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Mousumi RoyView profile

Professor

• Geophysics
• Tectonic Plate Dynamics
• Magma-Rock Interaction
• +5 more

Dr. Mousumi Roy is a Professor and Regents' Lecturer in the Department of Physics and Astronomy at the University of New Mexico (UNM). Her research focuses on geophysics and tectonic plate dynamics, particularly the interplay between magma-rock interactions, lithospheric evolution, and deformation patterns. She holds a PhD from MIT (1997), an MS from Boston University (1993), and a BS from Stanford University (1989). Current research projects include: Post-40 Ma lithosphere weakening in the Basin and Range Province (NSF-funded) Melt-migration within continental mantle lithosphere under disequilibrium conditions Mid-crustal magma intrusion modeling with seismic and geodetic data Volcano subsurface imaging using machine learning and muon radiography Stratovolcano topographic form analysis Her grants include NSF-EAR awards and UNM internal funding. She advises graduate students (e.g., Grant Block, Kellen Malone) and undergraduates like Stephanie Paiva-Flynn. Her research group uses advanced modeling techniques (finite-element, lattice Boltzmann) and collaborates with institutions globally, including LANL and the University of Tokyo. Teaching includes courses in geophysics, computational physics, and Earth deformation analysis. She emphasizes student research involvement, with past students securing NSF fellowships and industry positions.

Christian SchroerView profile

Professor

• X-ray Microscopy
• X-ray Optics
• Synchrotron Radiation
• +4 more

Christian Schroer is a Professor at the University of Hamburg and Leading Scientist at DESY, where he directs the scientific program of the PETRA III synchrotron radiation source. His research focuses on X-ray microscopy, X-ray optics, and their applications in materials science, nanotechnology, and condensed matter physics. He has contributed to the strategic development of PETRA IV, an ultra-low emittance synchrotron source, and co-founded the Helmholtz Imaging platform for data-driven imaging science. His career includes roles as a professor at TU Dresden (2006–2014), a scientist at DESY, and postdoctoral research at institutions like the University of Maryland and RWTH Aachen University. Schroer’s group develops advanced X-ray microscopy techniques and collaborates on projects ranging from solar cell analysis to catalyst dynamics. Education: PhD in Mathematical Physics, University of Cologne (1995) Studies in Physics at RWTH Aachen University (1986–1992) Habilitation in Physics, RWTH Aachen (2004) Research Interests: X-ray nanoscience and optics Aberration-corrected lenses and focusing techniques In situ and operando characterization of materials 3D tomography and multimodal imaging Synchrotron and free-electron laser applications Grants & Collaboration: Lead scientist for PETRA III and PETRA IV projects Member of Helmholtz Imaging initiative Active in international collaborations on X-ray microscopy and photon science Labs & Teams: X-ray microscopy group at DESY PtyNAMi (Ptychographic Nano-Analytical Microscope) facility

Stephen McKinnonView profile

Professor

• Mining Engineering
• Geomechanics
• Rockburst Analysis
• +7 more

Dr. Stephen McKinnon is a Professor in the Robert M. Buchan Department of Mining at Queen’s University, holding a cross-appointment in the Department of Geological Science and Geological Engineering within the Faculty of Arts and Science. He joined Queen’s in 1998 as the Professor and Chair in Mine Design. His expertise spans mine layout design, high-stress geomechanics, and seismic risk analysis in mining environments. He holds a B.A.Sc. and M.A.Sc. from the University of Toronto and a Ph.D. from the University of the Witwatersrand. Dr. McKinnon’s research focuses on mining-induced seismicity, rock mass characterization, and numerical modeling of subsidence and stability. His work integrates advanced techniques like LiDAR imaging for fracture mapping and machine learning for rockburst risk prediction. He has extensive international experience in mining geomechanics, working across Sweden, South Africa, Chile, the USA, and Canada. His publications reflect a strong emphasis on bridging geomechanical theory with practical mining challenges, particularly in deep and complex geological settings. He is a registered Professional Engineer in Ontario and actively contributes to industry-relevant research through collaborations and advanced modeling methodologies.

Abdurrahman ÖZBEYAZView profile

Associate Professor

• Optimization Algorithms
• Digital Image Processing
• Biomedical Signal Processing
• +7 more

Abdurrahman ÖZBEYAZ is an Associate Professor and Department Head of the Electrical and Electronics Engineering Department at Adıyaman University. He holds a PhD from Çukurova University (2015) and has extensive academic experience including visiting research at the University of Surrey (UK) under the TÜBİTAK 2219 program. His research focuses on Optimization Algorithms, Biomedical Signal Processing, Machine Learning, and Deep Learning applications. He has authored/co-authored over 24 peer-reviewed articles and actively participates in academic committees and editorial boards. Education: BSc in Computer Engineering, Erciyes University (2005) MSc in Electrical and Electronics Engineering, Kahramanmaraş Sütçü İmam University (2010) PhD in Electrical and Electronics Engineering, Çukurova University (2015) Research Interests: His work integrates computational intelligence with real-world applications like neuromarketing, environmental monitoring, and biomedical signal analysis. Notable projects include EEG-based brand impact studies and machine learning models for drought prediction. Professional Highlights: ERASMUS teaching mobility: Debrecen University (2010), Polytechnic Institute of Porto (2016) Over 8 academic awards including TÜBİTAK publication incentives Supervised projects on biogas optimization, solar energy modeling, and smart system design Awards: University of Surrey Postdoctoral Research Fellowship (2021-2022) Academic Incentive Award (Adıyaman University, 2022) Grants & Labs: Lead researcher in TÜBİTAK-funded projects and actively involved in university initiatives like the Land Consolidation Optimization research group. His lab focuses on developing smart environmental monitoring systems and neuromarketing tools.

Francisco RoblesView profile

Assistant Professor

• Optical Imaging and Spectroscopy
• Molecular and Functional Imaging
• Medical Devices
• +4 more

Dr. Francisco Robles is an Assistant Professor in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, and an Adjunct Assistant Professor in the School of ECE. He leads the Optical Imaging and Spectroscopy (OIS) Lab, which develops advanced optical technologies for biomedical applications, including cancer detection, tumor margin assessment, and hematology. His work focuses on label-free spectroscopic methods and signal processing to enhance functional and molecular contrast in biological systems. He holds a B.S. in Physics and Nuclear Engineering from North Carolina State University (2007), a Ph.D. in Medical Physics from Duke University (2011), and completed a Postdoctoral Fellowship in Chemistry at Duke (2016). His research spans optical imaging, nonlinear optics, and biomedical device development. Key research interests include: Quantitative phase imaging and tomography Deep-UV microscopy for label-free cell analysis Deep learning for virtual histology and diagnostics Point-of-care medical devices Notable awards include the NSF CAREER Award (2018) and the Burroughs Wellcome Fund Career Award (2015). His lab actively collaborates on biomanufacturing automation and digital twin technologies for cell therapies.

• Robotics and Automation
• Plant Disease Detection
• Spectral Imaging
• +3 more

Gerrit Polder is a Researcher at Wageningen University & Research, affiliated with the Robotics and Automation department (PE&RC). His work focuses on advancing technologies for plant disease detection, spectral imaging, and robotics in agricultural systems. He has contributed to projects such as OPTIMA, which aims to improve disease management in perennial crops and open-field vegetables using integrated pest management and smart camera systems. His research interests include machine learning applications in agriculture, non-destructive plant phenotyping, and the development of automated systems for crop monitoring. Key areas include spectral imaging for quality assessment, deep learning for disease detection, and 3D plant segmentation techniques. PhD supervision: Co-promotor for projects on 4D plant phenotyping, pre-symptomatic disease detection, and machine vision in hydroponic agriculture. Key contributions: Over 160+ publications, including patents on 3D plant reconstruction and smart camera systems. Affiliations: Active in international collaborations, such as EU-funded OPTIMA, and frequently presents at conferences on spectral imaging and precision agriculture. Recent work emphasizes the integration of spectral imaging and AI for early disease detection in crops like grape, apple, and carrot, as well as automated quality assessment in tomatoes and kiwifruit. His research bridges robotics, computer vision, and agricultural science to enhance sustainable farming practices.

Kaan InalView profile

Professor

• Computational Mechanics
• Materials Science
• Crystal Plasticity Modeling
• +3 more

Kaan Inal is a Professor at the Department of Mechanical and Mechatronics Engineering, University of Waterloo. He holds roles in faculty administration and is a full-time academic staff member. His research focuses on computational mechanics, materials science, and the integration of machine learning with engineering simulations. His work addresses challenges in crystal plasticity modeling, metallurgical processes, and microstructure characterization. Key areas of research include the development of advanced finite element methods for polycrystalline materials, thermal-mechanical modeling of alloys, and AI-driven approaches for material characterization and process optimization. He leads the Computational Mechanics Research Group, exploring innovations in composite materials, fatigue analysis, and additive manufacturing. His publications (2022–2025) highlight trends in data-driven modeling, phase-field simulations, and machine learning applications for predicting material behavior. These studies emphasize texture evolution, dynamic recrystallization, and microstructural analysis in metals and composites. No specific scientific awards are listed, but his contributions have advanced numerical methods in materials engineering. Advising and grant activities are integral to his role, though specific student names or grant details are not provided in the text. His work bridges theoretical mechanics with practical industrial applications, contributing to lightweight materials for automotive and aerospace sectors.

Behrad KhameseeView profile

Professor

• Magnetic Levitation Systems
• Microrobotics
• Additive Manufacturing
• +4 more

Behrad Khamesee is a Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, Canada. He is a full-time faculty member leading the Maglev Microrobotics Laboratory, specializing in magnetic levitation systems, microrobotics, and their applications in manufacturing and biomedical fields. His work integrates mechatronics, control systems, and advanced materials to develop innovative solutions for precision motion, non-destructive testing, and smart manufacturing. Research Interests: Magnetic Levitation Systems and Microrobotics Additive Manufacturing and Quality Assurance Control Systems for Industrial Automation Energy Harvesting via Nanogenerators Bio-Medical Robotics and Surgical Applications Key Projects: His lab has developed platforms like MagFloor and MagTable for flexible manufacturing, and pioneered the use of magnetic levitation in precision tasks such as additive manufacturing defect detection. Recent work emphasizes AI-driven control systems and modular magnetic levitation architectures. Labs/Teams: Director of the Maglev Microrobotics Laboratory, focusing on interdisciplinary projects involving robotics, materials science, and automation.

Felipe GonzalezView profile

Professor

• Aerial Robotics
• Automation
• Unmanned Aerial Systems (UAVs)
• +6 more

Professor Felipe Gonzalez is a Professor at the School of Electrical Engineering and Robotics (EER) within the Engineering Faculty at Queensland University of Technology (QUT). He leads the Airborne Sensing Lab and is a Chief Investigator (CI) in the QUT Centre for Robotics (QCR). His research focuses on aerial robotics, UAV automation, and remote sensing, with applications in environmental monitoring, biosecurity, and wildlife conservation. He holds a PhD in Aeronautical Engineering from the University of Sydney. Dr. Gonzalez has secured over $47.1M in grants, including ARC DP, ARC Linkages, CRC projects, and industry partnerships. His work emphasizes translating scientific outcomes into practical solutions for autonomy, sensor design, and UAV-based data analysis. Notable achievements include the Green Falcon solar-powered UAV, which won the iENA 2011 Gold Medal and EDN Innovation Award. His research spans UAV navigation in GPS-denied environments, precision agriculture, and AI-driven environmental monitoring. He has authored/co-authored nearly 130 refereed papers and several books on UAV design and optimization. Professional affiliations include Engineers Australia, AIAA, and the Royal Aeronautical Society. Awards include the Engineers Australia Excellence Award (2010), Vice-Chancellor’s Award (2011), and Chartered Professional Engineer (CPENG) certification. His teaching spans systems engineering, aerospace design, and UAV technologies, with consistently high student evaluations. Current projects include Antarctic vegetation monitoring, multi-UAV path planning, and AI-driven pest surveillance. The Airborne Sensing Lab collaborates with industry and international partners to advance UAV applications in environmental and industrial domains.

• Medical Ultrasound Imaging
• Image Quality Enhancement
• Beamforming Techniques
• +7 more

Svein-Erik Måsøy is a Professor in the Department of Circulation and Medical Imaging at Norwegian University of Science and Technology (NTNU), affiliated with the Faculty of Medicine and Health Sciences. His research focuses on improving medical ultrasound imaging quality through advanced beamforming techniques, aberration correction, and sound velocity estimation. From 2022-2024, he served as the Center Director of the Center for Innovative Ultrasound Solutions (CIUS). Research Interests: Måsøy’s work emphasizes enhancing image clarity in medical ultrasound to aid early disease detection and robust diagnostics. He specializes in beamforming optimization, adaptive image quality enhancements, and clinical validation of these techniques in echocardiography and fetal ultrasound. His recent projects include applying deep learning for automated measurements in salmon farming and improving 3D echocardiography imaging. Publications Trends: His recent publications span 2022-2025, addressing topics like deep learning-based image quality scoring, coherence-based aberration correction in fetal ultrasound, and Doppler ultrasound applications in borehole fracture analysis. These reflect a blend of clinical and geophysical ultrasound innovations. Lab/Teams: As former CIUS Director, he contributed to interdisciplinary ultrasound research collaborations. His work integrates medical imaging with machine learning and geophysical applications, fostering advancements in both clinical and industrial ultrasound technologies.

Enkhsaikhan BoldsaikhanView profile

Associate Professor

• Industrial Robotics
• Smart Manufacturing
• Automation Systems
• +2 more

Dr. Enkhsaikhan Boldsaikhan is an Associate Professor in Industrial, Systems, and Manufacturing Engineering at Wichita State University's College of Engineering. His teaching spans industrial robotics, automation, cyber-physical systems, and smart manufacturing. Research integrates artificial intelligence with manufacturing processes, focusing on robotic systems, automation, and sensor technologies. Key research areas include: Real-time toolpath planning for industrial robots Non-destructive testing of composite materials Soft robotics and compliant mechanisms Deep learning-based damage detection Friction stir welding applications Recent publications demonstrate innovation in combining finite element analysis with machine learning for robotic systems and advancing ultrasonic defect detection for aerospace composites.

Thomas DekeyserView profile

Lecturer

• Human Geography
• Digital Geographies
• AI & Deep Learning
• +6 more

Dr Thomas Dekeyser is a Lecturer in Human Geography at the University of Southampton. His research focuses on digital technologies, negative affects, and the politics of refusal, particularly exploring how technological changes reshape resistance movements and challenge traditional understandings of power and subjectivity. He holds a PhD in Geography from the University of Southampton, following BA/MA degrees in Media Studies and Geography from Artevelde University, Goldsmiths University, and Royal Holloway. Research Interests: Digital Geographies, AI ethics, critical theory, pessimism, experimental film, and posthumanist subjectivity. Recent creative projects include the documentary films MACHINES IN FLAMES (2022) and BREACHED (2024), examining historical and contemporary acts of technological resistance. Awards: British Academy Postdoctoral Fellow (Royal Holloway), 150th Anniversary Research Fellow (Aberystwyth). Current Research: Investigating 'AI-Pessimism' and the societal implications of AI saturation. Supervision: Accepting PhD applicants exploring technology critique, negativity studies, or digital geographies. His work bridges academic research with creative practice, using film and GeoHumanities methods to interrogate technology's role in colonialism, urban life, and affective politics.

• Biomedical Engineering
• Paediatrics and Reproductive Medicine
• Computation Theory and Mathematics
• +3 more

Priya Rani is a Lecturer in the School of Engineering at RMIT University. Her research focuses on interdisciplinary areas including Biomedical Engineering, Artificial Intelligence, and Neurosciences. She actively supervises research projects such as developing peripheral electronics for neural recording systems and applying machine learning for clinical delirium diagnosis. Dr. Rani’s work bridges computational methods with healthcare applications, emphasizing robust machine learning models and medical imaging analysis. She is affiliated with RMIT’s City Campus and contributes to advancing technologies for sustainable and intelligent systems. Research Interests: Her primary areas include computational theory, biomedical engineering, and AI-driven solutions for healthcare challenges. Notable projects involve semantic segmentation using attention-augmented networks and damage monitoring in advanced materials. Dr. Rani’s research trends highlight machine learning robustness, medical image analysis, and smart home technologies for aging populations. Supervision Focus: Neural recording systems, clinical AI applications Key Projects: Cortical neural interfaces, delirium prediction algorithms Technical Expertise: Machine learning, image processing, medical diagnostics Collaboration and Impact: Her work addresses real-world challenges through interdisciplinary approaches, with publications spanning medical IoT, nanosensors, and composite materials analysis. Dr. Rani’s contributions emphasize practical applications of AI and computational methods in healthcare and engineering domains.

Matthew K. NockView profile

Professor

• Suicide Prevention
• Self-Injury
• Self-Destructive Behaviors
• +3 more

Matthew K. Nock is the Edgar Pierce Professor of Psychology and Chair of the Department of Psychology at Harvard University. He leads the Nock Lab, focused on understanding self-destructive behaviors like suicide and self-injury. His work integrates epidemiology, lab experiments, and clinical studies, supported by grants from NIH, DoD, and private foundations. Nock holds a Ph.D. from Yale University (2003) and completed his clinical internship at Bellevue Hospital/NYU Child Study Center. His awards include the MacArthur Genius Grant (2011) and early career honors from APA and the American Association of Suicidology. He teaches courses on statistics, research methods, and cultural diversity at Harvard, earning multiple teaching awards. Research Interests: Suicide Prevention, Self-Injury, Developmental Psychopathology. His lab employs mixed-methods and community-based research to address mental health disparities, leveraging AI, speech analysis, and digital phenotyping. Collaborations include MGH’s Center for Precision Psychiatry and Harvard’s Chan School. Current projects focus on causal inference in suicide prevention, cultural factors in mental health, and scalable interventions. Grants & Awards: Over $10M in NIH/DoD funding; 100+ publications. Lab members include 12 research assistants, 8 grad students, and 5 postdocs (e.g., Jasmin Brooks Stephens, Chelsea Boccagno). The lab is expanding into global mental health surveys and psychedelic medicine research.

Tzu-Liang TsengView profile

Professor

• Additive Manufacturing
• Renewable Energy Systems
• Data Mining
• +2 more

Tzu-Liang (Bill) Tseng is Professor and Chair of Industrial, Manufacturing and Systems Engineering at UTEP. His research spans renewable energy, biomedical engineering, AI-driven manufacturing, and quality improvement technologies, with extensive publications in additive manufacturing and smart systems. He directs projects on 3D printing of sensor devices, AI-based non-destructive testing (funded by Lockheed Martin), and sustainable manufacturing. Recent work includes digital twins for aerospace quality assurance and medical imaging analysis. Key Areas: AI applications in manufacturing quality control 3D printed nanocomposites for sensors Cyber-physical production systems