Find Academic Supervisors

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +3 more

Ethan Goan is a Research Fellow at Queensland University of Technology (QUT) within the Faculty of Engineering, specifically affiliated with the School of Electrical Engineering & Robotics. He is actively involved with the Centre for Data Science and specializes in Signal Processing, Artificial Intelligence, and Vision Technologies research areas. His educational background includes: PhD pending conferral (as of February 27, 2024) from Queensland University of Technology Bachelor of Engineering (Electrical) from Queensland University of Technology Dr. Goan's research focuses on the intersection of artificial intelligence and signal processing, with particular expertise in Bayesian neural networks and their applications. His work spans multiple domains including medical imaging, radar signal processing, computer vision, and digital health. His recent publications demonstrate strong emphasis on uncertainty quantification in deep learning models, particularly for real-time applications on embedded systems. His research combines theoretical advances in probabilistic modeling with practical applications across healthcare, robotics, and signal processing domains. His publication record shows consistent output since 2016, with increasing focus on deep learning approaches to solve complex problems in signal processing and computer vision. Recent work demonstrates expertise in zero-shot learning frameworks, semantic segmentation, and the application of AI to medical diagnostics and digital health monitoring. Dr. Goan collaborates extensively with researchers across multiple institutions, as evidenced by his co-authorship on large-scale collaborative projects such as the Parkinson's Disease Digital Biomarker DREAM Challenge. His work appears in prestigious venues including IEEE conferences, Nature partner journals, and specialized machine learning publications. He is affiliated with the Centre for Data Science at QUT and works within the Signal Processing, Artificial Intelligence and Vision Technologies research group, contributing to QUT's growing reputation in AI and robotics research.

• Aerosol Instrumentation
• Particle Emissions
• Air Quality Analysis
• +3 more

Panu Karjalainen is an Assistant Professor at Tampere University, leading the Aerosol Instrumentation and Emissions research group under the Faculty of Engineering and Natural Sciences. He holds an Academy Research Fellowship (2023-2027). His work focuses on aerosol physics, particularly particle emissions and their impact on air quality, with specialization in aerosol instrumentation, combustion emissions, and secondary aerosol formation. He collaborates internationally on projects like the Envisum and Future Spaces initiatives. Research Interests: Characterization of vehicular and industrial emissions Indoor/outdoor particle dynamics Development of novel aerosol measurement technologies Secondary aerosol formation mechanisms Key Contributions: Advancement of Bayesian inversion methods for aerosol modeling Comprehensive studies on cold-weather vehicle emissions Analysis of scrubber effectiveness in marine engines Indoor air quality assessments across European offices Awards: Academy of Finland Research Fellowship (2023-2027). Labs/Teams: Aerosol Physics Laboratory and the Aerosol Instrumentation and Emissions group at Tampere University.

• Cloud Computing
• High-Performance Computing (HPC)
• Energy-Efficient Systems
• +4 more

Ivona Brandić is a University Professor for High Performance Computing Systems at TU Wien's Institute of Software Engineering and Interactive Systems. Born in Gradačac, Bosnia and Herzegovina, she moved to Austria in 1992 as a refugee during the Bosnian War. She earned a master's degree (2002) and doctorate (2007) in business computer science from TU Wien and completed her habilitation in applied computer science there in 2013. Her career includes roles as an assistant professor (University of Vienna, 2002–2007) and postdoctoral researcher (University of Melbourne, 2008). She transitioned to a tenure-track position at TU Wien in 2014 and became a full professor in 2016. Brandić’s research focuses on cloud computing, energy-efficient ultra-scale systems, and hybrid quantum-classical computing. She has been recognized with the MiA Award (2011), the Austrian Science Fund's Start-Preis (2015), and membership in the Austrian Academy of Sciences' Young Academy (2016). Her work emphasizes sustainable computing, edge systems, and optimizing resource management for distributed applications. Education: Bachelor's degree in Business Informatics (University of Vienna/TU Wien) Master's in Business Computer Science (University of Vienna, 2002) PhD in Applied Computer Science (TU Wien, 2007) Habilitation in Practical Computer Science (TU Wien, 2013) Research Interests: Brandić’s work spans cloud computing, energy efficiency in HPC systems, edge computing, and quantum-classical hybrid systems. She explores autonomic resource management, distributed system resilience, and sustainability in ultra-scale infrastructures. Her projects often address real-world applications like drug design, environmental monitoring, and smart energy grids. Publications: Her 2009 paper Cloud Computing and Emerging IT Platforms is a seminal work in the field. Recent publications focus on quantum-edge integration, energy optimization in AI models, and adaptive edge analytics frameworks. These contributions highlight trends toward sustainable, distributed, and hybrid computational paradigms. Awards: 2011: MiA Award for distinguished contributions by international backgrounds 2015: Austrian Science Fund’s Start Prize 2016: Austrian Academy of Sciences Young Academy Membership Advising & Grants: Brandić leads research groups and has secured grants for projects like NESSUS (energy-efficient cloud systems) and CHIST-ERA’s SDCDN (distributed networks). She mentors students in HPC, edge computing, and quantum systems. Advised topics include workload scheduling, fault tolerance, and energy-aware algorithms. Labs & Teams: She directs research on autonomic cloud management, edge intelligence frameworks (e.g., Sea-LEAP, FRESCO), and quantum-classical workflow systems (RIGOLETTO). Her teams collaborate internationally, integrating academia and industry for scalable, sustainable solutions.

• Motion Picture Science
• Imaging Systems
• Radiometric Calibration
• +7 more

Ricardo Figueroa is the School Director of the School of Film and Animation at the College of Art and Design, Rochester Institute of Technology (RIT), where he also holds the distinguished title of John Traver Endowed Professor. He earned his Ph.D. in Computing and Information Sciences from RIT, with prior degrees in Electrical Engineering from the University of Puerto Rico at Mayagüez. His research expertise lies at the intersection of engineering and motion picture science, with a focus on imaging systems, radiometric calibration, electronic image noise, and digital cinema workflows. His work bridges technical precision with creative application, emphasizing the scientific foundations of visual media. The recent publications and his U.S. patent highlight a consistent trajectory in advancing imaging technology for motion pictures, particularly in calibration and encoding. His research integrates statistical methods like Bayesian inference and explores neural networks for motion capture correction, indicating a forward-looking, interdisciplinary approach. SMPTE Fellow, 2020 Ricardo actively mentors students through senior projects and graduate theses, teaching core courses such as Introduction to Imaging and Video Systems, Film Projection and Digital Cinema, and Senior Project sequences. He also oversees independent studies and thesis research. His leadership has fostered industry partnerships, such as with The Dolby Institute, enhancing experiential learning. He supervises student projects in areas like latency perception and rotoscope automation, often leading to publications and real-world applications. He leads the School of Film and Animation, shaping curriculum and research direction, and promoting an interdisciplinary culture where technology and art converge. His lab and team activities are reflected in student capstone projects involving neural networks, deep learning, and digital imaging, supported by industry collaborations and professional society engagement like SMPTE.

Dmitry IgnatyevView profile

Senior Lecturer

• Aeronautical Systems
• Autonomous Systems
• Computing, Simulation & Modelling
• +7 more

Dr. Dmitry Ignatyev is a Senior Lecturer in Flight Dynamics and Control at Cranfield University's Centre for Autonomous and Cyberphysical Systems. With a PhD in Physics and Mathematics from Moscow Institute of Physics and Technology (2013), he previously spent eight years at Russia's Central Aerohydrodynamic Institute. His research spans aeronautical systems, autonomous vehicles, and structural dynamics. Education: PhD in Physics and Mathematics (2013) from Moscow Institute of Physics and Technology Affiliation: Cranfield University, School of Aerospace, Transport and Manufacturing Ignatyev's expertise includes Autonomous Systems , Flight Physics , and Computational Modeling . His work focuses on advanced control strategies and aerodynamic modeling to enhance vehicle performance and safety. Notable projects include developing autonomous emergency-responder aircraft with Cranfield's CraneAero team. His recent publications highlight trends in UAV control algorithms , Loewner framework applications for structural health monitoring, and Martian aerobot design . Awards include Fellowships in the Higher Education Academy (FHEA) and Royal Aeronautical Society (FRAeS). Scientific Recognition: Fellow of the Higher Education Academy (FHEA) Fellow of the Royal Aeronautical Society (FRAeS) Leadership: Deputy Course Director for the Autonomous Vehicle Dynamics and Control MSc program

• FPGA-based Systems
• Embedded Systems
• Hardware Accelerators
• +6 more

Thoma Yann is a Professor at the Haute école d'Ingénierie et de Gestion du Canton de Vaud (HEIG-VD), leading the Reconfigurable and Embedded Digital Systems (REDS) Institute. His work focuses on FPGA-based systems, hardware accelerators, and bio-medical applications. He holds a PhD in Computer Science from EPFL and has led multiple projects in therapeutic drug monitoring (TDM) and embedded systems. Research interests include FPGA design, bio-medical signal processing, and precision medicine. Notable projects include the SpikeOnChip neural activity analysis platform, TUCUXI TDM software, and the TheraPer POC system for drug dosage optimization. His work spans collaborations with institutions like CHUV, University of Geneva, and international partners. Funding includes projects like DNoC FPGA design (CHF 261,100), MOVABLE POC diagnostics (CHF 143,774), and therapeutic drug monitoring initiatives. Awards and recognitions are not explicitly listed in the provided text. Current roles include academic leadership at HEIG-VD and collaborative research in embedded systems and medical technology. No retirement status or former staff indication is present.

Phaedon-Stelios KoutsourelakisView profile

Professor

• Uncertainty Quantification
• Multiscale Modeling
• Computational Mechanics
• +3 more

Prof. Phaedon-Stelios Koutsourelakis is a Professor of Continuum Mechanics at the Technical University of Munich (TUM), affiliated with the TUM School of Engineering and Design. His research focuses on computational strategies for modeling and analyzing physical systems with a strong emphasis on uncertainty quantification, multiscale phenomena, and stochastic systems. He holds a PhD from Princeton University and has held academic positions at institutions including Cornell University, Heriot-Watt University, and the Lawrence Livermore National Laboratory. His educational background includes a degree from the National Technical University of Athens and a PhD from Princeton University (1998). Key research areas include data-driven coarse-graining techniques, Bayesian inference in engineering systems, and physics-aware machine learning models for high-dimensional problems. He has received notable awards such as the Top Teaching Trophy from the Munich School of Engineering (2014–2016) and the Dean’s First Year Merit Prize at Princeton (1998). His work bridges computational mechanics with statistical methods, addressing challenges in materials design, structural health monitoring, and predictive modeling under uncertainty. Recent projects include developing physics-constrained deep learning frameworks for surrogate modeling and quantifying model bias in digital twins of infrastructure systems. Notable contributions include the Predictive Coarse-Graining framework and probabilistic methods for uncertainty quantification in heterogeneous media. His research also extends to optimization under uncertainty and interdisciplinary applications in biomedical engineering and materials science.

Yann Le BihanView profile

Researcher

• Electromagnetic Engineering
• Non-Destructive Testing (NDT)
• RFID and NFC Technology
• +5 more

Yann Le Bihan is a researcher specializing in electromagnetic engineering, with a focus on non-destructive testing (NDT), RFID/NFC technology, wireless power transfer, and biomedical applications. He leads a laboratory at the Electrical and Electronic Engineering Department. His work integrates advanced techniques such as machine learning (neural networks, SVM) for material characterization and sensor design. Research interests include the development of novel sensors for semiconductor conductivity measurement, microwave dielectric properties analysis, and eddy current-based defect detection. He explores applications in healthcare (implantable devices), automotive (EV wireless charging), and industrial NDT. Key contributions involve optimizing RFID/NFC systems for medical monitoring, improving wireless power efficiency, and advancing computational tools for electromagnetic simulations. Recent articles highlight innovations in flexible coil configurations for RFID tracking, ANFIS-based microwave sensor design, and shielding optimization for EV charging systems. His research bridges theoretical modeling (e.g., 3D finite element methods) and experimental validation, addressing challenges in energy harvesting and electromagnetic compatibility.

• Artificial Intelligence
• Robotics
• Autonomous Systems
• +6 more

Sebastian B Thrun is an Adjunct Professor in the Department of Computer Science at Stanford University. His research focuses on artificial intelligence, robotics, autonomous systems, and medical imaging. He holds positions at Stanford and has affiliations with Udacity, Inc., contributing to innovations in autonomous driving systems, drone technology, and AI-driven healthcare solutions. His work often bridges theoretical advancements with practical applications in transportation and medicine. **Education**: Details not explicitly provided in the text. Research Interests include developing algorithms for autonomous vehicles, deep learning applications in healthcare (e.g., skin cancer detection), and improving medical imaging techniques. He explores Bayesian methods, decision trees, and clustering algorithms to enhance AI systems' efficiency and adaptability. His articles reflect a focus on autonomous systems (e.g., multicopter design, vehicle navigation), healthcare AI (skin cancer classification), and algorithm optimization. Notable trends include leveraging AI for real-world challenges in transportation and medical diagnostics. Scientific Awards**: None explicitly mentioned in the provided text. Advising & Grants**: No student advisees or grant details listed here. His contributions extend to industry partnerships, such as those in autonomous vehicle development and online education platforms like Udacity. Labs/Teams**: Likely associated with Stanford’s AI Lab and Udacity initiatives, though specific lab affiliations are not detailed in the text.

• Quantum Physics
• Quantum Optics
• Quantum Information
• +7 more

Professor John Jeffers is a leading academic in the Department of Physics at the University of Strathclyde, Faculty of Science, and a member of the Scottish Universities Physics Alliance (SUPA). His research lies at the forefront of quantum optics and quantum information science, with a focus on quantum illumination, quantum lidar, quantum retrodiction, and quantum key distribution. He is actively involved in both theoretical and experimental quantum technologies. His educational background is not explicitly detailed in the text, but his extensive research output and professorial rank suggest a strong foundation in physics, likely including a PhD in a quantum-related field. Jeffers' research interests span quantum sensing, quantum communication, and quantum foundations. He explores how quantum states can enhance detection and ranging capabilities beyond classical limits, particularly in noisy or adversarial environments. His work integrates theoretical modeling with practical implementations, often in collaboration with experimental groups. He is especially known for contributions to quantum retrodiction and the optimization of quantum illumination protocols using simple detection schemes. The most recent articles show a strong trend toward applied quantum technologies, particularly quantum-enhanced sensing and secure communication. His work frequently appears in high-impact journals like Philosophical Transactions A and Physical Review series, reflecting both theoretical depth and experimental relevance. Keywords across his publications include quantum optics, photonics, quantum information, and quantum metrology, with subfields such as quantum lidar, finite-key QKD, and entanglement-based sensing dominating recent efforts. Fellow of the Institute of Physics (2008) Award for Outstanding Refereeing (2008) Professor Jeffers has supervised multiple PhD students and postdoctoral researchers, including Richard Murchie and Roberto Gonzalez Pousa. He has led and co-led numerous research projects funded by EPSRC and NPL, such as the DTP 2224 studentship and the NPL iCASE project on quantum transduction. These grants support doctoral training and interdisciplinary research in quantum photonics. He also contributes to dataset creation and open science, supporting reproducibility in quantum experiments. He is actively involved in research labs and teams at the University of Strathclyde, particularly those working on quantum technologies. He collaborates with the Quantum Optics group and participates in the QuantIC hub. His projects often involve multidisciplinary teams combining theory and experiment, focusing on quantum sensing, communication, and imaging. He regularly presents at major conferences and hosts visiting researchers, indicating a vibrant and collaborative research environment.

• Artificial Intelligence
• Statistical Relational Learning
• Probabilistic Reasoning
• +4 more

David Poole is a Professor of Computer Science at the University of British Columbia, where he has established himself as a leading researcher in Artificial Intelligence with an h-index of 52 and over 19,000 total citations. His academic career spans more than three decades, with publications dating from 1985 to 2025, demonstrating sustained research productivity and relevance in the field. Professor Poole's research focuses on the integration of logical and probabilistic approaches to artificial intelligence. His work bridges symbolic AI and statistical methods, creating frameworks that handle uncertainty while maintaining logical expressiveness. He has made seminal contributions to probabilistic Horn abduction, Bayesian networks, and statistical relational artificial intelligence. His theoretical work on the Independent Choice Logic (ICL) has provided foundational frameworks for representing and reasoning with uncertainty in complex domains. His applied research spans geological analysis, educational technology, and mineral exploration. Professor Poole has authored influential textbooks including 'Computational Intelligence: A Logical Approach' (1998, 1,915 citations) and 'Artificial Intelligence: Foundations of Computational Agents' (2010, 1,750 citations), which have shaped AI education globally. His recent publications (2020-2025) demonstrate continued innovation in knowledge hypergraphs, relational neural networks, and geological hazard mapping applications. His most impactful research areas include: Statistical Relational Learning (as evidenced by the 2016 book 'Statistical Relational Artificial Intelligence') Knowledge Graph Embeddings and Hypergraphs CP-nets for preference representation (1,293 citations) Lifted probabilistic inference techniques Applications of AI in geological hazard prediction Professor Poole maintains active research collaborations with institutions worldwide, including Google DeepMind, universities in Hong Kong, Germany, and the United States. His work continues to influence both theoretical foundations and practical applications of artificial intelligence.

Vikram KrishnamurthyView profile

Professor

• Statistical Signal Processing
• Stochastic Control (POMDPs)
• Stochastic Optimization
• +10 more

Professor Vikram Krishnamurthy is a distinguished academic at Cornell University , affiliated with the School of Electrical & Computer Engineering , Center for Applied Mathematics , and Mechanical & Aerospace Engineering . He leads the Cornell Statistical Signal Processing Lab and is part of the Foundations of Information, Network, and Decision Systems (FIND) collaborative. His career spans institutions like the University of British Columbia (Canada Research Chair, 2002-2016) and University of Melbourne (1994-2002). His research bridges statistical signal processing , stochastic control (POMDPs), and network science , with applications in social networks , cognitive radar , and ion channel biosensors . Key themes include human-machine interfacing , inverse reinforcement learning , and behavioral economics . Recent publications focus on adaptive inverse reinforcement learning , LLM agent interactions , and quantum decision systems , reflecting his work in machine learning , stochastic optimization , and social network analytics . His book Partially Observed Markov Decision Processes (2nd ed., 2025) is a seminal text in the field. Scientific Awards: Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Distinguished Lecturer, IEEE Signal Processing Society Editor-in-Chief, IEEE Journal on Selected Topics in Signal Processing Honorary Doctorate from KTH Royal Institute of Technology, Sweden Jean Pierre LeCadre Award (2019) He currently advises PhD students including Shashwat Jain, Luke Snow, Adit Jain, and Yiming Zhang. His teaching includes advanced courses like Bayesian Estimation and Stochastic Optimization (ECE 7230) and Data Science for Engineers (ECE 2720).

Mehrisadat Makki AlamdariView profile

Senior Lecturer

• Structural Health Monitoring
• Structural Dynamics
• Signal Processing
• +3 more

Dr. Mehrisadat Makki Alamdari is a Senior Lecturer in the School of Civil and Environmental Engineering at the University of New South Wales (UNSW). She specializes in Structural Health Monitoring (SHM), structural dynamics, signal processing, and data mining. Her research bridges theoretical innovations with real-world infrastructure applications, including landmark projects on the Sydney Harbour Bridge and Gateway Bridge. Education: Ph.D. in Civil Engineering, University of Technology Sydney (2012–2014) M.Sc. in Mechanical Engineering, University of Manitoba (2011) M.Sc. in Aerospace Engineering, Iran University of Science and Technology (2006–2008) B.Sc. in Aerospace Engineering, Sharif University of Technology (2002–2006) Research Focus: Her work integrates machine learning, sensor technologies, and advanced signal processing for infrastructure resilience. Key areas include bridge health monitoring via drive-by inspections, piezoelectric energy harvesting, and damage detection using ultrasonic and computer vision techniques. Recent publications emphasize data-driven methodologies, unsupervised learning, and multi-sensor fusion for structural diagnostics. Awards & Honors: ARC Discovery Early Career Research Award (DECRA) JSPS Award (Japan Society for Promotion of Science) ITS Australia National Award (2020) Most Practical SHM Solutions Award (Stanford University, 2015) Best Paper Award (SHMII-8, 2017) Leadership & Service: She serves on the executive committee of the Australian Network of Structural Health Monitoring (ANSHM) and is an Associate Editor for Elsevier journals. She has secured over $5.5M in grants, including an ARC DECRA grant and a $5M Industrial Transformation Hub for resilient infrastructure.

Sabah Badri-HöherView profile

Professor

• Digital Signal Processing
• Underwater Robotics
• Sonar and Image Processing
• +4 more

Sabah Badri-Höher is a Professor at the Kiel University of Applied Sciences (UAS Kiel) in the Department of Digital Signal Processing. She leads the AUV team 'TomKyle' in collaboration with GEOMAR Helmholtz Centre for Ocean Research Kiel and oversees projects like MAUS (autonomous underwater vehicles) and UFOTriNet (automated fish detection). Research Focus: Her work spans digital signal processing, underwater robotics, sonar data interpretation, acoustic communication, and computer vision. Key applications include AUV navigation, fish tracking, and underwater sensor networks. Publications: Recent articles emphasize sonar-based fish detection, AUV collision avoidance, cooperative swarm navigation, and signal processing for underwater environments. Trends include machine learning integration (e.g., Kalman filters, deep learning) and multi-sensor fusion. Student Advising & Projects: She supervises doctoral candidates in signal processing, computer vision, and underwater systems. Major grants include: MAUS: Development of interconnected AUVs for coordinated missions. UFOTriNet: Automated fish population monitoring using low-light cameras/sonar. Labs & Teams: Heads the DSV research group, collaborating with GEOMAR on student AUV teams. The lab focuses on underwater robotics, sensor algorithms, and participates in competitions like SAUC-E.

Xin LiView profile

Assistant Professor

• Empirical Asset Pricing
• Bond Pricing
• Institutional Investments
• +5 more

Xin Li is an Assistant Professor of Finance at the College of Business, Michigan Technological University. His research focuses on Empirical Asset Pricing, Bond Pricing, Institutional Investments, Fintech, and Market Microstructure. He teaches courses in Asset Pricing, Corporate Finance, Investment, Fixed Income, and Risk Management. His research emphasizes innovative methodologies in financial markets, including the application of dual recognition systems in biosensors and data-driven frameworks for sensor optimization. Recent work explores convolutional neural networks for early-stage gas identification and multi-sensor systems for chemical analysis. Xin Li’s publications span analytical chemistry, sensor technology, and materials science, with notable contributions to electrochemical sensors, nanomaterials, and high-temperature gas detection. His work bridges finance and engineering through interdisciplinary research on Fintech and sensor innovation.