Find Academic Supervisors

• Digital Signal Processing
• Radar Imaging
• Terahertz Time Domain Spectroscopy
• +3 more

Associate Professor Brian Ng is an academic at the University of Adelaide, affiliated with the School of Electrical and Mechanical Engineering within the Faculty of Sciences, Engineering and Technology. His primary role is as an Associate Professor/Reader, focusing on teaching and research in electrical engineering and signal processing. He holds a position in the Department of Electrical and Electronic Engineering and is eligible to supervise Masters and PhD students in areas such as radar systems, signal processing, and terahertz technology. His research expertise includes digital signal processing, wavelet systems, pattern recognition, radar imaging (including 3D ISAR and maritime applications), and terahertz time-domain spectroscopy. He has led or co-investigated several grants, such as the SmartSat CRC Project (2021-22), DST Group-funded research on 3D ISAR classification, and terahertz sensing projects. His work spans defense applications, environmental monitoring, and material science. Ng teaches courses like Digital Signal Processing, Radar Principles, and Signal Processing Applications. He has contributed to educational initiatives, including e-learning projects and curriculum design. His professional activities include editorial roles in radar conferences and collaborations with defense and industry partners. Key facilities and labs include those supporting terahertz fiber optics and radar systems at the University of Adelaide's North Terrace campus. His research emphasizes practical applications of signal processing in aerospace, maritime, and biomedical contexts.

Magnus JanssonView profile

Professor

• Signal Processing
• Machine Learning
• Navigation and Positioning
• +3 more

Magnus Jansson is a Professor of Signal Processing at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Division of Information Science and Engineering. He holds a Ph.D. from KTH (1997) and has held academic positions at KTH since 1998, progressing from Assistant Professor (1998-2003) to Associate Professor (2003-2013) before becoming a full Professor in 2013. His research focuses on statistical signal processing, machine learning, navigation systems, sensor array processing, and system identification, with applications in radar, communication systems, and sensor fusion. Education: M.Sc. (1992), Licentiate (1995), and Ph.D. (1997) in Electrical Engineering (Automatic Control) from KTH. Postdoctoral work at the University of Minnesota (1998-1999). Served as an editor for IEEE Signal Processing Letters, Elsevier Signal Processing, and EURASIP Journal on Advances in Signal Processing. Current roles include Associate Editor for Elsevier Signal Processing (since 2024). Research emphasizes model selection, low-rank matrix reconstruction, and Bayesian methods, with recent contributions to drone classification via CNNs, robust model selection in high-dimensional data, and navigation algorithms leveraging inertial and vision systems. His work bridges theoretical signal processing with practical applications in robotics, wireless localization, and radar systems. Teaching responsibilities include courses on estimation theory, stochastic signals, and adaptive signal processing. Active in collaborative research on positioning systems and sensor networks, with contributions to IMU-camera calibration and visual-inertial navigation. No specific grants or awards listed, but recognized for editorial roles and academic leadership.

Chris PhillipsView profile

Associate Professor

• Internet Technologies
• Optical Networking
• Network Security
• +5 more

Dr. Chris Phillips is a Reader in the School of Electronic Engineering and Computer Science at Queen Mary University of London. He holds a BEng in Telecommunications Engineering and a PhD in Telecommunications from Queen Mary. His roles include Director of Recruitment and Admissions for EECS, Programme Director of Computer Systems Engineering, and member of the Networks Research Group. His research focuses on Internet Technologies, Optical Networking, Network Security, and Energy-Efficient systems, with emphasis on resilient communication, wireless sensor networks, and nomadic computing. Education: BEng Telecommunications Engineering (1st Class Honours), Queen Mary University of London (1984–1987); PhD in Telecommunications, Queen Mary University of London (1987–1991). Professional experience includes roles at Bell Northern Research, Siemens–Roke Manor Research, Nortel Networks, and academic leadership at Queen Mary since 2000. He has supervised over 20 PhD students and contributed to multiple research grants, including projects on energy management in wired networks and resilient overlay systems. Research Interests: Internet protocols, optical networking, network security, energy efficiency, wireless sensor networks, and resilient communication systems. His work spans theoretical advancements and practical implementations, with applications in healthcare, agriculture, and vehicular systems. Recent publications explore IoT optimization, cooperative routing in MANETs, and energy-efficient optical networks. Grants and Awards: Secured funding from EPSRC, Royal Society, Nortel, Nokia, and the UK-China Government Office for Science. Notable grants include the EPSRC-funded project on machine learning in optical networks and Royal Society collaborations on intelligent connection management. Teaching: Currently teaches Computer Architecture and Networks (Postgraduate) and Microprocessor Systems Design (Undergraduate). Previously lectured on topics like MPLS networks, G-MPLS, and real-time transport protocols. His teaching integrates practical lab work with theoretical frameworks, emphasizing hands-on experience with microcontrollers and network protocols. Labs/Teams: Active member of the Networks Research Group, collaborating with institutions like Technical University of Denmark and University of Girona. His research team focuses on developing innovative solutions for network resilience, energy efficiency, and distributed systems.

Khalid RajabView profile

Lecturer

• Millimeter-Wave Radar
• Sensing for Health and Wellbeing
• IoT Sensors
• +3 more

Dr. Khalid Rajab is a Lecturer in the School of Electronic Engineering and Computer Science at Queen Mary University of London (QMUL). His research focuses on millimeter-wave radar systems, IoT sensors for health monitoring, and machine learning applications in ambient sensing. He holds a PhD and is a Fellow of the Higher Education Academy (FHEA) and a Chartered Engineer (CEng). His research interests include radar-based activity recognition, health and wellbeing sensing, and sensor fusion technologies. He leads projects in wearable-free healthcare monitoring and has contributed to innovations in radar localization and non-invasive health screening. Dr. Rajab has secured a £396,049 grant from EPSRC for developing smart sensors in 'virtual wards' using mm-wave radar. His work spans interdisciplinary collaborations, including with the Centre for Networks, Communications and Systems at QMUL. His publications highlight advancements in radar calibration, multi-target tracking, and metamaterials. He is affiliated with research centers focusing on electronics, networks, and brain-behavior interfaces.

• Computer Vision
• Robust Estimation
• Object Tracking
• +6 more

Professor Jiri Matas is a Vice Chancellor Fellow at the University of Surrey's School of Computer Science and Electronic Engineering, affiliated with the Centre for Vision, Speech and Signal Processing (CVSSP). His primary research focuses on computer vision, robust estimation, and neural network applications in domains like autonomous systems, medical imaging, and audio-visual perception. He has contributed to advanced tracking algorithms, including discriminative filters and Siamese networks for object tracking, as well as methods for binaural sound processing and anomaly detection in autonomous vehicles. His academic work spans over 50 peer-reviewed publications since 2020, covering topics such as robust geometric estimation (MAGSAC++), lightweight depth estimation architectures, and AI-driven species recognition systems for fungi and plants. Notable collaborations include developing the Danish Fungi 2020 dataset and deploying citizen-science platforms for mycology. He also explores interdisciplinary applications, such as transgenerational immunity in ant colonies and audio-based vehicle monitoring systems. His grants and advising span both technical and biological domains, with labs focused on advancing perception systems in real-world conditions. Recent work emphasizes energy-efficient tracking (FEAR tracker), cross-modal learning (Binaural SoundNet), and handling prior shifts in classification tasks.

Nic ZhangView profile

Lecturer

• Internal Combustion Engineering
• Electric Vehicle Technology
• Energy Systems
• +5 more

Nic Zhang is a Lecturer in the Department of Mechanical Engineering at the University of Bath. His affiliations include the Made Smarter Innovation: Centre for People-Led Digitalisation and IAAPS (Innovative Automotive and Autonomous Power Systems). He earned his PhD in Mechanical Engineering from the University of Bath in 2016, supervised by Prof. C. Brace and others, focusing on two-stage turbocharging systems for diesel engines. His research interests span internal combustion engineering, electric vehicle technology, energy systems optimization, and sustainable mobility solutions. He actively collaborates on projects addressing zero-emission transportation, hybrid powertrains, and advanced vehicle dynamics. Key areas of focus include vibration analysis in electric vehicles, multi-camera tracking systems, and computational modal analysis of motor components. Zhang leads or co-leads six research projects, including the EU-funded 2Zero Partnership targeting road transportation decarbonization and the Clean Hybrid Alternative Marine Powertrain (CHAMP) initiative. His work contributes to UN Sustainable Development Goals related to affordable and clean energy and industry innovation. Notable contributions include advancements in battery equivalent circuit modeling and vehicle tracking algorithms using CityFlow datasets. He has presented at international conferences and engaged in public outreach, such as the SomerScience AAPS CDT stand in 2024.

Kevin WagnerView profile

Research Fellow

• Exoplanets
• Circumstellar disks
• Planet formation
• +3 more

Kevin Wagner is a NASA Hubble/Sagan Fellow at the University of Arizona, specializing in exoplanet research with a focus on circumstellar disks and planet formation. His work leverages advanced telescopic systems such as the James Webb Space Telescope (JWST), Very Large Telescope (VLT), and MagAO-X for high-contrast imaging. He has contributed to key projects like the HOSTS Survey and the JWST Early Release Science Program, advancing techniques for detecting protoplanets and characterizing exoplanetary systems. Education: Ph.D. in Astronomy (2020, University of Arizona). Research interests include imaging young planetary systems, studying accretion processes in protoplanetary disks, and developing mid-infrared spectroscopy methods for exoplanet detection. His recent work emphasizes direct imaging of exoplanets in habitable zones and AGN outflow dynamics using the Large Binocular Telescope (LBT). Articles highlight breakthroughs in detecting Saturn/Jupiter analogs with JWST MIRI, orbital modeling of eccentric substellar companions, and validating planet candidates around accelerating stars. His contributions also include improving thermal background subtraction techniques for ELT-class telescopes. Key awards: NASA Hubble/Sagan Fellowship (Research Excellence). Advising/grants: Active in the Giant Accreting Protoplanet Survey (GAPlanetS) and the Bioverse project targeting exo-Earth biosignatures. Collaborates on instrument development (e.g., SCALES for aperture masking). Labs/Teams: Affiliated with the Lunar and Planetary Laboratory (LPL), contributing to the LBT Interferometer and SCExAO/CHARIS imaging campaigns.

Susie MoloneyView profile

Associate Professor

• Climate Change Adaptation
• Urban Sustainability
• Social Equity and Justice
• +3 more

Dr. Susie Moloney is an Associate Professor in Sustainability and Urban Planning at RMIT University's School of Global Urban and Social Studies (GUSS), affiliated with the Centre for Urban Research. Her work focuses on climate change adaptation, social equity, urban sustainability, and policy governance at local and regional scales. She has collaborated with governments, NGOs, and community organizations on projects such as the Victorian Government's Place-based Adaptation Guidance and the Climate Change Exchange initiative. Research interests: Climate justice, urban resilience, low-carbon transitions, and participatory policy design Key roles: Co-founder of The Climate Change Exchange, Executive Director of the Centre for Just Places (2021–2023) Publications: Co-edited Local Action on Climate Change (2018) and authored Planning Melbourne (2016) Her research emphasizes equitable climate policies and innovative frameworks for monitoring adaptation efforts. She has led international projects, including a European-funded network on Low Carbon Urban Transitions involving researchers from multiple countries. Current supervision focuses on sustainable urban spaces and climate justice, with a commitment to bridging academic research with practical policy implementation.

• Communications engineering
• Electronics, sensors and digital hardware
• Aerospace engineering
• +7 more

Bill Moran is a Professor (Research Focused) in the School of Engineering at RMIT University. He specializes in research areas including communications engineering, applied mathematics, and sensor systems. His work focuses on localization algorithms, sensor networks, and signal processing applications. His email is bill.moran@rmit.edu.au. Research Interests: Moran’s research spans communications engineering, electronics, aerospace engineering, and applied mathematics. He has contributed to advancements in multi-sensor positioning, hidden Markov models, and Bayesian algorithms. His work often integrates theoretical frameworks with practical applications in robotics, underwater surveillance, and autonomous systems. Articles Trends: His publications (2018–2021) emphasize localization systems, sensor fusion, and gravitational wave detection. Key themes include Bayesian algorithms, multi-objective optimization, and trajectory planning for autonomous vehicles. Recent work explores underwater threat detection and RRT* algorithms for path planning. No scientific awards or honors explicitly mentioned. Supervised one project: Localization Using Hidden Markov Models in a Multipath Environment (2017). Labs/Teams: Affiliated with RMIT’s School of Engineering research groups focused on signal processing and autonomous systems.

• Unmanned Aerial Vehicles (UAVs)
• Swarm Intelligence
• Immunology
• +3 more

Lizhen Wu is an Associate Research Scientist at the Yale School of Medicine, Yale University. His research spans interdisciplinary fields including immunology, aerospace engineering, and computational biology. He focuses on molecular mechanisms of immune response modulation, particularly in somatic hypermutation and class switch recombination, as well as developing advanced UAV coordination algorithms and swarm intelligence systems. His work integrates genetic studies with bioinformatics, and he collaborates with leading researchers such as David G. Schatz and Anurupa Devi Yadavalli. Key research areas include: Immunology: Study of epigenetic factors (e.g., ELOF1, STING) in antibody diversification and cancer immune evasion Robotics: Multi-target tracking, swarm task planning, and manned-unmanned teaming strategies for aerospace applications Genetics: Roles of histone demethylases (KDM5A) in cancer progression and senescence Dr. Wu has published extensively in journals like Molecular Cell and IEEE Signal Processing Letters, with a focus on translating computational models into real-world applications. His recent work combines large language models (LLMs) with swarm robotics for optimized task allocation.

Zhen QuView profile

Assistant Professor

• Atmospheric Chemistry
• Climate Science
• Air Quality Modeling
• +5 more

Zhen Qu is an Assistant Professor in the Department of Marine, Earth, and Atmospheric Sciences at North Carolina State University. She holds a PhD in Mechanical Engineering (Air Quality Track) from the University of Colorado Boulder (2019) and a B.S. in Physics from Peking University (2014). Her research focuses on atmospheric chemistry, using satellite observations and advanced modeling techniques to study air pollutants and greenhouse gases. Key areas include methane emissions, tropospheric ozone analysis, and Bayesian statistical methods for emission inversion. Dr. Qu’s work integrates data-driven approaches with atmospheric models to address climate and environmental challenges, such as tracing greenhouse gas emissions and understanding climate interactions. She teaches MEA 425/525 (Introduction to Atmospheric Chemistry) and MEA 412 (Atmospheric Physics). Her research group develops tools like the Integrated Methane Inversion (IMI 1.0) to analyze high-resolution methane emissions from satellite data. Recent publications highlight contributions to methane surge attribution, ozone analysis via chemical reanalyses, and wildfire smoke impacts on health. She collaborates with global networks to improve emission inventories and supports policy through transparent data methodologies. Dr. Qu actively seeks motivated graduate students to join her team, emphasizing interdisciplinary approaches to environmental science.

Shigang YueView profile

Professor

• Artificial Intelligence
• Computer Vision
• Robotics
• +4 more

Professor Shigang Yue is a Professor of Computer Science at the University of Leicester's Computing and Mathematical Sciences department. His research focuses on artificial intelligence, computer vision, robotics, neuroscience, and bio-inspired systems. He has published over 200 papers and led EU-funded projects. He holds a PhD from Beijing University of Technology (1996) and was an Alexander von Humboldt Research Fellow (2000-2001). Research interests include bio-inspired visual neural networks, autonomous robotics, medical image analysis, machine learning, and swarm intelligence. He has coordinated EU FP7 and Horizon 2020 projects and examined over 20 PhD theses. His work bridges biological systems and robotics, emphasizing collision avoidance, motion detection, and swarm robotics. Notable contributions include the I2Bot platform for insect navigation simulation, bio-inspired collision detection models (e.g., LGMD), and frameworks for small target tracking. He is an EPSRC peer reviewer and editorial board member for international journals. Labs and teams: Active in developing bio-inspired robotics systems and collaborative platforms like Colcos φ for swarm robotics. His group welcomes PhD and visiting students in AI and robotics.

James C. SpallView profile

Research Professor

• Stochastic systems
• Parameter estimation
• Stochastic optimization
• +5 more

James C. Spall holds multiple positions at Johns Hopkins University, including Research Professor in the Department of Applied Mathematics and Statistics, Chair of the Applied and Computational Mathematics Program, and Co-Chair of the Data Science Program at the Whiting School of Engineering. He is also a Principal Professional Staff member at the Johns Hopkins Applied Physics Laboratory (APL). His work focuses on stochastic systems, statistical analysis, simulation methods, and optimization algorithms. Spall has authored three books and numerous publications in control and statistics, including pioneering work on the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. He holds two U.S. patents for control systems and has served as Program Chair for the 2007 IEEE Conference on Decision and Control. His editorial roles include Senior Editor for IEEE Transactions on Automatic Control and Contributing Editor for Current Index to Statistics. His research interests span stochastic optimization, parameter estimation, Monte Carlo methods, and neural networks, with applications in control systems, system identification, and Kalman filtering. Spall has received multiple awards, including the IEEE Fellow distinction, Tau Beta Pi Fellowship, and teaching accolades from Johns Hopkins Engineering for Professionals. He currently chairs the new Industrial and Operations Engineering Program at JHU Engineering for Professionals, set to launch in 2024. His interdisciplinary work bridges academia, industry, and applied research, emphasizing methodological innovation in computational and statistical sciences.

• Marine Mammal Ecology
• Ecological Modeling
• Fisheries Management
• +3 more

Sophie Smout is a Senior Lecturer in the School of Biology at the University of St Andrews. She is affiliated with the Sea Mammal Research Unit, Scottish Oceans Institute, and the Centre for Research into Ecological & Environmental Modelling. Her research focuses on marine mammal ecology, ecological modeling, fisheries management, and climate change impacts. Dr. Smout leads projects like ACTNOW (Cumulative Impacts on European marine biodiversity) and SEAwise (Ecosystem-based fisheries management), addressing challenges in marine conservation and sustainable resource use. Her work integrates diverse datasets, including animal tracking and environmental data, to inform policy and management. Key research interests include species distribution modeling, predator-prey interactions, and the application of particle algorithms for animal movement analysis. Dr. Smout collaborates internationally on initiatives such as the ICES/NAMMCO seal modeling workshops and the North Sea ecosystem studies. Her recent publications emphasize spatial ecology, fisheries data integration, and biomarker analysis in seals. Notable contributions include frameworks for passive acoustic telemetry and studies on multi-decadal environmental changes recorded in seal teeth. Dr. Smout also supervises PhD students in topics like marine mammal energetics and elasmobranch conservation. She actively participates in policy advisory roles, including ICES and NAMMCO committees, ensuring science-informed marine management decisions.

Amirali Khodadadian GostarView profile

Senior Lecturer

• Machine Learning
• Data Analytics
• Multi-Object Tracking
• +5 more

Dr. Amirali Khodadadian Gostar is a Senior Lecturer at the School of Engineering, RMIT University. His research focuses on machine learning, data analytics, multi-object tracking, sensor management, and data-driven manufacturing. He has contributed to advancements in autonomous systems, anomaly detection, and multi-agent coordination through projects like distributed information fusion for connected vehicles and geometrically-informed tracking algorithms. Research Interests : Image processing, sensor fusion, robotics, control systems, and industrial automation. Key Projects : Development of electronic pre-tension systems for seatbelts, AI-driven logistics optimization, and stereo vision systems for defect detection. Publications : Over 60 peer-reviewed articles in top journals/conferences such as IEEE Transactions on Intelligent Transportation Systems and ISA Transactions, focusing on tracking algorithms, anomaly detection, and multi-agent systems. His work bridges theoretical frameworks (e.g., random finite set theory) with practical applications in robotics, transportation, and manufacturing. He actively supervises PhD/Master's students on topics ranging from quantum AI in logistics to eye gaze tracking for visual attention modeling.