Find Academic Supervisors

Farnaz Adib YaghmaieView profile

Assistant Professor

• Control Theory
• Reinforcement Learning
• Machine Learning for Control Systems
• +5 more

Farnaz Adib Yaghmaie serves as an Assistant Professor and Docent at Linköping University's Department of Electrical Engineering (ISY), specializing in the Automatic Control division. Her research bridges control theory and machine learning, with significant contributions to reinforcement learning applications in complex systems. Education: Ph.D. in Electronic and Electrical Engineering, Nanyang Technological University (NTU), Singapore (2017, Best Thesis Award) Master's Degree in Electrical Engineering (Control), K. N. Toosi University of Technology, Tehran, Iran (2011) Bachelor's Degree in Electrical Engineering (Control), K. N. Toosi University of Technology, Tehran, Iran (2009) Her research focuses on redefining machine learning paradigms for control problems , particularly through reinforcement learning applications. Current projects include Foundation Models and RL for General-Purpose Control (2025-present), Online Control with Adversarial Noise (2022-present), and RL for Partially Observable Systems (2021-present). She explores how foundation models can enable control systems to generalize across diverse tasks and adapt to unseen scenarios, with applications in robotics and autonomous systems. Her work integrates large language models and generative AI into control frameworks, addressing fundamental challenges in sequential decision-making under uncertainty. Scientific Recognition: Best Thesis Award at Nanyang Technological University (2017) CENIIT research grant (2020) Co-founded SEDDIT competence centre (2024) As an educator, she teaches Ph.D.-level courses including Reinforcement Learning (2021-2024) and Advanced Robotics (2025), with her WASP course ranking as the second-best Ph.D. course in the program (3.9/5 evaluation). She actively mentors Ph.D. students including Abbas Pasdar, who is working on Foundation Models for General-Purpose Control. Her research is supported by grants including CENIIT and collaborations through the SEDDIT competence centre. She contributes to the academic community through workshops like the LINK-SIC Core Competence Workshops on Reinforcement Learning and by chairing sessions at major conferences including ECC 2025.

Xiaoming ZhengView profile

Professor

• Computational Mathematics
• Fluid Dynamics
• Numerical Partial Differential Equations
• +4 more

Xiaoming Zheng is a Professor of Mathematics at Central Michigan University (CMU), specializing in computational mathematics and mathematical biology. His research focuses on fluid dynamics, numerical partial differential equations, and biological systems such as tumor growth and angiogenesis. He leads a collaborative NSF-funded project (DMS-2309747) investigating computational methods for incompressible fluids and magnetohydrodynamic turbulence, with implications for weather prediction and plasma physics. Zheng earned his Ph.D. in Applied Mathematics from the University of California, Irvine (2005), and holds M.S. and B.S. degrees in Mathematics from Peking University and Nanjing University, respectively. Research & Grants: Zheng’s work bridges numerical analysis and biological modeling, with contributions to tumor growth dynamics, angiogenesis mechanisms, and fluid-structure interactions. His NSF project (2023–2026) develops novel computational tools for fluid dynamics, promoting interdisciplinary training and community engagement through K-12 outreach. Education: Ph.D., Applied Mathematics, University of California, Irvine (2005) M.S., Computational Mathematics, Peking University (1999) B.S., Mathematics, Nanjing University (1996) Research Interests: Computational Mathematics: Incompressible fluids, Navier-Stokes equations, interface-fitted adaptive mesh methods. Mathematical Biology: Tumor growth modeling, angiogenesis, infectious disease dynamics. Advising & Training: Zheng mentors graduate and undergraduate students in computational and biological mathematics, emphasizing interdisciplinary research. His projects include training underrepresented groups and fostering collaborations with institutions like Notre Dame. Labs & Simulations: His lab produces simulations of tumor growth under Darcy’s Law and Boussinesq equations, visualizing phenomena such as plume formation and temperature redistribution in buoyancy-driven flows.

• Applied Mathematics
• Numerical Analysis
• Mathematical Biology
• +3 more

Dr. Chandrasekhar Venkataraman is an Associate Professor in Mathematics at the University of Sussex's School of Mathematical and Physical Sciences. His research focuses on applied mathematics, numerical analysis, and mathematical biology, with a particular emphasis on modeling cell migration, reaction-diffusion systems, and biological pattern formation. He has contributed to understanding mechanisms in cancer biology, immunology, and plant pathology through computational frameworks. Key research interests include multiscale modeling, free boundary problems, and parameter identification in biological systems. He has secured grants from institutions like the Leverhulme Trust and Innovate UK for projects addressing drug discovery, cell manufacturing, and 3D cell migration dynamics. His work integrates mathematical theory with experimental data, as seen in collaborations analyzing tumor-immune interactions and cellular force estimation. Teaching responsibilities include courses on numerical analysis, discrete mathematics, and partial differential equations. Professional activities include editorial roles at Royal Society Open Science and participation in international workshops on multiscale modeling and oncology. His research has been published in high-impact journals such as Nature, Royal Society Open Science, and the Journal of Theoretical Biology.

Michael FosterView profile

Researcher

• Software Testing
• Causal Inference
• Model Inference
• +3 more

Dr. Michael Foster is a Researcher and Research Software Engineer at the School of Computer Science, University of Sheffield. He specializes in software testing methodologies, causal inference, and formal methods for system modeling. His work focuses on developing tools for automated model inference, particularly Extended Finite State Machines (EFSMs), and applying causal reasoning to improve software testing. Education: PhD in Computer Science (2017-2020) from the University of Sheffield, supervised by Prof. John Derrick and Dr. Achim Brucker. His doctoral research addressed EFSM inference from black-box software execution traces, integrating genetic programming and formal verification techniques. Research Projects: Principal contributor to the CITCoM project, applying causal inference to computational model testing. Developed the causal testing framework and EFSM inference tool, which formalizes model merging and data dependency analysis. Explored metamorphic testing, automated verification of trace properties, and active learning techniques for EFSMs. Key Contributions: Published extensively on causal testing, EFSM inference, and algorithmic testing strategies. His work bridges formal methods with practical software engineering challenges, emphasizing automated tools and rigorous validation frameworks.

• Electromagnetics
• Microwave and Antenna Technologies
• Optics
• +3 more

Assoc. Prof. Dr. Mehmet Nuri Akinci is affiliated with the Department of Electronics and Communication Engineering at Istanbul Technical University (ITU). His academic career includes roles as an Associate Professor (since 2019) and Research Assistant at ITU, with additional research experiences at Virginia Commonwealth University and the National Research Council of Italy. His research focuses on electromagnetics, microwave and antenna technologies, optics, and photonics, with applications in biomedical imaging, non-destructive testing, and material characterization. Education: Integrated PhD in Telecommunication Engineering, ITU (2013–2017) Bachelor's in Electronics and Communication Engineering, ITU (2010–2013) Research Interests: Dr. Akinci specializes in microwave imaging systems, qualitative inverse scattering methods, antenna design, and biomedical applications such as hyperthermia treatments and brain imaging. His work integrates deep learning techniques for improved accuracy in medical diagnostics and material analysis. Awards: IEEE Türkiye PhD Thesis Award (2017) TUBITAK National Physics Olympiad Bronze Medal (2010) Academic Excellence Awards (2006–2010) Advising & Grants: He supervises PhD and master’s students in topics like microwave imaging algorithms and hyperthermia treatment optimization. Active projects include designing imaging systems for walnut quality assessment and developing low-cost biomedical phantoms. Labs/Teams: His research group focuses on advanced microwave imaging, antenna technology, and biomedical engineering applications at ITU's Ayazağa Campus.

Diletta GiuntiniView profile

Assistant Professor

• Ceramics Processing
• Additive Manufacturing
• Sintering
• +4 more

Diletta Giuntini serves as Assistant Professor in the Mechanics of Materials section of the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e), with affiliations to the Institute for Complex Molecular Systems (ICMS) and EIRES Research. She concurrently holds a position in the Eindhoven Young Academy of Engineering (2021-2025). Her academic foundation includes a BSc and MSc in Aerospace Engineering from the University of Pisa, Italy, followed by a PhD in Engineering Sciences through the joint UCSD-SDSU program (2016). Postdoctoral work at Hamburg University of Technology under a Humboldt Fellowship (2018) preceded her current TU/e appointment. Giuntini's research pioneers advanced ceramic processing via ultra-fast sintering and additive manufacturing, focusing on nano-architected multiscale materials. Her work on supercrystalline nanocomposites—nanoparticle assemblies mimicking atomic crystals—explores emergent mechanical properties through nanoscale structural tailoring, heavily incorporating biomimetic principles for innovative material design. Analysis of her 15 most recent publications reveals dominant themes: supercrystalline nanocomposite characterization via nanoindentation/fatigue/creep testing (73% of works), computational sintering optimization (13%), and biomimetic composite design (13%). This output demonstrates consistent integration of experimental validation with predictive modeling to enhance ceramic toughness and functional properties. Her scientific recognition includes: Acta Materialia Outstanding Reviewer Award (2019) TMS-AIME Champion H. Mathewson Award (2022) As Project Manager for ACROPOROUS (2021-2028), she leads research on sustainable ceramics and porous materials, while supervising 11 graduate students. Her teaching encompasses Advanced and Additive Manufacturing, Engineering Design, and Fracture Mechanics. She actively promotes diversity through initiatives like the Power Hour at the 2018 Gordon Research Seminar on Solid State Studies in Ceramics.

Dieter De WitteView profile

Professor

• Artificial Intelligence
• Data Science
• FAIR Data
• +3 more

Prof. Dieter De Witte serves as a Professor at Ghent University, dedicating 50% of his time to the Internet Technology and Data Science Lab (IDLab) while simultaneously contributing 50% to the Royal Museums of Fine Arts Belgium (RMFAB) in Brussels through a FED-tWIN mandate from Belspo. At RMFAB, he spearheads the strategic overhaul of digital infrastructure toward FAIR-compliant and data-driven systems, while at Ghent University he collaborates with Prof. Steven Verstockt on applied AI projects across heritage, mental healthcare, and education domains. His academic foundation includes a Master's in Engineering Physics from Ghent University (2008) followed by doctoral research on Big Data technologies and FAIR data for life sciences. Prior to returning to academia in 2021, he gained industry experience as an AI consultant and team lead at Telenet and Ordina. De Witte's research centers on AI-driven transformation of cultural heritage through FAIR data publication , collection enrichment , and intuitive querying interfaces . His technical expertise spans multimodal algorithms, image segmentation, pose estimation, large language models (LLMs), and semantic technologies including SPARQL and IIIF. Current projects focus on human-in-the-loop AI systems that combine diverse AI building blocks for practical heritage applications. Analysis of his 15 most recent publications reveals a clear trajectory from early bioinformatics work (2007-2018) on genomic motif discovery and life sciences data infrastructure toward contemporary cultural heritage applications (2023-2024). Recent outputs demonstrate innovative fusion of pose estimation, linked data frameworks, and multimodal AI for museum contexts, highlighting increasing specialization in AI enrichment of digital collections while maintaining core expertise in FAIR data principles. His FED-tWIN grant enables critical knowledge transfer between academic research and cultural heritage institutions, supporting development of next-generation digital infrastructure at RMFAB. Current projects involve creating AI tools for intuitive collection exploration and systematic enrichment of heritage assets through advanced computational methods. De Witte operates within Ghent University's Internet Technology and Data Science Lab (IDLab), participating in interdisciplinary teams developing applied AI solutions. His work bridges technical innovation with practical implementation in cultural institutions, focusing on sustainable, interoperable systems that enhance public access to digital heritage collections through cutting-edge AI interaction paradigms.

Sarah GlozerView profile

Professor

• Corporate Social Responsibility (CSR) Communication
• Digital Ethics
• Sustainability
• +3 more

Sarah Glozer is a Professor and Head of Marketing in the Business & Society Division at the University of Bath School of Management. She holds affiliations with the Centre for Business, Organisations and Society (CBOS), the Centre for Qualitative Research, and the Centre for the Future of Work. Her research focuses on responsible business communication in digital contexts, particularly influencer marketing, content moderation, and ethical advertising. She co-authored a leading European Business Ethics textbook and serves on the editorial board of Business & Society . Education: PhD in Corporate Social Responsibility Communication from the University of Nottingham (2014), Fellow of the Higher Education Academy from Royal Holloway, University of London (2015). External roles include External Examiner at ESCP Business School (2018–2023). Recent projects include leading the 7th International CSR Communications Conference (2024), studying content moderation’s human rights implications, and exploring platform responsibility in influencer marketing. Current research areas emphasize digital ethics, stakeholder dialogue frameworks, and sustainable development goals. Key projects include: "Bringing Consumers Back Down to Earth" (ecstatic dance wellness project) "Content Moderation: Interdisciplinary Insights" (2021–2022) "Platform Responsibility and Influencer Marketing" (2020–2024) Her work contributes to UN Sustainable Development Goals related to quality education, decent work, and climate action. Over 47 peer-reviewed publications span journals like Business & Society , Journal of Business Ethics , and Organization Studies .

Enyue Annie LuView profile

Professor

• High Performance Computing
• Computer and Communication Networks
• Algorithm Design and Analysis
• +5 more

Dr. Enyue (Annie) Lu is a Professor of Computer Science at Salisbury University, affiliated with the Department of Computer Science within the Richard A. Henson School of Science & Technology. She holds a Ph.D. from the University of Texas at Dallas. Her research focuses on High Performance Computing, including parallel and distributed processing, cloud computing, and GPU computing. She also explores Computer and Communication Networks, such as network security and wireless sensor networks, alongside Algorithm Design and Graph Theory applications in bioinformatics and medical imaging. Dr. Lu has led and contributed to numerous research projects, including the REU (Research Experience for Undergraduates) program, which emphasizes emerging computing in science and engineering. Her work spans interdisciplinary areas like medical imaging algorithms and stable matching problems, leveraging GPU acceleration and parallel computing techniques. She has advised over 20 students in these areas and collaborates with institutions like the University of Maryland Eastern Shore and Johns Hopkins University. Her publications emphasize practical applications of theoretical computer science, such as improving tomographic imaging and network intrusion detection through machine learning and distributed systems. Despite not listing explicit awards, her extensive contributions to undergraduate research and algorithm development highlight her scholarly impact.

• Stochastic Optimization
• Robust Optimization
• Data-Driven Decision-Making
• +3 more

Nam Ho-Nguyen is a Senior Lecturer in the Discipline of Business Analytics at the University of Sydney Business School. He holds a PhD from the Tepper School of Business at Carnegie Mellon University and an Honours degree in Mathematics from the Australian National University. His research focuses on stochastic, robust, and data-driven optimization techniques for decision-making under uncertainty, combining methods from statistics, machine learning, and traditional optimization. Education: PhD in Operations Research, Carnegie Mellon University (2019) Bachelor of Science (Honours) in Mathematics, Australian National University Research Interests: Nam’s work addresses decision-making in uncertain environments through optimization frameworks. He explores applications in finance, healthcare, transportation, and technology, emphasizing scalability and computational efficiency. Key areas include bilevel optimization, adversarial machine learning, and distributionally robust chance-constrained programming. Awards: INFORMS Optimization Society Young Researchers Prize (2022) Gerald L. Thompson Doctoral Dissertation Award (2019) Australian Research Council Discovery Early Career Researcher Award (DECRA) ($470K AUD) Teaching & Grants: Teaches QBUS1040, QBUS2310, BUSS4932, and QBUS6820 courses. Recipient of ARC grants for research on multi-stage optimization with contextual data and forecast reconciliation ($625K AUD). Supervises research students on scheduling problems and multi-agent systems. Lab/Team: His work involves collaborations on optimization tools for choice modeling, electronic trading, and solution design in IT services, supported by interdisciplinary teams at the University of Sydney and international institutions.

Doris GrosseView profile

Research Fellow

• Adaptive Optics
• Space Situational Awareness
• Space Debris Remediation
• +4 more

Dr. Doris Grosse is a Research Fellow at the Advanced Instrumentation and Technology Centre (AITC) within the Research School of Astronomy & Astrophysics at the Australian National University (ANU). She specializes in adaptive optics, with a focus on space situational awareness, space debris remediation, and optical communication systems. Her work includes developing the ANU Optical Communications Ground Station and contributing to laser guide star systems for debris tracking. Education: PhD in Electrical Engineering and Information Technology from Ruhr-University Bochum, Germany. Postdoctoral research at ANU AITC since 2016. Research Interests: Adaptive optics for space debris tracking, laser communication systems, atmospheric turbulence analysis, and holography. Key projects include the Space Environment Research Centre (CRC) and development of optical ground station instrumentation. Recent Work: Focused on deep space communication systems, lunar optical communication, and turbulence profiling for free-space optical networks. Her research addresses challenges in space debris collision mitigation and satellite tracking using advanced adaptive optics. Grants/Projects: Supervises projects such as the 'Deep-Space and Lunar Optical Communication System' and 'Adaptive Optics for Laser Communication'. Involved in international collaborations for space surveillance and optical infrastructure. Labs & Teams: Part of AITC and ANU's RSAA teams, contributing to observatory operations at Mount Stromlo and Siding Spring Observatory.

Mahyar AmirgholyView profile

Assistant Professor

• Cooperative Control of Connected Automated Vehicles
• Transportation Electrification and Decarbonization
• Machine Learning and Reinforcement Learning
• +3 more

Dr. Mahyar Amirgholy is an Assistant Professor at Kennesaw State University (KSU) within the Southern Polytechnic College of Engineering and Engineering Technology, and a Research Affiliate at Lawrence Berkeley National Laboratory. His research focuses on transportation systems, with expertise in cooperative control of connected automated vehicles, transportation electrification, and machine learning applications. Education: Ph.D. in Civil and Environmental Engineering (Transportation Systems) from the University of Massachusetts Amherst Postdoctoral Scholar at Cornell University’s Civil and Environmental Engineering Department M.S. from Rutgers University (Transportation Engineering) and Sharif University of Technology (Transportation Planning) B.S. in Civil Engineering from Amirkabir University of Technology Research Interests: Dr. Amirgholy’s work emphasizes optimizing large-scale transportation systems through machine learning, network modeling, and energy efficiency strategies. Key areas include decarbonization of transportation networks, cooperative control of automated vehicles, and traffic flow optimization in signal-free urban environments. Publications: His recent work addresses challenges in mixed traffic management, signal-free networks, and energy-efficient vehicle operations. Themes include leveraging AI for dynamic traffic modeling and predicting emissions from EV charging infrastructure. Awards: Recipient of the CUTC Milton Pikarsky Award for his doctoral dissertation on transportation systems. Advising & Students: Supervises a diverse team of doctoral, master’s, and undergraduate researchers focusing on smart transportation systems. Notable projects include developing the Atlanta Energy and Emission Modeling and Analysis Tool (AEEMAT). Labs & Collaborations: Leads the Futra Lab, which collaborates with entities like the Atlanta Regional Commission to advance sustainable mobility solutions. Current initiatives include predictive energy modeling for EV adoption and equitable infrastructure design.

Gavin TaylorView profile

Professor

• Machine Learning
• Artificial Intelligence
• Reinforcement Learning
• +5 more

Gavin Taylor is a Professor of Computer Science at the United States Naval Academy (USNA), where he chairs the development of the new Data Science major. His research focuses on Machine Learning, including neural network vulnerabilities, reinforcement learning for IoT devices, and adversarial machine learning. He is currently on sabbatical as a Visiting Professor at the Norwegian OpenAI Lab (NTNU), collaborating with institutions like the University of Maryland and NTNU. Professor Taylor has been recognized with the 2022 Class of 1951 Civilian Faculty Teaching Excellence Award, emphasizing his commitment to education. He teaches courses such as SD312: Machine Learning and has developed curricula across Data Science, Cyber Operations, and Operations Research. His sabbatical work in Norway involves advancing AI research through interdisciplinary collaborations. His research trends emphasize practical AI applications—such as autonomous IoT systems—and ethical concerns like adversarial attacks and data poisoning. He actively seeks partnerships to engage students in real-world data science projects through the new major, scheduled to graduate its first class in 2025.

Frédéric MAZENCView profile

Researcher

• Automatic Control
• Systems and Control Theory
• Robust Control
• +7 more

Frédéric MAZENC is a Research Director (DR) at Inria Saclay, part of the EPI DISCO team, and a member of the Laboratory of Signal and Systems (L2S) at CentraleSupélec, affiliated with Université Paris-Sud. His roles include leadership in research, editorial responsibilities, and service on scientific committees. Education HDR (Habilitation to Supervise Research in Mathematics), University of Montpellier II, 2007 PhD in Mathematics and Automatics, École Nationale Supérieure des Mines de Paris, 1996 Master’s in Mathematics, University of Nice-Sophia Antipolis, 1991 Research Interests Focuses on automatic control, systems modeling, robust control, and stability analysis, particularly in nonlinear systems, delay systems, and Lyapunov methods. His work spans theoretical advancements and practical applications in control systems, including extremum seeking, event-triggered control, and observer design. Key areas include: Stability analysis using Lyapunov functions and Halanay’s inequality Design of interval observers and stabilization techniques for time-varying systems Applications in bioreactors, telecommunication systems, and cancer stem cell dynamics Publications & Contributions Author/co-author of 321 publications, including a monograph on Lyapunov functions and over 160 conference papers. Recent works emphasize fixed-time stabilization, extremum seeking algorithms, and robust control strategies for systems with delays. His research bridges theoretical control theory and practical engineering challenges. Awards & Service Editorial roles include Associate Editor of Automatica and IEEE Control Systems Letters . Former roles include membership on the Scientific Commission of Inria’s CRI Saclay. Active in conference program committees, including the American Control Conference and IFAC workshops. Labs & Teams Associated with L2S, a leading laboratory in signal and systems research, and collaborates with interdisciplinary teams on projects like cancer dynamics modeling and energy systems.

• Medical Imaging
• Pediatric Cardiology
• MRI Reconstruction
• +3 more

Dr. Qing Zou is an Assistant Professor in the Division of Pediatric Cardiology, Department of Pediatrics at the University of Texas Southwestern Medical Center (UTSW). He holds secondary appointments in the Department of Radiology and the Advanced Imaging Research Center (AIRC). His research focuses on medical imaging techniques, particularly magnetic resonance imaging (MRI), with an emphasis on pediatric cardiology applications. Dr. Zou’s work integrates deep learning and advanced reconstruction algorithms to address challenges in dynamic imaging, motion compensation, and undersampling problems. Education: Ph.D. in Mathematics/Computer Science (University of Iowa) Postdoctoral Training in Cardiac MRI (University of Iowa) Research Interests: Dr. Zou explores cutting-edge methodologies in MRI reconstruction, including deep learning-driven approaches for free-breathing and non-contrast-enhanced imaging. His projects address pediatric-specific needs, such as congenital heart disease diagnostics and rapid whole-heart angiography. He also investigates mathematical foundations of sampling theory and high-dimensional data analysis. Recent Research Trends: His publications highlight advancements in motion-resolved imaging, Gaussian representation-based reconstruction, and generative models for dynamic MRI. Key themes include improving temporal resolution, reducing imaging time, and enhancing vascular visualization without contrast agents. Awards: No honors or awards explicitly listed in the provided text. Advising & Grants: No formal advisees/students or grant details mentioned. His research is likely funded through institutional resources or collaborative grants. Labs/Teams: Dr. Zou is affiliated with the Advanced Imaging Research Center (AIRC) at UTSW, a hub for innovative imaging technologies and interdisciplinary research.