Nasim Dadashi Serej is a Lecturer in Artificial Intelligence at the School of Computing and Engineering, University of West London. Her expertise lies in applying AI to healthcare challenges, particularly in medical data analysis, collaborating closely with clinicians and healthcare organizations. Her research focuses on machine learning, deep learning, computer vision, and medical image-guided interventions. Her research interests include advanced AI techniques such as 3D scene analysis, natural language processing, stochastic search methods, and combinatorial optimization. She actively contributes to the development of software solutions for medical applications like image-guided navigation, medical image/video processing, and dataset collection. Nasim teaches across multiple programs, including BSc and MSc courses in Artificial Intelligence, Data Science, and related fields. Her recent publications span AI-driven healthcare innovations, from seizure detection to cardiovascular imaging analysis and pandemic forecasting. She emphasizes collaboration with clinical partners to ensure practical, real-world impact. Her work bridges theoretical AI advancements with clinical practice, aiming to improve diagnostic accuracy and patient outcomes through technology.
Professor Hyo-sang Shin is a Lecturer at Cranfield University, specializing in Guidance, Navigation, and Control within the Autonomous and Intelligent Systems Group. He holds an MSc in Aerospace Engineering from KAIST and a PhD in cooperative missile guidance from Cranfield University. His expertise includes Aeronautical Systems, Autonomous Systems, and Vehicle Health Management. Research focuses on cooperative guidance/control for multiple vehicles, coordinated health monitoring, and information-driven sensing. He collaborates with industry partners like Leonardo, Airbus, and Lockheed Martin. Key awards include Silver and Most Popular Team awards from the 2004 Korea Robot Aircraft Competition. Publications emphasize advanced control algorithms, UAV trajectory optimization, and sensor fusion techniques. Over 150 peer-reviewed articles span journals like IEEE Transactions on Aerospace and Electronic Systems and International Journal of Robust and Nonlinear Control. Labs/Teams: Active contributor to the Centre for Autonomous and Cyberphysical Systems at Cranfield, leading projects on UAV swarms, autonomous systems, and hypersonic vehicle control.
Linwei Wang is the Bruce B Bates Endowed Professor in the Department of Computing and Information Sciences at Rochester Institute of Technology (RIT). She leads the Computational Biomedicine Laboratory (CBL), focusing on integrating domain knowledge with machine learning to address critical medical challenges, particularly in cardiac electrophysiology and arrhythmia treatment. Her research bridges physics-based models with data-driven inference, emphasizing personalized healthcare solutions. Education: PhD in Computing and Information Science from RIT (2009), MPhil from Hong Kong University of Science and Technology (2007), and BE in Optic-Electronic Information Engineering from Zhejiang University (2005). Research interests include Electrocardiographic Imaging (ECGi), uncertainty quantification in cardiac models, and machine learning for medical data. Key projects involve noninvasive imaging of ventricular tachycardia, improving ECGi accessibility through camera-based systems, and developing hybrid models for personalized cardiac simulations. Scientific awards include the NSF CAREER Award (2014) and PECASE (2019). Her lab collaborates with institutions like NIH, Siemens Healthineers, and the University of Pennsylvania. Current teaching includes CISC-820 (Quantitative Foundations) and CISC-862 (Computational Modeling). Grants: NIH R01HL145590 (2019-2024) and NSF awards. Advises numerous PhD students in machine learning and biomedical applications. Lab activities include developing AI tools for real-time clinical guidance and risk prediction in heart failure.
Alexander C. Loui is a Professor in the Department of Computer Engineering at Rochester Institute of Technology (RIT), leading the Multimodal Analysis and Perception (MAP) Lab. He holds a Ph.D., M.A.Sc., and B.A.Sc. in Electrical Engineering from the University of Toronto, Canada. His academic roles include Senior Area Editor of IEEE Transactions on Image Processing and Senior Editor of SPIE/IS&T Journal of Electronic Imaging. Loui specializes in computer vision, image/video processing, machine learning, and multimedia systems, with over 100 peer-reviewed publications and 95+ patents. Research interests span video summarization, medical image segmentation, and AI-driven multimedia applications. He has led technical teams at Kodak Alaris, Kodak Research Labs, and Bell Communications Research. Awards include IEEE Region 1 Technological Innovation Award and Fellowships from IEEE and SPIE. His work bridges academia and industry, emphasizing practical algorithm development and patent innovations. Teaching responsibilities include Digital Signal Processing and Multidisciplinary Senior Design courses. Key contributions: MAP Lab leadership, over 95 patents (e.g., video object segmentation, salient foreground detection), and editorial roles in top journals. His research trends focus on label-efficient learning, trustworthy AI, and predictive content curation systems.
Paris Mastorocostas is a Professor at the University of West Attica, specializing in computational intelligence, signal processing, and algorithmic data mining. His research focuses on neuro-fuzzy systems, deep learning applications, and their integration into domains like energy systems, transportation networks, and industrial automation. He has pioneered methodologies for short-term load forecasting, telecommunications fraud detection, and adaptive noise cancellation in medical signals. Key research themes include: Neuro-fuzzy modeling for dynamic systems Machine learning in energy and transportation Data warehouse development using Python/MySQL UAV-based inventory quantification Graph neural networks for urban metro flow His work demonstrates a strong interdisciplinary approach, combining algorithm design with practical industrial applications. Notable contributions include the ReNFuzz-LF model for electricity load forecasting and TMD-BERT for transportation mode detection. His publications span over 25 years, showing sustained innovation in computational intelligence techniques and their real-world implementation.
Chandra Kambhamettu is a Professor in the Department of Computer and Information Sciences at the University of Delaware, and Director of the Video/Image Modeling and Synthesis (VIMS) Lab. His research focuses on computer vision, robotics, and autonomous systems with applications in biomedical imaging, remote sensing, and multimedia analysis. Education: PhD in Computer Science and Engineering from the University of South Florida (1991-1994), M.S. in Computer Science and Engineering from the same institution (1989-1991), and B.S. from Osmania University (1985-1989). Research Themes: His work spans salient object detection, 3D point cloud analysis, thermal imaging, and biomedical applications such as sickle cell retinopathy detection. Recent projects include deep learning frameworks for SAR imagery analysis, autonomous systems for polar environments, and medical image segmentation. Publications: Recent articles emphasize advancements in neural network architectures (e.g., SODAWideNet++), thermal material classification, and Arctic sea ice motion estimation. Themes include fusion of RGB-IR imagery, salient object detection without pre-training, and transformer-based models. Labs and Teams: Leads the VIMS Lab, which develops cutting-edge solutions for video modeling, image synthesis, and multispectral data analysis.
Dr. Pratheepan Yogarajah is a Lecturer in Computer Science at the University of Ulster, affiliated with the School of Computing, Engineering and Intelligent Systems (Derry~Londonderry campus). He has held academic roles since 2007, including Research Associate and Teaching Assistant, and has contributed over 20 peer-reviewed publications. His research focuses on biometrics, computer vision, and machine learning with applications in healthcare and security. Yogarajah holds a PhD from Ulster University (2015), an MPhil in Computer Vision from Oxford Brookes University (2006), and a first-class honours degree in Computer Science from the University of Jaffna (2001). He is a member of the British Computer Society (BCS) and IEEE. His research interests span biometric identification, medical image analysis (e.g., cervical cancer screening, cardiac MRI segmentation), and explainable AI. Notable achievements include patents filed, HMGCC scholarship (2005), and grants from Ulster University (PoP 2012) and Invest NI (PoC 2013). Recent work emphasizes AI-driven healthcare solutions, including lead toxicity prediction in maternal health, glaucoma detection via retinal imaging, and autism spectrum disorder classification via gait analysis. He actively explores multimodal medical imaging fusion and deep learning model interpretability. Yogarajah has collaborated on projects like ChPOS (non-contact heart rate estimation) and MMC-Net (cardiac MRI segmentation). He maintains an active publication record with contributions to conferences like IEEE and journals in medical AI and computer vision.
George Bebis is a Foundation Professor at the Department of Computer Science & Engineering, University of Nevada, Reno, affiliated with the College of Engineering. He has published extensively in computer vision, machine learning, and image processing. Research Interests: Computer vision, Image processing, Pattern recognition, Machine learning, Evolutionary computing Email: bebis@unr.edu Recent research trends include crater detection using convolutional neural networks, horizon/sky line detection via semantic segmentation, low-resolution face recognition, Gabor feature optimization, and dynamic programming integration with machine learning. His work spans applications in remote sensing, transportation safety, medical imaging, and biometrics.
Emily M. Hand is an Associate Professor and Graduate Director in the Department of Computer Science & Engineering at the University of Nevada, Reno (UNR), where she directs the Machine Perception Laboratory (MPL). Her research bridges Machine Learning, Computer Vision, and Human Perception with a mission to develop wearable assistive technologies for individuals with visual impairments or on the Autism spectrum. Education Doctor of Philosophy, University of Maryland, College Park (2018) Master of Science, University of Maryland, College Park (2015) Bachelor of Science in Computer Science and Engineering, University of Nevada, Reno (2013) Bachelor of Science in Applied Mathematics, University of Nevada, Reno (2013) Research Focus Dr. Hand's work centers on explainable facial feature modeling , human-perception-inspired machine learning , and real-world assistive applications . Her MPL lab pioneers techniques for social interaction enhancement through visual and natural language processing, with emphasis on robustness in noisy environments. Key contributions include facial attribute recognition under unconstrained conditions, deep learning architectures for label noise resilience, and novel approaches to multi-task learning leveraging implicit feature relationships. Publication Trends Analysis of her 14 most recent publications (2012-2020) reveals a consistent trajectory toward socially impactful computer vision: early work focused on foundational techniques in facial recognition and neural network optimization, evolving toward assistive applications by 2017. Her research increasingly integrates temporal modeling (2018), noise-robust systems (2019), and real-world deployment challenges (2020), with 70% of recent work directly addressing accessibility needs through wearable technologies and social interaction aids. Scientific Recognition NSF CAREER-level grant for facial caricature research ($419,979) University of Maryland Future Faculty Fellow NSF Graduate Fellowship Honorable Mention Multiple conference paper acceptances at CVPR, AAAI, and ICRA Senior Scholar Mentor awards for undergraduate researchers Academic Leadership As Graduate Director and Faculty Advisor for UNR's Women in Computer Science and Engineering (WiCSE), Dr. Hand mentors students through the Senior Scholar program while securing significant external funding. Her $419,979 NSF grant develops facial verification systems using caricatures, and her SCO-funded CV-SIGHTT project advances synthetic image generation for defense applications. She actively shapes curriculum through courses in Machine Learning and Computational Linguistics. Laboratory & Outreach The Machine Perception Laboratory (MPL) operates from UNR's WPEB 415, developing wearable social interaction aids through interdisciplinary collaboration. Dr. Hand co-founded Reno's Girls Who Code chapter and participates in State Department speaker series, demonstrating commitment to broadening participation in computing through hands-on outreach and policy engagement.
Massimo Robberto is a Researcher at Johns Hopkins University (Department of Physics and Astronomy) and an AURA Observatory Scientist affiliated with the Space Telescope Science Institute (STScI) in Baltimore. He specializes in astronomical instrumentation, star formation (particularly in the Orion Nebula Cluster), and galaxy evolution. Previously, he held roles including Branch Manager for the JWST's NIRCam instrument, instrument scientist for Hubble's WFC3, and contributed to missions like the Frontier Fields and the ATLAS probe concept. Robberto earned a Summa cum Laude degree and PhD in Astronomy from the University of Torino. His work spans instrument development (e.g., SCORPIO for Gemini South) and observational studies using HST, Spitzer, and VLT/SPHERE. Key achievements include leading Hubble Treasury Programs on Orion and discovering brown dwarf companions to A-type stars. He has served on Decadal Survey panels and contributed to over 100 publications. Education: PhD in Astronomy, University of Torino (Italy) Summa cum Laude Degree in Physics, University of Torino Research Interests: His research focuses on instrumentation for space and ground telescopes, star formation processes (e.g., Orion Nebula), and cosmological studies using galaxy surveys. He has pioneered techniques in high-contrast imaging for exoplanets and developed software tools like TA-DA for astrophysical data analysis. Articles Overview: Recent work includes studies on exoplanet demographics around A-type stars, the Initial Mass Function in Orion, and instrument design for Gemini South's SCORPIO. His publications blend observational astronomy with instrumental innovation, emphasizing large-scale surveys and cosmic evolution. Grants & Advising: Robberto has secured significant funding for HST Treasury Programs and led international collaborations. While no formal awards are listed, an asteroid (2008 QE12) bears his name. He mentors projects on instrument development and observational strategies, contributing to the next generation of space missions like JWST and WFIRST. Labs/Teams: He leads teams at STScI and JHU, coordinating efforts for instruments like NIRCam and SCORPIO. His lab focuses on cutting-edge spectrograph design and multi-wavelength observational techniques.
Luciano Spinello is a Research Fellow affiliated with the University of Freiburg's Department of Computer Science, working within the AIS Lab led by Prof. W. Burgard. Previously, he held roles at Amazon Research (Seattle), ETH Zurich (PhD under Prof. Roland Siegwart), and EPFL Lausanne as a research assistant. His research focuses on the intersection of computer vision and robotics, specializing in robot perception, SLAM, and autonomous systems. He has contributed to projects involving RGB-D data processing, terrain classification, and socially-aware navigation algorithms. Education: PhD in Computer Science from ETH Zurich (2009), Electrical Engineering degree from Rome, Italy. Academic activities include organizing workshops (RSS 2014, IROS 2012), serving on program committees for robotics conferences, and editorial roles (IROS associate editor). His work emphasizes multimodal sensing, object detection in 3D environments, and robust localization across dynamic conditions. Key technical contributions include methods for RGB-D fusion, adaptive domain adaptation, and large-scale place recognition. His research bridges theoretical advancements with practical applications in autonomous robotics, including navigation systems and human-robot interaction protocols.
Paul Fieguth is a Professor and Associate Vice President - Academic Operations at the University of Waterloo. He holds affiliations with the Full-time Faculty, Faculty of Mathematics, and the Intelligent and Autonomous Systems research group. His work focuses on interdisciplinary areas including machine learning, computer vision, medical imaging, and deep learning techniques for solving complex engineering and biological problems. He has contributed to advancements in photoacoustic remote sensing, autonomous systems, and large-scale biodiversity datasets like BIOSCAN-5M. His research bridges theoretical foundations (e.g., pattern recognition, inverse problems) with practical applications in robotics, medical diagnostics, and environmental monitoring. Education details are not explicitly provided in the text, but his professional roles suggest advanced training in computer science and engineering disciplines. His research interests span a wide range, including but not limited to: pattern recognition algorithms, deep learning architectures, remote sensing technologies, and computational methods for medical imaging. Recent work emphasizes innovations in rail defect detection, 3D reconstruction, and biodiversity assessment through multimodal datasets. Publications from 2022–2025 highlight contributions to fields like neural network optimization, uncertainty quantification, and generative adversarial networks for medical applications. While no specific awards are listed, his prolific publication record reflects recognition in academic circles. Advising and grants sections remain underdeveloped in the provided information, though his leadership roles suggest involvement in institutional research initiatives. He is a key member of teams advancing technologies such as PARS imaging and autonomous systems at the University of Waterloo.
Aleksandra Pizurica is a Professor in statistical image modelling at Ghent University, Belgium, affiliated with the Group for Artificial Intelligence and Sparse Modelling (GAIM). She serves as Senior Area Editor for IEEE Transactions on Image Processing (2016–) and Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology (2016–), having previously held editorial roles at IEEE Transactions on Image Processing (2012–2016). Her educational background includes: Dipl. Ing. in Electrical Engineering, University of Novi Sad (1994) MSc in Telecommunications, University of Belgrade (1997) PhD in Engineering, Ghent University (2002) Prof. Pizurica's research centers on statistical modelling , probabilistic graphical models , and Bayesian inference , with significant contributions to sparse coding , signal/image processing , and machine learning . Her work bridges theoretical advances with applications in medical imaging, remote sensing, and cultural heritage preservation, particularly in image denoising, inpainting, and hyperspectral analysis. Analysis of her 15 most recent publications (2023–2025) reveals dominant themes in hyperspectral image processing (clustering/classification via model-aware deep learning), medical imaging (3D foot/ankle alignment, musculoskeletal segmentation), and cultural heritage (crack detection in paintings). Emerging trends include fairness in AI (skin color bias mitigation), scalable seabed mapping, and generative models for point cloud processing. Her notable recognition includes: Scientific Prize “de Boelpaepe” for 2013-2014 from the Royal Academy of Science, Letters and Fine Arts of Belgium While specific grant details and student advisement records aren't provided in available sources, her editorial leadership and research output indicate active supervision of graduate researchers. She leads initiatives in the Group for Artificial Intelligence and Sparse Modelling (GAIM), focusing on statistical image modeling and sparse representations for real-world applications. The GAIM research unit under her affiliation drives innovation in probabilistic modeling and machine learning, with projects spanning medical diagnostics, remote sensing, and digital art restoration, evidenced by recent publications in IEEE Transactions and high-impact journals.
Sander Oude Elberink is an Associate Professor at the Department of Earth Observation Science, International Institute for Geo-Information Science and Earth Observation (ITC), University of Twente. He is also affiliated with the Digital Society Institute. He holds a PhD from ITC (2010) and a Geodetic Engineering degree from Delft University of Technology (2000). His research focuses on information extraction from point clouds and 3D reconstruction using fused topographic maps and laser data. Education: PhD: 'Acquisition of 3D Topography' (ITC, 2010) Master's: Geodetic Engineering (Delft University of Technology, 2000) Research Interests: His work spans 3D point cloud analysis , LiDAR technology , UAV-based remote sensing , and digital twin development . Recent efforts include semantic segmentation algorithms for urban modeling and energy-related infrastructure analysis. His research bridges geospatial data science with practical applications in urban planning and environmental monitoring. Notable Projects: 3D BGT tool development for hydrological applications 3DTOP10NL national 3D topography model collaboration Capacity-building initiatives in India (IIRS joint program) Awards: ISPRS Giuseppi Inghilleri Award (2016), ITC Research Award (2009), and ISPRS Young Author Award (2008). Advising: Supervised over 25 master’s students and co-promoted PhD candidates including Biao Xiong, Sudan Xu, and Amrollah Seifoddini. Current projects involve UAVPal dataset development and semantic segmentation in complex urban environments. Labs & Teams: Active in the ITC research group, contributing to UN SDG initiatives related to sustainable cities (SDG 11) and responsible consumption (SDG 12) through 3D modeling innovations.
Dr. Irene M.L. Chew is a Senior Lecturer at the Malaysia School of Engineering, Monash University. She holds a BEng from Universiti Teknologi Malaysia and a PhD from the University of Nottingham, Malaysia, focusing on process integration for resource conservation. Her research emphasizes mathematical modeling (GAMS/LINGO) and data-driven approaches like artificial neural networks for optimizing industrial processes. She currently supervises two PhD and one Master's student under Monash and Ministry of Higher Education scholarships. Key research areas include waste heat recovery systems, process system optimization, eco-industry park design, and biorefinery development. She contributes to UN Sustainable Development Goals through projects addressing environmental sustainability and energy efficiency. Notable collaborations include work on fractal-induced turbulence in heat exchangers and POSS-based nanomaterials. Teaching: CHE4173 - Sustainable Processing 2, CHE2162 - Material and Energy Balances Editorial role: Editor-in-chief of Scientific Reports (Journal) Active projects (2024-2027): Unlocking Advanced Properties of 3D Polyhedral Oligomeric Silsesquioxanes Cages Functionalised with Covalent Organic Framework
