSERC Postdoctoral Fellowship , Teaching Achievement Award (UCalgary), and multiple scholarships including the Eyes High International Doctoral Scholarship . Advising & Collaborations Supervises Master’s students in areas like curdlan recovery, xanthan gum extraction, and cold-climate wetland performance. Collaborates with industry partners and municipalities on applied research projects. His team includes researchers like Erik Groenenberg (MASc) and Manveer Kaur (MASc). Labs & Teams Hosts an active research team at UNBC focusing on AGS bioreactors, biofilm dynamics, and sustainable water solutions. Facilities include advanced wastewater treatment setups and analytical laboratories.
Dr. Shaon Bhatta Shuvo is an Assistant Professor at the University of Windsor's School of Computer Science. He holds a Ph.D. from the University of Windsor (2020–2024), an M.Sc. from South Asian University (2013–2015), and a B.Sc. from Noakhali Science and Technology University, Bangladesh. Research Interests: Deep Learning & Reinforcement Learning: Development and application of advanced algorithms for computer vision and natural language processing. AI-Based Decision Support Systems: Designing AI-driven systems for healthcare and social network analysis. Modelling and Simulation: Multi-agent simulations and mathematical modeling for complex systems, including pandemic preparedness and healthcare optimization. Research Trends in Publications: His work focuses on AI-driven solutions for healthcare (e.g., pandemic modeling, PPE demand prediction), social network analysis (link/node classification, knowledge graphs), and computational epidemiology. He emphasizes hybrid simulation models and data-driven approaches for real-world challenges. Awards: No scientific awards explicitly mentioned. Advising & Grants: No student advisees or grants listed. Labs/Teams: Not specified in available information.
Dr. Rishad Irani is Associate Professor in Mechanical and Aerospace Engineering at Carleton University, specializing in mechatronic systems for marine applications. His research focuses on motion control and robotic systems for challenging maritime environments. Core research areas: Marine motion compensation Underwater vehicle launch/recovery Robotic machining precision Offshore crane control Current projects develop advanced control algorithms for shipboard crane systems and novel actuation methods for marine platforms, with industry partnerships including Rolls-Royce Marine.
Abdelghny Orogat is a Contract Instructor at the School of Computer Science, Carleton University, located at Herzberg Laboratories in Ottawa. He specializes in Knowledge Graphs, Question Answering Systems, and Web Technologies with a focus on Data Management and Machine Learning applications. His research includes developing benchmarks for evaluating QA systems over Knowledge Graphs and advancing data analytics in telecom systems through Ericsson's frameworks. He has authored several articles on Knowledge Graph applications, automated benchmarking tools, and web adaptation techniques. Research interests prominently feature Knowledge Graphs, with contributions to datasets like QueryBridge and frameworks such as Maestro and SmartBench. His work bridges theoretical advancements in AI with practical applications in enterprise data management and web technologies. Articles from 2021-2024 highlight trends in QA system evaluation, automated benchmark generation, and scalable data systems. Dr. Orogat has no listed scientific awards or grants in the provided information. He currently holds no documented roles in labs or research teams beyond his instructional duties.
Dr. Edward Brown is an Associate Professor in the Department of Computer Science at Memorial University of Newfoundland, part of the Faculty of Science. His research focuses on human-computer interaction, hypertext systems, scientific visualization, and legal aspects of technology. He holds degrees from Memorial University (B.Sc.), the University of Toronto (M.Sc., Ph.D.), and the University of Victoria (LL.B.). His current research includes augmented reality applications for marine navigation, multimedia teaching strategies for K-12 education, and the design of user interface agents. Notable projects involve developing hypermedia tools for educational environments and studying complexity in finite-domain problems using genetic algorithms. Dr. Brown also practices technology law and has expertise in intellectual property issues. Teaching responsibilities include courses such as CS2760 (Encountering the Computer) and CS3718 (Programming in the Small). His work bridges computer science with pedagogical innovation and legal implications of emerging technologies.
Kevin Englehart is a Professor in the Department of Electrical and Computer Engineering at the University of New Brunswick and currently serves as the Associate Dean of Graduate Studies. He holds a PhD and is a Professional Engineer (PEng). His primary affiliation is with the Institute of Biomedical Engineering, where he conducts groundbreaking research on advanced prosthetic control systems. Englehart's work focuses on improving the usability and adaptability of myoelectric prostheses through machine learning, signal processing, and biomechanical modeling. He previously served as Director of the Institute of Biomedical Engineering, demonstrating leadership in fostering interdisciplinary research. His research interests include electromyography (EMG) signal analysis, human-machine interfaces, and the application of artificial intelligence in healthcare technologies. Englehart's publications span over two decades, with a strong emphasis on real-time control systems, noise reduction techniques, and user-centric design principles for assistive devices. His academic contributions extend to the development of novel training paradigms for prosthetic users and the mitigation of technical challenges such as electrode shift and signal instability. Englehart collaborates extensively with clinical and engineering teams to translate theoretical advancements into practical solutions for individuals with physical disabilities. His office is located at R.N. Scott Hall 219 in Fredericton, New Brunswick.
Jason Jaskolka is an Associate Professor in the Department of Systems and Computer Engineering at Carleton University, part of the Faculty of Engineering and Design. He holds a Ph.D. from McMaster University and is a licensed Professional Engineer in Ontario. His research focuses on cyber security evaluation and assurance, formal methods, and secure software engineering. He leads the Cyber Security Evaluation and Assurance (CyberSEA) Lab, emphasizing security-by-design principles for complex systems like industrial control systems and IoT-enabled healthcare. Education: Ph.D. (Software Engineering, McMaster University, 2015), M.A.Sc. (Software Engineering, McMaster University, 2010), B.Eng. (Software Engineering and Game Design, McMaster University, 2009). Research interests include: Cyber Security Evaluation & Assurance, Data-Driven Security Metrics, Formal Verification of Security Properties, Secure Software Architecture Design, and Industrial Cyber-Physical Systems Security. His work addresses challenges in threat modeling, compliance with security standards, and mitigating implicit system vulnerabilities. Recognition includes the 2021 New Faculty Excellence in Teaching Award for innovative pedagogy. He actively supervises graduate students in funded research positions. Key collaborations include Health Canada's Scientific Advisory Committee on Digital Health Technologies and the U.S. Department of Homeland Security’s Cybersecurity Postdoctoral Fellowship at Stanford University. Labs/Teams: CyberSEA Lab, focusing on developing rigorous security evaluation frameworks and tools for software-dependent systems.
Lucas Lehnert is an Assistant Professor in the Department of Computer Science at the University of Saskatchewan, specializing in Artificial Intelligence and Reinforcement Learning (RL). His research focuses on how intelligent systems can learn to solve complex decision-making tasks through representation learning, abstraction mechanisms, and lifelong learning strategies. He also explores applications of AI/RL in scientific and engineering domains. Education: PhD in Computer Science (Brown University, 2021), MSc (McGill University, 2016), BSc (McGill University, 2014). Postdoctoral positions included Meta's FAIR team (2022–2024) and the Mila Quebec AI Institute (2021–2022). Research interests include reinforcement learning fundamentals, generative AI reasoning, exploration strategies, and reward-predictive representations. His work bridges model-based and model-free RL paradigms, emphasizing scalable and generalizable solutions. Awards include the Best Student Workshop Paper Award (2017) and an NIMH training grant in cognitive neuroscience. His research has been published in top conferences like NeurIPS, ICML, and ICLR. He advises graduate students in RL and collaborates on projects involving transformer-based planning, exploration algorithms, and multi-agent systems. Current work includes developing SearchFormer for efficient planning tasks and exploring maximum entropy exploration methods.
Chadi Assi is a Professor and Tier II Concordia Research Chair at the Concordia Institute for Information Systems Engineering, Concordia University. His research focuses on wireless networks, information security, and smart grid systems, with particular emphasis on reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), cybersecurity for electric vehicles (EVs), and machine learning-driven network optimization. He has pioneered work on mitigating cyber-physical attacks in power grids and IoT ecosystems, while advancing cooperative communication protocols like RSMA and NOMA. His technical contributions span theoretical frameworks for energy efficiency maximization in hybrid SDMA/NOMA schemes, low-complexity RIS element selection algorithms, and adversarial PINN models for grid dynamics. He also investigates vulnerabilities in EV charging infrastructure and O-RAN synchronization protocols, proposing robust detection mechanisms like PEACE and Grid Mirror. His interdisciplinary work bridges communications, power systems, and AI, addressing challenges in 5G/6G security and resilient IoT provisioning. Key Research Areas: RIS-enabled ISAC networks, EV cybersecurity, meta-learning in communications, IoT malware analysis Current Projects: Grid resilience against load-altering attacks, Movable antenna optimization, federated learning for AGC systems Recent publications (2024-2025) emphasize deep reinforcement learning frameworks for RIS-aided networks, cooperative RSMA performance enhancement, and defense mechanisms against dynamic trigger-based attacks. He has also developed novel datasets for advanced persistent threats and frameworks like ChargePrint for EV charging security analysis. His work is published in top venues including IEEE Transactions on Smart Grid, IEEE JSAC, and IEEE ICC, reflecting contributions to both theoretical advancements and practical system implementations.
Rachida Dssouli is a Professor at the Concordia Institute for Information Systems Engineering (Concordia University). Her research focuses on advanced software engineering methodologies, quality assurance systems, and distributed computing frameworks. She specializes in model-based testing, federated learning optimization, and big data quality management. Her work integrates formal verification techniques with modern machine learning approaches to address challenges in edge computing, IoT, and safety-critical systems. Key research areas include: Development of hybrid swarm intelligence algorithms for optimizing large language model deployment in edge-cloud environments Design of reinforcement learning frameworks for robotics motion planning and IoT device scheduling Creation of interpretable machine learning tools for fault detection in software systems Establishment of holistic big data quality frameworks for continuous monitoring and unstructured data analysis Formal verification methods for avionics systems using multi-agent models Her recent work demonstrates trends toward AI-driven solutions for testing methodologies (e.g., SHAP-Driven fault detection) and edge-cloud integration (e.g., MIMO-based computation offloading optimization). The 2025 publications highlight advancements in federated learning and trust-aware IoT scheduling. Earlier works (2018-2020) emphasize foundational contributions to cloud trust models, big data quality metrics, and safety-critical system testing. Her research also addresses emerging technologies for developing countries through frameworks like neurodegenerative disease monitoring systems and mobile application requirements engineering. She has contributed to service-oriented architectures for healthcare systems and cloud-based resource orchestration strategies.
Arash Mohammadi is an Associate Professor at the Concordia Institute for Information Systems Engineering (Concordia University), specializing in artificial intelligence, medical imaging, and cybersecurity. His research focuses on advancing AI-driven solutions in healthcare diagnostics, autonomous systems, federated learning, and edge computing. He supervises graduate programs in Information Systems Security, Electrical and Computer Engineering, and related disciplines. Key research interests include medical image classification using deep learning, reliability of autonomous driving systems, and secure communication in smart grids. His work integrates transformer architectures, Bayesian methods, and graph neural networks to address challenges in healthcare, robotics, and network optimization. Notable contributions include frameworks for lung nodule malignancy prediction, ECG-based emotion recognition, and secure federated learning in resource-constrained environments. Dr. Mohammadi has published extensively in top-tier venues such as IEEE conferences and journals, with a focus on practical applications of AI in healthcare and engineering systems. He actively engages in interdisciplinary projects involving biomedical signal processing, human-computer interaction, and resilience engineering for cyber-physical systems.
Sandra Cespedes is an Assistant Professor at Concordia University's Department of Computer Science & Software Engineering within the Gina Cody School of Engineering and Computer Science. She previously held roles as an Associate Professor and Head of Research at NIC Chile Research Labs, Universidad de Chile, and maintains an honorary Adjunct Professorship at Universidad Icesi, Colombia. She is also an Associate Researcher at the Advance Center of Electrical and Electronic Engineering (AC3E), Chile. Education: B.Eng. (Telematics Engineering) and Specialization (Management of Information Systems), Universidad Icesi, Colombia (2003, 2007) Ph.D. (Electrical and Computer Engineering), University of Waterloo, Canada (2012) Research Interests: Her research focuses on wireless networks, IoT, vehicular communications, and satellite networking. Key areas include: Design of protocols for constrained IoT devices Vehicular safety systems and cooperative communication Direct-to-satellite IoT (DtS-IoT) architectures Rural connectivity solutions Publications: Her recent work explores satellite IoT protocols, vehicular network optimization, and distributed hypothesis testing. Notable contributions include MAC protocol design for satellite IoT and safety message dissemination in VANETs. Teaching: At Concordia, she teaches undergraduate courses like Data Communications and Computer Networks and Embedded Systems , and graduate courses such as Computer Networks and Protocols . Previously, she taught wireless networking and IoT development at Universidad de Chile.
