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Hadj BatatiaView profile

Associate Professor

• Computational Imaging
• Machine Learning
• Medical Imaging
• +6 more

Hadj Batatia is an Associate Professor at the School of Mathematical and Computer Sciences (MACS) at Heriot-Watt University Dubai (HWUD) since 2020, serving as Associate Director of Research. He previously held roles at the National Polytechnic Institute of Toulouse (INP Toulouse), Conservatoire National des Arts et Métiers (CNAM Toulouse), and University of Toulouse, where he led research teams and departments. His academic journey includes a PhD in Computer Vision from the University of Toulouse (1992) and an MSc in Artificial Intelligence (1987), with undergraduate studies at the University of Algiers. His research focuses on computational imaging, machine learning, and statistical modeling applied to medical imaging and computer vision. Key areas include deep learning, inverse problems, and biomedical image processing. He has supervised 15 PhD theses and contributed to 82 research outputs, achieving an h-index of 26. Notable grants include the ESMS project (2021-2023) for healthcare smart home systems and CAMM4D (2008-2012) for skin oncology imaging. Dr. Batatia teaches Advanced Machine Learning, Biologically Inspired Computation, and Big Data Management. He has led international collaborations across Europe, Latin America, and Asia, addressing challenges in medical imaging, cybersecurity, and education. His work aligns with UN SDGs, particularly in health and innovation. Awards include qualifications to professorship in Computer Science and Engineering.

Marta VallejoView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Evolutionary Algorithms
• +7 more

Dr. Marta Vallejo is an Assistant Professor in the Department of Computer Science at the School of Mathematical & Computer Sciences, Heriot-Watt University. She also serves as Programme Director for the Graduate Apprenticeship in Data Science. Previously, she held a tenure-track Research Fellow position in biomedical signal/image processing and was Head of AI at ClearSky Medical Diagnostics Ltd. She is a Fellow of the Higher Education Academy and has contributed to EU-funded projects in Spain. Education: PhD in Computational Intelligence and Predictive Modelling MSc in Artificial Intelligence Her research focuses on machine learning and evolutionary algorithms applied to healthcare, specifically neurodegenerative diseases (Parkinson’s, Huntington’s, ALS) and cancer. Key projects include developing AI-driven clinical assessments, biomarker detection platforms, and image co-registration systems using deep learning. She leads the ML-Healthcare group and collaborates with ClearSky MD and the Proteus project. Her work emphasizes translating academic research into clinical tools, such as virtual staining techniques and wearable tremor monitoring devices. In public engagement, she organizes initiatives like Rubbish Robots (recycled materials for robotics) and contributes to events like the UK-RAS Robot Lab Live. She has received awards for research culture and outward-looking collaborations, including the Spirit of Heriot-Watt Awards 2017 and the 2024 Research Culture Recognition Award. Grants & Leadership: Principal Investigator: Neuro BioMark (EPSRC IAA) Co-Investigator: Lung Cancer Registration (Cancer Research UK) Successfully secured 96,432 GPU hours via EPSRC for high-performance computing Organized EvoStar workshops and led the SPECIES society for evolutionary computation She hosts educational programs at the Robotic Assisted Living Testbed, introducing students to robots like Pepper and Tiago. Her research also aligns with UN Sustainable Development Goals, particularly in advancing healthcare technologies and promoting STEM education.

Steven WaslanderView profile

Associate Professor

• Autonomous Vehicles
• Robotics
• Simultaneous Localization and Mapping (SLAM)
• +12 more

Steven Waslander is an Associate Professor at the University of Toronto Institute for Aerospace Studies and an Adjunct Professor in Mechanical and Mechatronics Engineering at the University of Waterloo. He previously directed the Waterloo Autonomous Vehicles Laboratory (WAVELab). Education: Doctorate in Aeronautics and Astronautics from Stanford University (2007) Master's in Aeronautics and Astronautics from Stanford University (2002) Bachelor's in Applied Mathematics and Mechanical Engineering from Queen's University (1998) His research focuses on autonomous vehicles , including aerial and ground systems, with an emphasis on Simultaneous Localization and Mapping (SLAM) , nonlinear estimation , multi-robot systems , and autonomous driving . He also explores convolutional and recurrent neural networks for motion planning and object detection . Recent articles highlight trends in 3D detection , LiDAR-based tracking , neural radiance fields for autonomous driving, and model-agnostic pretraining for motion prediction. These works span journals like IEEE Transactions on Robotics and conferences such as ICCV and NeurIPS . Professor Waslander has advised the University of Waterloo Robotics Team, mentored students like Evan Cook, Barza, and Marc, and collaborated with industry partners including Aeryon Labs and Clearpath Robotics. He is a member of the NSERC Canadian Field Robotics Network .

Javad KhazaeiView profile

Assistant Professor

• Smart Grid Dynamics & Control
• Data-Driven Modeling and Control
• Microgrid Systems
• +3 more

Javad Khazaei is an Assistant Professor in the Electrical & Computer Engineering Department at Lehigh University and director of the INTEGRITY Laboratory. Previously, he held an Assistant Professor position at Penn State Harrisburg and was affiliated with Penn State University Park's Architectural Engineering Department. He earned his Ph.D. in Electrical Engineering (Power and Energy) from the University of South Florida in 2016. Research Interests : His work focuses on smart grid dynamics and control, data-driven modeling and control in power systems, water-energy microgrids, smart grid cybersecurity, and renewable energy integration. His laboratory, INTEGRITY Lab, addresses challenges in resilient and intelligent energy systems. Recent Work Trends : His publications emphasize data-centric approaches for system identification, model predictive control (MPC) for microgrids, and enhancing resilience through advanced control strategies. Key themes include cybersecurity in energy systems, hybrid energy-water nexus optimization, and naval/marine microgrid resilience. Grants & Collaborations : Awarded a $12M DOE grant to expand marine energy initiatives, with partnerships focusing on naval microgrid resilience and offshore renewable integration. His work often involves interdisciplinary collaborations between electrical engineering, cybersecurity, and infrastructure systems. Lab & Infrastructure : Directs the INTEGRITY Lab, which develops cutting-edge solutions for cyber-physical energy systems. Research includes hardware-software integration for real-time monitoring and control of microgrid components.

• Machine Learning
• Spatial Data Mining
• AI in Mental Health
• +4 more

Dr. Saida Elmi is an Assistant Professor Adjunct at Yale University School of Medicine and an Assistant Professor at the University of New Haven. She holds a Ph.D. in Computer Science from the National School of Mechanics and Aerotechnics (2017), followed by postdoctoral research at Korea University of Technology and Education (2017–2018) and National University of Singapore (2018–2021). Her research focuses on AI applications in healthcare, transportation systems, and spatial data mining. She developed the Automated Test of Embodied Cognition (ATEC), an AI-driven system using motion capture and machine learning for mental disease detection. Key contributions include predicting taxi fares, travel times, and energy consumption in transportation networks using deep learning architectures like RNNs and CNNs, and designing group-oriented recommendation systems for points-of-interest (POIs) using graph convolution networks and attention mechanisms. Education : Ph.D., Computer Science, National School of Mechanics and Aerotechnics (2017) Postdoctoral Research, Korea University of Technology and Education (2017–2018) Postdoctoral Research, National University of Singapore (2018–2021) Her research interests span machine learning, spatial data mining, and AI ethics. She has pioneered frameworks for spatio-temporal data analysis, including a transfer learning approach for travel time prediction in data-scarce regions. Her work on energy consumption modeling and POI prediction has been published in top-tier conferences like WWW, MOBIQUITOUS, and ECIR. Dr. Elmi’s interdisciplinary projects integrate AI with healthcare and urban systems, emphasizing automated cognitive assessment and smart city technologies. Her current efforts focus on refining action recognition algorithms for clinical applications and optimizing recommendation systems for group mobility decisions.

Wonjun LeeView profile

Assistant Professor

• Artificial Intelligence
• Security Analytics
• Deep Learning
• +4 more

Wonjun Lee is an Assistant Professor of Artificial Intelligence at the Katz School of Science and Health at Yeshiva University . His academic journey includes a Ph.D. and M.S. from Purdue University (2011 and 2002, respectively) and a B.A. from Korea University (2000). Educational Background Ph.D., Purdue University, 2011 MS, Purdue University, 2002 BA, Korea University, 2000 Dr. Lee specializes in AI-based analytics platforms for multidisciplinary applications, focusing on knowledge-based response systems inspired by human immunology. His work explores scalable AI models for cloud-based information and communication systems (ICS), leveraging lightweight virtualization and microservice architecture. He collaborates with institutions like NOAA , University of Texas at San Antonio (UTSA) , Auburn University , and University of Georgia . His research spans AI-driven cybersecurity, including analysis of malign network traffic and IoT privacy issues. At Samsung , he previously led cybersecurity projects such as mobile app penetration testing and cloud computing assessments. Recent publications highlight his expertise in encrypted network traffic detection using Perlin Noise and CNNs, container rootkit detection, and cache event analysis on AMD Zen micro-architecture. These works reflect his integration of AI with cybersecurity and hardware-level analysis.

Liliana CaldeiraView profile

Researcher

• Artificial Intelligence in Medicine
• Medical Image Analysis
• Radiology
• +4 more

Dr. Liliana Caldeira serves as a Core Scientist and team leader at the Center for Data and Simulation Science (CDS) within the University Hospital of Cologne, University of Cologne. She directs innovative data science research in the Department of Diagnostic and Interventional Radiology, specializing in AI algorithm development for 3D medical imaging analysis including Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Research Focus: Her work centers on clinical AI implementation in radiology, with expertise in deep learning for medical image segmentation (brain tumors, lymph nodes, organs), image denoising, and disease classification. Key applications include brain metastasis detection in melanoma, lymph node staging for cancer, kidney segmentation in polycystic disease, and pulmonary hypertension assessment—emphasizing clinical validation and multicenter study designs to bridge AI research with practical medical workflows. Publication Trends: Analysis of her 2020-2023 publications reveals consistent focus on clinically validated AI tools, particularly using 3D convolutional neural networks for segmentation tasks. Her research demonstrates strong collaboration across radiology subspecialties, with shared last authorship indicating principal investigator leadership in translating algorithms to clinical practice. Scientific Awards: No awards were documented in the provided text. Advising and Grants: Student mentorship and grant funding details were not specified in the source material. Labs and Teams: Dr. Caldeira leads a dedicated data science team within the radiology department, collaborating with CDS under Scientific Domain 2 (Computational Biology and Medicine). The team focuses on high-quality dataset creation, AI evaluation frameworks, and algorithm implementation for medical imaging diagnostics, operating from the University Hospital campus at Kerpener Str. 62.

Hussein AbdullahView profile

Professor

• Biomedical Robotics
• Assistive Devices
• Mechatronic Systems Design
• +3 more

Hussein Abdullah is a Full Professor at the School of Engineering, University of Guelph, specializing in biomedical robotics and mechatronics. He holds a BEng from the University of Technology in Baghdad, and an MSc and PhD from the University of Glasgow. His career includes roles as Associate Professor at Sultan Qaboos University (Oman) and Vice President of Academics and Research at the Australian University in Kuwait (2020–2021). At UoG, he served as Associate Director for Graduate Studies (2008–2009), School Director (2010–2018), and has led over 30 years of academic and industry contributions. Research focuses on assistive robotics, therapeutic devices, and mechatronic systems, funded by NSERC, NRC, and OMAFRA. His 90+ publications include work on robotic exoskeletons, spinal injury treatment models, and thermal energy storage. He has mentored 34 master’s and PhD students, many entering academia and industry globally. He serves as Associate Editor of the Journal of Frontiers in Robotics and AI and chairs committees in engineering education and accreditation. Teaching includes graduate courses in advanced robotics and manufacturing systems. His professional affiliations include the Canadian Council of Deans of Engineering and membership in the Professional Engineers Ontario (PEO).

Tae-Hoon KimView profile

Professor

• Cybersecurity Education
• Network Security
• Data Science
• +8 more

Tae-Hoon Kim is a Professor in the Department of Computer Information Technology and Graphics at Purdue University Northwest. He holds a Ph.D. in Information Science from the University of Pittsburgh, an M.S. in Interdisciplinary Telecommunications from the University of Colorado Boulder, and a B.S. in Mechanical Engineering from Oklahoma State University. His research focuses on cybersecurity education, wireless sensor networks, data-driven analysis using machine learning, and AR-based educational tool development. Current projects are funded by the Army Research Office, NSF, NSA, and industry partners. Research interests span network security, data science, and innovative educational technologies. He actively develops interactive tools for K-12 and college cybersecurity education using augmented reality and data visualization techniques. Teaching specialties include network protocols, IoT, blockchain, and big data analytics. His publications address topics ranging from biomass energy prediction models to cultural heritage glyph analysis. Teaching responsibilities include courses on LAN/WAN design, network security, AI, and mobile applications. Over 20 years of academic experience includes advising student projects in cybersecurity and data science.

Md Liakat AliView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Behavioral Biometrics
• +3 more

Md Liakat Ali is an Associate Professor in the Department of Computer Science and Physics at Rider University, New Jersey. He holds a Ph.D. in Computer Science from Pace University and multiple Master's degrees from Blekinge Institute of Technology and International Islamic University Chittagong. His academic career includes roles at Rider University since 2018, prior positions at Caldwell University, Rowan University, and adjunct roles at several institutions. His research focuses on Machine Learning, Artificial Intelligence, Behavioral Biometrics, Cybersecurity, and Data Science. Notable contributions include studies on keystroke dynamics for authentication, phishing detection, and cybersecurity threats. He has received grants including an NSF award (Co-PI) for STEM education and multiple Best Paper Awards at IEEE conferences (2022, 2021, 2018, 2017). Ali has authored/co-authored over 40 publications, including a book on wireless security and journals in Electronics , IEEE Transactions , and conference proceedings. His work spans fraud detection, healthcare analytics, and blockchain security. He has led presentations on topics like biometrics, neural networks, and cybersecurity at international venues such as CVCI, IVSP, and IEEE conferences. Education: Ph.D., Computer Science, Pace University, New York M.S., Electrical Engineering, Blekinge Institute of Technology, Sweden B.S., Computer Science & Engineering, International Islamic University Chittagong His grants include NSF funding for STEM education initiatives and a Rider University grant for online course development. He has collaborated on projects like CUE-T to broaden computing participation and enhance active learning strategies. Ali’s lab focuses on cybersecurity, biometric systems, and machine learning applications. His research teams address challenges in fraud detection, medical diagnosis, and secure software development, reflecting his interdisciplinary approach to technological innovation.

Peter HommelhoffView profile

Professor

• Dielectric Laser Acceleration
• Nanophotonics
• Ultrafast Electron Dynamics
• +4 more

Peter Hommelhoff is a Professor in the Chair of Laser Physics at Friedrich-Alexander University Erlangen-Nürnberg (FAU) . His research focuses on dielectric laser acceleration , nanostructured electron sources , and quantum nanophotonics . Key Research Areas: Quantum-coherent control of free electrons Attosecond electron pulse generation Ultrafast dynamics in 2D materials (graphene, hexagonal systems) On-chip photonic particle acceleration Light-driven electron emission from nanotips Quantum interference in electron-photon interactions Recent Publications highlight advancements in dielectric laser accelerators (Nature, 2023), auto-ponderomotive beam control (Phys. Rev. Lett., 2024), and non-classical electron emission (Nature Physics, 2024). His work also explores graphene valley control and Bloch electron interferometry for material band-structure analysis. Laboratory Context: The Chair of Laser Physics at FAU investigates nanostructured electron sources , photonic control of charged particles , and quantum applications in electron microscopy and sensing. Collaborations span quantum nanophotonics , attosecond science , and integrated photonic circuits .

• Air Vehicle Design (Derivative and Unconventional)
• Computational Aerodynamics
• Aeroelasticity
• +2 more

Professor Andrea Da Ronch is a faculty member at the University of Southampton's School of Engineering, holding the academic rank of Professor and serving as Director of the Boeing Flight Simulators Laboratory. She maintains active research leadership and teaching responsibilities within the Aeronautics and Astronautics program, with a demonstrated commitment to high-quality education evidenced by two nominations for "Outstanding Lecturer" and Fellowship of the Higher Education Academy. Her research spans Air Vehicle Design (Derivative and Unconventional), Computational Aerodynamics, Aeroelasticity, Weather Modeling, and Multidisciplinary Optimisation. She develops scalable computational methods at the intersection of aerospace engineering, mathematics, and computer science to support next-generation sustainable air transportation, targeting aviation decarbonization, Advanced Air Mobility (AAM) connectivity, and accelerated industry product development through physics-informed machine learning approaches. Recent publications (2024-2025) reveal a pronounced trend toward geometric deep learning and graph neural networks for transonic flow prediction, morphing wing analysis, and propeller-wing interaction. These works emphasize computational efficiency in unsteady aerodynamics while addressing critical challenges in aircraft design validation and dynamic maneuver simulation. Scientific awards include: Rotary International Award (2012) Aerospace Speakers Travel Grant (2010) T.I.M.E. Double Degree Award (2008) AIAA Atmospheric Flight Mechanics Best Paper Award (2019) Fellow of the Higher Education Academy (2014) Senior Member of AIAA (2017) She currently supervises three PhD students (Declan Salazar Clifford, Gabriele Immordino, Giuseppe Morichetti) with funding secured from EPSRC, European Union H2020, US Air Force Office of Scientific Research, and Royal Academy of Engineering projects including IMPACT anti-ice coatings optimization, Sparsified Reduced Modeling, and transonic buffet analysis. As Director of the Boeing Flight Simulators Laboratory, she provides hands-on curriculum enhancement for engineering students. Her active membership in the Aerodynamics and Flight Mechanics Research Group facilitates collaboration on sustainable aviation initiatives while supporting her leadership in major projects like HOMER- with Professor Bharath Ganapathisubramani.

• Medical Imaging
• PET/CT Imaging
• Quantitative Image Analysis
• +5 more

Drew A. Torigian is a Professor of Radiology at the Perelman School of Medicine, University of Pennsylvania, with extensive contributions to medical imaging research. His work focuses on developing advanced methodologies for PET/CT image analysis, disease quantification, and anatomical segmentation through innovative applications of deep learning and computer vision techniques. Dr. Torigian's research interests span multiple critical areas in medical imaging including quantitative image analysis, PET/CT applications, deep learning in medicine, radiation therapy planning, and anatomical segmentation. His work has significantly advanced the field of medical image analysis through the development of novel algorithms for disease quantification without explicit object delineation, standardized anatomic space frameworks, and attention-based neural networks for medical image interpretation. His recent publications demonstrate a strong trend toward integrating artificial intelligence with medical imaging, particularly in developing gaze-guided neural networks, geographical attention mechanisms, and hybrid transformer-convolutional architectures for improved medical image analysis. These works consistently address critical challenges in radiology including disease quantification, anatomical segmentation, and the development of clinically interpretable AI models. Dr. Torigian has received recognition through his substantial publication record in top-tier medical imaging venues including Medical Image Analysis, IEEE Transactions on Biomedical Engineering, and leading medical imaging conferences. His research has been consistently funded through various mechanisms supporting innovation in medical imaging technology. Through his mentorship and collaborative research, Dr. Torigian has contributed to advancing the field of medical imaging with practical applications in radiation therapy planning, disease quantification, and the development of standardized methodologies for image analysis. His work bridges the gap between computer science innovation and clinical radiology applications.

• Computer Science
• Machine Learning
• IoT
• +7 more

Ali Hassan is a researcher affiliated with the National University of Sciences and Technology (NUST), School of Electrical Engineering and Computer Science, Department of Computer and Software Engineering. His work spans multiple domains in computer science, engineering, and applied mathematics, focusing on areas such as machine learning, IoT, energy systems, and medical informatics. His research explores: Reinforcement learning applications for battlefield information systems IoT antenna design and performance evaluation Optimization of second-life battery systems in electric vehicles Transformers for real-time vehicle collision avoidance Image hashing techniques using visual attention models Neural network-based phasor estimation for power grids Biomedical sensor systems for non-invasive health monitoring Mathematical modeling of viral dynamics Recent publications demonstrate his emphasis on interdisciplinary approaches combining AI, signal processing, and sustainability. He has collaborated with institutions across Pakistan, France, Saudi Arabia, and the USA, with a focus on practical implementations in cybersecurity, energy optimization, and healthcare technology.

Gültekin IşıkView profile

Associate Professor

• Deep Learning
• Machine Learning
• Computer Vision
• +4 more

Assoc. Prof. Gültekin Işık is affiliated with the Department of Computer Hardware at Iğdır University's Faculty of Engineering. He holds a PhD in Computer Engineering from Hacettepe University (2011–2019), focusing on Turkish dialect recognition using deep learning. His academic roles include serving as Head of the Department from 2019 to 2021. His research interests span deep learning applications in computer vision (e.g., YOLO-based crowd detection), optimization algorithms (e.g., Slime Mould Algorithm), environmental modeling (solar power efficiency prediction), and healthcare (breast cancer detection). He has authored numerous peer-reviewed articles and books, including works on convolutional neural networks for plant disease identification and hybrid optimization techniques for data clustering. Prof. Işık teaches advanced courses such as İleri Derin Öğrenme (Advanced Deep Learning) and Sinir Ağları (Neural Networks). His recent work emphasizes real-time video analysis for public health (social distancing) and energy systems optimization. Despite no listed awards, his contributions to interdisciplinary fields like bioacoustics and renewable energy are notable. He has advised two master’s students: Mehmet Şirin Gündüz (2023) on YOLO-based crowd detection and Seda Bayat (2021) on bird species recognition using deep learning. His research integrates theoretical models with practical applications in domains ranging from agriculture to medical imaging.