Find Academic Supervisors

Seyed Mojtaba SajadiView profile

Lecturer

• Simulation-Based Optimisation
• Applied Operation Research
• Artificial Intelligence and Meta-heuristic Algorithms
• +4 more

Dr. Seyed Mojtaba Sajadi is an Assistant Professor (Lecturer) in Operations and Supply Chain Simulation at Aston Business School, part of Aston University's College of Business and Social Sciences. He holds a PhD in Industrial Engineering from Amirkabir University of Technology and has extensive academic experience, including roles as Associate Professor and Assistant Professor at the University of Tehran (2010–2020), and Assistant Professor at Coventry Business School (2021–2022). His research focuses on simulation-based optimization in supply chain management, healthcare systems, and disaster management, leveraging techniques like Discrete Event Simulation (DES), Agent-Based Simulation (ABS), and metaheuristic algorithms. Education: PhD in Industrial Engineering, Amirkabir University of Technology MSc in Industrial Engineering, University of Tehran BSc in Industrial Engineering, Sharif University of Technology PGCert in Higher Education, Coventry University Research Interests: Simulation-Based Optimization Applied Operations Research Machine Learning Supply Chain and Production Optimization Healthcare and Disaster Management Editorial Roles: Associate Editor, Journal of Simulation (2023–Present) Editorial Advisory Board, Journal of Enterprise Information Management (2024–Present) Advising and Grants: Supervised over 20 PhD candidates, including current projects on disaster-resilient smart cities and Industry 4.0 in food sustainability. Active in securing funded research projects, such as an Aston PhD Studentship and a Saudi Government-funded initiative. Labs/Teams: Engaged in collaborative research across international institutions, including the Operational Research Society Simulation Workshop (SW25, 2025) and interdisciplinary projects in healthcare logistics and smart supply chains.

Marco AmatiView profile

Professor

• Urban Planning
• Environmental Science
• Urban Greening
• +6 more

Professor Marco Amati is a faculty member at RMIT University, affiliated with the School of GUSS, where he holds the rank of Professor. He serves as the Program Manager for the Master of Urban Planning and Environment program, focusing on developing impactful educational initiatives. His research emphasizes urban green spaces, environmental sustainability, and the intersection of planning with health and social sciences. Key areas include the cooling benefits of trees, urban agriculture, and social-ecological systems. Professor Amati’s work integrates interdisciplinary approaches, such as ecological design frameworks and machine learning for urban policy. His supervision spans topics like urban green spaces, peri-urban food systems, and land reform in developing countries. Notable contributions include studies on Melbourne’s urban vegetation, heat mitigation strategies, and the social dimensions of urban planning. He actively collaborates with institutions like the Clean Air and Urban Landscape Hub and has authored influential publications on biodiversity integration, multicultural urban landscapes, and climate-responsive design. His expertise bridges technical fields (e.g., geomatic engineering) with humanities, reflecting a commitment to holistic urban development.

Jacco ThijssenView profile

Professor

• Real Options Theory
• Game-Theoretic Models
• Environmental Economics
• +3 more

Professor Jacco Thijssen is a leading academic in Mathematical Finance at the University of York's Department of Mathematics, with a visiting professorship at Lisbon School of Economics and Management (ISEG). He holds a PhD and MSc in Econometrics & Operations Research from Tilburg University. His research focuses on mathematical models of investment under uncertainty, particularly real options theory, game-theoretic dynamics, and applications in environmental and infrastructure investment. He collaborates with institutions across Europe, including NTNU, Tilburg, and Antwerp universities. His work spans projects like CCUS technology valuation, infrastructure investment uncertainty, and competitive investment timing under demand volatility. He currently advises four PhD students and has led grants from NERC on topics like carbon markets and coastal ecosystem recovery financing. He is also part of the Mathematical Finance and Stochastic Analysis Research Group at York. Education: BSc and PhD in Econometrics & Operations Research from Tilburg University. Former positions include roles at Trinity College Dublin and York's Department of Economics and Management School (now School for Business and Society). Research interests emphasize strategic investment timing, ambiguity aversion, and stochastic modeling in economics and finance. His projects include analyzing carbon markets, renewable energy investments, and policy impacts on green technologies. He actively engages with international collaborators and supervises research in real options applications. Recent grants include leadership roles in NERC-funded projects exploring tree-landscape benefits and coastal ecosystem recovery through insurance mechanisms. His publications bridge theory and practice, addressing issues like pandemic intervention timing and risk-free asset proxies in financial markets.

Yunshi LanView profile

Associate Professor

• Knowledge Bases
• Question Answering
• Information Extraction
• +6 more

Yunshi Lan is an Associate Professor at the School of Data Science and Engineering, East China Normal University (ECNU). He holds a Ph.D. from Singapore Management University (SMU) and a Bachelor's degree from Southwest University (SWU). His research focuses on knowledge bases, question answering systems, deep learning, and large language models. He has led multiple grants, including projects from the National Science Foundation of China and the Shanghai Pujiang Talent Program. Lan has published extensively in top venues like ACL, EMNLP, and AAAI, with notable contributions to visual question answering, grammatical error correction, and educational NLP. He serves on program committees for ACL, EMNLP, and AAAI, and has given invited talks on large language models and knowledge-based systems. His teaching includes undergraduate and graduate courses on deep learning, emphasizing practical applications and LLM integration. Lan mentors a vibrant research group, with students contributing to impactful projects like MWPToolkit and FlaCGEC datasets. Education: Ph.D., SMU (201X); B.Sc., SWU (201X). Key grants include 'Knowledge Base Question Answering System in Interactive Environments' (NSFC, 2023-2025) and 'Intelligent Low Carbon Oriented Theories for Cross-Border Electric Power Trading' (NSFC, 2024-2028). Awards include the First Prize in the Financial GraphRAG Competition (2024) and the Shanghai Pujiang Talent Program (2022). His research highlights include benchmarking Visual Chinese GEC, developing TreeEval for LLM evaluation, and creating educational NLP tools like 中介语智能批改 for CFL learning.

Connie KoView profile

Adjunct Professor

• LiDAR Data Processing
• 3D Object Detection
• Geographic Information Systems
• +4 more

Connie Ko is Adjunct Professor in the Faculty of Environmental and Urban Change at York University, where she serves as GIS/Remote Sensing Technician and Research Associate. Her research focuses on advancing LiDAR data processing techniques and developing AI applications for 3D object detection in environmental contexts. Education credentials: PhD in Earth and Space Science & Engineering (York University, 2014) MSc in Geography (York University, 2004) Bachelor in Environmental Science (York University, 2001) Research specializes in: AI-driven LiDAR analysis for environmental mapping 3D object detection methodologies Tree genus classification using geometric features Large-scale dataset creation for machine learning Publications demonstrate consistent focus on LiDAR applications (80% of works), particularly in forestry and hydrological contexts. Recent shift toward AI integration evident since 2018. Authored foundational datasets like YUTO Tree5000 and Yuto Semantic to support geospatial AI development. Teaches GIS/Remote Sensing techniques and mentors students in spatial analysis. Transformed GIS computer lab into collaborative learning hub supporting interdisciplinary environmental research.

• Neuroscience
• Computational Neuroscience
• Cognitive Neuroscience
• +5 more

Ralf Wimmer is a Research Assistant Professor at Tufts University School of Medicine in the Department of Neuroscience. His work focuses on computational and neurophysiological mechanisms underlying cognitive functions and disorders. Current affiliation: Tufts University (2023–present) Wimmer’s research investigates thalamocortical circuits in schizophrenia, attentional control, sleep spindles, and computational models of neural networks. His studies integrate electrophysiology, optogenetics, and behavioral analysis in animal models. Recent publications highlight his work on: Cognitive dysfunction in schizophrenia via prefrontal-thalamic networks Computational models of neural sequence processing Thalamic regulation of attention and uncertainty Role of sleep spindles in memory and disease He can be reached at ralf.wimmer@tufts.edu.

• Maritime Transportation
• Failure Mode Effect Analysis
• Marine Engineering Systems
• +6 more

Kadir Cicek is an Associate Professor at the Department of Marine Engineering, Istanbul Technical University. His research focuses on maritime safety systems, failure analysis, and maritime education innovation. He has published extensively on topics like ship inspection analytics, risk assessment models, and digital transformation in maritime management. He leads research on failure mode effect analysis (FMEA), intelligent ship inspection methods, and compliance strategies for international maritime regulations (e.g., ISM Code). His work contributes to UN Sustainable Development Goals related to safe and sustainable marine operations. Projects : Developing TMSA 3.0-based competency monitoring for tanker management Ship Engine Reliability House project Digitalization of tanker fleet safety systems Awards : Best Reviewer Award 2017 Above Threshold Reward (2015) Above Threshold Award (2018) Research Themes : Maritime safety and risk mitigation AI-driven decision support systems Maritime education methodology His recent work emphasizes predictive analytics for ship deficiencies, cost evaluation frameworks in maritime logistics, and fuzzy logic applications in instructional design. He collaborates globally on port state control optimization and digital maturity assessments for ship management firms.

Ashwin VenkataramanView profile

Assistant Professor

• Operations Management
• Demand Prediction
• Choice Models
• +6 more

Ashwin Venkataraman is an Assistant Professor of Operations Management at the Naveen Jindal School of Management, University of Texas, Dallas. He holds a Ph.D. in Computer Science from NYU's Courant Institute, advised by Srikanth Jagabathula and Lakshminarayanan Subramanian. His research focuses on demand prediction, choice modeling, and machine learning applications in operations management and retail analytics. Education: Ph.D. in Computer Science (NYU, 2018); Master's and Bachelor's degrees not explicitly stated Research interests include nonparametric demand estimation, feature-based choice models, and leveraging unobserved factors in decision-making systems. His work spans commodity pricing forecasting, crowdsourcing system optimization, and educational technology platforms like CollectiveTeach. Recent articles explore advancements in tree logit models, stochastic preference frameworks, and data-driven demand prediction techniques. These contributions address scalability challenges in large-scale systems and improve accuracy through algorithmic innovations. Awards: Honorable Mention for INFORMS Dantzig Dissertation Award (2019), NYU GSAS Dissertation Fellowship (2017-18), Jacob T. Schwartz Fellowship (2015) His work integrates theoretical rigor with applied impact, including collaborations on retail analytics and commodity market forecasting. No explicit grant details or lab affiliations are mentioned, but contributions to the TreeLogitEstimation repository indicate active engagement in collaborative research.

• Parallel Functional Programming
• Parallel Symbolic Computation
• Resource Analysis and Control
• +4 more

Dr. Patrick Maier is a Lecturer in Computing Science at the University of Stirling's Division of Computing Science and Mathematics. His research focuses on parallel computing, including parallel functional programming, symbolic computation, and resource analysis. He has contributed to projects such as AJITPar (2013-2017), HPC-GAP (2009-2013), and MOBIUS (2006-2009). His work spans parallel algorithm frameworks (e.g., YewPar), high-performance computing systems, and formal methods in software verification. Research interests include parallel functional programming, symbolic computation optimization, temporal logic, and compositional verification. Notable publications cover neuromorphic vision systems, antibiotic dosing optimization, scalable combinatorial search, and fault-tolerant functional computation. Collaborations include work on distributed memory parallel Haskell (HdpH) and formal verification techniques for resource management. He is affiliated with the Data Science and Intelligent Systems, and Computational Mathematics and Optimisation research groups. His recent work emphasizes reproducible parallel search algorithms and interdisciplinary applications like computational biology and neuromorphic engineering.

Aysun ÖZTUNA KAPLANView profile

Associate Professor

• Science Education
• STEM/STEAM Education
• Nature of Science
• +12 more

Aysun ÖZTUNA KAPLAN is an Associate Professor in the Department of Mathematics and Science Education at the Faculty of Education, Sakarya University. She actively contributes to science education through teaching, research, and academic leadership. Her work spans undergraduate and graduate programs, with a strong focus on pedagogical innovation and teacher development. Her research interests include science education, STEM/STEAM integration, the nature of science, socio-scientific issues, epistemological beliefs, scientific literacy, out-of-school learning environments (such as science centers and museums), values in science education, thinking skills, and astronomy education. She emphasizes interdisciplinary and experiential approaches to enhance student engagement and understanding. Her recent publications reflect a consistent trend in informal and formal science learning, teacher education, and curriculum development. Articles such as "Educational Games in Science Center" , "STEAM from Soil to Table" , and "Visible Thinking in Science Education" demonstrate her commitment to innovative, context-based, and student-centered pedagogies. Her work often involves collaboration with pre-service and in-service teachers, focusing on self-efficacy, awareness, and practical implementation of modern educational frameworks. Editor, Sakarya University Journal of Education (SUJE) – Statistics and Subject Editor (2011–2023) Editor of books including Fizik 1 – Problem Solutions and Fen Öğretimi Active participant in COST Actions and TÜBİTAK-funded projects She has supervised numerous master’s and doctoral theses, contributing significantly to graduate education. Her leadership extends to departmental administration, having served as Head of Department in previous years. She also organizes and chairs academic sessions at national and international conferences, promoting scholarly dialogue in science education. She is involved in research projects such as "Topraktan Sofraya STEAM" , "Müzelerde Bilimin Gücünü Keşfediyoruz" , and "Fen Bilimleri Öğretmen Adayları Bilim Merkezini Keşfediyor" , which highlight her dedication to bridging formal and informal learning contexts and fostering scientific curiosity through real-world applications.

Krzysztof PancerzView profile

Professor

• Machine Learning
• Computational Intelligence
• Knowledge Discovery from Data
• +3 more

Krzysztof Pancerz is a Professor at the Department of Computer Science within the Faculty of Philosophy at John Paul II Catholic University of Lublin (KUL), Poland. His institutional affiliation is formalized as "prof. KUL" with contact address at Al. Racławickie 14, 20-950 Lublin. He holds dual doctorates: PhD in Computer Science (2006) and DSc in technical sciences (2018) from the Polish Academy of Sciences in Warsaw. His research spans Computational Intelligence and Medical Diagnostics , with emphasis on machine learning for cancer detection using Raman/FTIR spectroscopy. Key areas include: Knowledge discovery from biomedical spectral data Ontology-based information systems Unconventional computing models Deep learning for disease biomarker identification Recent publications (2022-2025) demonstrate consistent focus on cross-disciplinary applications, particularly in oncology and reproductive health diagnostics. He actively organizes scientific workshops including: International Workshop on AI in Medical Applications (FedCSIS conferences, 2011-2018) Knowledge and Data Engineering in Medicine (KES conferences since 2018) His research leadership includes managing the Ministry of Science-funded project on non-invasive laryngeal disease diagnostics and contributing to the EU FP7 PhyChip project on slime mould computing. Current thesis supervision includes topics like fruit leaf recognition and financial deposit forecasting using neural networks. Professional engagement: Member of Polish Fuzzy Sets Society (POLFUZZ) ORCID: 0000-0002-5452-6310 Publications indexed in arXiv, dblp, PubMed

• Evolutionary Computation
• Swarm Robotics
• Optimisation Algorithms
• +4 more

Professor Emma Hart is a leading academic at Edinburgh Napier University , affiliated with the School of Computing Engineering and the Built Environment and the Centre for Algorithms, Visualisation and Evolving Systems . Her research focuses on evolutionary computation , swarm robotics , and optimisation algorithms , with applications in ecology, cybersecurity, and AI-driven design. Education: 1 st Class Honours in Chemistry (Oxford), MSc and PhD in AI (Edinburgh) Leadership: Former Editor-in-Chief of Evolutionary Computation (MIT Press, 2017–2023), ACM SIGEVO Board member, and key contributor to Scotland’s AI Strategy (2021). Her research spans evolutionary robotics, lifelong learning systems, and explainable AI. Recent work includes using novelty search to engineer diverse robot swarms, XAI for algorithm configuration, and LLM-evolved heuristics for bin packing. She explores fitness landscapes and attractor analysis to improve optimisation model robustness. Scientific Awards include the ACM SIGEVO Outstanding Contribution Award (2023) Fellow of the Royal Society of Edinburgh (2022) Bronze Humies Award (2018) Best Paper Award@GECCO 2019 She supervises PhD students such as Grant Anderson Kirsty Montague Kelly Hunter Magnus Janson Fiona Stewart and has led over £2M in grants, including EPSRC , Innovate UK , and EU projects on autonomous robot evolution and lifelong learning systems.

Necati ARASView profile

Professor

• Inventory Control
• Product Recovery Network Design
• Reverse Logistics Management
• +7 more

Necati ARAS is a Professor in the Department of Industrial Engineering at Bogazici University. He holds a PhD from Bogazici University (1999). His research focuses on inventory control, reverse logistics, supply chain optimization, and the application of artificial neural networks in complex systems. He has published extensively on topics including facility location, wireless sensor networks, disaster preparedness, and competitive market strategies. Education: PhD, Bogazici University, 1999 ARAS's work bridges theoretical optimization with real-world applications, particularly in logistics, network security, and smart cities. His contributions include models for minimizing contagion spread in networks, optimizing multi-tier cloud computing, and improving vehicle routing efficiency. Recent research emphasizes strategic resource allocation and mitigating misinformation in social networks. Despite no listed awards, his prolific publication record (over 70 articles) reflects sustained academic impact. His research often addresses humanitarian challenges like disaster response and infrastructure protection. ARAS has advised numerous projects but no specific student names are provided in the text. His research teams likely focus on interdisciplinary collaboration between operations research, computer science, and civil engineering.

• Theory of Machine Learning
• Randomized Algorithms
• Convex Optimization
• +4 more

Nicholas Harvey is a Professor in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. He holds affiliations as an Associate Member in the Department of Mathematics and serves as Director of the Mathematics of Information, Learning, and Data (MILD) research cluster. His research focuses on randomized algorithms, convex optimization, and machine learning theory, with notable contributions to online learning, submodular functions, and algorithmic aspects of discrepancy theory. Harvey's academic journey includes significant work on problems such as prediction with expert advice, learning mixtures of Gaussians, and graph algorithms. He has supervised numerous graduate students, including notable researchers like Huang Fang and Victor Sanches Portella. His work has been recognized with awards such as the Best Paper Award at NeurIPS 2018 and the Excellence in Teaching Award in 2024. His research outputs span theoretical computer science, optimization, and machine learning, with key contributions in algorithms for submodular functions, online convex optimization, and probabilistic methods. He has also contributed to practical applications, such as algorithms for network coding and efficient failure notification systems in distributed networks. Harvey is actively involved in teaching, including pioneering courses on randomized algorithms and machine learning theory. His research group collaborates on projects related to the MILD cluster, focusing on interdisciplinary applications of data science and mathematics.

Haitian WangView profile

Researcher

• 3D Urban Mapping
• Remote Sensing
• LiDAR and GNSS Data Fusion
• +6 more

Haitian Wang is a Research Officer at the University of Western Australia (UWA) in the School of Engineering, Department of Electrical, Electronic and Computer Engineering, and also serves as a Research Scientist at the Department of Primary Industries and Regional Development (DPIRD) in Western Australia. His work bridges urban mapping and agricultural intelligence through advanced sensing technologies. His research interests span 3D Urban Mapping , Remote Sensing , LiDAR and GNSS Data Fusion , Precision Agriculture , and Deep Learning . He focuses on developing high-precision mapping pipelines for urban environments and AI-powered solutions for agricultural challenges, including weed detection and real-time decision-making platforms using UAV-based multispectral imaging. Analysis of his recent publications reveals a strong trend in applying LiDAR and remote sensing technologies to both urban and agricultural contexts. His work integrates multimodal sensing (LiDAR, GNSS, and imaging) with deep learning for 3D point cloud processing, object detection, and environmental monitoring, contributing to smart city and sustainable farming initiatives.