Professor Shigeru Fujimura is affiliated with Waseda University as a faculty member of the Faculty of Science and Engineering , specifically in the Graduate School of Information, Production, and Systems . With a PhD in Engineering from Waseda University, his research focuses on intelligent informatics and system engineering , particularly in scheduling, optimization, and human-robot collaboration. His research spans multiple domains including: Deep Reinforcement Learning for combinatorial optimization Energy-efficient manufacturing systems Multi-agent collaboration frameworks Augmented Reality interfaces IoT business modeling Recent publications demonstrate expertise in graph neural networks , particle swarm optimization , and generative adversarial networks applied to industrial problems. He has received multiple awards including the Invention Encouragement Prize (2003) and IEEJ Paper Presentation Award (1994) .
Alaa Sheta is a tenured Professor of Computer Science at Southern Connecticut State University , New Haven, CT, USA. With over 180 refereed publications, three authored books, and extensive funded research, he is a globally recognized authority in machine learning, evolutionary computation, image processing, and robotics. Education B.E. Electronics & Communication Engineering, Cairo University, 1988 M.Sc. Electronics & Communication Engineering, Cairo University, 1994 Ph.D. Computer Science, George Mason University, USA, 1997 Research Interests Prof. Sheta’s research integrates machine learning , deep learning , and evolutionary algorithms to solve complex real-world problems. Core themes include image and signal processing for medical and industrial applications, autonomous robotics for navigation and inspection, big-data analytics for environmental and financial forecasting, and software reliability modeling using computational intelligence. His work frequently leverages meta-heuristic optimization techniques such as genetic algorithms, particle swarm optimization, and hybrid neuro-fuzzy systems. Publication Trends From 2015-2021, Prof. Sheta’s publications reveal a clear pivot toward deep learning and healthcare informatics , with multiple studies on obstructive sleep-apnea diagnosis using ECG and depth-sensor data, brain-tumor detection in MR images, and mobile-health applications. Earlier work emphasizes industrial process modeling , power-system optimization , and software effort estimation , reflecting sustained contributions across both theoretical algorithmic advances and high-impact interdisciplinary applications. Scientific Awards & Honors Best Poster Award, SGAI International Conference on Artificial Intelligence, Cambridge, UK, 2011 Senior Member, IEEE Vice-President, Arab Computer Society (2011) Associate Editor, International Journal of Advanced Computer Science and Applications (IJACSA) Associate Editor, International Journal of Computational Complexity and Intelligent Algorithms (IJCCIA) Advising & Grants Prof. Sheta has successfully supervised more than 30 master’s and Ph.D. students in the United States, United Kingdom, Jordan, and Syria. His research has been funded by the U.S. National Science Foundation , as well as agencies in Egypt, Saudi Arabia, and Jordan. He has also consulted for the Egyptian Ministry of Communication & IT (2002-2004) and UNDP Smart Schools project (2003). Labs, Workshops & Leadership He is the founder and chair of the Advanced Computation for Engineering Applications (ACEA) workshop series, held five times across Egypt, Jordan, and Saudi Arabia. He served as Program Chair of the Science and Information Conference 2013 in London and has held academic leadership roles such as Associate Dean (2008-2009) and Assistant Dean for Planning & Development (2006-2008) at Al-Balqa Applied University, Jordan.
Nico Lang is an Assistant Professor at the University of Copenhagen's Department of Computer Science, associated with the Pioneer Centre for AI and Global Wetland Centre. He holds a PhD from ETH Zurich where he developed methods for global forest structure mapping. His research bridges computer vision, machine learning, and remote sensing to address environmental challenges like deforestation monitoring and climate change mitigation. Lang's research focuses on: Developing probabilistic deep learning models for global canopy height estimation Advancing open-set recognition under adversarial conditions Creating multi-modal representation learning frameworks for geospatial data Applying computer vision to biodiversity monitoring and conservation His work frequently appears in top venues like Nature, CVPR, and ECCV. His publications show strong emphasis on: environmental applications of AI, uncertainty-aware deep learning, and global-scale geospatial analysis. Recent work explores vision-language models and fine-grained open-set recognition. Awards & Honors: Culmann Prize for outstanding doctoral thesis (2023) Outstanding Reviewer for CVPR 2023 U.V. Helava Award for best paper in ISPRS Journal (2019) Associate PhD Fellow at Max Planck ETH Center (2018) Collaborations & Labs: Directs research at the intersection of computer vision and environmental science. Key affiliations include NASA GEDI mission, Swiss Federal Institute for Forest, Snow and Landscape Research, and the Pioneer Centre for AI. Organizes workshops like FGVC at CVPR and SSL4EO summer schools.
Luís A. Nunes Amaral is the Erastus Otis Haven Professor of Engineering Sciences and Applied Mathematics at Northwestern University's McCormick School of Engineering. He also holds courtesy appointments in Physics and Astronomy, and Medicine (Pulmonary and Critical Care). His research focuses on complex systems, systems biology, and the science of science, addressing challenges in healthcare, innovation, and machine intelligence. He has published over 180 peer-reviewed papers in prestigious journals like Nature, Science, and Cell. Education: PhD in Physics (Boston University), M.S. and B.S. in Physics (Universidade de Lisboa). Research Interests: Emergence of complex systems, healthcare analytics, creativity in science, and AI ethics. His work spans interdisciplinary collaborations, including projects on electronic health records, gene expression dynamics, and open justice systems. Selected Awards: NIH CAREER Award, Keck Foundation Distinguished Scholar, HHMI Early Career Scientist, Fellowships from APS, AAAS, and Network Science Society. He received the 2020 Provost's Award for Exemplary Faculty Service. Lab: Amaral Lab focuses on network science, systems biology, and quantitative social science. Current Students: Includes PhD candidates Maalavika Pillai, Feihong Xu, and Huaxia Zhou. Grants: Active funding in healthcare analytics, AI ethics, and systems biology.
Raquel Dias is an Assistant Professor in the Department of Microbiology & Cell Science at the University of Florida. Her research focuses on integrating computational biology, genomics, and machine learning to address challenges in agriculture, microbial ecology, and human health. Key areas include genomic sequence analysis, protein structure-function relationships, and the application of AI in clinical diagnostics. Education details are not explicitly provided in the text, but her publications suggest advanced training in computational biology, bioinformatics, and microbiology. Her work spans diverse topics such as soil microbiome dynamics in agricultural systems, genetic risk prediction for hearing disorders, and developing novel machine learning models for genotype imputation. Research interests emphasize interdisciplinary approaches, including: Machine learning applications in genomics and proteomics Microbial community analysis in environmental and clinical contexts Development of predictive models for disease progression and genetic associations Her recent publications highlight trends in AI-driven genomic analyses, polygenic risk scoring, and the functional characterization of uncharacterized proteins. Notable projects include the STICI and InteracTor tools for genomic and protein analysis. No scientific awards or grants are explicitly listed in the provided text. She maintains an active lab focused on computational biology and microbial genomics, collaborating with institutions in Brazil and the U.S. on projects related to crop improvement, soil health, and human health informatics.
Samiran (Shomu) Banerjee is a Teaching Professor in the Department of Economics at Emory University. He holds a PhD in Economics from the University of Minnesota, an MA from Delhi School of Economics, and a BA in Commerce from Shri Ram College of Commerce, University of Delhi. His research interests focus on Applied Microeconomic Theory , Industrial Organization , Experimental Economics , and Environmental Economics . He has authored the textbook Intermediate Microeconomics: A Tool-Building Approach (2nd ed., Routledge, 2021), which emphasizes constrained optimization and learning-by-doing methodologies. His work bridges theoretical rigor with practical applications in economics education. Notable contributions include studies on net neutrality regulation, healthcare resilience strategies, and optimization in power distribution systems. His interdisciplinary approach spans economics, policy analysis, and computational methods. His book has garnered acclaim for its pedagogical clarity, with praise from MIT’s David Autor and Cornell’s Kaushik Basu. Banerjee’s research also extends to medical imaging technology and oncology case studies, reflecting his diverse academic engagement.
Dr. Zia Ush Shamszaman is a Senior Lecturer in Computer Science at Teesside University's School of Computing, Engineering & Digital Technologies (SCEDT). He holds a PhD in Computer Science from the University of Galway and a Master's from Hankuk University of Foreign Studies. His research focuses on Cybersecurity, AI Ethics, IoT Resilience, and Game Theory applications, with a strong emphasis on bridging academic research with industry applications. Teaching responsibilities include postgraduate modules on AI Ethics, Cyber Risk Management, and Ethical Hacking, alongside undergraduate courses in Secure Data Acquisition and Ethical Hacking. He actively supervises five PhD students in cybersecurity and AI, advocating for inclusive technology education and societal impact. Professional memberships include IEEE (Senior Member), Elsevier Advisory Panel, and W3C. He has organized conferences like the International Conference on Suitable Technologies 4.0 and served on program committees for major events. Recognized for exceptional peer reviewing, he has received awards from leading journals like the Journal of Network and Computer Applications and Future Generation Computer Systems. Current projects funded by Innovate UK include CyberPathway (diversity in cybersecurity education), SafeSCMS (AI-driven supply chain security), and Opera-CyberThemis (trustworthy AI frameworks). Past industry roles include technical leadership in Bangladesh's government projects like the Machine Readable Passport system and World Bank-funded Bangladesh Automated Clearing House initiative. Shamszaman's work spans industry collaborations, academic leadership, and community engagement, with recent media contributions on GenAI governance and cyber resilience strategies. His research integrates theoretical advancements with practical solutions for SMEs, healthcare systems, and underserved communities.
Ahmed Abdelaty is an Assistant Professor of Civil and Architectural Engineering at the University of Wyoming's College of Engineering and Physical Sciences. His research focuses on infrastructure asset management, data analytics in construction management, and digital project delivery. He holds a Ph.D. from Iowa State University and M.S./B.S. degrees from Cairo University. Abdelaty's work integrates emerging technologies like BIM, UAVs, and machine learning into construction safety and efficiency solutions. Education: B.S. Construction Engineering and Management, Cairo University (2010) M.S. Civil Engineering, Cairo University (2013) Ph.D. Civil Engineering (Construction), Iowa State University (2017) Research Interests: Abdelaty explores innovative approaches to preconstruction services, pavement management systems, and data-driven decision-making in construction. His work emphasizes practical frameworks for local agencies, including dynamic pavement treatment evaluation and enhanced life cycle cost analysis. Recent studies involve 3D photogrammetry for safety training and UAV-based infrastructure assessment. Publications: His recent articles address UAV limitations, BIM integration with game engines, and deep learning for energy consumption prediction. Themes include digital transformation in construction, risk mitigation, and regulatory challenges in emerging technologies. Advising & Grants: While specific student advisees are not listed, his research aligns with funded studies on project scheduling, adverse weather modeling, and social media analytics in construction. No awards are explicitly noted in the provided materials. Labs/Teams: Abdelaty collaborates on projects involving the University of Wyoming's Civil Engineering department and external partners like Iowa State University, focusing on data-driven construction solutions.
Michael Möller is a Professor at the University of Siegen, leading the Lehrstuhl für Computer Vision within the Institute for Vision and Graphics. His research spans computer vision, machine learning, and optimization, with applications in medical imaging and computational geometry. Research Interests: Focuses on solving inverse problems in imaging, neural architecture optimization, and quantum-hybrid computational methods. His work integrates deep learning with traditional optimization for scalable solutions in 3D reconstruction and image analysis. Publications: Recent articles explore advancements in neural architecture search robustness, quantum annealing for shape matching, and data-driven regularization techniques for CT reconstruction, reflecting cross-disciplinary applications.
Fu Hu, Ph.D., is a Professor in the Department of Anesthesiology at the University of Maryland School of Medicine. He holds secondary appointments in Epidemiology & Public Health and Surgery. His research focuses on trauma care, predictive analytics, telemedicine, and medical informatics, with an emphasis on leveraging continuous vital signs and machine learning to improve patient outcomes. Key areas include traumatic brain injury monitoring, blood transfusion prediction, and critical care systems design. Education: B.S. in Electrical Engineering (Shanghai University, China, 1984), M.S. and Ph.D. in Computer Science (University of Maryland Baltimore County, 1992 and 2013). Research interests span medical AI, surgical workflow optimization, and telemedicine platforms. His work integrates advanced computational methods with clinical data to address challenges in trauma and critical care. Recent efforts include developing algorithms for real-time patient monitoring and decision support systems. He has led/co-investigated multiple grants on topics like non-invasive monitoring, trauma patient outcomes, and telemedicine. His publications highlight innovations in predictive modeling, medical imaging analysis (e.g., retinal disease detection), and clinical informatics. Notable collaborations include studies on automated ICP monitoring and the ONPOINT project for trauma care.
Dr. Bin Zhang is an Associate Professor in the Department of Electrical Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina. With over 20 years of experience in prognostics and health management, intelligent systems and control, and robotics, he has established himself as a leading researcher in battery management systems, power electronics, and fault-tolerant control systems. Dr. Zhang's educational background includes: Ph.D. in Electrical Engineering from Nanyang Technological University, Singapore M.E. in Mechanical Engineering from Nanjing University of Science and Technology, China B.E. in Mechanical Engineering from Nanjing University of Science and Technology, China His research focuses on active approaches to achieve intelligent smart systems with self-situational-awareness and self-adapting capabilities. Primary interests include prognostics and health management (PHM), which covers fault detection and isolation, failure prognosis, and fault tolerance; robotics and unmanned systems; intelligent systems and control; and dynamic systems design, modeling, simulation and control. His work integrates physics-based models with data-driven techniques and computational intelligence, including pattern recognition and machine learning. Analysis of Dr. Zhang's recent publications reveals a strong emphasis on battery modeling (particularly lithium-ion batteries), power electronics control (including fractional order delay and virtual variable sampling techniques), and deep learning methods (including graph neural networks, deep residual convolutional neural networks, and deep belief networks). His research spans multiple application domains including power grids, batteries, aircraft, helicopters, and manned/unmanned vehicles. Dr. Zhang serves as Associate Editor for prestigious journals including IEEE Transactions on Industrial Electronics, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and Neurocomputing. He is a Senior Member of IEEE and a member of ASME. As director of the Resilient Systems Laboratory, Dr. Zhang advises numerous graduate students working on cutting-edge research in battery technology, power cable insulation, and control systems. His lab is equipped with advanced facilities including an 8-channel ARBIN BT-Smart battery testing system, power electronics control systems, cable/wire testing systems, rotating machinery testing systems, and unmanned vehicles including quadrotors and hexacopters.
Daniel Peralta Cámara is a Postdoctoral Researcher at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology (EA05). His work spans Machine Learning , Bioinformatics , High Performance Computing , and Biometrics , with a focus on Single-Cell Data Analysis , Image Cytometry , and Missing Value Handling . Affiliation: Ghent University Academic Rank: Researcher Research Disciplines: Data Mining, Parallel Computing, Bioinformatics, High Performance Computing His research designs scalable machine learning frameworks for biological and engineering applications, including SCIP for morphological profiling and MSDeepAMR for antimicrobial resistance prediction. He explores advanced techniques like Polar Encoding for missing data and Fuzzy Rough Sets for classification uncertainty, contributing to one-class classification and novelty detection through Python libraries like Fuzzy-rough-learn 0.2 . Key Trends: Hybrid CNN-LSTM for sports analytics Time-series feature selection in healthcare UWB/IMU wearable integration for animal monitoring Dr. Peralta supervises PhD candidates like Maxim Lippeveld (2025) and Oliver Urs Lenz (2023). His collaborations span Ghent University researchers including Eli De Poorter , Chris Cornelis , and Yvan Saeys . Applications: Badminton strategy analysis Goat activity classification White blood cell identification
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.
Prof. Huan Nguyen is a Professor of Digital Communication Engineering at Middlesex University, affiliated with the Faculty of Science and Technology's Design Engineering & Mathematics department. He leads the London Digital Twin Research Centre and the 5G/6G & IoT Research Group. He holds a PhD from the University of New South Wales (2007) and a BSc from Hanoi University of Science & Technology (2000). Research Interests: Digital twin modeling, machine learning for disaster management and structural health monitoring, 5G/6G communication systems, and IoT. His work focuses on smart factories, critical infrastructure resilience, and healthcare applications like stroke care via digital twins. Grants & Leadership: Secured £2.5M+ in grants, including projects on quantum-assisted digital twinning (QuanDT, £93K), cognitive twins for Dubai manufacturing (£270K), and stroke care in Vietnam/Indonesia (StrokeDT, £99.8K). Served as PI or Co-I on 15+ grants from EPSRC, British Council, and industry partners. Teaching: Currently teaches Digital Communication Systems and Major Project modules. Previously instructed courses in digital communications, wireless systems, and telecommunications at Middlesex and other institutions. Awards & Roles: Senior Fellow of the HEA (2019), Senior IEEE Member, and Chair of the Vietnamese Intellectual Society in the UK/Ireland. Editor for KSII Transactions on Internet and Information Systems and REV Journal on Electronics and Communications . Labs & Teams: Leads research groups focused on digital twins, 5G/6G, and IoT. Collaborates with global partners like VinGroup, Vietnamese universities, and UK institutions on Industry 4.0 and healthcare innovation.
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.
