Find Academic Supervisors

• Computer Science
• Information Technology
• Marine Conservation Technology
• +3 more

Jason Holdsworth is a Lecturer at James Cook University (JCU), specializing in Digital Technology and Computer Science. He holds a PhD in Computer Science from JCU, with earlier degrees including a BSc (Hons) and a double major BSc in Computer Science from the same institution. His research focuses on applying computer technology to real-world challenges, such as marine conservation through mobile apps and image classification systems for WWF, and improving abalone farming via image segmentation techniques. He is also engaged in educational technology, particularly enhancing adult learner engagement through digital methods. Holdsworth has been recognized with awards including the Advance Queensland Community Digital Champion (2017) and CSIRO STEM Professional (2017). He actively contributes to JCU’s Indigenous Australian Datathon Steering Committee and has advised students on topics like autonomous CNN architecture selection. His work spans healthcare technology (e.g., VR in hemodialysis), embedded systems (C++20 coroutines on microcontrollers), and collaborative learning tools (e.g., academagogy frameworks). Over 41 publications highlight his interdisciplinary research, including studies on asynchronous IoT programming, mobile application performance models, and collaborative emergency management systems. His work often bridges theory and practice, emphasizing sustainable tech solutions for education, healthcare, and environmental conservation.

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

Eva Cernadas García is a University Lecturer at the Department of Computer Science and Artificial Intelligence, University of Seville. She teaches core courses in Computer Science, Mathematics, and Physics programs, focusing on foundational topics such as Informatics for Scientists, Scientific Programming, and Machine Learning fundamentals. Research affiliation: Applied Artificial Intelligence research group Her teaching spans undergraduate and master's programs, including the Bachelor of Mathematics, Double Bachelor in Mathematics and Physics, and the University Master in Computer Vision. She has been active in academic years 2022/2023 to 2026/2027, instructing subjects like 'Fundamentals of Image Processing and Analysis' and 'Machine Learning Applied to Computer Vision'. No specific awards or grants are listed in the provided information. Her current role emphasizes undergraduate education and master's-level specialization in AI and computer vision applications.

• Applied Mathematics
• Approximation Theory
• Orthogonal Polynomials
• +5 more

Darío Ramos López is a Professor of Applied Mathematics at the Department of Mathematics, Universidad de Almería (UAL). He coordinates the Double Degree in Economics and Mathematics and teaches subjects such as Mathematics II and Numerical Methods and Optimization . His research focuses on Approximation Theory, Orthogonal Polynomials, Bayesian Networks, and Machine Learning applications in biomedical optics and data streams. He leads the research group on Approximation Theory and Orthogonal Polynomials (FQM229). His work integrates mathematical modeling with real-world applications, including corneal data analysis (via the rPACI R package) and environmental conservation projects. He developed the AMIDST toolbox , a Java library for scalable probabilistic machine learning, supporting analysis of massive data streams in finance and other domains. He has authored over 30 publications since 2010, with recent contributions to nonstationary data stream inference and keratoconus detection using Bayesian methods. Tutoring sessions are held in-person or online at CITE III INFORMATICA-MATEMAT (Office 1-400), with flexible scheduling via email appointment.

• Artificial Intelligence
• Machine Learning
• Architectural Design
• +4 more

Dr. Ana Cocho-Bermejo is a Senior Lecturer in the School of Engineering and the Built Environment at Anglia Ruskin University (ARU), part of the Faculty of Science and Engineering. She joined ARU in January 2022 and previously held roles such as Lecturer and Researcher at UIC Barcelona, Academic Manager and Vice-Dean for Student Affairs at UIC Barcelona, and Co-Director of the Master's program in Artificial Intelligence for Architectural Design at Barcelona Tech. Education: PhD in Technology in Architecture and Building Construction, Barcelona Tech (2012) MArch, Architectural Association of London (2006) MRes in Adaptive Architecture and Computation, The Bartlett (2011) MPhil in Artificial Intelligence, Barcelona Tech (2022) Research focuses on AI and machine learning applied to architectural and urban design, including hybrid design processes, complex systems, and distributed intelligence. Her work explores computational methods for sustainable architectural systems, ETFE membrane systems, and biomimetic strategies. Key areas include generative design, evolutionary algorithms, and urban optimization. Publications highlight contributions in architectural computing, biomimetics, and intelligent façade systems. Notable works include studies on evolutionary computation in urban design and ETFE membrane behavior analysis. She has served on editorial boards for journals like International Journal of Architectural Computing and Sustainability ArchiDOCT . Grants include scholarships from Santander Universia (2011-12) and Pedro Barrie de la Maza/British Council (2004-06). Active in professional organizations such as ACADIA, IACM, and ECAADE. Her teaching spans architecture technology, sustainable design, and computational methods.

Amyn HabibView profile

Professor

• Glioblastoma Research
• Cancer Biology
• Signal Transduction
• +3 more

Dr. Amyn Habib is a Professor in the Department of Neurology at the University of Texas Southwestern Medical Center and a Staff Physician at the VA North Texas Health Care System. As a physician-scientist, he combines clinical care of neurological disorders with basic science research focused on cancer biology. His laboratory investigates mechanisms of resistance to targeted therapies in glioblastoma and lung cancer, particularly those involving EGFR signaling pathways. Key research areas include understanding adaptive responses to EGFR inhibition, mechanisms of tumor invasion, and therapeutic strategies to overcome treatment resistance. Dr. Habib received his medical degree from Dow Medical College in Pakistan. He completed residency training in Neurology at the University of Chicago Medical Center and held research fellowships at the University of Chicago and Beth Israel Deaconess Medical Center/Harvard Medical School. Before joining UT Southwestern, he was an Instructor of Neurology at Harvard Medical School. His research is funded by the NIH and the Department of Veterans Affairs. The Habib Lab collaborates closely on translational research, with ongoing clinical trials testing novel therapeutic approaches derived from their findings. Key recent studies include identifying the role of EGFR ligands in suppressing tumor invasion via BIN3 upregulation and exploring glucocorticoid-based strategies to target resistance mechanisms in lung cancer. Lab members include postdoctoral researchers (e.g., Dr. Gao Guo, Dr. Xiaoyao Yang) and medical students engaged in projects such as investigating EGFR signaling in glioblastoma invasion. The lab’s work emphasizes bridging basic science discoveries with clinical applications to improve outcomes for cancer patients.

• Smart Grids
• Power System Operational Planning
• Renewable Energy Integration
• +7 more

Vincent Debusschere is an Associate Professor at the Grenoble Institute of Technology, working within the National School of Energy, Water and Environment (ENSE 3 ) and the Grenoble Electrical Engineering Laboratory (G2Elab). He has been with the institution since 2010 and is a member of the SYREL research team. His current responsibilities include serving as co-coordinator of the international master's degree in Electrical Engineering for Smart Grid and Buildings, head of international relations for the Energy Systems and Markets department, and member of the scientific committee of the national JCGE conference since 2013. Dr. Debusschere's research focuses on integrating renewable energies into power systems, with particular emphasis on maximizing grid capacity for variable renewables while ensuring operational stability. His work spans multiple dimensions: residential and tertiary flexibility estimation and control, renewable production management through storage systems, operational planning of distribution grids under uncertainty, microgrid stability in isolated environments, and long-term energy system prospective modeling. His research combines technical, economic, environmental, and societal criteria to address the challenges of sustainable energy transition. His publication record shows a clear evolution toward energy communities, storage market participation, and advanced control strategies for grid-forming converters. Recent work increasingly focuses on the intersection of machine learning techniques with power system operation, particularly for voltage control under uncertainty and distributed resource management. His research maintains strong connections between theoretical developments and practical applications through numerous industrial collaborations. Dr. Debusschere has directed or co-directed more than 10 PhD theses (5 completed, 6 ongoing) and maintains active research collaborations with institutions worldwide including NTU Singapore, University of Oulu (Finland), Institute Teknologi Bandung (Indonesia), and Danang University (Vietnam), as well as French institutions like LIG, GIPSA, CEA, and GAEL. He manages three industrial research contracts with partners including ENEDIS, Schneider Electric, and ITE Supergrid. Based at the G2Elab in the GreEn-ER Building in Grenoble, Dr. Debusschere leads research activities within the SYREL team, focusing on experimental validation of regulation methods for smart grids and the application of artificial intelligence to grid operation. His laboratory work particularly emphasizes the stability of microgrids with low inertia and the development of virtual synchronous generator solutions.

Mark TurnerView profile

Lecturer

• Software Engineering
• Evidence-based Software Engineering
• Service-based Software Engineering
• +3 more

Dr. Mark Turner is a Lecturer in Computer Science (Projects & Engagement) at Keele University's School of Computer Science and Mathematics. His academic background includes an MSc in Information Technology (2001) and a PhD (2007) from Keele University, focusing on 'Controlling Access to Distributed Data Services.' His research interests center on Software Engineering, particularly evidence-based practices, service-based systems, and cybersecurity/access control. He coordinates academic projects and industrial placements across Computer Science and Mathematics, including roles as Examinations Officer and Project Coordinator for both undergraduate and postgraduate programs. Dr. Turner's teaching portfolio includes advanced modules like Third Year Double Project, MSc Industrial Placement, and Mathematics Work Placement, alongside foundational courses in Database Systems and Object-Oriented Programming. His administrative roles reflect his commitment to academic governance and student support. His publications, primarily in software engineering and related fields, are accessible via ResearchGate. Although no scientific awards are explicitly listed, his work contributes to key areas like cybersecurity and project management methodologies. His research and teaching emphasize practical applications of theoretical computer science principles. Dr. Turner collaborates closely with industry partners through project placements, bridging academic theory with real-world software development challenges. His administrative contributions ensure smooth academic operations, including placement coordination and examinations administration.

Deji AremuView profile

Lecturer

• Additive Manufacturing
• Mechanical Engineering
• Materials Science
• +8 more

Deji Aremu is a Lecturer in Mechanical Engineering at the CEES School of Engineering. He holds a Doctorate in Mechanical and Manufacturing Engineering from Loughborough University, specializing in Topology Optimization for Additive Manufacturing, alongside an MSc in Manufacturing Systems Engineering from the University of Warwick and a degree in Metallurgical and Material Engineering from Obafemi Awolowo University. His research focuses on additive manufacturing, composites, and finite element analysis, with notable contributions to machine learning applications in materials science. Education: Doctorate in Mechanical and Manufacturing Engineering (2009–2013), Loughborough University MSc in Manufacturing Systems Engineering (2006–2007), University of Warwick Bachelor’s in Metallurgical and Material Engineering (1998–2005), Obafemi Awolowo University His research interests include additive manufacturing techniques, mechanical properties of composite materials, lattice structures, and structural optimization. He employs machine learning to enhance parameter optimization and predictive modeling in materials processing. Recent work explores porosity effects in thermoplastic composites and crashworthiness of polymer-based honeycomb structures. Dr. Aremu supervises PhD projects on additive manufacturing for developing nations, algorithm development for lattice structures, and material design for cost-effective metal AM processes. He actively collaborates on global engineering challenges, particularly leveraging AM for third-world infrastructure solutions.

• Artificial Intelligence
• Machine Learning
• Explainable Artificial Intelligence
• +3 more

Dr. Jaime Pizarroso Gonzalo is an Assistant Professor at the Department of Telematics and Computing within the Higher Technical School of Engineering (ICAI) at Comillas Pontifical University. He joined the university in 2019 and specializes in teaching undergraduate courses on machine learning, natural language processing, time series analysis, and deep learning. His research focuses on data analysis and artificial intelligence applications, particularly in explainable AI (XAI), neural networks, and sensitivity analysis. Education: PhD in Industrial Engineering (2023), Comillas Pontifical University Double Master's in Industrial Engineering and Connected Industry (2019) Industrial Engineering (Electronics specialization, 2017) Research Interests: Jaime's work combines theoretical advancements in AI with practical applications. His current projects include developing XAI techniques using partial derivatives, analyzing neural network sensitivity, and applying machine learning to real-world problems such as energy demand forecasting and satellite image analysis for environmental monitoring. He emphasizes interpretable AI to ensure transparency in algorithmic decision-making. Key Projects (2020–2025): ENDESA: Satellite imagery analysis for cork oak tree identification (2025) Santalucía Chair: Interactive neural network application for education (2024–2025) IDB: Energy demand scenarios during the pandemic (2020) Airbus: Intelligent inertial positioning system (2019–2020) Dissemination & Training: Jaime has delivered invited lectures on XAI, conducted training courses in generative AI and machine learning, and contributed to media discussions on AI's societal impacts. He actively participates in academic conferences, including presenting at the RTGF 2024 Geometry-Physics Meeting.

• Biomedical Engineering
• Neurorehabilitation
• Alzheimer's Disease Diagnosis and Treatment
• +4 more

Zahra Moussavi is a Professor and Canada Research Chair in the Department of Electrical and Computer Engineering at the University of Manitoba’s Price Faculty of Engineering. Her research focuses on biomedical engineering applications, including Alzheimer’s disease diagnosis and treatment, sleep apnea detection, and neurorehabilitation using virtual reality (VR) and transcranial magnetic stimulation (TMS). She leads the Biomedical Engineering Graduate Program and has pioneered EVestG technology for vestibular diagnostics. Education: PhD in Electrical Engineering (University of Manitoba, 1997), Postdoctoral Fellow in Biomedical Engineering at Johns Hopkins University (1999). Active in translational research, she has published over 310 peer-reviewed articles and given 126+ invited talks, including TEDx and keynote presentations. Awards include Engineers Geoscientists Manitoba’s Technical Excellence Award (2018) and recognition as one of Canada’s Top 100 Most Powerful Women. Research Themes: Alzheimer’s therapy via rTMS and VR, OSA screening during wakefulness using tracheal breathing sounds, EVestG-based vestibular diagnostics, and fluid-structure interaction modeling of upper airways. Her work bridges engineering and clinical practice, emphasizing non-pharmaceutical interventions and patient-centered technology. Lab/Team: Directs labs focused on medical device innovation and neurotechnology. Collaborates on CanStim platform for non-invasive brain stimulation trials. Engaged in public outreach, authoring science articles and developing memory fitness programs for seniors.

• Applied Mathematics
• Computational Neuroscience
• Mathematical Biology
• +4 more

Thoa Thieu is an Assistant Professor at the School of Mathematical and Statistical Sciences and College of Health Professions at the University of Texas Rio Grande Valley. She holds email addresses at thoa.thieu@utrgv.edu and thoathieu28@gmail.com . Dr. Thieu's education includes a Double Ph.D. in Mathematics from Gran Sasso Science Institute (Italy) and Karlstad University (Sweden), supervised by Prof. Adrian Muntean and Dr. Matteo Colangeli. She earned a Master's in Applied Mathematics from the University of Orleans and Tours (France), and a B.S. in Mathematics and Statistics from the University of Science in Vietnam. Her research focuses on stochastic systems modeling with applications in: Neuroscience (neuronal dynamics, synaptic plasticity) Crowd behavior analysis (pedestrian flows, active-passive interactions) Biological systems (insulin transport in beta cells, cellular cytoskeleton dynamics) Interdisciplinary computational methods (coupled Skorokhod SDEs, lattice gas frameworks) Her recent work emphasizes multi-fidelity modeling and well-posedness analysis in complex systems. She has contributed to neuromorphic computing applications and disaster-evacuation simulations through her mathematical frameworks. Professional experience includes Postdoctoral Fellowships at Indiana University (2023-2024) under Dr. William Holmes, and at Wilfrid Laurier University (2021-2022) under Prof. Roderick Melnik. Her research outputs bridge pure mathematics (stochastic PDEs, uniqueness proofs) with applied domains in health sciences and social dynamics.

Claudia TimmreckView profile

Researcher

• Volcanology
• Climate Modeling
• Atmospheric Science
• +3 more

Claudia Timmreck is a Researcher and Group Leader at the Max-Planck-Institute for Meteorology in Hamburg, leading the 'Stratospheric Forcing and Climate' group within the Department of Climate Physics. She holds a Ph.D. in Meteorology (1997) and a Habilitation in Meteorology (2018) from the University of Hamburg. Her career includes roles as a researcher and project leader at the Max-Planck-Institute since 2001, notably heading the 'Supervolcano' project (2006–2011). Her research focuses on volcanic influences on climate, particularly the stratospheric aerosol impacts of large eruptions. Key interests include radiative forcing pathways, Earth system interactions, and historical eruption climatic tracing. She has contributed to major initiatives like VolMIP and IMPRS-ESM, advancing climate modeling and volcanic response studies. Recent work highlights include analyzing volcanic aerosol dynamics, decadal climate prediction readiness for eruptions, and machine learning applications in detecting volcanic signals. Her publications span over 100 peer-reviewed articles, with a focus on stratospheric processes, climate variability, and eruption impacts. Dr. Timmreck actively collaborates in international projects, such as the SPARC/DCPP Volcanic Response Readiness Exercise, and contributes to climate policy through scientific assessments. She advises on volcanic hazards and climate model development, emphasizing preparation for future eruptions' societal impacts.

Sergey ChurakovView profile

Professor

• Geochemical Modeling
• Atomistic Simulations
• Thermodynamics
• +2 more

Prof. Sergey Churakov is a Full Professor of Mineralogy at the University of Bern and Head of the Laboratory for Waste Management at the Paul Scherrer Institute (PSI) in Switzerland. His research integrates computational and experimental approaches to address geochemical processes, nuclear waste safety, and material science challenges. Education: PhD in Physical-Chemical Properties of Complex Natural Fluids (TU Berlin, 2001) M.S. in Applied Mathematics (Moscow State University, 1997) M.S. in Geochemistry (Moscow State University, 1996) Research Focus: Churakov's work spans multi-scale geochemical modeling, atomistic simulations of fluid-solid interfaces, thermodynamic equilibrium in high-pressure systems, and machine learning applications in geochemistry. His lab investigates mineral reactivity, radionuclide transport, and sustainable material design for environmental and industrial applications. Publication Trends: Recent articles (2024-2025) emphasize machine learning-enhanced material discovery, nuclear waste containment mechanisms, and atomistic studies of mineral surfaces. His interdisciplinary output bridges geochemistry, materials science, and computational physics. Awards: No specific scientific awards are listed in the provided text. Labs & Teams: Leads the Laboratory for Waste Management at PSI, focusing on geochemical barriers for nuclear waste, reactive transport modeling, and collaborative projects like EURAD on nuclear safety digitalization.

• Astrophysics
• Relativity
• Cosmology
• +5 more

Robert J. Nemiroff is a Professor of Physics at Michigan Technological University and a Fellow of the American Physical Society. He is best known for his role as co-creator, writer, and editor of NASA's Astronomy Picture of the Day (APOD), a globally popular science website. His research focuses on astrophysics, relativity, and cosmology, particularly gamma-ray bursts (GRBs), relativistic phenomena, and superluminal motion. Education: Earned his PhD in Physics from the University of Pennsylvania. His work bridges academic research with public outreach, exemplified by his book Faster than Light (2023) and contributions to APOD. Research Interests: Includes studying GRB energetics, gravitational lensing, light echoes, and the implications of superluminal motion. He explores topics such as dark matter detection via millilensing and constraints on the Weak Equivalence Principle using distant GRBs. Publications: Over 200+ papers, with recent works addressing relativistic image doubling, AI-driven APOD curation, and the sustainability of research software. His articles often highlight interdisciplinary connections between physics and outreach. Awards: Recognized as a Fellow of the American Physical Society for his contributions to astrophysics and science communication. Contributions: Pioneered the Astrophysics Source Code Library (ASCL) to enhance software discoverability, and engages in educational initiatives like the APOD Guest Writing Program.

• Mining Engineering
• Environmental Engineering
• Hydrogeology
• +5 more

Dr Behshad Jodeiri Shokri is a researcher at the Centre for Future Materials at the University of Southern Queensland (UNISQ). With degrees in Mining Engineering (BMineExEng, MMineExEng) from Shahrood University and a PhD from USQ, his work bridges mining and environmental engineering. Research Focus: Acid mine drainage (AMD) prediction, rock bolt systems, machine learning applications in mining, sustainable practices, and groundwater contamination. Recent Work Highlights: Development of empirical-analytical AMD models, numerical simulation of rock bolt mechanics, and data-driven XGBoost/ensemble techniques for mine safety. Collaborations: Projects with BHP Group Limited and ACARP (34018 Carbolt™ prototype). Notable Trends: Interdisciplinary use of machine learning for geotechnical challenges and innovative solutions for mine safety and sustainability. Labs/Teams: Affiliated with the Centre for Future Materials , focusing on research-driven environmental and geotechnical solutions.