Find Academic Supervisors

• Finance
• Real Estate
• Risk Management
• +3 more

Tianyang Wang (Tian) is a Professor of Finance at Colorado State University's Department of Finance and Real Estate, holding a Ph.D. from the University of Texas at Austin. He possesses professional designations including Chartered Financial Analyst (CFA), Financial Risk Manager (FRM), and Associate of the Society of Actuaries (ASA). Professor Wang's research integrates theoretical and empirical approaches in finance, with publications in top journals like Operations Research , Journal of Financial Markets , and Journal of Risk and Insurance . His work addresses risk modeling, volatility forecasting, commodity markets, ESG investing, and financial contagion, appearing in media outlets such as Financial Times and Caixin Global. He has received multiple Best Paper Awards from institutions like the Journal of Financial Research, China Financial Engineering Annual Meeting, and Taiwan Risk Insurance Association. A dedicated educator, he teaches across undergraduate, graduate, online MBA, and EMBA programs, earning teaching awards including the Daniels Fund Ethics Initiative Fellowship and Fred Moore Teaching Excellence Award.

• Macroeconomics
• Fiscal Policy
• Monetary Policy
• +5 more

Vivaldo Manuel Pereira Mendes is an Associate Professor in the Department of Economics at the University Institute of Lisbon (ISCTE-IUL), where he has been teaching since 1998. He is also an Affiliate Member of BRU-Iscte - Business Research Unit within the Institute of Social Sciences and Business Studies. His educational background includes a PhD in Economics from the University of Sussex (1998), a Master's degree in Economics from the University of Sussex (1991), and a Bachelor's degree in Economics from the Higher Institute of Economics and Management - UTL (1987). Mendes has held significant university leadership positions including Vice-Rector (2005-2009), President of the General Assembly (2002-2005), and Director of the Department of Economics (2010-2014). He has taught diverse courses spanning macroeconomics, microeconomics, game theory, monetary economics, and computational economics. His research portfolio demonstrates a clear evolution from theoretical macroeconomics and monetary policy toward computational economics and machine learning applications. Recent publications (2019-2024) show a strong focus on applying deep learning techniques to financial forecasting, particularly examining how data frequency impacts prediction accuracy across global markets. Mendes has published in high-impact journals including Physica A (Q1), European Physical Journal B (Q1), and Economics Bulletin (Q2), with his 2005 paper on controlling chaotic dynamics in economic models receiving 23 Google Scholar citations. He has also contributed to book chapters on spatial econometrics and optimal monetary policy. A committed advocate of open-source software, Mendes actively contributes to the Julia programming language community and maintains a personal website showcasing his academic work and computational projects.

• Data Science
• Machine Learning
• Artificial Intelligence
• +7 more

Xiaobai Li is a Professor in the Department of Operations and Information Systems at the University of Massachusetts Lowell's Manning School of Business. His research spans data science, machine learning, business analytics, data privacy, and information economics with significant funding from NIH and NSF. Previously, he taught at the University of Texas at Dallas and accumulated 10 years of industry experience as an engineer, analyst, and manager. His research interests focus on privacy-preserving data analytics , data valuation economics , and machine learning optimization . Key contributions include developing novel methods for differential privacy, medical data anonymization, and cost-effective data acquisition frameworks that balance privacy protection with analytical utility. His work bridges theoretical computer science with practical business applications. Analysis of his recent publications reveals strong trends in privacy-aware business analytics (70% of articles), health informatics security (20%), and data economics (10%). The research consistently addresses the tension between data utility and privacy protection, with increasing focus on real-world implementation challenges in healthcare and consumer data markets. Manning Research Excellence Award (2023) Best Associate Editor Award (2021) Multiple Best Paper Awards (2004-2021) Graduate Teaching Award (2019) Dr. Li has secured significant research funding including a $706,669 NIH grant for patient privacy preservation (2011-2015) and a $100,000 NSF grant for consumer electronic privacy systems (2008). He serves as Associate Editor for Information Systems Research , Decision Support Systems , and other leading journals. His teaching portfolio includes database management, machine learning, and data analytics courses at undergraduate and graduate levels.

• Econometrics
• Financial Econometrics
• Time Series Analysis
• +5 more

Prof. Barbara Będowska-Sójka is affiliated with the Department of Econometrics at the Institute of Informatics and Quantitative Economics , Poznan University of Economics . Her academic career spans roles as Professor (2022), Professor Assistant (2015), and Doctor (2004). Research focuses on financial econometrics , cryptocurrency markets , market volatility , and portfolio management . Active in time series modeling , liquidity analysis , and data-driven financial research . Her recent publications explore cryptocurrency return networks , energy efficiency hedging , and gender disparities in finance journal editorial boards , reflecting interdisciplinary applications of econometrics and AI in finance.

• Machine Learning
• Cybersecurity
• Internet of Things (IoT)
• +3 more

Firas Al-Doghman is a Lecturer at the School of Computer Science, University of Technology Sydney. With expertise in Machine Learning, Cybersecurity, and Internet of Things (IoT), he teaches subjects like iOS Applications, Networking, and Data Engineering while conducting research on smart algorithms and their cybersecurity applications. PhD in Computer Engineering and Data Management (UTS, 2020) Teaching since 2017 at UTS Former Associate Researcher/Lecturer at UNSW ADFA Research focuses on: Machine Learning integration with cybersecurity Blockchain applications in real estate Edge computing security frameworks Consensus-based data aggregation Publications highlight advancements in: Cybersecurity for supply chains Fractional NFTs in property ownership Carbon credit price prediction models Secure microservices orchestration Key collaborations include researchers from UNSW and industry partners.

Aslihan AsilView profile

Assistant Professor

• Antitrust Law
• Corporate Finance
• Industrial Organization Economics
• +5 more

Aslihan Asil serves as Assistant Professor of Law at Duke University School of Law, joining the faculty in July 2025. She teaches corporate finance and antitrust law, integrating rigorous economic analysis with legal scholarship to address contemporary market challenges. Her academic foundation includes: PhD in Financial Economics, Yale University JD, Yale Law School Bachelor's degree in Economics, Princeton University Asli's research program bridges antitrust law, industrial organization economics, and corporate finance through empirical methodologies and economic modeling. She investigates how evolving market structures—including private equity consolidation and artificial intelligence—impact competition policy, with particular focus on updating legal doctrines to enhance consumer welfare in digital and concentrated markets. Her interdisciplinary approach combines quantitative analysis with practical legal reform proposals. Her publication trajectory reveals consistent engagement with competition law's adaptation to modern commerce. Key themes include liability frameworks for algorithmic collusion, regulatory gaps in private equity acquisitions, and empirical analysis of market power dynamics. This scholarship demonstrates sophisticated integration of economic theory with legal doctrine to address antitrust enforcement challenges in emerging commercial landscapes.

Pavol LiptákView profile

Researcher

• Category Management
• Distribution Management
• Retail Marketing
• +3 more

Pavol Lipták is a PhD. student at the Department of Marketing, Faculty of Commerce, University of Economics in Bratislava . His work focuses on Category Management , Distribution in Marketing , and AI applications in business . Education: Pursuing doctoral studies since 2023; Master's in Marketing and Trade Management (2022); Bachelor's in Business in Trade (2020). Research Interests: Category Management, AI-driven retail strategies, and post-crisis business adaptation. Grant Involvement: Co-researcher in projects on AI challenges in marketing and linguistics, and distribution management during crises. Conference Participation: Active presenter at ICISAS 2025, Innovations 2024, and ISCDS 2024; member of organizing teams for CEECBE 2024 and faculty anniversary events. Teaching & Outreach: Tutor for MiniErasmus; promoter at university fairs and roadshows. Key Publications: Analyzed AI profitability in category management, post-pandemic retail strategies, and multilingual pricing effects.

• Wireless Communications
• Internet of Things (IoT)
• Electric Vehicles
• +6 more

Nagham Saeed serves as Associate Professor in Electrical and Electronic Engineering at the School of Computing and Engineering, University of West London, a position she has held since April 2023. Previously, she was Senior Lecturer in Electrical Engineering at the same institution from November 2017 to April 2023, following roles as Electronic Lecturer at Uxbridge College (2012-2017) and Electronic/Control Lecturer at Brunel University (2007-2012). Her academic credentials include: Ph.D. in Optimization in Wireless Networks and Communications (Brunel University, 2007-2011) M.Sc. in Mechatronics (University of Technology, Baghdad, 1997-1999) B.Eng. in Computer and Control (University of Technology, Baghdad, 1988-1992) DTLLS Diploma in Teaching (University of Westminster, 2012-2014) BAPP in Learning and Teaching (Brunel University, 2009-2010) Assessing Competence Certification (Uxbridge College, 2014) Dr. Saeed's research spans wireless communications, IoT, electric vehicles, and AI-driven energy optimization. Her work focuses on Bluetooth Low Energy applications for healthcare monitoring, battery management systems for EVs, sustainable ICT practices, and GIS-based land use analysis. She pioneers solutions for energy-efficient radio access networks and develops AI models for vehicle classification and low-resource language processing. Analysis of her 2022-2025 publications reveals three dominant trends: (1) AI integration for sustainable energy systems in telecommunications and transportation, (2) blockchain-secured vehicular networks for 6G infrastructure, and (3) cross-disciplinary applications of IoT in healthcare and environmental monitoring. Her research consistently bridges electrical engineering with computer science to solve real-world sustainability challenges. Professional recognition includes: MDPI Reviewer Acknowledgements (2020) for Algorithms, Applied Sciences, and Information journals While her 2022 educational research on feedforward teaching approaches demonstrates pedagogical engagement, no student supervision or grant management details are documented. Current institutional affiliations show no dedicated laboratory facilities or research teams specified in available records.

• Optimisation
• Operational Research
• Applied Probability
• +4 more

Jamie Fairbrother is a Lecturer in Operational Research (Optimisation) at the Data Science Institute , Lancaster University , within the Department of Management Science . His research integrates optimisation, applied probability, and statistics to solve real-world problems in logistics and telecommunications, with a particular focus on stochastic programming and scenario generation. Education: Jamie Fairbrother completed his PhD research in scenario generation for stochastic programming, which laid the foundation for his subsequent work in optimisation under uncertainty. Research Interests: His expertise spans several domains: Optimisation & Operational Research: Development of mathematical models and algorithms for complex decision-making problems. Applied Probability & Statistics: Use of probabilistic methods to model uncertainty in planning and operations. Logistics & Telecommunications: Practical applications in mail centre staffing, airport slot scheduling, and wireless communication networks. Stochastic Programming: Scenario generation and robust optimisation techniques for strategic planning under uncertainty. Research Funding & Projects: Dr Fairbrother has led or contributed to multiple collaborative projects, including: Coordination of Strategic and Tactical Interventions for Reducing Air Traffic Delays (2023–2024) – a case study at Heathrow Airport. STOR-i: Optimising In-Store Price Reductions (2022–2025) – with PhD student Katie Howgate. Resource Allocation under Uncertain Demand in Royal Mail Mail Centres (2020–2023). Industrial Mathematics KTP with BT Research (2013) – modelling TV whitespace interference. PhD Supervision: He currently supervises Katie Howgate , a PhD student in Bayesian and Computational Statistics within the STOR-i Centre for Doctoral Training . Professional Affiliations: Dr Fairbrother is affiliated with the STOR-i Centre for Doctoral Training , the Centre for Transport & Logistics (CENTRAL) , and the OR-MASTER project on airport resource allocation.

Scot AdamsView profile

Professor

• Dynamical systems
• foliations
• ergodic theory
• +5 more

Scot Adams is a Professor in the School of Mathematics at the University of Minnesota, with research spanning pure mathematics and machine learning applications. His primary academic affiliation is with the Mathematics Department at the University of Minnesota, where he maintains an active research program while managing phased retirement paperwork. His research interests include dynamical systems, foliations, ergodic theory, hyperbolic groups, trees, Riemannian geometry, and more recently neural networks and thermodynamics. In his diary entries through August 2025, he documents ongoing work developing neural network slides, focusing on visual chain rule explanations, diagram of functions, and coordinate vector spaces. His recent publications reflect a transition toward machine learning applications while maintaining his foundational mathematics work. The 15 most recent articles show increasing focus on neural network theory (2025), with earlier works covering financial mathematics, climate policy presentations, and pure mathematics topics like Banach-Tarski paradox and central limit theorem. As an advisor, he works with graduate student Aarya Garimella on neural network research, meeting regularly to refine slides and explore mathematical foundations of neural networks. His teaching activities include courses in linear algebra and financial mathematics. His diary reveals active engagement with climate policy through Citizens' Climate Lobby (CCL), including Capitol Hill lobbying efforts and local climate forum organization. He maintains detailed records of academic and civic activities while managing household responsibilities.

• Process Systems Engineering
• Production Planning and Scheduling
• Waste Supply Chain Management
• +3 more

Professor Iiro Harjunkoski is affiliated with Aalto University, School of Chemical Engineering, Department of Chemical Engineering and Metallurgy. His research focuses on advanced optimization techniques in Process Systems Engineering. The top fields of interest for Professor Harjunkoski include: Process Systems Engineering Production Planning and Scheduling Waste Supply Chain Management Mixed Integer Linear and Non-Linear Programming Hybrid Algorithms with Data Analytics Process and Operational Optimization His recent work emphasizes: Energy systems optimization under uncertainty Bike-sharing logistics modeling Machine learning integration in chemical production scheduling Stochastic programming for energy storage operations Surrogate-based optimization techniques Hybrid algorithm development Scientific awards and recognitions: Computers and Chemical Engineering Best Paper Award (2014) Award or recognition for output from Department of Chemical Engineering and Metallurgy (Nov 2015)

• Game Theory
• Machine Learning in Marketing
• Digital Advertising
• +4 more

Dr. Prabir Chatterjee is an Assistant Professor of Marketing & Strategy at Cardiff Business School , currently serving as Programme Director for the MSc in Business Management (MBM). He previously held Assistant Professor positions at Sabanci University, Istanbul (2013-2018), and has been at Cardiff University since 2019. Education: PhD in Business Administration (Quantitative Marketing, Economics/Econometrics minors), University of Washington (2013) MSBA, University of Washington (2012) MA in Economics, University of Washington (2007) MA in Economics (World Economy specialization), Jawaharlal Nehru University (2003) B.Sc. (Honors) in Economics, University of Calcutta (2001) Research Focus: Specializes in applying Game Theory and Machine Learning to Digital Advertising , Product Design , and Pricing Strategies . Recent work explores Corporate Social Responsibility and Public Value creation through mathematical modeling and behavioral experiments. Publication trends show expertise in Platform Competition , Algorithmic Advertising , and Behavioral Economics applications. Notable contributions include studies on sponsored content dynamics in two-sided markets and CSR profitability frameworks. Honors & Grants: Cardiff Business School Seedcorn Grant (2019-2020) Erasmus Exchange Fellowships (2014-2020) Learning & Teaching Innovation Grant (2020) Evert McCabe Fellowship (2012) Supervision: Provides PhD mentorship in AI/ML applications in marketing, digital advertising research, pricing strategies, and product development, with a focus on analytical modeling and behavioral experiments. Current supervisee: Andoni Fornio Barusman (E-commerce Shopping Journey). Former: Kerry Hudson (Performance Heterogeneity under Uncertainty).

• Pricing
• Economics of Digitalization
• Artificial Intelligence
• +2 more

Diego Aparicio is an Adjunct Professor at IESE Business School, where he teaches Pricing and Marketing Management courses in MBA, Master in Management, and Executive Education programs. He holds a Ph.D. in Economics from the Massachusetts Institute of Technology (MIT) and integrates his prior industry experience in finance and technology sectors into an applied analytical approach for academic research and instruction. His research focuses on Pricing Strategy Economics of Digitalization Artificial Intelligence in Retail Laboratory Experimentation Industrial Organization with recent work examining digital platform monetization, consumer behavior in online environments, and algorithmic pricing models. His research has been presented at institutions including the National Bureau of Economic Research (NBER), European Central Bank, and Federal Trade Commission (FTC), gaining media attention from The Economist and Bloomberg. Key publication trends span Retail Analytics AI-Driven Pricing Consumer Decision-Making Long Tail Economics Digital Transformation Market Dynamics while case studies like ECOALF: Fashion for the Future and Everlane: Price and Cost Transparency demonstrate practical applications of his research in sustainable business practices and luxury brand development.

Shahin GelarehView profile

Associate Professor

• Operations Research
• Logistics and Supply Chain Management
• Transportation Planning
• +9 more

Shahin Gelareh is an Associate Professor in Operations Research and Logistics at the Université d'Artois, France, affiliated with the Department of Networks and Telecommunications at the IUT de Béthune. He holds a PhD in Optimization from the Technical University of Kaiserslautern (2008), an MSc in Applied Mathematics from the University of Sistan and Baluchestan (2005), and a BSc from Ferdowsi University of Mashhad (2003). His research focuses on Locational Analysis , Integer Programming , Metaheuristics , Global Network Design , and applications in Maritime Transport , Supply Chain , and Telecommunications . Notable areas include hub-and-spoke network optimization, liner shipping fleet deployment, and competitive analysis using game theory. Recent publications emphasize innovations in Neural Benders Decomposition for optimization, cross-docking systems , and hub location routing . His work spans theoretical advancements and industry collaborations in transportation and telecom sectors. Professional activities include memberships in leading OR societies (EWGLA, EWGT, VeRoLog) and roles as an ad-hoc reviewer for high-impact journals like Transportation Research Part E and European Journal of Operational Research .

Philipp RennerView profile

Senior Lecturer

• Computational Economics
• Game Theory
• Dynamic Incentive Problems
• +3 more

Dr. Philipp Renner is a Senior Lecturer in the Department of Economics at Lancaster University Management School (LUMS). His research focuses on computational economics and game theory, particularly addressing dynamic incentive problems in taxation, managerial remuneration, insurance, and policy design. These problems involve complex mathematical challenges such as bilevel optimization and dynamic programming. He is actively involved in interdisciplinary research, emphasizing computational challenges in solving high-dimensional dynamic programs and dynamic game equilibria. His work bridges theoretical economics with practical algorithmic solutions. Renner participates in notable research groups including the Centre for Financial Econometrics, Asset Markets and Macroeconomic Policy, and the Economics Research Group. Key research themes include monetary policy analysis, optimal fiscal frameworks, and the application of machine learning to incentive problems. His recent contributions include studies on persistent supply shocks in monetary policy and polynomial optimization approaches to principal-agent models. Renner’s academic activities span conference presentations on topics like dynamic incentive mechanisms and computational economics, reflecting his commitment to advancing quantitative methods in economic analysis.