Find Academic Supervisors

• 2D Materials
• Silicon Nanomembranes
• Micro/Nanofabrication
• +4 more

Houk Jang is a Staff Scientist in the AI Accelerated Nanoscience division at Brookhaven National Laboratory's Center for Functional Nanomaterials. His expertise spans 2D materials, silicon nanomembranes, micro/nanofabrication, and analog neural networks. He leads the QPress initiative, developing autonomous tools for 2D material-stack production and characterization, aiming to automate and accelerate material research for CFN users. Education: B.S. (2010) in Materials Science and Ph.D. (2014) in Nano Engineering, both from Sungkyunkwan University, Korea. Postdoctoral research at Yonsei University and Harvard University. His work focuses on integrating AI with nanomaterials to advance optoelectronics, flexible electronics, and quantum systems. Research highlights include creating graphene-based flexible electronics, developing optical machine vision processors, and pioneering methods for 2D material layer detection. His 2022 Nature Electronics publication demonstrated in-sensor optoelectronic computing using silicon, while 2020 Advanced Materials work introduced atomically thin optoelectronic processors. Publications span over 40 peer-reviewed articles, emphasizing photonics, quantum effects in 2D materials, and neuromorphic devices. His lab collaborates on projects involving terahertz spectroscopy, nanoscale imaging, and bioinspired optical systems. Currently directs nanofabrication efforts and advises on scalable material synthesis techniques. Active in developing high-performance infrared photodetectors and tunable electronic devices leveraging graphene and topological materials.

• Computer Vision
• Robotics
• Biomedical Engineering
• +3 more

Javaan Singh Chahl is a Professor at the School of Engineering within UniSA STEM at the University of South Australia, based at the Mawson Lakes Campus. His roles include DST Joint Chair of Sensor Systems. His research focuses on biomimetic systems, computer vision, UAV control, thermal management, and biomedical applications. He has authored/co-authored over 20 peer-reviewed publications since 2013, including work on ocular disease detection via deep learning, UAV gesture recognition datasets, and thermal management of electrical motors using Peltier technology. Education: Not explicitly listed in provided texts. Affiliations: UniSA STEM, School of Engineering, DST Joint Chair role. Research highlights include: Bioinspired flight control systems mimicking insect optical sensors and appendages. Development of vision-based systems for non-invasive medical diagnostics (e.g., ocular diseases, neonatal jaundice) using computer vision and Raspberry Pi platforms. Innovative thermal management solutions for electrical motors leveraging thermoelectric coolers. Creation of UAV-GESTURE dataset advancing gesture-based drone control. His work bridges robotics, biomedical engineering, and environmental monitoring, with applications in healthcare, disaster relief, and autonomous systems.

Cuong DangView profile

Associate Professor

• Nanophotonics
• Nanomaterials
• Optoelectronics
• +7 more

Assoc Prof (Steve) Cuong Dang is an Associate Professor at the School of Electrical & Electronic Engineering, Nanyang Technological University (NTU), Singapore. He leads the Cuong Dang Group (CDG), focusing on nanophotonics, nanomaterials, and their applications in sensors, photovoltaics, light-emitting diodes, and lasers. His research has been published in top international journals including Nature Photonics and Nature Nanotechnology. Previously, he worked at Brown University as a postdoctoral research associate, senior research associate, and manager of the Nano-Photonics laboratory before joining NTU as a senior research fellow at LUMINOUS! Centre of Excellence for Semiconductor Lighting and Displays. He became an Assistant Professor at NTU in September 2014 and was promoted to Associate Professor. Dr. Dang's research spans multiple areas including nanophotonics and nanomaterials engineering for optoelectronic applications, perovskite optoelectronics (photovoltaics, LEDs, scintillators), focusing light through scattering media, imaging through scattering media, and computational imaging and display. His group is actively working on novel nonradiative energy transfer, strong light-matter coupling, hybrid plasmonic nano-emitters, perovskite scintillators, and computational approaches for imaging and light field control. His research and development with QD Vision Inc. was recognized with the first quantum dot product for solid-state lighting: "Quantum LightTM Optic," which later evolved into "Color-IQ," the world's first high-volume Quantum Dot product for LCD Televisions. His 15 most recent publications (2020-2021) demonstrate a strong focus on nanophotonics, perovskite optoelectronics, and energy applications. His work spans materials science, photonics, and energy conversion, with particular emphasis on quantum dot lasers, perovskite scintillators, and light manipulation through scattering media. The publications show interdisciplinary research connecting physics, materials science, and engineering. Vietnam Education Fellowship (VEF) in 2004 VEF professional development grant in 2009 Dr. Dang actively mentors students and has multiple PhD positions available in his group, focusing on nanomaterials and optoelectronic/photonic applications, as well as optical imaging through strongly scattering media. His group has received funding from various sources to support research on optoelectronic devices with nanomaterials, prototype development for gas imaging cameras, and algorithm development for imaging through complex media. The Cuong Dang Group maintains several specialized laboratories including the Nanophotonics Lab, Photonics I and II Labs, and the Luminous! Lab.

• Artificial Intelligence in Healthcare
• Medical Image Analysis
• ADHD Diagnosis Systems
• +5 more

Abdul Rehman is a computer science researcher currently affiliated with IMT Atlantique Bretagne Pays de la Loire in Brest, France, where he recently completed his PhD dissertation on 'Practical watermarking for multimedia traitor tracing' in 2024. His research spans multiple domains at the intersection of artificial intelligence and healthcare applications, with significant contributions in medical image analysis, neurodevelopmental disorder diagnosis, and wireless communications. His primary research interests focus on applying AI techniques to healthcare challenges, particularly in ADHD diagnosis systems using visual attention tasks, eye tracking, and serious games. He has also made substantial contributions to medical image analysis for breast cancer and leukemia detection, as well as work in multimedia security, serverless computing, and wireless sensor networks. His interdisciplinary approach combines computer vision, deep learning, and human-computer interaction to develop practical healthcare solutions. Analysis of his recent publications (2023-2025) reveals a strong trend toward healthcare AI applications, particularly in neurodevelopmental disorders and medical imaging. His work demonstrates expertise in developing explainable AI frameworks that bridge technical innovation with clinical needs, often collaborating with psychologists and medical professionals. The publications show increasing sophistication in multimodal approaches combining eye tracking, visual attention analysis, and deep learning. Abdul Rehman has established productive collaborations with researchers including Ilona Heldal, Jerry Chun-Wei Lin, and Waqas Sultani, with whom he has co-authored multiple papers on ADHD diagnosis systems. His work appears in reputable venues including IEEE Access, IEEE Big Data, and MICCAI. As a recent PhD graduate transitioning into research positions, he is actively contributing to multiple research directions while establishing himself as a specialist in AI applications for healthcare diagnostics, particularly in the areas of neurodevelopmental disorders and medical image analysis.

• Biomedical and bioinspired engineering
• Optical/Infrared/mm-Wave/T-ray vision systems
• Game theory and information theory
• +8 more

Professor Derek Abbott is a faculty member in the School of Electrical and Mechanical Engineering at the University of Adelaide, within the Faculty of Sciences, Engineering and Technology. He holds the academic rank of Professor and is actively involved in supervising Masters and PhD students. His research interests span a wide range of interdisciplinary fields, including biomedical engineering, optical systems, game theory, quantum mechanics, stochastic phenomena, computational neuroscience, and forensic engineering. Professor Abbott has also contributed to energy systems optimization and space-based sensor technology. His recent work includes studies on neural network robustness, gravitational redshift measurement, and cardiovascular disease prediction using machine learning. His research often bridges theoretical concepts with practical applications in engineering and healthcare. Key research areas include the development of terahertz technology, bio-inspired engineering systems, and quantum game theory frameworks. He has published extensively on topics ranging from deep learning security to renewable energy grid optimization. His interdisciplinary approach integrates principles from physics, mathematics, and computer science to address complex real-world problems. Professor Abbott is eligible to supervise postgraduate research students and collaborates internationally on projects related to forensic genealogy, platonism in science, and energy storage solutions. His work often emphasizes the intersection of technology and societal challenges, such as climate change and healthcare innovation.

• Composite Materials
• Fluid-Structure Interactions
• Hydrodynamics
• +5 more

Ivan Stenius is a full-time Associate Professor at the Department of Engineering Mechanics, KTH Royal Institute of Technology. He holds a M.Sc. (2003) and Ph.D. (2009) in Lightweight Structures from KTH, with a licentiate degree (2007). His research focuses on composite materials, fluid-structure interactions, hydrodynamics, marine robotics, and model-based systems engineering. He leads the Swedish Maritime Robotics Centre (SMaRC), Sweden’s largest academic initiative in underwater robotics, and co-founded Zparq AB for marine electric propulsion. Education: M.Sc. in Lightweight Structures (KTH, 2003) Technical Licentiate in Lightweight Structures (KTH, 2007) Ph.D. in Hydroelasticity and Fluid-Structure Interactions (KTH, 2009) Research & Collaboration: Stenius develops advanced software tools with FMV and the Swedish Coast Guard. He leads cross-disciplinary projects involving computer vision, electrochemistry, and networked control. His work on hydrofoiling and electric propulsion has spun off Zparq AB. Recent projects include autonomous seaweed farm inspection, bioinspired underwater robots, and reinforcement learning for AUV maneuvering. Labs & Initiatives: PI of SMaRC, which integrates advanced robotics and maritime systems. Active in courses like Underwater Technology (SD2709) and Vehicle Engineering (SD1002).

• Integrated Photonics
• Imaging Systems
• Medical Diagnostics
• +7 more

George Giakos is a Professor and Chair of the Department of Electrical and Computer Engineering at Manhattan College, NY, and Director of the Graduate Program. Previously, he served as a Professor at the University of Akron for over 20 years, directing imaging technologies and surveillance laboratories. His research focuses on imaging systems, photonics, medical diagnostics, and defense applications. Giakos holds a PhD from Marquette University and has authored over 200 peer-reviewed articles, 16 patents, and received prestigious awards including the IEEE Fellow distinction and the 2014 IEEE-USA Professional Achievement Award. Education Ph.D., Electrical & Computer Engineering, Marquette University M.S., Nuclear Physics, Ohio University Postgraduate Diploma, Nuclear Instrumentation, University of Edinburgh Laurea, Applied Physics, University of Turin Research Interests Giakos pioneers advancements in imaging devices and systems, integrating photonics, nanotechnology, and bio-inspired techniques. His work spans medical diagnostics (e.g., cancer detection via polarimetric imaging), defense (space surveillance, autonomous systems), and semiconductor characterization. He emphasizes interdisciplinary innovation, bridging defense and bioscience through novel imaging paradigms. Articles Overview Recent work includes neuromorphic architectures for space IoT, adversarial machine learning in power grids, and interdisciplinary frameworks for systematic innovation. His research often combines AI with domain-specific applications, addressing challenges in healthcare site selection, cybersecurity, and quantum sensing. Awards IEEE Fellow (2014) ONR Distinguished Faculty Fellow (2005) AFRL Space Surveillance Fellowships (2007–2009) NASA Faculty Fellowships (2000, 2001, 2004) Advising & Grants Giakos has secured over $6M in grants as PI, collaborating with AFRL, NASA, and industry partners like Lockheed Martin. His leadership spans IEEE committees, including chairing the TC-19 Imaging Systems Technical Committee and organizing international conferences like IEEE International Conference on Imaging Systems and Techniques. Labs & Teams Directs Manhattan College’s imaging and surveillance laboratories, advancing technologies such as multifusion polarimetric detection and bio-inspired robotic vision systems. His team develops platforms like the AFRL Multifunctional Imaging Surveillance system for defense and medical applications.

Christopher BuckinghamView profile

Research Fellow

• Artificial Intelligence
• Clinical Decision Making
• Psychological Classification
• +3 more

Christopher Buckingham is a Research Fellow at Aston University's College of Engineering and Physical Sciences and School of Engineering and Applied Science. His work focuses on artificial intelligence applications in clinical decision support systems, psychological classification, and expertise development. He holds a BSc in Psychology from the University of Southampton and MSc/PhD in Computer Science from the University of Birmingham. Buckingham is also an Associate Editor for BMC Medical Informatics and Decision Making since 2010. Education: BSc Psychology, University of Southampton MSc Computer Science, University of Birmingham (1987) PhD Computer Science, University of Birmingham (Thesis: 'Galatean model of human classification implemented in a decision support system') Research Interests: Clinical decision-making and risk assessment, particularly in mental health Intelligent knowledge-based systems and AI-driven clinical tools Human expertise modeling and cognitive processes Development of decision support systems (e.g., GRiST) Applications of fuzzy logic and evolutionary algorithms His work emphasizes bridging cognitive science and clinical practice, with notable contributions to mental health risk management systems and emotion recognition technologies. Buckingham's research often integrates real-world domain challenges like infrastructure maintenance and healthcare collaboration. Key Contributions: Over 49 research outputs, including the GRiST decision support system for mental health risk assessment and the ARRIVE algorithm for knowledge elicitation. Active in interdisciplinary projects combining AI, healthcare, and engineering.

Giuseppe LoiannoView profile

Associate Professor

• Robotics
• Autonomous Systems
• Micro Aerial Vehicles (MAVs)
• +7 more

Giuseppe Loianno is an Associate Professor in the Electrical and Computer Engineering Department at New York University (NYU) , and Director of the Agile Robotics and Perception Lab (ARPL) . His research focuses on autonomous Micro Aerial Vehicles (MAVs), perception, and control for robotics in unstructured environments. Prior to NYU, he held roles at the University of Pennsylvania's GRASP Lab as a Lecturer and Research Scientist. Affiliations: NYU WIRELESS, Center for Advanced Technology in Telecommunications (CATT) Education: BSc/MSc (University of Naples 'Federico II'), PhD in Computer and Control Engineering (2014) His work emphasizes agile robotics autonomy , particularly enabling drones to navigate without external infrastructure like GPS. Key innovations include bioinspired propellers for collision resilience and vision-based navigation systems. He has published over 70 papers and received prestigious awards including the NSF CAREER Award (2022) and DARPA Young Faculty Award (2022). Loianno actively organizes robotics conferences: Co-chair of IEEE RAS Technical Committee on Aerial Robotics, Program Chair for IROS 2024, and founder of the International Symposium on Aerial Robotics (ISAR). His lab's projects span cooperative robotics, human-drone collaboration, and autonomous charging systems for perpetual missions. Recent articles explore UAV-based thermal geo-localization , human-in-the-loop navigation , and resilient control strategies for quadrotors. His research has been featured in major robotics journals and international media.

• Cognitive Robotics
• Artificial Intelligence
• Biomimetic Systems
• +7 more

Professor Tony Prescott is a prominent academic in cognitive robotics at the University of Sheffield, leading the School of Computer Science. He is co-founder and former director of Sheffield Robotics and an active member of the Sheffield Neuroscience Institute. His research integrates robotics, AI, psychology, and neuroscience to explore human-technology relationships, societal impacts of emerging technologies, and bioinspired systems like Scratchbot and MiRo-e. He has authored over 240 publications and secured £10M in grants, co-founding companies such as Consequential Robotics and BOW. His work frequently features in media including BBC, Discovery Channel, and CNN. Education: MA Psychology (Edinburgh), MSc Applied AI (Aberdeen), PhD Machine Learning (Sheffield) Research focuses on biomimetic systems, active touch sensing, assistive robotics, and ethical AI. Key achievements include the Scholarpedia of Touch , Living Machines conference co-chair role, and contributions to robotic telepresence. Awards include BPS Fellowship and Sage Centre distinction. He advises on STEM education and chairs the Skills and Education in Robotics and AI network. Publications span cognitive neuroscience, AI, and robotics, with recent emphasis on synthetic self-awareness, tactile perception, and ethical implications of technology. His labs include the Adaptive Behaviour Research Group and Active Touch Laboratory.

Stanislao GraziosoView profile

Assistant Professor

• Soft Robotics
• Nonlinear Modeling
• Bio-inspired Design
• +4 more

Stanislao Grazioso serves as Assistant Professor in the Department of Industrial Engineering at the University of Naples Federico II (UNINA), where he leads the BIOIC (Bioinspired soft robotic systems for cognitive production) project. His work focuses on transforming manufacturing through soft robotic technologies, with expertise spanning design, modeling, and control of compliant robotic systems for industrial and biomedical applications. Education: Bachelor of Science in Mechanical Engineering, University of Naples Federico II Master of Science in Mechanical Engineering, University of Naples Federico II Ph.D. in Industrial Engineering, University of Naples Federico II (2018), specializing in soft robotics Research Focus: Dr. Grazioso investigates the entire soft robot development lifecycle, emphasizing innovative design concepts for variable-stiffness actuators, advanced modeling using Cosserat rod theory and finite element methods, and human-robot interaction for safe collaboration. His research platforms include soft grippers for agrifood automation, continuum manipulators for confined spaces, and exoskeletons for industrial assistance. Key applications target nuclear remote handling (DTT fusion reactor), biomechanical analysis, and cognitive manufacturing systems. Publication Trends (2022-2024): Recent work demonstrates accelerating innovation in soft growing robots for remote sensing, bioinspired grippers with embedded adhesion mechanisms, and wearable biomechanics for athlete monitoring. His publications bridge theoretical modeling (e.g., screw theory for continuum robots) with industrial implementation, particularly in fusion reactor maintenance and agrifood automation, reflecting strong technology transfer orientation. Project Leadership: As BIOIC project lead, he drives integration of biological principles into manufacturing robotics. His involvement in DTT remote handling and NEFERTARI projects highlights expertise in nuclear robotics, while collaborations with clinicians demonstrate commitment to medical applications like soft exoskeletons for rehabilitation. Current efforts focus on virtual reality integration for ergonomic workplace assessment and EMG-based wearable systems.

Georgia FARGETTAView profile

Research Fellow

• Optimization
• Machine Learning
• Game Theory
• +9 more

Georgia Fargetta is a Research Fellow (RTD-A) in Computer Science at the Department of Mathematics and Computer Science, University of Catania, Italy. She holds a PhD in Computer Science (2022) and both Bachelor's (2017) and Master's (2019) degrees in Mathematics from the University of Catania. Current academic affiliation: Department of Mathematics and Computer Science, University of Catania Academic rank: Research Fellow RTD-A (INF/01) Research themes: Optimization, Machine Learning, Game Theory, and applications to supply chain networks and crowd evacuation modeling Research Interests Georgia's research spans multiple domains including: Optimization Game Theory Metaheuristic Algorithms Crowd Simulation Medical Supply Chain Her publications demonstrate expertise in applying these methodologies to diverse problems such as social media content competition, emergency evacuation planning, and medical supply allocation. The scientific awards section highlights: Young Women in Operations Research Award (EURO WISDOM, 2021) Young Women for Operational Research (2022) She actively participates in international conferences including ODS, EUROPT, and MIC. As member of IPLAB (Image Processing Laboratory), she contributes to research in Computer Vision and Multimedia.

• Bioinspired Robotics
• Biomechanics
• Aerodynamics
• +3 more

Associate Professor Sridhar Ravi is Deputy Head of the School of Engineering and Technology at UNSW Canberra and leads the Bio-Engineering Research Group . He holds a Bachelors of Engineering (Aerospace) and PhD from RMIT University (2007 & 2011), where his doctoral research focused on wind turbulence effects on wing aerodynamics. Postdoctoral stints at Harvard University (2012-2014), Chiba University (JSPS Fellowship), and University of Bielefeld (Alexander von Humboldt Fellowship) expanded his expertise in insect/bird flight and bio-inspired design. Research Interests span Bioinspired Robotics Biomechanics of Flight Fluid-Structure Interactions Neuroethology of Navigation Unsteady Aerodynamics Autonomous Systems His work integrates biological principles into engineering solutions, particularly for miniature aerial/underwater vehicles. Recent Publications emphasize fluid dynamics in bio-inspired propulsion, obstacle avoidance, and swarm robotics. Key trends include passive wing rotation mechanisms, ground effect optimization, and sensory integration for autonomous control. Scientific Honors include JSPS Fellowship (Chiba University) Alexander von Humboldt Fellowship (University of Bielefeld) His teaching at UNSW Canberra includes Rotorcraft Engineering and Fundamentals of Flight , reflecting his technical depth.

Rasoul SadeghianView profile

Researcher

• Soft Robotics
• Deep Learning
• Control Systems
• +3 more

Rasoul Sadeghian is a Researcher at the Royal College of Art (RCA), specializing in robotics and autonomous systems since 2019. He holds an MSc in Mechatronics Engineering (Islamic Azad University, 2015) and a BSc in Applied Mathematics (Vali E Asr University, 2012). Prior to RCA, he served as a Research Fellow/Instructor at Tehran University's TaarLab (2013–2018) and Senior Researcher at INGV Co., Italy (2018–2019). His research focuses on Soft Robotics, Deep Learning, and Control Systems , with an emphasis on autonomous systems for extreme environments. He leads the MIMRee project , developing robotic solutions for offshore wind turbine inspection and repair. His work integrates vision-based control, adaptive gripping, and decision-making algorithms for robotics. Rasoul has published extensively on topics including robotic anchoring systems, telerobotics, and AI-driven medical imaging. His expertise spans robotics hardware design, simulation (ROS/Gazebo), and machine learning applications in automation. Current affiliations include the RCA Robotics Laboratory and contributions to the Multi-Platform Inspection Maintenance & Repair In Extreme Environment (MIMRee) initiative. His research bridges theory and practice, addressing challenges in robotics for renewable energy and industrial maintenance.

• Fish Locomotion
• Functional Morphology
• Bioinspired Robotics
• +5 more

Brooke Flammang is an Associate Professor in the Department of Biological Sciences at the New Jersey Institute of Technology (NJIT) . Her research focuses on the functional morphology and biomechanics of aquatic organisms, with an emphasis on fish locomotion, bioinspired robotics, and evolutionary adaptations. She leads federally funded projects including NSF grants on remora adhesion systems, flying fish robotics, and endangered marine organism telemetry. Key areas of expertise include: Biomechanics of swimming and flying fish Adhesive mechanisms in remoras Bioinspired robotic designs Evolutionary functional morphology Recent work involves developing amphibious energy harvesting systems and studying the hydrodynamics of flying fish models. She has published extensively on topics ranging from shark denticle orientation to robotic fish tail design, with over 45 peer-reviewed articles and significant media coverage for her innovative conservation-focused projects. Notable collaborations include: BRC-BIO Grant (2025-2028) : Duck foot morphology and swimming ability NSF BioDesign Grant (2024-2027) : Remora-inspired telemetry devices FELS RAISE Grant (2019-2025) : Evolution of terrestrial locomotion models