جست‌وجوی استاد و پژوهشگر

Ahmet Soyluمشاهده پروفایل

دانشیار

• Knowledge Graphs
• Semantic Web
• Data Integration
• +۳ مورد دیگر

Ahmet Soylu is Associate Professor in Data and Knowledge Management at the University of Oslo. His research develops semantic technologies and knowledge systems for applications in healthcare, cloud computing, and industrial domains. Research Focus Creates knowledge graph systems for data integration and analysis, with applications in medical diagnostics (cancer/brain tumor classification), cloud cost optimization, and industrial monitoring systems. Develops transformer-based models for SQL recommendation and data linking. Publication Trends Recent work shows strong emphasis on healthcare AI (particularly cancer diagnostics), cloud storage optimization, knowledge graph embeddings, and industrial applications at Bosch. Increasing focus on imbalanced learning techniques for medical data. Project Leadership Developed Koala-UI for tabular data linking Semantic systems for Bosch welding quality monitoring Open energy management systems for EV charging

Shiliang Zhangمشاهده پروفایل

پژوهشگر ارشد

• Smart Grid
• Transactive Energy Management
• Privacy Preservation
• +۴ مورد دیگر

Shiliang Zhang is a Postdoctoral Fellow at the University of Oslo working with the Networks and Distributed Systems research group. His research focuses on privacy preservation in smart grid and transactive energy management systems, including the Privacy preserving Transactive Energy Management (PriTEM) project. He teaches Energy Informatics and Artificial Intelligence for Energy Informatics courses while developing interactive visualization tools for Norwegian energy infrastructure. His primary research areas encompass Smart Grid systems, Transactive Energy Management, and Privacy Preservation through Differential Privacy techniques. He applies Artificial Intelligence and Energy Informatics to address renewable energy integration challenges, grid resilience, and autonomous navigation systems. Recent work explores bionic data-driven approaches for underwater navigation and anomaly-resistant control mechanisms. Publications from 2022-2025 reveal strong interdisciplinary focus on machine learning applications for energy systems and navigation. Key themes include privacy-preserving pricing schemes for smart grids, robust control under system uncertainties, and deep reinforcement learning for geomagnetic navigation. His work spans IEEE Transactions, SmartGridComm, and Mathematics journals with significant contributions to resilient energy infrastructure. No scientific awards were mentioned in available sources. Zhang has no listed advisees but actively contributes to the PriTEM project within Networks and Distributed Systems. His research leverages collaborations across energy informatics domains, evidenced by publications in high-impact venues and development of practical tools like Norway's energy consumption and solar panel distribution maps. He operates within the Networks and Distributed Systems research group, developing interactive visualizations for Norwegian energy infrastructure including municipal energy consumption (May 2025), Oslo's solar panel distribution (April 2025), and national power lines (May 2025). His work bridges theoretical control systems with real-world energy applications through the PriTEM project.

Jörgen Blomvallمشاهده پروفایل

دانشیار

• Financial Engineering
• Stochastic Programming
• Quantitative Finance
• +۳ مورد دیگر

Jörgen Blomvall is an Associate Professor at Linköping University's Department of Management and Engineering (IEI). His research focuses on optimal financial decision-making, accurate financial measurement, and stochastic optimization models applied to financial markets. He develops methods to enhance measurement accuracy for quantities like forward rates, default intensities, and local volatilities, which are critical for equity, interest, credit, and derivative markets. Blomvall's work integrates stochastic programming and dynamic programming to address portfolio optimization, risk management, and transaction cost reduction. He is affiliated with the Operations Management and Finance research group, exploring resource optimization in manufacturing and service industries. His key research areas include financial engineering, stochastic programming, and quantitative risk management. Recent work emphasizes improving dividend estimation from intraday quotes, reducing transaction costs in hedging strategies, and analyzing stage complexity in stochastic programming for portfolio choice. Blomvall's methodologies have advanced the modeling of systematic risks and optimal investment decisions under real-world market constraints. Blomvall has contributed extensively to the development of optimization-based frameworks for performance attribution and yield curve estimation. His articles frequently address practical applications of stochastic models in financial markets, balancing theoretical rigor with computational feasibility. Despite his prolific output, no specific academic awards or student advisees are documented in the provided texts. He is active in the Production Economics (PEK) research group at Linköping University.

Mogana Darshini Ganggayahمشاهده پروفایل

مدرس

• Data Science and Analytics
• Machine Learning
• Health Informatics
• +۵ مورد دیگر

Dr. Mogana Darshini Ganggayah is a Lecturer at the Malaysia School of Business, Monash University Malaysia, specializing in Business Analytics and Data Science. She holds a PhD in Data Science (2021) and a BSc in Bioinformatics (2018) from Universiti Malaya. Her research integrates machine learning and data analytics in healthcare and business domains, with a focus on health informatics, predictive modeling, and data literacy. She collaborates with institutions like Cambridge University, University Malaya Medical Centre (UMMC), and Homi Baba Cancer Hospital. Dr. Ganggayah has received two Silver Awards in 2024 for innovative mental health and statistical assessment tools. She actively engages in public lectures, conferences, and peer review, and is an HRDF-certified trainer with over 1,000 trainees in data science and programming. Education: PhD in Data Science, Universiti Malaya (2021) BSc Bioinformatics (Distinction), Universiti Malaya (2018) Research Interests: Her work spans machine learning applications in healthcare (e.g., cancer diagnostics, mental health tracking) and business analytics (e.g., financial forecasting, consumer behavior). She emphasizes interdisciplinary collaboration and practical implementation of algorithms in real-world settings. Recent projects include developing data-driven solutions for the Habib Group and analyzing pandemic impacts on education and shopping behavior. Grants & Projects: PPRN Project (2025-2026): Building a Data-Driven Organisation with Data Warehouse and AI for the Habib Group (Chief Investigator) Labs/Teams: Engaged with UMMC’s medical data science initiatives and Monash Business School’s analytics team.

Tobias Heilmannمشاهده پروفایل

مدرس

• Statistical Inference
• Change Point Detection
• Differential Privacy
• +۵ مورد دیگر

Dr. Tobias Heilmann is a Lecturer at the Department of Humanities, Social and Political Sciences at ETH Zurich. His research focuses on statistical inference, change point detection, differential privacy, high-dimensional data analysis, network analysis, and machine learning. He holds a PhD in Statistics from Fudan University, advised by Zhiliang Ying, and has contributed to software packages such as changepoints , GMPro , and APPLE . His work bridges theoretical statistics and practical applications, emphasizing privacy-preserving techniques, dynamic systems analysis, and algorithmic robustness. Key areas include nonparametric methods, contextual bandits, federated learning, and functional data analysis. Recent contributions address challenges in distributed systems, adversarial robustness, and high-dimensional model selection. He has published extensively in top journals like Annals of Statistics , SIAM Journal on Mathematics of Data Science , and IEEE Transactions on Information Theory . Dr. Heilmann's research also involves interdisciplinary collaborations, including social psychology of groups and biomedical applications. His software tools are widely used for change point analysis and community detection in networks. Current projects explore optimal privacy-utility trade-offs and adaptive algorithms for non-stationary environments.

• Operations and Supply Chain Management
• Digital Innovation
• Complex Systems Modelling
• +۳ مورد دیگر

Professor Kieran Jude Fernandes serves as Executive Dean of Durham University Business School, a globally ranked institution renowned for research excellence and societal impact. He holds additional affiliations as Fellow of the Academy of Social Sciences, University College Durham, and the Wolfson Research Institute. His extensive advisory roles include Trustee of the BALTIC Centre for Contemporary Art, Vice-Chair of the UK National Commission for UNESCO, and Director of the Northern Powerhouse Innovation Observatory. His research expertise spans three interconnected domains: Operations and Supply Chain Management focusing on optimization, digitization, sustainability, and risk mitigation Innovation and Technology Management examining digital business models and technology adoption Complex Systems Modelling analyzing innovation diffusion in social and economic networks His work bridges theoretical rigor with practical application across pharmaceutical, construction, and digital sectors. Professor Fernandes' publication portfolio reveals consistent leadership in supply chain digitalization, with recent emphasis on blockchain implementation, sustainability-performance linkages, and network effects in innovation systems. His 2023-2025 articles demonstrate growing integration of machine learning techniques with traditional operations frameworks. His distinguished recognition includes: NASA Space Shuttle Main Engine Continuous Improvement Award Rutherford Excellence in Engineering Award Financial Times Professor of the Week designation University of York Vice-Chancellor's Gold Medal As an academic leader, he supervises doctoral candidates including Jafni Bin Johari Jiken and Kieran Purvis while directing significant research initiatives. His grant portfolio features funding from ESRC, EPSRC, NASA, Royal Academy of Engineering, and Swiss Federal Department of Foreign Affairs, supporting projects that translate complex systems modeling into sector-specific innovations. His UNESCO advisory work extends this impact to global sustainable development frameworks.

Justin T. Huangمشاهده پروفایل

استادیار

• Digital Platform Design
• User Behavior Analysis
• Online Marketplaces
• +۷ مورد دیگر

Justin T. Huang is an Assistant Professor of Marketing at the University of Michigan Ross School of Business. He holds a PhD from Stanford University (2018) and dual Bachelor's degrees from the University of Maryland (2013). His research focuses on digital platform design, user behavior, and online marketplaces, addressing topics like content moderation, influencer marketing, and platform governance. Key areas include the impact of political bias on social networks, the economics of livestreaming, and the societal costs of anti-Asian racism during the pandemic. His work bridges economic theory, marketing tools, and machine learning to analyze issues such as organic content creation incentives, long-term platform growth, and policy implications of platform governance. Recent studies include analyses of Reddit's echo chamber formation via biased moderation and the psychological drivers of tipping in live-streaming environments. Media coverage highlights his expertise in TikTok's regulatory challenges and the broader implications of data security laws. Huang collaborates with industry leaders to address practical concerns in digital ecosystem management. His academic contributions span multiple disciplines, including marketing, sociology, and computational social science.

Basem Suleimanمشاهده پروفایل

مدرس ارشد

• AI-driven Software Services
• Applied Artificial Intelligence (GenAI/LLM, Machine Learning)
• Education Technologies (Personalized Learning)
• +۴ مورد دیگر

Dr. Basem Suleiman is an Adjunct Senior Lecturer in Advanced Computing at the University of Sydney and Deputy Director of the Software Engineering Program at UNSW Sydney. He holds a Ph.D. in Computer Science from UNSW (2012) and has over a decade of experience in academia and industry, including roles at SAP, CSIRO’s Data61, and leading universities. Education: Ph.D. in Computer Science, UNSW Sydney (2012) Graduate Certificate in Research Commercialization & Management, UNSW Sydney Master of Computing (Software Engineering), Western Sydney University Research Interests: AI-driven Software Services: Federated Learning, Recommender Systems, Human-Machine Interaction Applied AI: GenAI/LLMs, Machine Learning, Deep Learning for FinTech, Healthcare, and Education Cloud Computing: Elasticity, Workload Management, Privacy-Preserving Techniques Key Contributions: Developed AI models for structural health monitoring, personalized learning systems, and anomaly detection Secured research grants and published 80+ papers in venues like ACM Transactions, IEEE Conferences, and IJCAI Supervised 15+ PhD/MPhil students and led industry partnerships with SAP, CSIRO, and global tech firms Awards & Recognition: Runner-up Best Paper Award (2023) Dean’s Commendation for Teaching Excellence Invited Keynote Speaker (2022 ICoSEIT) Engagement: Leads industry collaborations as UNSW’s Industry Capstone Liaison Officer, fostering partnerships for student projects and curriculum innovation.

• Bayesian Methodology
• Statistical Machine Learning
• Variational Bayes
• +۳ مورد دیگر

Dr. Minh-Ngoc Tran is an Associate Professor in Business Analytics at the University of Sydney Business School and an Associate Investigator in the ARC Centre of Excellence for Mathematical and Statistical Frontiers. His research develops Bayesian and machine learning methods for complex models with big data. His methodological innovations include quantum-enhanced variational Bayes, manifold optimization techniques, and subsampling MCMC algorithms. Current research focuses on time series modeling for financial markets, quantum computing applications in statistics, and variational inference for models with intractable likelihoods. Dr. Tran teaches quantitative methods, data mining, and predictive analytics. He currently supervises PhD students in deep learning for financial time series, explainable AI, and quantum machine learning. Recent research grants support quantum computation in business analytics and deep learning-based financial forecasting. His work has been recognized through awards including the University of Sydney Business School Emerging Scholar Research Fellowship and designation as Australia's top researcher in Probability and Statistics with Applications in 2021.

Mohsen Afsharianمشاهده پروفایل

استاد

• Operations Research
• Data Envelopment Analysis (DEA)
• Supply Chain Optimization
• +۵ مورد دیگر

Prof. Mohsen Afsharian is a Professor of Production and Logistics at Leibniz Fachhochschule Hannover, Germany. He specializes in Operations Research with focus on Data Envelopment Analysis (DEA), incentive regulation mechanisms, and performance analytics. His research bridges theoretical advancements in DEA methodologies with practical applications in supply chain optimization, facility location, and regulatory policy design. Education: Holds a Habilitation in Prescriptive Analytics (2022) and a Doctorate in Operations Research. Notable academic contributions include monographs on cutting-stock problems with defects (2013) and prescriptive analytics frameworks (2022). Research interests span efficiency evaluation systems, productivity measurement over time, and data-driven decision models. Recent work emphasizes incentive mechanisms for regulated sectors and multi-criteria optimization under centralized management structures. Publications (2020–2024) highlight contributions to DEA flexibility control, metafrontier analysis, and emerging data science applications in business. He co-edited special issues on incentive regulation analytics (2022) and public sector DEA applications (2018). Active in editorial roles for Decision Analytics Journal and Socio-Economic Planning Sciences. His work integrates mathematical programming with real-world challenges in logistics, education systems, and banking efficiency analysis.

• Technical Analysis
• Algorithmic Trading
• Cryptocurrency
• +۲ مورد دیگر

Dr. Daniel Svogun is an Assistant Professor of Finance and Director of Finance at the Busch School of Business, The Catholic University of America. He holds a PhD in Economics from Fordham University (2020), along with MA (2017) and BA (2014) degrees in Economics from the same institution. His research focuses on technical analysis, cryptocurrency markets, life-cycle investing, and faith-based investment guidelines aligned with Catholic principles. He teaches courses including Introduction to Finance, Corporate Finance, and Advanced Corporate Finance, emphasizing topics like time value of money, corporate taxation, and portfolio valuation. Dr. Svogun’s work bridges traditional finance with emerging digital assets, exploring cryptocurrency portfolio optimization, transaction cost impacts, and the integration of USCCB socially responsible investing principles. His research has been presented at conferences such as the Eastern Economic Association and Southwestern Finance Association. While no formal awards are listed, his contributions address critical questions in algorithmic trading, market efficiency, and lifecycle wealth management strategies. His advising and grants section remains underdeveloped in the provided text, though his teaching spans foundational to advanced finance topics. No lab affiliations or teams are explicitly mentioned, though his research collaborations likely involve econometric and computational finance methodologies.

Suhaib Fahmyمشاهده پروفایل

استاد

• Reconfigurable computing
• Field Programmable Gate Arrays (FPGAs)
• Compute acceleration
• +۳ مورد دیگر

Suhaib Fahmy is a Professor of Computer Science at KAUST, having previously held the position of Professor in the School of Engineering at the University of Warwick. He leads the Adaptive Reconfigurable Computing Lab and was previously the head of the Connected Systems Research Group (2016–2020) and a Turing Fellow at The Alan Turing Institute (2017–2021). He earned a First Class Honours MEng in Information Systems Engineering (2003) and a PhD in Electrical and Electronic Engineering (2008) from Imperial College London. His career includes postdoctoral research at Trinity College Dublin (2007–2009) and a role as Assistant Professor at Nanyang Technological University (2009–2015). His research focuses on reconfigurable computing, FPGAs, compute acceleration, automotive systems, and adaptive radio systems. Notable contributions include work on automotive cybersecurity, FPGA-based neural networks, and low-latency accelerator interfaces. He has received awards such as the IEEE High Performance Extreme Computing Conference Best Paper Award (2021) and ACM Transactions Best Paper Award (2019). Dr. Fahmy has supervised numerous students, including PhD candidates focusing on FPGA acceleration of HPC applications, edge ML, and automotive networks. His teams have pioneered frameworks like NAIL for FPGA offloading and developed security solutions for automotive E/E architectures. He actively contributes to conferences and holds senior memberships in IEEE and ACM.

• Empirical Asset Pricing
• Market Microstructure
• Socially Responsible Investment
• +۲ مورد دیگر

Yung Chiang Yang serves as a Senior Lecturer in Finance at the University of Liverpool Management School, holding a PhD from Nanyang Technological University. His academic profile centers on empirical finance research with strong connections to Chinese financial markets and sustainable investment practices. Education: PhD in Finance, Nanyang Technological University Dr. Yang's research spans Empirical Asset Pricing , Market Microstructure , Socially Responsible Investment , International Capital Markets , and Machine Learning in Finance . His work frequently investigates Chinese market dynamics, corporate social responsibility impacts, and quantitative trading strategies through rigorous empirical analysis. Analysis of his 2020-2025 publications reveals dominant themes in market microstructure (particularly futures markets), CSR-finance linkages, and time-series momentum phenomena. His research consistently employs advanced statistical methods with practical applications for market practitioners and policy makers. Dr. Yang actively contributes to academic governance through external examiner roles at Sheffield Hallam University (2023-present), Australian National University (2024), and King's College London (2021). Within the Management School, he serves as Director of Studies for the MSc Finance & Investment Management program and Module Co-ordinator for Research Projects, while teaching core finance modules including Dissertation and Principles of Finance with Excel.

Hatem Ben Ameurمشاهده پروفایل

استاد

• Financial engineering
• Stochastic processes
• Stochastic simulation
• +۴ مورد دیگر

Hatem Ben Ameur is a Full Professor in the Department of Decision Sciences at HEC Montréal, Canada, and a member of the Group for Research in Decision Analysis (GERAD). He holds a Ph.D. in Financial Engineering from HEC Montréal, an Engineer degree in statistics and economics from ENSAE Paris, and a Master’s in Mathematics from Tunis University. His research interests focus on financial engineering, stochastic processes, and risk measurement, with applications in option pricing, bankruptcy prediction, and dynamic programming. Recent publications emphasize structural models for corporate securities valuation and the integration of machine learning techniques in financial analysis. Professor Ben Ameur has supervised 3 Master’s theses and 5 project supervisions, collaborating with students on topics like neural networks in bankruptcy prediction and Monte Carlo simulation for option valuation. His teaching includes courses on business analytics, applied mathematics, and Monte Carlo methods. He is actively involved in GERAD, contributing to interdisciplinary research in decision sciences and quantitative finance. His work bridges theoretical advancements with practical applications in financial markets and risk management.

• Quantitative Finance
• Stochastic Control
• Statistical Machine Learning
• +۵ مورد دیگر

Sébastien LLEO is a Full Professor of Quantitative Finance at NEOMA Business School in France. He holds a Ph.D. in Mathematics and an HDR (Habilitation à Diriger des Recherches) in Social Sciences and Humanities, alongside certifications such as CFA Charterholder and Professional Risk Manager. His academic leadership includes serving as the inaugural Director of NEOMA's Doctoral School, where he developed groundbreaking Ph.D. and DBA programs. He also coordinated the RISKPERFORM project funded by Région Champagne Ardenne and FEDER, and designed a risk analytics system for the CMHC Pension Fund. His research focuses on stochastic control, asset management, behavioral finance, and risk prediction. Notable contributions include work on portfolio theory inspired by Harry Markowitz and William Ziemba, as well as predictive models for stock market crashes. His recent articles (2023–2025) emphasize integration of alternative data in asset management and the application of jump-diffusion processes in risk-sensitive strategies. He has authored/co-authored books such as Risk-Sensitive Investment Management and Stock Market Crashes: Predictable and Unpredictable . LLEO actively participates in academic conferences globally, addressing topics like behavioral finance, stochastic optimization, and systemic risk. His interdisciplinary approach bridges mathematics and finance, with a commitment to practical applications for both academia and industry.