Pasi Lautala is a Professor in the Department of Civil, Environmental, and Geospatial Engineering at Michigan Technological University (Michigan Tech) and currently serves as the Associate Dean for Research. He holds a BS from Tampere University of Technology (Finland) and MS/PhD from Michigan Tech. His research focuses on rail and highway transportation engineering, with emphasis on grade crossing safety, multimodal logistics, sustainability, railway capacity analysis, and engineering education development. Since 2007, Lautala has directed the Rail Transportation Program (RTP) within the Michigan Tech Transportation Institute (MTTI), expanding rail research collaborations across disciplines. He leads over $10M in external research funding, including projects on trespasser safety, freight logistics, and lifecycle analysis. Lautala is a key figure in rail education revitalization, serving as Rail Group Chair at the Transportation Research Board (TRB) and advising the Michigan Commission for Supply Chain Logistics. His teaching spans courses like Transportation Engineering, Railroad Design, and Logistics Management. Lautala has advised numerous undergraduate and graduate projects, emphasizing industry partnerships. Recent contributions include developing in-vehicle auditory alerts for rail crossings and AI-driven safety systems like RAIILS. Key collaborations include the Federal Railroad Administration (FRA) on grade crossing safety ($641K+ projects), U.S. DOT on rail modal analysis, and Battelle on connected vehicle systems. He mentors the Tracks to the Future youth program and co-leads international rail education initiatives.
Dr. Ahmad Alsharif is an Assistant Professor in the Department of Computer Science at the University of Alabama's College of Engineering. His research expertise spans applied cryptography, IoT security, cyber-physical systems security, and blockchain applications. He received his B.S. and M.S. in Electrical Engineering from Benha University, Egypt, and Ph.D. in Electrical and Computer Engineering from Tennessee Tech University. Research focuses on security challenges in critical infrastructure systems including smart grids, IoT networks, and UAV systems. Current projects investigate privacy-preserving machine learning techniques, adversarial attack resilience, secure data marketplaces, and attack detection mechanisms for distributed energy systems. His work combines cryptographic protocols with machine learning for trustworthy systems. Awards include the NSF Research Initiation Initiative Grant (NSF CRII) and Young Innovator Award from Egyptian Industrial Modernization Center.
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.
Safa Otoum is an Assistant Professor at the College of Technological Innovation (CTI), Zayed University, UAE, and holds an adjunct role at the School of Computer Science and Electrical Engineering. She is a licensed Professional Engineer (P.Eng.) in Ontario and a member of IEEE and ACM. Her expertise spans network security, blockchain, AI, and IoT, with a focus on intrusion detection and prevention systems. Education: She earned a M.A.Sc. (2015) and Ph.D. (2019) in Computer Engineering from the University of Ottawa, Canada, followed by postdoctoral research there. She has held roles as a data scientist at Cheetah Networks and as a researcher in reputable institutions. Research Interests: Her work emphasizes AI-driven security solutions, blockchain applications in IoT, federated learning, and sustainable smart city infrastructure. She explores machine/deep learning for cybersecurity and has pioneered architectures for secure vehicular networks and healthcare systems. Publications: Over 15 peer-reviewed articles in top journals/conferences like IEEE Transactions on Network, ACM TOIT, and GLOBECOM. Notable contributions include highly cited blockchain surveys and award-winning intrusion detection frameworks. Awards: Recipient of prestigious scholarships (NSERC, Canada Graduate Scholarship) and grants (RIF, TII). Honored with a Best Paper Award for intrusion detection research in critical infrastructure. Service Roles: She serves as Area Editor for Springer's Cluster Computing, chairs international workshops on securing healthcare systems, and organizes conferences on network security and intelligent transportation. She also guest-edits special issues on AI-driven healthcare in journals like Electronics.
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.
Lorenzo Sani is a PhD student in the Department of Computer Science and Technology at the University of Cambridge, supervised by Prof. Nicholas D. Lane and part of the CaMLSys research group. His work focuses on federated learning, edge computing, and privacy-preserving machine learning algorithms for large-scale distributed systems. Education: He holds a Bachelor's Degree in Physics from the University of Bologna (2019) and a Master's Degree in Applied Physics from the same institution (2021), with a thesis on unsupervised clustering of MDS data using federated learning. During his studies, he contributed to the GenoMed4All project and collaborated with the CaMLSys group on the Flower Framework. Research Interests: Sani's research emphasizes optimizing federated learning efficiency, privacy in distributed machine learning, and the application of federated techniques to large language models. His work addresses challenges in communication efficiency, client collaboration, and ethical data usage in decentralized systems. Teaching: He serves as a Teaching Assistant for the Principles of Machine Learning Systems (L46) and Federated Learning: Theory and Practice (L361) courses, and supervises students at Jesus College for Algorithm and Artificial Intelligence modules. Publications: His recent work includes innovations in federated optimization (DES-LOC, SparsyFed), LLM unlearning (LUNAR), and global federated training systems (Photon, Worldwide federated training). The 2020 Flower Framework paper established a foundational research tool for federated learning experimentation.
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).
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.
Tara Javidi holds the Jerzy (George) Lewak Endowed Chair and is a Professor in the Department of Electrical and Computer Engineering and Halicioglu Data Science at the University of California San Diego (UCSD). She leads multiple initiatives, including serving as Founding CTO of KavAI, Co-Director of the Center for Machine Intelligence, Computing and Security, and Co-Principal Investigator (CoPI) of the NSF AI Institute TILOS. Her research focuses on stochastic analysis, design, and control of information systems, emphasizing active learning, decentralized optimization, and wireless networks. Key areas include information acquisition/utilization, stochastic control, and AI-driven communication solutions. Her work bridges theoretical foundations and practical implementations, such as drone systems for information gathering (via detecdrone.ucsd.edu) and optical data center networking. Notable contributions include end-to-end scheduling for all-optical data centers and hybrid wireless-optical architectures. Javidi is an IEEE Fellow and has received significant grants, including leading UCSD’s Schmidt AI in Science Postdoctoral Fellowship program. She actively collaborates with industry and academia, with a focus on next-generation wireless networks and decentralized systems. Education: Ph.D. in Electrical Engineering (implied from title). Affiliations: IEEE Journal of Selected Areas in Information Theory (Editor-in-Chief), CALIT2, CNS, and TILOS. Grants: NSF AI Institute TILOS ($20M over 5 years), Schmidt AI Fellowship program. Her research group emphasizes both theoretical rigor (e.g., sequential hypothesis testing) and practical testing, with applications in service drones, cognitive networks, and federated learning. Recent articles highlight advancements in optical networking, secure communication, and distributed learning protocols. Awards: IEEE Fellow, Jerzy Lewak Chair. Labs/Teams: Center for Machine Intelligence, TILOS Institute, KavAI, and UCSD’s AI in Science initiatives.
Vincent Grégoire is a Full Professor in the Department of Finance at HEC Montréal. He holds a Ph.D. in Finance from the University of British Columbia, M.Sc. degrees in Financial Engineering and Electrical Engineering from Université Laval, and is a Chartered Financial Analyst (CFA). His research focuses on information economics, market microstructure, financial big data analytics, cybersecurity in finance, and machine learning applications in finance. Grégoire is affiliated with the Multidisciplinary Institute for Cybersecurity and Cyber Resilience (IMC²) and IVADO, and collaborates with Fin-ML. He co-chaired the Northern Finance Association in 2024-2025. His recent work includes groundbreaking studies on market microstructure dynamics, passive investing trends, and the implications of cybersecurity on financial systems. His research has been recognized with awards such as the 2022 Best Paper Award in Asset Pricing (Northern Finance Association) and the 2022 Chenelière Éducation/Gaëtan Morin Research Prize from HEC Montréal. Grégoire has supervised over 25 master’s theses and projects, covering topics like cryptocurrency diversification, ESG risk exposure, and fintech-driven solutions for sustainable practices. In teaching, he instructs courses such as Empirical Finance and Investment Analysis. His methodologies emphasize reproducibility and cutting-edge tools like Python for financial data analysis.
Anees Baqir is an Assistant Professor of Data Science at Northeastern University London, affiliated with the CoMENS Faculty's Data and AI department. He is also a research fellow at the Complex Human Behavior (CHuB) lab at Fondazione Bruno Kessler (FBK), Trento, Italy, contributing to the European-funded AI-CODE project studying misinformation and polarization in social media. His research focuses on analyzing online information dynamics, polarization, and machine learning applications in healthcare, crime prediction, and language processing. Education details are not explicitly provided here, but his work bridges computer science and social sciences through interdisciplinary projects. He is part of the Complex Systems Society and collaborates globally, leveraging Northeastern University's transnational network across 13 campuses in the UK, US, and Canada. Research interests include: (1) Misinformation spread and polarization in digital ecosystems, (2) Machine learning for health analytics and behavioral prediction, (3) NLP innovations for Urdu and multilingual content analysis, (4) Spatio-temporal crime modeling for smart cities, and (5) Computational social science methods for political and societal dynamics. His recent publications span 2020–2025, exploring topics like Twitter-based PTSD detection, Urdu language processing systems, and AI-driven crime prediction. His work frequently intersects technical methodologies with societal impact, such as analyzing polarization in Pakistan’s political discourse or developing frameworks for university course scheduling. He holds no explicitly listed academic awards but is actively involved in research initiatives addressing global challenges like misinformation and public health surveillance. His advisory roles and grant activities are not detailed here, though his CHuB lab affiliation suggests collaborative funding opportunities. Key affiliations include the CHuB lab (FBK, Italy), Complex Systems Society, and Northeastern’s global network. He contributes to projects like AI-CODE, focusing on federated social media analysis and European misinformation trends.
Norman R. Swanson is a Distinguished Professor and James Cullen Chair in Economics at Rutgers University. He holds a PhD from the University of California, San Diego, and a degree from the University of Waterloo. Primary Affiliations: Department of Economics, Rutgers University Previous Positions: Pennsylvania State University, Texas A&M University, Purdue University, IBM Canada His research focuses on financial econometrics , forecasting , machine learning and big data , and time series analysis . He has published over 100 peer-reviewed articles and served as editor for journals like the Journal of Econometrics and Journal of Business and Economic Statistics . His work often bridges theoretical econometrics with practical applications in finance and macroeconomics, emphasizing robustness and predictive accuracy. The articles listed reflect his expertise in volatility modeling , jump detection , data reduction , and forecasting methodology . Key trends include the use of shrinkage methods, factor models, and simulation-based testing in high-frequency financial and macroeconomic contexts. Scientific Awards: Fellow of the Journal of Econometrics Fellow of the International Association of Applied Econometrics He has acted as a visiting scholar at institutions like the University of Maryland and the Federal Reserve Bank of Philadelphia. His consulting work spans firms such as Union Bank of Switzerland and DFA Capital Management, with expertise as a legal expert witness in financial services cases.
Dr. Yimei Zhang is an Assistant Professor at the Jake Jabs College of Business & Entrepreneurship, Montana State University. She holds a Ph.D. in Accounting from the University of South Florida (2023) and an M.S. in Accounting from Northern Illinois University (2019). Her research focuses on textual analysis, natural language processing, machine learning, auditing, and accounting information systems. She teaches courses in taxation, financial accounting, and data analytics. Her recent publications explore the impacts of iXBRL adoption on data usability, data breaches on audit fees, and machine learning's role in assessing going concern accuracy. These studies highlight her expertise in integrating technology with traditional accounting practices. No scientific awards are listed, and no advising/grants are mentioned. Courses taught include Federal Income Taxation and Principles of Financial Accounting. No specific lab or team affiliations are noted.
Jin Lu is an Assistant Professor at the University of Georgia's School of Computing, part of the Franklin College of Arts & Sciences. He earned his Ph.D. (2019) and M.S. (2019) in Computer Science and Engineering from the University of Connecticut. Prior to his current role, he served as an Assistant Professor at the University of Michigan–Dearborn (2019–2023). Educational Background: Ph.D. in Computer Science and Engineering, University of Connecticut, 2019 M.S. in Computer Science and Engineering, University of Connecticut, 2019 Research Interests: Dr. Lu focuses on machine learning, optimization, bio-informatics, and smart mobility. His work spans federated learning, healthcare applications (e.g., depression and BMI monitoring), IoT systems, and computer vision. Recent projects explore AGI's potential in medical and educational contexts, leveraging models like CycleGAN and reinforcement learning. Grants & Funding: Develop digital brains to advance portable diagnosis of neurological conditions (Google, 2025) Lab/Teams: While specific lab affiliations are not explicitly stated, his research involves collaborations in interdisciplinary areas such as health informatics and smart mobility.
Yasmeen George is a Senior Lecturer in the Department of Data Science & AI at Monash University's Faculty of Information Technology. She holds a PhD in Medical Image Processing from the University of Melbourne (2018) and a Master's in Computer Science from the University of Ain Shams (2013). Her research focuses on AI applications in healthcare, particularly medical image analysis for conditions like cancer, glaucoma, and psoriasis. She co-founded the AIM for Health Lab at Monash IT and is a research affiliate at the Victorian Institute of Forensic Medicine. Education: PhD (Medical Image Processing), University of Melbourne (2018); Master of Computer Science (Medical Image Analytics), University of Ain Shams (2013). Research interests include AI-driven medical image analysis, machine learning, and cross-domain healthcare analytics in radiology, dermatology, and ophthalmology. Her work addresses challenges in automated disease detection, lesion segmentation, and severity assessment across modalities like 2D/3D imaging and text data. Recent articles focus on kidney segmentation, glaucoma detection, federated learning for breast cancer, and AI frameworks for hazardous waste policy. Her research has led to patents with IBM and grants from MRFF and NHMRC. Awards: Recipient of the 2023 Heidelberg Laureate Foundation Alumni Award. Collaborations include projects on clean energy sustainability and hazardous waste management through advanced AI techniques. She actively advises PhD students and engages in interdisciplinary research teams across Monash and industry partners. Labs/Teams: Co-founder of the AIM for Health Lab, affiliated with the Victorian Institute of Forensic Medicine.
