Find Academic Supervisors

Babak TaatiView profile

Associate Professor

• Computer Vision
• Healthcare Technology
• Artificial Intelligence
• +3 more

Babak Taati is an Associate Professor at the University of Toronto (UofT), affiliated with the Department of Computer Science, Institute of Biomedical Engineering (BME), and Rehabilitation Sciences Institute (RSI). He holds the Barbara G. Stymiest Chair in Rehabilitation Technology Research at UHN and is a Senior Scientist at KITE, UHN's research arm. He is also a Vector Institute Faculty Affiliate. His work focuses on applying computer vision and machine learning to healthcare challenges, particularly in rehabilitation technologies for aging populations, gait analysis, fall prevention, and dementia care. Taati leads the Aging team at KITE and is affiliated with the Intelligent Assistive Technology and Systems Lab (IATSL) and the Computational Vision group. Research interests include noninvasive monitoring of health conditions such as Parkinsonism, sleep apnea, and pain management in older adults. His contributions span datasets like the Toronto NeuroFace Dataset and TOAGA archive, emphasizing clinical applications. Taati has taught CSC420 (Image Understanding) repeatedly and has organized workshops on topics like AI in dementia care and ambient intelligence in healthcare. His awards include the TRI-UHN Best Paper Award (2017) and the AMS Healthcare Fellow in Compassion and Artificial Intelligence (2021). He has advised students like Michael Li and collaborates on grants involving federal initiatives (e.g., FedDev Ontario). His research bridges theoretical computer science with practical healthcare solutions, addressing unmet clinical needs through vision-based systems.

Soner SonmezogluView profile

Assistant Professor

• Integrated Circuits
• Micro/Nano Electro Mechanical Systems (M/NEMS)
• Implantable Medical Devices
• +4 more

Soner Sonmezoglu is an Assistant Professor of Electrical and Computer Engineering at Northeastern University's College of Engineering. His research focuses on implantable and wearable medical devices enabled by advanced microelectronics and microfabrication for neurological, diagnostic, and therapeutic applications. He leads the Sonmezoglu Lab and has secured major grants including a $13M ARPA-H award for developing photoacoustic imaging systems for early lung cancer detection. Education: PhD in Electrical and Computer Engineering, UC Davis (2017) BSc and MSc in Electrical Engineering with a minor in Solid-State Physics, Middle East Technical University (2010-2012) Postdoctoral Researcher, UC Berkeley EECS (pre-2022) Research Interests: His work spans integrated circuits, micro/nano electromechanical systems (M/NEMS), neural interfaces, and medical device integration. Key projects include ultrasonic wireless neural interfaces and millimeter-scale oxygen sensors for deep-tissue monitoring. Current initiatives include the PAIL project for lung cancer diagnostics. Awards: UC Davis Graduate Division Fellowship Scientific and Technical Research Council of Turkey Graduate Fellowship Grants & Collaborations: Principal Investigator of ARPA-H's $13M PAIL initiative. Active in the Institute for NanoSystems Innovation, contributing to chip-level technology advancements. Labs/Teams: Directs the Sonmezoglu Lab at Northeastern, focusing on next-generation biomedical device innovation through interdisciplinary microsystems engineering.

• Structural Health Monitoring
• Seismic Analysis
• Bayesian Calibration
• +14 more

Gaetano Miraglia is a Fixed-term Assistant Professor in the Department of Structural, Building and Geotechnical Engineering (DISEG) at Politecnico di Torino, where he conducts research in structural health monitoring, seismic analysis, and computational modeling. He is a member of the Interdepartmental Center R3C – Responsible Risk Resilience Centre, contributing to interdisciplinary efforts in risk mitigation and infrastructure resilience. His work spans both theoretical and applied domains, with strong emphasis on heritage preservation and sustainable urban development. His research interests include Bayesian calibration of nonlinear models, hybrid simulation, peridynamics, masonry structures, and the integration of satellite interferometric (InSAR) data with in-situ measurements for structural monitoring. He applies advanced computational and machine learning techniques to improve the accuracy and reliability of structural assessments, particularly in historical and monumental buildings. His work supports UN Sustainable Development Goals 9, 11, and 13. His recent publications demonstrate a consistent focus on data fusion, digital twinning, domain adaptation, and real-time damage detection. He frequently collaborates with researchers such as Rosario Ceravolo and Erica Lenticchia, publishing in high-impact journals like Computer-Aided Civil and Infrastructure Engineering , Structures , and Scientific Reports , as well as at major conferences including EWSHM, SAHC, and EVACES. His research is applied in projects such as the monitoring of the Vicoforte Sanctuary and the development of the CAMELOT and HY-LEARN toolboxes. Research Projects: MONITORAGGIO VICOFORTE (2024–2026) – Member of Research Group CAMELOT – PoC Transition (2023–2024) – Member of Research Group HY-LEARN – Model Calibration via Hybrid Simulation and ML (2022–2024) – Scientific Manager (PNRR Mission 4) He teaches in various programs, including as a course collaborator in PhD, Master’s, and Bachelor’s level courses such as Earthquake Engineering , Structural Consolidation , and Seismic Risk of Cultural Heritage . He is also an inventor on national and international patents and software related to the CAMELOT toolbox, highlighting the translational impact of his research. He has no listed scientific awards or formal advisees in the provided text.

Michael StevensView profile

Senior Lecturer

• Biomedical Engineering
• Advanced Manufacturing
• Digital Manufacturing
• +6 more

Dr. Michael Stevens is a Senior Lecturer at University of New South Wales (UNSW) Canberra , where he focuses on advanced manufacturing and biomedical device control systems . His work bridges digital manufacturing for SMEs with smart artificial heart technologies , emphasizing industry collaboration and translational research. Specializes in physiological control systems for rotary blood pumps Develops unobtrusive fall detection systems for dementia patients Leads international projects on total artificial heart development Education : B.Eng (Medical - First Class Honours), Queensland University of Technology (2010) PhD in Physiological Control for Biventricular Assist Devices, University of Queensland (2014) Research Trends show consistent focus on: Machine learning for biomedical diagnostics (2018–2025) mmWave radar and thermal sensors in patient monitoring (2021–2024) Computational fluid dynamics in artificial heart modeling (2016–2024) Physiological control algorithms for rotary blood pumps (2011–2025) Scientific Awards : UNSW Scientia Education Award (2021) for contextual teaching Heart Foundation Runner-up for "Smart Artificial Hearts" pitch (2021) ARC PGC Supervisor Award (2017) for mentoring Grants & Supervision : Holds over $6 million in competitive funding including MRFF and ARC grants. Currently supervises 4 PhD students while maintaining industry partnerships with VitalCare and BiVACOR. Labs & Facilities : Works across UNSW Engineering labs and Graduate School of Biomedical Engineering platforms, including mock circulation loops and high-performance computing clusters for CFD simulations.

Christos MiasView profile

Associate Professor

• Electromagnetic Shielding
• Computational Electromagnetics
• Microwave Engineering
• +3 more

Dr Christos Mias is a Reader in Electrical and Electronic Engineering at the School of Engineering, University of Warwick, UK. He serves as Director of Studies for the Electrical and Electronic Engineering stream and has held roles such as UK URSI Commission B (Fields and Waves) representative from 2009-2014. His research focuses on electromagnetic shielding, computational electromagnetics, microwave engineering, antennas, periodic structures, and radiowave propagation. Dr Mias' work bridges theoretical advancements with practical applications in antenna design, frequency selective surfaces, and electromagnetic modeling. His research interests emphasize innovative solutions in antenna array analysis, periodic structure optimization, and advanced simulation techniques such as finite element time-domain (FETD) modeling. He has contributed to understanding scattering phenomena, non-reflecting boundary conditions, and molecular vs electromagnetic wave propagation comparisons. Recent studies include railway infrastructure monitoring and critiques of superlensing concepts in plasmonics. Publications highlight advancements in MoM formulations, FSS analysis, and sparse matrix methodologies for boundary condition simulations. His work often integrates computational methods with real-world engineering challenges, such as optimizing antenna performance and improving electromagnetic compatibility in complex environments. Dr Mias advises through his role as Director of Studies, fostering academic excellence in electrical engineering education. His office is located in A325, with advice hours on Fridays 10 AM-12 PM during term time.

• Microwave Engineering
• Power Amplifiers
• Gallium Nitride (GaN) Technology
• +7 more

Marco Pirola is a Full Professor at the Department of Electronics and Telecommunications (DET) of the Polytechnic University of Turin, Italy. He is a member of the Interdepartmental Center 'CleanWaterCenter@PoliTo' and actively contributes to research in high-frequency electronics and microwave engineering. His work focuses on power amplifiers, device characterization, and advanced microwave circuit design. Research Interests: Microwave power devices, GaN technology, 5G/mm-Wave applications, space communications, and smart pipeline monitoring systems. Awards: IEEE Fellow (since 2019), IEEE Senior Member. Recent Publications address topics like Ka-band MMIC amplifiers for SAR systems, broadband Doherty amplifiers using GaN, and harmonic analysis of current-mode power stages. His projects include STARGATE (European GaAs power architectures) and Millimetre-Wave GaN Radar for UAV detection. Teaching: He leads courses on 'Radio Frequency Integrated Circuits' and 'Advanced Devices for High Frequency Applications' at the Polytechnic University of Turin. Supervised PhD students include Wenjun Zhang and Abbas Nasri, who worked on III-V HEMT circuits and GaN power amplifiers.

Kejun HuangView profile

Assistant Professor

• Machine Learning
• Signal Processing
• Optimization
• +2 more

Kejun Huang is an Assistant Professor in the Department of Computer and Information Science and Engineering at the University of Florida's Herbert Wertheim College of Engineering. His primary research area is Machine Learning, with additional interests in algorithms, computer vision, and data science. He received his Ph.D. in Electrical Engineering from the University of Minnesota in 2016. His research focuses on machine learning, signal processing, optimization, and statistics. Recent work tackles unsupervised learning challenges and AI-powered medical research through NIH-funded projects. Dr. Huang's publications demonstrate consistent focus on optimization techniques for tensor decomposition, dictionary learning identifiability, and nonnegative matrix factorization. Key themes include algorithmic efficiency and theoretical guarantees in machine learning models.

Kwanghee JeongView profile

Research Fellow

• Flow Assurance
• Hydrogen Liquefaction
• Carbon Capture and Storage
• +5 more

Kwanghee Jeong is a Research Fellow at the University of Western Australia , affiliated with the Fluid Science and Resources research group within the School of Engineering and Chemical Engineering Department . His work focuses on energy transport, decarbonisation technologies, and flow assurance. Education: PhD in Chemical Engineering (UWA, 2020), BSc in Mechanical Engineering (Dongguk University, 2014) Research Themes include: Flow Assurance for hydrogen, CO2, and natural gas pipelines Carbon Capture & Emissions Management (MOFs, Raman spectroscopy) Cryogenic Hydrogen Process Engineering (liquefaction, boil-off gas) Cold Energy Utilisation and Waste Heat Recovery Hydrate Formation Kinetics via Acoustic Levitation Article Trends reflect expertise in: Using Raman spectroscopy for real-time adsorption and phase transition analysis Developing Joule-Thomson loops to simulate pipeline conditions Optimizing Metal-Organic Frameworks for GHG separation Advancing hydrogen liquefaction efficiency through catalysis Addressing microplastics and hydrate nucleation via spectroscopic methods Scientific Awards : Best Poster Award (2023) - Natural Gas UWA Travel Award (2017) ARC PhD Scholarship (2016) He contributes to UN Sustainable Development Goals via decarbonisation research and has collaborated with Chevron, Woodside Energy, and Curtin University. His technical skills include Aspen HYSYS, OLGA simulations, HAZOP studies, and Differential Scanning Calorimetry (DSC).

Luis Duffaut EspinosaView profile

Assistant Professor

• Control Theory
• Estimation Theory
• Robotics
• +7 more

Summary Luis A. Duffaut Espinosa is an Assistant Professor in the Department of Electrical and Biomedical Engineering at the University of Vermont (UVM), affiliated with the College of Engineering and Mathematical Sciences. His research focuses on control theory, estimation, robotics, and nonlinear systems with applications in autonomy, quantum control, and environmental monitoring. He holds a Ph.D. in Electrical and Computer Engineering from Old Dominion University (2009) and has held academic positions at George Mason University and postdoctoral roles at Johns Hopkins University and the University of New South Wales. Education: Ph.D. in Electrical and Computer Engineering (2009), Old Dominion University M.S. in Mathematics (2005), Pontificia Universidad Católica del Perú B.S. in Physics (2003), Universidad Nacional de Ingeniería, Peru Research Interests: His work emphasizes data-driven control and estimation methodologies, including model-free approaches for power systems, environmental monitoring, and quantum control. Current projects include real-time data assimilation in harsh environments, resilient robotics in GPS-denied conditions, and SAR with small satellites. He co-directs the Autonomous and Intelligent Systems Research Laboratory (AIRLab) and is part of the CREATE center. Recognition: 2024 NSF CAREER Award for work on safety-aware data-driven control frameworks Teaching & Advising: He teaches courses in estimation theory, control systems, and signal processing. Advises a team of graduate and undergraduate students focusing on autonomy, robotics, and control systems. Notable students include Danial Waleed (Ph.D. 2024), Jacob Friz-Trillo (M.S. 2025), and Farnaz Boudaghi (Ph.D. candidate). Labs & Collaborations: AIRLab: Focuses on data-driven control for autonomy in robotics and engineered systems CREATE: Research on resilient energy and autonomous technologies

• Machine Learning
• Deep Learning
• Anomaly Detection
• +5 more

Peter Karsmakers serves as Associate Professor at KU Leuven's Department of Computer Science within the Faculty of Engineering Technology, based at the Geel Campus. He coordinates the Declarative Languages and Artificial Intelligence (DTAI) research group and holds leadership roles including coordinator of Research and Education for Computer Science across Geel and Diepenbeek Campuses. Karsmakers earned his PhD in Engineering Science in May 2010, focusing on kernel-based learning algorithms for sparse modeling and efficient predictions from large datasets. His doctoral work established foundations for his current research trajectory in resource-constrained machine learning systems. His research integrates machine learning with signal processing for real-time sensor data interpretation, specializing in anomaly detection from acoustic, radar, and accelerometer signals on embedded devices. Current projects address industrial condition monitoring, elderly care systems, and livestock facility monitoring through three main tracks: acoustic monitoring (e.g., SINS, WATCHDOG), radar-based systems (e.g., FARADAY, NextPerception), and smart electronics for power converters. Recent publications demonstrate strong trends in constraint-guided deep learning architectures for industrial applications, cross-environment robustness in sensor systems, and domain-knowledge integration to reduce data requirements. His work consistently bridges theoretical machine learning with practical implementations in resource-constrained environments. No scientific awards or fellowships were mentioned in the provided materials. Karsmakers supervises over 10 master's theses annually and coordinates a research team of 10 PhD students and a post-doc within DTAI-ADVISE. He has secured approximately 2.3 million euros in funding through VLAIO, EU-ECSEL, and bilateral industry contracts, including 10 active projects such as AutoEdgeML (2024-2028) and Fault Tolerant Neural Networks for Space Applications (2024-2027). He leads the DTAI-ADVISE research group focused on developing software that attaches semantics to sensor data on resource-constrained devices. The team operates across multiple campuses with specialized labs for acoustic monitoring (Geel), radar-based systems (in collaboration with ESAT-TELEMIC), and smart electronics (with Electrical Engineering department), maintaining strong industry partnerships with companies in healthcare, manufacturing, and agriculture sectors.

• Microfluidics
• Biomedical Engineering
• Circulating Tumor Cells (CTCs)
• +12 more

Brian Kirby is the Meinig Family Professor in the Department of Mechanical Engineering at the College of Engineering, Cornell University. He is a leading researcher in microfluidics, biomedical engineering, and cancer diagnostics, with a strong emphasis on circulating tumor cells (CTCs), rare cell isolation, and biophysical forces in disease. His work bridges engineering, biology, and clinical medicine. Institution: Cornell University School: College of Engineering Department: Mechanical Engineering Rank: Professor Education: Stanford University, 2001 Brian Kirby's research focuses on developing and applying microfluidic technologies to solve biomedical challenges. His work centers on microfluidic rare cell capture , particularly circulating tumor cells (CTCs) , enabling early cancer detection and monitoring treatment response. He investigates biophysical forces such as shear stress and surface interactions in conditions like thrombosis and cancer metastasis. His lab also works on dielectrophoresis , acoustophoresis , and electrokinetics for cell separation and analysis. Additional interests include bioinstrumentation , lab-on-a-chip devices , and fluid mechanics in biological systems . His recent publications show a consistent focus on microfluidic diagnostics, cancer biophysics, and smart fluid systems. Articles span topics from CTC isolation in prostate and pancreatic cancers to thrombosis in medical devices and programmable viscosity metamaterials . The research integrates engineering design with clinical applications, often involving interdisciplinary collaboration. Scientific Awards: Creative Teaching Award, Cornell Center for Teaching Innovation Advising Award, College of Engineering, Cornell University, 2015 Research Award, College of Engineering, Cornell University, 2015 Brian Kirby is actively involved in advising and research mentorship. While specific student names are not listed in the provided text, his extensive publication record and leadership of a research group indicate active supervision of graduate students and postdoctoral researchers. His research is supported by grants related to cancer diagnostics, microfluidics, and biomedical engineering, though specific grant details are not provided. He has contributed to the development of novel microfluidic devices such as the GEDI (Geometrically Enhanced Differential Immunocapture) platform for CTC capture and functional analysis. Labs and Teams: Kirby leads a research laboratory at Cornell focused on microfluidics and biomedical instrumentation. His team develops and applies microfluidic platforms for clinical diagnostics, particularly in oncology and hematology. The lab collaborates with clinicians and scientists across disciplines to translate engineering innovations into medical applications.

Qipei MeiView profile

Assistant Professor

• Civil Engineering
• Artificial Intelligence
• Robotics
• +4 more

Qipei Mei is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Alberta's Faculty of Engineering. With an MSc in Computer Science and a PhD in Structural Engineering, he bridges civil engineering with artificial intelligence to enhance infrastructure productivity and sustainability. His research spans AI-driven design automation, robotics for construction safety, and IoT-based condition assessment. PhD, Structural Engineering - University of Alberta (2020) MSc, Computer Science - Georgia Institute of Technology (2018) MSc, Structural Engineering - University of Alberta (2014) B.E., Civil Engineering - Huazhong University of Science and Technology (2011) Mei's work focuses on three key areas: leveraging data-driven methods for design automation, applying sensing/robotics to construction operations, and using digital twins for infrastructure assessment. His team explores generative AI for housing design, robotic construction in remote communities, and smart monitoring systems. Recent publications highlight advancements in: lateral capacity prediction for monopile foundations, transformer-based architectural layout analysis, large language models for building code compliance, vision-language models for safety hazard detection, and sensor networks for bridge monitoring. These works demonstrate interdisciplinary integration of AI, structural engineering, and IoT. Mei actively collaborates with diverse researchers and welcomes graduate students to his Smart Infrastructure Technologies (SITE) Research Group, part of the Infrastructure and Human Tech Lab (IHT-Lab). He teaches advanced topics in structural and civil engineering while pursuing industry-funded projects through NSERC, CFI, and Alberta Innovates.

• Power Electronic Converters
• Embedded Systems
• Analog and Mixed-Signal Circuit Design
• +5 more

Dr. Anwar Ali is a Lecturer in the Department of Electronic and Electrical Engineering at Swansea University's Bay Campus, affiliated with the School of Aerospace, Civil, Electrical and Mechanical Engineering. He holds an M.S. in Electronic Engineering (2010) and a Ph.D. in Electronic and Communication Engineering (2014) from Politecnico di Torino, Italy. His research focuses on: Power electronic converters and conditioning systems Embedded systems for aerospace applications Analog/mixed-signal circuit design Satellite technologies including power management Attitude determination and control systems Thermal modeling of aerospace systems Dr. Ali has authored over 50 publications with recent works concentrated in satellite power systems, thermal analysis of spacecraft, machine learning applications in healthcare/robotics, and energy harvesting techniques. His research demonstrates consistent innovation in small satellite technologies and cross-disciplinary applications of electrical engineering principles. He currently supervises PhD projects on: Wireless power transfer for implantable medical devices Integrated power and attitude control optimization for small spacecraft and teaches modules including Analogue Design, Software Engineering, Embedded System Design, and Integrated Circuit Design.

• Tribology
• Lubrication Engineering
• Acoustic Sensor Development
• +3 more

Professor Rob Dwyer-Joyce is a leading academic in Tribology and Lubrication Engineering at the University of Sheffield , School of Mechanical, Aerospace and Civil Engineering. He serves as Director of the Centre for Doctoral Training in Integrated Tribology and manages the Leonardo Centre for Tribology. A Fellow of both the Royal Academy of Engineering and the Institution of Mechanical Engineers, his work focuses on developing ultrasonic sensors for real-time lubrication and wear monitoring in industrial systems. Academic Affiliation: University of Sheffield (since 1994) Education: BEng Mechanical Engineering (Imperial College), PhD Tribology Industry Experience: Former British Gas engineer (Rough gas field) His research interests center on industrial wear problems , lubrication metrology , and acoustic sensor development . Key applications include wind turbine bearings, marine diesel engines, and automotive systems. His team’s innovations in tribo-acoustic sensors have enabled non-invasive oil film thickness and viscosity measurements in challenging environments. Recent scientific contributions span lithium-ion battery monitoring, wind turbine bearing dynamics, and marine engine lubrication, with over 30 publications since 2020. Awards include the EPSRC Advanced Career Fellowship in Tribo-Acoustic Sensors and recognition as a Royal Academy of Engineering Fellow . Contact: r.dwyer-joyce@sheffield.ac.uk

Jyh-Hone WangView profile

Professor

• Transportation Human Factors
• Driving Safety
• Intelligent Transportation Systems
• +5 more

Professor Jyh-Hone Wang holds a faculty position in the Department of Mechanical, Industrial and Systems Engineering at the University of Rhode Island (URI). His research focuses on transportation human factors, driving safety, and intelligent transportation systems, with particular emphasis on variable message sign (VMS) design, driver behavior analysis, and automation technology acceptance in elderly drivers. He has conducted studies on dynamic message sign efficacy, traffic flow management, and roadway safety improvement strategies. Education: Ph.D. and M.S. in Industrial Engineering from the University of Iowa (1989 and 1986), and B.S. in Industrial Engineering from Tunghai University, Taiwan (1980). Recent grants include a 2020 National Institute for Undersea Vehicle Technology grant (Co-PI) on stress monitoring via wearable devices, and a 2017 Rhode Island Department of Transportation grant (PI) assessing sidewalk quality compliance. His work bridges engineering principles with human factors to enhance traffic safety and transportation efficiency. Key research contributions include optimizing VMS message design for clarity, analyzing driver responses to automation levels, and addressing tailgating issues through behavioral interventions. He has advised multiple graduate students and collaborated on interdisciplinary projects involving traffic data analysis and manufacturing process optimization.