جست‌وجوی استاد و پژوهشگر

Alex Shestopaloffمشاهده پروفایل

مدرس

• Markov Chain Monte Carlo (MCMC) Methods
• High-Dimensional Time Series Analysis
• Network Science
• +۴ مورد دیگر

Alex Shestopaloff is a Lecturer in Statistics at Queen Mary University of London (QMUL), affiliated with the School of Mathematical Sciences. Previously, he was a Research Fellow at the Alan Turing Institute (2017–2020) and a Junior Research Fellow at Campion Hall, Oxford. He holds a PhD in Statistics from the University of Toronto (2016), supervised by Radford M. Neal. His research focuses on developing efficient MCMC methods, high-dimensional time series analysis, network science, and applications in financial market microstructure. Education: PhD in Statistics, University of Toronto (2016) Supervisor: Radford M. Neal Research Interests: Bayesian online learning in non-stationary environments Limit order book modeling and trading strategies Graph clustering and network analysis Statistical methods for high-dimensional data Algorithmic trading and cryptocurrency markets His recent work spans financial engineering, machine learning, and statistical methodologies. Notable contributions include cluster-based trading strategies (ClusterLOB), generalized Bayesian filtering frameworks, and scalable graph analysis techniques. Collaborations with industry partners (e.g., Wise Plc) highlight applied research in financial systems. Advising & Alumni: Current advisees include Yichi Zhang (Oxford), Maria Fernanda Pintado (QMUL), and Dave Lui (Oxford) Alumni: Gerardo Duran-Martin (Postdoc at Oxford-Man Institute), Claudio Bellani (Citadel Securities) Labs/Teams: Leads interdisciplinary projects at QMUL and collaborates with the Alan Turing Institute on financial and network science initiatives.

Georgios Sermpinisمشاهده پروفایل

استاد

• Machine Learning
• Financial Trading
• Forecasting
• +۳ مورد دیگر

Professor Georgios Sermpinis is a faculty member in Accounting & Finance at the Adam Smith Business School, University of Glasgow. He joined the institution in 2011, bringing expertise in machine learning, financial trading, forecasting, econometrics, and financial risk management. Degrees from National Kapodistrian University of Athens and Liverpool John Moores University Former roles at University of Bedfordshire and Liverpool John Moores University Consultancy for major banks including Goldman Sachs and BNP Paribas Research Interests: Focus on machine learning applications in finance, including portfolio optimization, cryptocurrency markets, and risk modeling. His work integrates AI with traditional financial theories, emphasizing sustainability and behavioral aspects. Scientific Awards: Acanto Research Grant (2017) Santander & University of Oviedo Grant (2017) Mobility Grant, Universidad de La Laguna (2017) University of Oviedo Grant (2014) Early Career Research Grant, University of Bedfordshire (2010) Students: Supervised PhD candidates include Feng Xin and Sun Longguang, focusing on ESG investment and financial markets. Editorial Roles: Senior Editor of Decision Support Systems, Associate Editor of Information Systems and Operational Research, and Guest Editor for journals like Quantitative Finance.

Emanuel Sallingerمشاهده پروفایل

استاد

• Information Systems Engineering
• Computational Logic
• Scalable Artificial Intelligence
• +۸ مورد دیگر

Emanuel Sallinger is a Full Professor at TU Wien's Databases and Artificial Intelligence Group and Vice Dean of Academic Affairs for Business Informatics and Data Science. He leads the Knowledge Graph Lab, focusing on scalable knowledge-based systems, reasoning in knowledge graphs, and AI integration. His research spans computational logic, database theory, and blockchain applications. Education: PhD in Computer Science (awarded 'sub auspiciis praesidentis rei publicae'), Master's degrees in Computational Intelligence and Informatics Management, and a Bachelor's in Software and Information Engineering. Research Interests: Knowledge graphs (construction, reasoning, scalability), logic-based systems, AI/ML integration with databases, enterprise architecture modeling, and financial knowledge systems. His work emphasizes practical applications like enterprise modeling, sustainable waste management, and regulatory compliance. Grants & Projects: Lead Vienna Science and Technology Fund (WWTF)-funded Knowledge Graph Lab. Involved in projects like 'Knowledge Graph-driven Tour Management' (sustainability), 'SustainGraph' (waste processing), and 'Enterprise Architecture Knowledge Graphs'. Teaching: Offers courses on Knowledge Graphs, Generative AI, Database Systems, and research methodology. Supervises doctoral and master's students in AI, databases, and knowledge representation. Labs/Teams: Knowledge Graph Lab at TU Wien, collaborating with industry on blockchain-based systems, financial AI, and enterprise architecture frameworks.

• Taxation
• Data Analytics
• Financial Reporting
• +۱ مورد دیگر

Christine Cheng serves as Assistant Professor of Accountancy at the University of Mississippi's Patterson School of Accountancy, specializing in Tax and Data Analytics. She previously held a visiting scholar position at the Securities and Exchange Commission Division of Economic and Risk Analysis (2020-2022) and currently contributes to the Financial Accounting Standards Board Taxonomy Advisory Group. Her academic credentials include: Ph.D. in Business Administration from Pennsylvania State University (2011) M.B.A. in Business Administration from Pennsylvania State University Harrisburg (2003) Dr. Cheng's research examines machine-readable financial reporting determinants, tax-influenced decision making, and the intersection of tax analytics with corporate strategy. Her work bridges theoretical accounting frameworks with practical data science applications, particularly in post-Wayfair e-commerce taxation and marriage tax policy analysis. She employs advanced tools like Alteryx and robotic process automation to model complex tax scenarios. Publication trends reveal a strategic shift toward data-driven tax education and regulatory compliance, with 60% of recent work integrating analytics into financial reporting. Her articles frequently address real-world policy impacts, such as same-sex marriage tax implications and hail damage fraud detection, demonstrating applied relevance to both academic and practitioner audiences. Major recognitions include: 2023 Public Interest Section Best Paper Award (American Taxation Association) 2023 Graduate Teacher Award (American Accounting Association) Three ATA/Deloitte Teaching Innovation Awards (2019-2022) 2019 Best Article Award from The Tax Adviser As an educator, she pioneered Ole Miss's Master's of Taxation and Data Analytics program and maintains a YouTube channel with 200+ instructional videos. Her advising includes master's student Taylor, J. (lead author on a 2015 publication), and she has secured multiple curriculum development grants through Deloitte partnerships. Current projects focus on SEC disclosure analytics and blockchain-based tax compliance systems.

Ooi Beng Chinمشاهده پروفایل

استاد

• Database Systems
• Distributed Systems
• Blockchain
• +۶ مورد دیگر

Ooi Beng Chin is a Professor at the School of Computing , National University of Singapore (NUS). He holds concurrent roles as an adjunct Chang Jiang Professor at Zhejiang University, Visiting Distinguished Professor at Tsinghua University, and Director of NUS AI Innovation and Commercialization Centre in Suzhou, China. He earned his B.Sc. (1st Class Honours, 1985) and Ph.D. (1989) from Monash University, Australia. His research spans database systems, blockchain, machine learning, and large-scale analytics , focusing on system architectures, security, and cross-domain applications. Notable contributions include initiating the Apache SINGA distributed deep learning platform and developing Blockbench, the first blockchain benchmarking system. He also co-founded MZH Technologies (2018) for healthcare analytics. Key publications highlight his work in blockchain-database integration, AI for healthcare/finance, and 5G-enabled data systems. Awards include the ACM SIGMOD EF Codd Innovation Award (2020), Singapore President's Science Award (2011), and fellowships from SNAS, IEEE, ACM , and SAEng (2023). He leads the Singapore Blockchain Innovation Programme (SBIP) and contributes to industry collaborations with healthcare institutions and financial organizations. Fellow, Singapore National Academy of Science (SNAS) Fellow, IEEE Fellow, ACM Singapore President's Science Award, 2011 IEEE Kanai Award, 2012 NUS Outstanding Researcher Award, 2013 ACM SIGMOD EF Codd Innovation Award, 2020 Foreign Member, Chinese Academy of Sciences, 2023

• Cybersecurity
• Data Security and Privacy
• GRC (Governance, Risk, Compliance)
• +۶ مورد دیگر

Dr. Gowri Sankar Ramachandran is a Senior Lecturer in the School of Information Systems at Queensland University of Technology (QUT), specializing in cybersecurity and distributed systems. She holds a PhD from KU Leuven (Belgium) and a postdoctoral position at the University of Southern California (USC). Her research focuses on open-source software security, runtime threat detection, blockchain applications, and IoT vulnerabilities. Notable contributions include the FUSE tool for detecting malicious packages and the discovery of hyperlink hijacking vulnerabilities affecting millions of domains. Research interests span software supply chain security, metadata-based risk analysis, and generative AI for cyber risk modeling. Awards include Best Paper Awards at ACM CBSE (2016), Mobiquitous (2017), and BigMM (2019). Collaborations include projects with CSIRO, the City of Los Angeles, and the University of São Paulo. She teaches courses on cybersecurity, database management, and network security, and actively supervises PhD students in cybersecurity and blockchain domains. Recent publications address blockchain-based data governance, quantum-resilient IoT protocols, and decentralized identity systems. Her work bridges academic research with real-world impact, addressing critical challenges in digital systems security and privacy.

Ying Zhuمشاهده پروفایل

دانشیار

• Digital Marketing
• Consumer Behavior
• Marketing Analytics
• +۵ مورد دیگر

Dr. Ying Zhu is an Associate Professor in the Department of Marketing and Consumer Studies at the Lang School of Business and Economics, University of Guelph. She holds a Ph.D. in Marketing from Texas A&M University, alongside master’s degrees in Computer Science (Minzu University, China) and Management (University of Lethbridge, Canada). Previously, she served as an Associate Professor at UBC Okanagan. Her research focuses on digital marketing, consumer behavior, marketing analytics, and the impact of technology (e.g., AI, Metaverse) on consumer decisions. She has published 19 peer-reviewed articles since 2017, many in top-tier journals like American Psychologist and European Journal of Marketing . Her work explores themes such as brand extension strategies, digital advertising effectiveness, and sustainability initiatives. Dr. Zhu has received significant funding through SSHRC grants and has been recognized for her teaching excellence, including the Golden Apple Award. She actively advises graduate students and volunteers with community organizations like the Kelowna Museums Society. Her research on smartphone-induced consumer behavior has garnered media attention in outlets like Time Magazine , Global News , and Science Daily .

Emre Seferمشاهده پروفایل

دانشیار

• Bioinformatics
• Machine Learning
• Financial Networks
• +۳ مورد دیگر

Dr. Emre Sefer is an Associate Professor at the Faculty of Engineering, Özyeğin University, specializing in machine learning and bioinformatics. He holds a Ph.D. in Computational Biology from Carnegie Mellon University (2015), an M.S. in Computer Science from University of Maryland College Park (2011), and a B.S. in Computer Engineering from Boğaziçi University (2008). His research bridges graph machine learning with financial networks, bioinformatics, and data engineering. Ph.D.: Computational Biology, Carnegie Mellon University M.S.: Computer Science, University of Maryland College Park B.S.: Computer Engineering, Boğaziçi University Research focuses on applying machine learning to financial and biological networks: Bioinformatics : 3D genome modeling, protein modifications, transcriptomic analysis Graph Machine Learning : GNNs for fraud detection, drug response prediction, and network evolution Financial Networks : Cryptocurrency investment strategies, asset price prediction His lab (OzU Machine Learning in Finance and Bioinformatics Lab) develops graph-based deep learning methods for cross-domain applications, including NFT market analysis and chromatin structure prediction. He received the Best research paper award at Recomb 2016 for work on 3D genome architecture. Former postdoc at CMU Machine Learning Department Industry experience as Quantitative Strategist at Goldman Sachs and JPMorgan

Mawuli Kouami Segnonمشاهده پروفایل

پژوهشگر

• Econometrics
• Time Series Analysis
• Financial Forecasting
• +۵ مورد دیگر

Dr. Mawuli Kouami Segnon is a researcher at the Chair of Empirical Economics, School of Business and Economics, University of Münster. His work focuses on econometric modeling, financial time series analysis, and volatility forecasting across various domains including cryptocurrencies, energy markets, and macroeconomic indicators. Research interests include: Development of advanced volatility models (GARCH, multifractal, regime-switching) Applications to financial markets, energy economics, and macroeconomic policy High-frequency data analysis and mixed-frequency forecasting Count data modeling with conditional heteroscedasticity Portfolio risk management using copula and multifractal approaches Recent publications demonstrate expertise in: Geopolitical risk impacts on stock volatility Comparative analysis of realized variance measures Inflation uncertainty modeling in G7 countries Electricity price volatility in Australian markets Bitcoin market forecasting Historical economic data analysis Current projects (since 2020) involve: Innovative economic/financial time series forecasting Financial market volatility modeling Applications of multifractal structures in econometrics

Yang Chengمشاهده پروفایل

دانشیار

• Supply Chain Management
• Manufacturing Strategy
• Manufacturing Network
• +۵ مورد دیگر

Yang Cheng is an Associate Professor at the Department of Materials and Production, Aalborg University, Denmark. He holds a PhD in Mechanical Engineering from the same institution (2011), focusing on manufacturing strategy and network dynamics. His research spans supply chain management, sustainability, and global operations, with a focus on integrating technology and environmental policies into manufacturing systems. He leads or participates in high-impact projects like MAASive (2024–2026) and the Sino-Danish Center Research Project (2011–present), addressing resilience in value networks and global operations innovation. Research Interests: Supply Chain Management & Integration Sustainability & Green Technologies Manufacturing Strategy & Networks Technology Policy & Digitalization Global Operations & Cross-Border Collaboration Recent Work Trends: Prof. Cheng's 2025 articles emphasize blockchain in sustainable supply chains, green technology investments under carbon policies, and digitalization's ethical implications. His 2024 research explores smart factories, EU battery regulations, and robotization in manufacturing. These studies blend quantitative models with case-based analysis to address real-world challenges. Awards: 2024 Emerald Literati Awards – Outstanding Reviewer Advising & Grants: As PI for multiple Global Operations Management PhD programs (2019–2025), he guides research on digital transformation and university-industry collaboration. His projects receive funding from Danish and international grants, focusing on innovation and resilience in manufacturing networks. Labs/Teams: Collaborates with the Center for Industrial Production at Aalborg University and engages in international partnerships through the Sino-Danish Center. Active editorial roles include Production Planning & Control and Journal of Manufacturing Technology Management .

Naren Ramakrishnanمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Urban Analytics
• +۲ مورد دیگر

Naren Ramakrishnan is the Thomas L. Phillips Professor of Engineering in the Department of Computer Science at Virginia Tech, where he directs the Sanghani Center for AI and Data Analytics. He also serves as AI and Machine Learning Lead for the Virginia Tech Innovation Campus. His research spans data science, machine learning, urban analytics, forecasting, and computational epidemiology. Recent publications (2024-2025) focus on language model optimization, AI applications in government and environmental conservation, and spatiotemporal data analysis. Work demonstrates strong emphasis on real-world AI deployments in regulatory compliance, supply chain verification, and network optimization. Methodological innovations include prompt engineering techniques, world models for reinforcement learning, and specialized embedding methods. Dr. Ramakrishnan has received prestigious fellowships from ACM, AAAS, and IEEE. His research has been supported by numerous agencies including DARPA, NSF, NIH, and industry partners like Amazon and Boeing, with 36 PhD students mentored to completion.

Tarek Alskaifمشاهده پروفایل

دانشیار

• Energy Informatics
• Smart Grids
• Artificial Intelligence
• +۹ مورد دیگر

Dr. Tarek Alskaif is an Associate Professor of Energy Informatics at Wageningen University & Research, specializing in the intersection of information technology and energy systems. He leads research on smart energy systems, focusing on electricity markets, distributed energy resources, and AI-driven solutions. His work integrates modeling, optimization, and big data analytics to advance the sustainable energy transition. Education: PhD in Energy Informatics (2012–2016, Cum Laude) from Universitat Politècnica de Catalunya, Spain. Postdoc at Utrecht University’s Copernicus Institute (2016–2020). Current roles include coordinating the BSc Data Science Minor and teaching Python and Big Data courses. Research interests emphasize leveraging digitalization for energy systems, including smart grids, electric mobility, and battery storage. Notable projects include HighLO Energy Markets (EU-funded, using particle physics and AI for market transparency) and MESSM (coordinated via TKI Urban Energy). He also leads the AI ELSA Lab (NWO-funded). Editorial roles include Associate Editor for IEEE Transactions on Smart Grid and IEEE Power Engineering Letters . Member of IEEE, the Netherlands Institute for Research on ICT (4TU.NIRICT), and the Technical Program Committee for IEEE SmartGridComm and PSCC 2026. Has supervised over 50 students (MSc/BSc) and 7 PhDs. Projects address challenges like grid congestion, EV charging optimization, and decentralized energy trading. His work bridges academic research with industry collaborations, including partnerships with CERN and ACER.

Fotios Petropoulosمشاهده پروفایل

استاد

• Time Series Forecasting
• Judgmental Forecasting
• Statistical Model Selection
• +۳ مورد دیگر

Fotios Petropoulos is a Professor at the University of Bath, holding the Management Chair in Management Science within the School of Management's Information, Decisions & Operations department. He also served as the Spyros Makridakis Chair in Forecasting at the University of Nicosia (2023–2023). His research focuses on time series forecasting, judgmental approaches, and integrating statistical and human judgment in decision-making processes. He has contributed to improving forecasting accuracy through temporal aggregation and hierarchical methods. Petropoulos holds a Doctor of Engineering (2012) and Bachelor of Engineering (2007) from the National Technical University of Athens. Editor of the International Journal of Forecasting (2020–present) Associate Editor of Foresight: The International Journal of Applied Forecasting (2015–2022) Director of the International Institute of Forecasters (2016–2018) His research interests emphasize forecasting processes, model selection, and the role of judgment in statistical models. Key areas include temporal aggregation, forecast reconciliation, and behavioral operations analytics. He has published over 100 peer-reviewed articles, focusing on topics like computational cost optimization, probabilistic forecasting, and scalable reconciliation methods. His work contributes to Sustainable Development Goals related to education and innovation. Recent articles highlight advancements in univariate forecasting efficiency, forecast selection criteria, and dynamic reconciliation. Petropoulos is a member of the Smart Warehousing and Logistics Systems group and actively participates in editorial boards of leading forecasting journals. His academic and professional roles bridge theoretical research and practical applications in operational decision-making.

Raja Jurdakمشاهده پروفایل

استاد

• Distributed Systems
• Blockchain Technology
• Network Modeling
• +۷ مورد دیگر

Professor Raja Jurdak is a leading academic in distributed systems and applied data sciences at Queensland University of Technology (QUT), where he directs the Trusted Networks Lab. He holds dual roles as Professor of Distributed Systems and Chair in Applied Data Sciences, alongside leadership in the Centre for Data Science. His research focuses on dynamic network modeling, blockchain-based trust frameworks, and IoT applications, with particular emphasis on cybersecurity, energy efficiency, and mobility-driven diffusion processes. Jurdak formerly led CSIRO's Distributed Sensing Systems Group and maintains a visiting scientist role there. Education: PhD in Information and Computer Science, University of California, Irvine MS in Computer Networks and Distributed Computing, University of California, Irvine BE in Computer and Communications Engineering, American University of Beirut Research Interests: Network science, blockchain technology, IoT security, sustainable energy systems, and data-driven decision-making. His work bridges theoretical advancements with practical applications in smart grids, health surveillance, and urban mobility. Awards: Finalist for the 2019 Eureka Prize, multiple CSIRO accolades, and IEEE Senior Member status. His research has received industry recognition for interdisciplinary innovation, including the DiNeMo project's real-time disease surveillance system. Advisory & Grants: Leads high-impact projects funded by government and industry partnerships. Supervises PhD candidates in areas like decentralized data processing and privacy-preserving AI. Holds editorial roles at journals such as Ad Hoc Networks and PLoS ONE . Labs & Teams: Directs the Trusted Networks Lab at QUT, fostering collaborations with institutions like Oxford University and MIT. His work emphasizes cross-disciplinary teams to address global challenges in cybersecurity and sustainable systems.

• Open Innovation
• Technology Transfer
• Food Industry Innovation
• +۴ مورد دیگر

Barbara Bigliardi is an Associate Professor at the Department of Engineering and Architecture, University of Parma, with national scientific qualification for full professor in Economic-Management Engineering (SSD ING-IND/35). She serves as President of the Management Engineering Program at University of Parma and Director of Bachelor's/Master's programs at University of San Marino, co-leading double-degree initiatives between the two institutions. Over 100 publications (57 SCOPUS-indexed) with H-index=20 Editorial roles: Cambridge Scholars Publishing (2019), MDPI Sustainability, Sci, European Journal of Innovation Management Key research areas: Open Innovation, Technology Transfer, Food Industry Innovation, Industry 4.0, Supply Chain Sustainability Recent Publications (2024-2025) demonstrate leadership in: Industry 4.0 integration with circular economy AI applications in public administration and healthcare Digitalization of food supply chains Green startup resource orchestration Simulation-based optimization in remanufacturing Sustainable additive manufacturing Scientific Recognition : 2005 Emerald Highly Commended Award 2013 Most Cited Paper in Trends in Food Science & Technology 2019 Highly Cited Paper in Review of Policy Research Research Leadership includes: National Observatory on Start-ups (President since 2021) National Observatory on Reputation (Vice President since 2019) INAIL-funded mobile risk assessment systems (2018-2020) INAF space technology transfer projects (2018-present) Academic Contributions : Supervised over 300 theses Deputy Coordinator of Industrial Engineering Doctoral Program Director of Management Engineering Programs (Parma & San Marino) Founder of academic spin-offs: Sistemi per il marketing di contenuto S.r.l. (2016-present), Univenture SrL (2006-2010)