جست‌وجوی استاد و پژوهشگر

Amedeo Piolattoمشاهده پروفایل

دانشیار

• Industrial Organisation
• Microeconomic Theory
• Political Economy
• +۱۲ مورد دیگر

Amedeo Piolatto is an Associate Professor (with tenure) in the Department of Economics and Economic History at the Autonomous University of Barcelona (UAB), where he also serves as Co-director of Graduate Studies (IDEA) and Chair of the Hiring Committee. He is an Affiliated Professor at the Barcelona School of Economics (BSE) and a Research Affiliate at the Barcelona Institute of Economics (IEB), maintaining a strong presence in the European economics research community. Research Interests: Industrial Organisation Microeconomic Theory Political Economy Public Economics Federalism and Fiscal Federalism Media, Information, and Piracy Taxation and Tax Evasion Political Centralization and Accountability Education Economics His research employs applied theoretical models to study interactions between firms, governments, and citizens. The 15 most recent publications reflect a consistent focus on institutional design, regulatory policy, and strategic behavior in political and market environments. Key themes include the economics of digital platforms, political business cycles in federations, partisan bias in disaster relief, and the role of information in media and electoral systems. His work frequently appears in top journals such as the Quarterly Journal of Economics , Journal of Public Economics , and Regional Science and Urban Economics . Scientific Awards and Recognition: Ramón y Cajal Fellowship (2018–2023) BBVA Foundation Grant for Researchers and Cultural Creators (2016–2018) Advising and Grants: Piolatto has advised PhD students, including Francisco de Lima Cavalcanti. He has secured significant competitive funding as Principal Investigator, including grants from the Spanish Government (Generación de conocimiento, Consolidación investigadora), the Ramón y Cajal program, and BSE Seeds. He has also been a team member on multiple national and regional research projects. He actively organizes academic events, serves on editorial boards (including as Co-editor of Economics ), and participates in PhD juries and institutional governance at UAB, BSE, and other universities. Labs and Teams: He is affiliated with the Barcelona Institute of Economics (IEB) and the Barcelona School of Economics (BSE), where he contributes to research workshops and collaborative initiatives in public economics and political economy.

Gerald Englmairمشاهده پروفایل

دانشیار

• Thermal Energy Storage
• Phase Change Materials
• Flexible Heating and Cooling Systems
• +۱۲ مورد دیگر

Gerald Englmair is an Associate Professor in the Section of Energy and Services within the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU). He leads research and teaching initiatives in thermal energy storage, flexible heating and cooling systems, and solar energy integration at DTU campuses in Lyngby and Sisimiut. He actively contributes to international energy research collaborations, including EU-funded projects and IEA tasks. His research focuses on the experimental development, testing, and demonstration of thermal energy storage technologies, particularly those based on phase change materials (PCM) and supercooled sodium acetate trihydrate. His work enables compact, decentralized, and efficient energy storage solutions that support energy flexibility and sustainability. The recent publications highlight a strong trend in advancing the stability, performance, and real-world integration of thermal storage systems. Key areas include discharging dynamics, material degradation, and application in buildings and data centers. His research bridges engineering science with practical implementation in energy systems. Kalundborg Refinery Prize (2024) PhD Paper of the Year 2018 Innovation Award by FH Upper Austria (2015) Gerald Englmair has led multiple research projects, including EU-CETP “La-Flex”, EU "TREASURE", and IEA SHC Task 67/ES Task 40, where he serves as subtask lead and national coordinator for Denmark. He is also vice chair of the thermal energy storage working group at DaCES. His advisory roles include consultancy for public sector and international projects like STES4D in Spain. He mentors students and collaborates widely across Europe and China. He is actively involved in research networks, public outreach, and educational initiatives, including guest lectures, media appearances, and scientific advisory roles. His laboratory work focuses on experimental validation of thermal storage units and their integration into real-world energy systems.

Ruben Taelmanمشاهده پروفایل

پژوهشگر

• Decentralization
• Linked Data Publishing
• Web Querying
• +۳ مورد دیگر

Ruben Taelman is a postdoctoral researcher at IDLab , Ghent University – imec, specializing in decentralization, Linked Data publishing, and querying. He develops open-source JavaScript libraries like the Comunica engine for decentralized data access and focuses on intelligent infrastructure for data publication and retrieval. His research bridges academic and industrial perspectives, addressing challenges in decentralized knowledge graphs, policy negotiation, and data governance. Key projects include Triple Storage for versioned RDF querying and contributions to the Solid ecosystem for user-centric data control.

Jie Dingمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Statistics
• Scientific Computing
• +۶ مورد دیگر

Jie Ding is an Associate Professor at the University of Minnesota's School of Statistics with graduate faculty appointments in Electrical Engineering, Computer Science, and the Data Science Program. He serves as a core faculty member of the Data Science and AI Hub and holds an Amazon Scholar position with the Amazon AGI Team focusing on foundation model training. His educational background includes a Ph.D. in Engineering Sciences from Harvard University (2017), postdoctoral work at Duke University (2018), and a B.S. from Tsinghua University where he participated in both the Math & Physics Academic Talent Program and Electrical Engineering program. Ding's research sits at the intersection of artificial intelligence, statistics, and scientific computing, with focus areas including Agentic AI for autonomous data science workflows, AI Foundations for interpretability and trustworthiness, Scalable Modeling for broader AI accessibility, Decentralized and Collaborative AI systems, and AI Safety addressing privacy and security concerns. He developed the STAT 8931 Generative AI course with open-source materials available at genai-course.jding.org . His recent publications demonstrate strong activity across multiple AI subfields, particularly in value alignment (MAP framework), AI safety mechanisms, federated learning innovations, and statistical foundations for modern AI systems. The breadth of venues (ICML, ICLR, NeurIPS) indicates significant impact across the AI research community. NSF CAREER Award (2024) Army Early Career Program (Young Investigator) Award (2023) Cisco Research Award (2022-25) AWS Cloud Credits for Research (2021-22) Meta/Facebook Faculty Research Award (2021-22) UMN Thank-A-Teacher Teaching Award (2019-20) Ding leads the Agentic AI for Data Science Benchmark initiative, collaborating with University of Minnesota colleagues and Minnesota industry partners to evaluate AI agent capabilities across healthcare, insurance, retail, energy and other sectors. His research group actively recruits PhD students interested in AI/Statistics intersections, with focus on developing theoretically grounded yet practically impactful AI systems.

Jonathan Yu-Meng Liمشاهده پروفایل

دانشیار

• Business Analytics
• Financial Engineering
• Machine Learning
• +۱۲ مورد دیگر

Jonathan Yu-Meng Li is an Associate Professor and holder of the RBC Financial Group Professorship in Financial Risk Analytics at the Telfer School of Management , University of Ottawa. He serves as Principal Coordinator of the Center for Responsible Wealth Transition and leads the Risk Intelligence and Resilient Solutions cluster. B.Sc. in Electrical Engineering from National Sun Yat-Sen University M.A.Sc. in Electrical and Computer Engineering from McMaster University Ph.D. in Operations Research (Financial Engineering specialization) from the University of Toronto Specializing in quantitative methodologies , his research focuses on: Optimization and machine learning tools for data-driven decisions Financial risk analytics (portfolio management, asset pricing, fraud detection) Operations management (inventory control, revenue management) Healthcare analytics (clinical risk assessment, patient triage) Climate risk modeling and technology disruption management His recent publications demonstrate an evolving research trajectory : 2024: Distributional robustness under distorted expectations 2023: Deep reinforcement learning for financial derivatives 2022: Equal risk pricing with convex measures 2018-2021: Inverse optimization and robust modeling Key scientific awards include: Emerging Researcher Award at Telfer First place in CORS Paper Competition Finalist in INFORMS Financial Services Section Major funded research projects: 2023-2029: NSERC-funded framework for robust machine learning 2023-2026: Telfer School grant on decentralized finance security 2022-2024: SSHRC-funded research on DeFi criminal activity detection 2014-2019: NSERC grant on risk measure modeling

• Interdependent infrastructure network resilience
• Supply chain performance
• Decentralized and distributed control and optimization models
• +۲ مورد دیگر

Andrés D. González serves as an Associate Professor in the Department of Industrial & Systems Engineering at the University of Oklahoma, applying interdisciplinary analytical frameworks to complex system dynamics across engineering and social domains. His educational foundation includes: Ph.D. in Civil Engineering from Rice University Ph.D. in Engineering from Universidad de los Andes, Colombia M.Eng. in Industrial Engineering from Universidad de los Andes, Colombia B.Sc. in Physics from Universidad de los Andes, Colombia Six Sigma Black-Belt certification from Arizona State University Research focuses on developing methodologies from systems dynamics, statistical physics, and operations research to model social-physical systems. Core contributions span infrastructure network resilience, supply chain optimization, and distributed control systems, with significant impact in Cyber-Physical-Social Systems and Aerospace/Defense engineering through novel analytical frameworks for interdependent critical networks. Professional recognition includes industry certification in process optimization, though formal scientific awards are not documented in this profile. Mentorship details and grant activities remain unspecified in current materials, with no laboratory or research team affiliations explicitly referenced.

Marwa Keshkمشاهده پروفایل

مدرس

• Cyber Security
• Artificial Intelligence
• Privacy Preservation
• +۵ مورد دیگر

Dr. Marwa Keshk is a Lecturer in Cyber Security at the School of Professional Studies, University of New South Wales (UNSW) Canberra. Her academic career focuses on the intersection of cybersecurity and artificial intelligence, with particular expertise in privacy preservation, anomaly detection, and threat intelligence. Her educational background includes: PhD in Cyber Security and Privacy Preservation (2021) from UNSW Master's degree in Evolutionary Computation (2017) from UNSW Bachelor's degree in Computer Science (2012) from the Faculty of Computers and Information at Helwan University, Egypt Dr. Keshk's research interests primarily center around the application of artificial intelligence and machine learning techniques to enhance cybersecurity measures. Her work explores how computational intelligence, statistical methods, and emerging technologies can be leveraged for privacy preservation, anomaly detection, and threat intelligence. She has made significant contributions to the security of Internet of Things (IoT) networks, cyber-physical systems, and critical infrastructure protection. Her research often combines theoretical advancements with practical applications, addressing real-world security challenges in increasingly connected digital environments. Analysis of Dr. Keshk's publication history reveals a strong focus on applying AI and machine learning to cybersecurity challenges, particularly in IoT environments. Her work demonstrates a progression from foundational research in evolutionary computation to specialized applications in cybersecurity. Recent publications show increasing emphasis on explainable AI for security applications, reflecting the growing need for transparency in security decision-making processes. Her research spans multiple domains including smart cities, industrial control systems, and power networks, demonstrating the versatility and wide applicability of her security frameworks. Dr. Keshk has received notable recognition for her academic work: UNSW Tuition Fee Scholarship for PhD Research (2018-2021) Data61/CSIRO Scholarship (2019-2021) During her PhD studies, Dr. Keshk was a research candidate at Data61-CSIRO in Australia, providing her with valuable industry experience alongside her academic pursuits. Her collaborative approach to research is evident in her extensive co-authorship with colleagues across various institutions, particularly with researchers from UNSW and other Australian universities. Dr. Keshk's work contributes significantly to several research teams and initiatives focused on cybersecurity and AI at UNSW Canberra. Her expertise complements broader efforts in secure systems development, particularly in the context of increasingly connected environments where traditional security boundaries are challenged by new technologies.

Jack Clark Francisمشاهده پروفایل

استاد

• Investments
• Portfolio Management
• Derivatives
• +۸ مورد دیگر

Jack Clark Francis is a Professor at the Bert W. Wasserman Department of Economics and Finance within the Zicklin School of Business at Baruch College, City University of New York . He holds a PhD in Finance from the University of Washington (1969), an MBA in Finance from Indiana University (1964), and a BS in Marketing from Indiana University (1962). Education: PhD, University of Washington, Finance, 1969 MBA, Indiana University, Finance, 1964 BS, Indiana University, Business Marketing, 1962 Research interests span Investments , Portfolio Management , Derivatives , and Financial Markets . His work includes analyses of cryptocurrencies , timberland investments , utility theories , and market efficiency . He has contributed extensively to asset pricing , tax policy impacts , and executive compensation models . Scientific awards include editorial board memberships for journals like Review of Pacific Basin Financial Markets & Policy (1995–Present) and International Review of Financial Analysis (1992–2025). His advising role involves evaluating finance professors and mentoring interns via reports and committee work. He has participated in grants and research initiatives related to market modeling, real estate, and derivatives. Labs and teams include collaborations with institutions like Rutgers University, Pacific Basin Finance Conference, and Federal Reserve Bank, with presentations at international conferences and editorial contributions to key finance journals.

• Model Predictive Control
• Distributed Control Systems
• Renewable Energy Systems
• +۴ مورد دیگر

Paula Chanfreut Palacio is an Assistant Professor in the Control Systems Technology section of the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e). She joined TU/e in February 2023 after previously contributing to the ERC Advanced Grant project OCONTSOLAR (Optimal Control of Thermal Solar Energy Systems). Education: B.Sc. and M.Sc. in Industrial Engineering (University of Seville, 2017/2019) Ph.D. in Automation Engineering (University of Seville, 2018–2022) Visiting scholar at UC Berkeley (2021) and MIT (2022) Research Interests: Her work centers on decentralized and data-driven model predictive control (MPC) for large-scale interconnected systems. Key applications include renewable energy plants, traffic networks, and smart grids, with emphasis on scalability, cooperative multi-agent frameworks, and real-time optimization. She also investigates cyber-security in control systems and learning-based supervisory methods. Publications: Recent articles (2025) focus on advanced MPC techniques for safety-critical systems like vehicle platooning and power grids, highlighting themes such as cyber-attack resilience, nonlinear cooperative control, and invariant set design. Cross-cutting topics include distributed optimization, robustness, and machine learning integration. Projects: Active member of the Model Predictive Pandemic Control project (2023–2025), funded by an ERC Advanced Grant, focusing on control strategies for large-scale health crises. Affiliation: Part of the Cyber-Physical Systems and Systems Engineering research group at TU/e, contributing to interdisciplinary work in control theory and real-world engineering solutions.

Katja Hoseمشاهده پروفایل

استاد

• Query Processing
• Semantic Web
• Linked Data
• +۷ مورد دیگر

Katja Hose is a Professor in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. Her research focuses on Data, Knowledge and Web Engineering with specializations in AI for the People and Artificial Intelligence and Machine Learning. She maintains an active research profile with numerous publications and projects. Department of Computer Science Technical Faculty of IT and Design Aalborg University Research areas: Query Processing, Semantic Web, Linked Data, Knowledge Graphs Professor Hose's research interests center on knowledge representation, semantic web technologies, and AI applications. Her work spans from theoretical database systems to practical applications in healthcare, environmental assessment, and microbial data analysis. She has made significant contributions to knowledge graphs, large language models, and semantic search technologies, with particular emphasis on addressing hallucinations in AI systems and improving table search in semantic data lakes. Her recent publications demonstrate a strong trend toward integrating knowledge graphs with large language models, developing evaluation frameworks for AI hallucinations, and applying data science to diverse domains including healthcare and environmental sustainability. Her research bridges theoretical computer science with practical applications that address real-world challenges. NLP4KGC Best Paper Award (2023) ESWC 2023 Best Demo Award (2023) 2020 AMiner AI 2000 Most Influential Scholars AIME 2020 Best Paper Nomination (2020) ESWC 2019 Best Demo Award Nomination (2019) Professor Hose leads multiple significant research projects including ARISTOTLE (AI for clinical risk assessment), DarkScience (microbial data analysis), and the Poul Due Jensen Professorate in Big Data and AI. She has supervised numerous PhD students and collaborates extensively across disciplines, particularly in healthcare applications of AI and environmental assessment technologies. Her research has attracted substantial funding from sources like Villum Fonden and Danish E-infrastructure Cooperation. She is actively involved in several interdisciplinary research teams, including collaborations with microbiologists on microbial dark matter projects and with environmental scientists on digital environmental assessment systems. Her work on the ARISTOTLE project demonstrates strong connections between AI research and clinical applications, while her DarkScience project bridges computer science with microbiology.

Zoltán Sütőمشاهده پروفایل

دانشیار

• Power Electronics
• Control Systems
• Power Converters
• +۵ مورد دیگر

Zoltán Sütő is an Associate Professor at the Budapest University of Technology and Economics, affiliated with the Department of Automation and Applied Informatics. His research focuses on advanced power electronics and control systems, with expertise in real-time implementation using FPGA technology. He maintains an active presence through institutional contacts at Budapest 1117, Magyar tudósok krt. 2., Q.B114, and can be reached via phone (+36 1 463-2337) or email (Suto.Zoltan@aut.bme.hu). Dr. Sütő's research encompasses: Design and optimization of power converters (dual active bridge, multilevel inverters) Real-time control algorithms for grid-connected systems and microgrids FPGA-based hardware-in-the-loop simulation methodologies Nonlinear dynamics in power electronic systems Artificial intelligence applications for fault diagnosis in drive systems His work bridges theoretical control models with practical implementations in renewable energy integration and power quality management. Recent publications demonstrate a strong focus on predictive control techniques, adaptive compensation methods, and optimization of power converter topologies. Research trends emphasize real-time validation, FPGA implementation, and AI-enhanced diagnostics across applications ranging from energy storage systems to industrial drives. Articles consistently address efficiency improvements, stability challenges, and novel modulation strategies in power conversion.

Shaileshh Bojja Venkatakrishnanمشاهده پروفایل

استادیار

• Blockchain Networking
• Cryptocurrency Algorithms
• Network Systems
• +۵ مورد دیگر

Shaileshh Bojja Venkatakrishnan is an Assistant Professor in the Department of Computer Science and Engineering at The Ohio State University's College of Engineering. His research spans multiple domains at the intersection of networking, distributed systems, and machine learning with a particular focus on blockchain technologies and cryptocurrency networks. Educational Background: PhD in Computer Science from University of Illinois Urbana-Champaign (UIUC), 2017 Bachelor's degree from Indian Institute of Technology Madras, 2012 Dr. Bojja Venkatakrishnan's research interests center around fundamental algorithmic questions at the networking layer of Bitcoin and other cryptocurrency networks. He is particularly interested in applying machine learning techniques for developing algorithms over networks and graphs, with emphasis on 'first-principles' thinking to solve complex distributed systems problems. His work extends to scheduling algorithms for data center networks, peer-to-peer networks, and information theory. The breadth of his research is evident in his publications spanning blockchain networking, payment channel networks, decentralized applications, and machine learning applications for network optimization. Research Impact: Recipient of the Joan and Lalit Bahl fellowship at UIUC His publication record demonstrates significant contributions to the field, with papers appearing in top venues including SIGCOMM, NSDI, SIGMETRICS, and IEEE Transactions. His recent work focuses on enhancing blockchain scalability through peer sampling techniques, transaction reordering mitigation, and efficient peer-to-peer network design for decentralized applications. The trajectory of his research shows a consistent focus on addressing fundamental challenges in distributed systems with practical implications for real-world blockchain implementations and network architectures.

Michael K. Reiterمشاهده پروفایل

استاد

• Computer Security
• Distributed Systems
• Applied Cryptography
• +۳ مورد دیگر

Michael K. Reiter is a Professor at Duke University in the Department of Computer Science and Department of Electrical & Computer Engineering . He received his Ph.D. in Computer Science from Cornell University (1993) and held academic positions at Carnegie Mellon University and UNC Chapel Hill. Research Focus: Computer and network security, distributed systems, and applied cryptography. Notable Contributions: Pioneering work in Byzantine fault tolerance, malware detection, privacy-preserving protocols, and human-verifiable authentication. Leadership: Founding Technical Director of CyLab at Carnegie Mellon, program committee roles at top security conferences. Recent Publications emphasize: Machine learning security (adversarial training, backdoor detection) Blockchain consensus mechanisms (asynchronous BFT, random beacons) Privacy-enhancing technologies (differential privacy, private set intersection) Side-channel mitigation in cloud environments Awards & Recognition: ACM Fellow (2008) James B. Duke Distinguished Professor

• Process Mining
• Anomaly Detection
• Blockchain
• +۳ مورد دیگر

Ko, Jonghyeon is a researcher affiliated with the Ulsan National Institute of Science and Technology (UNIST) , specifically the Department of Materials Science and Engineering within the College of Natural Science and Engineering. His work spans multiple disciplines including process mining, anomaly detection, blockchain technology, AI computing, and environmental engineering. His research interests include: Anomaly detection in business process event logs Blockchain-based systems for nuclear/radioactive waste management AI computing using neuromorphic devices Statistical leverage and information-theoretic approaches to process mining Optimization of autonomous vehicle safety systems Recent publications demonstrate expertise in developing formal languages for data quality simulation, probabilistic trace alignment methods, and practical tools for anomaly detection like AIR-BAGEL. While no explicit scientific awards are mentioned in the text, his work has been published in venues such as Information Systems , npj Unconventional Computing , and Expert Systems with Applications .

Núria Bosch Rocaمشاهده پروفایل

استاد

• Efficiency analysis
• Fiscal federalism
• Territorial fiscal flows

Núria Bosch Roca is a Professor at the University of Barcelona and a researcher at the Institut d'Economia de Barcelona (IEB), specializing in fiscal federalism and public economics. She serves as the Director of the Fiscal Federalism Observatory and has contributed extensively to the analysis of intergovernmental fiscal relations, territorial fiscal flows, and local governance efficiency. Research Interests: Fiscal federalism Efficiency analysis of public services Political economy of fiscal flows Intergovernmental financial relations Selected Publications explore topics such as revenue allocation in autonomous regions, municipal fiscal governance, and the political dimensions of fiscal decentralization. Her work spans comparative studies of Spain, Germany, and Canada, as well as empirical analyses of Spanish municipalities. Contact: nbosch@ub.edu