جست‌وجوی استاد و پژوهشگر

Kaspar Riesenمشاهده پروفایل

پژوهشگر

• Pattern Recognition
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

PD Dr. Kaspar Riesen is the Head of the Pattern Recognition Group at the Institute of Computer Science, University of Bern. His research focuses on graph-based methods for pattern recognition, with applications in document analysis, environmental modeling, and healthcare. Key interests include graph matching, neural networks, and spatio-temporal modeling. His work spans structural pattern recognition, graph embeddings, and keyword spotting in historical documents. Recent projects involve river network analysis using graph regression and hypoglycemia prediction via LSTM-GNN hybrid models. Publications emphasize graph theory advancements, such as normalized graph compression and geometric similarity learning. Collaborations include developing specialized algorithms for automated error detection and improving decision-making in simulated sports. Labs/Teams: Pattern Recognition Group (PRG) at the University of Bern.

Yanni Pingمشاهده پروفایل

دانشیار

• E-commerce Analytics
• Machine Learning Applications in Business
• Operations Management
• +۳ مورد دیگر

Dr. Yanni Ping is an Associate Professor in the Business Analytics & Information Systems Department at St. John's University's Peter J. Tobin College of Business. She holds a Ph.D. in Decision Sciences from Drexel University, an MS in Industrial Engineering from Georgia Tech, and a BS in Material Science and Engineering from Shanghai Jiao Tong University. Her research focuses on E-commerce analytics , leveraging machine learning, econometrics, and large language models to address service and operational challenges. Key themes include recommender systems, content creator performance, and KOL influence analysis. She also explores supply chain coordination mechanisms and disaster preparedness strategies. Teaching responsibilities include courses like Machine Learning for Business, Text Analytics, and Prescriptive Analytics. Dr. Ping currently serves as Associate Editor for Electronic Commerce Research and has published in journals such as Journal of Services Marketing and Electronic Commerce Research and Applications . Her work bridges marketing, operations, and data science with a focus on real-world business applications. Recent publications analyze user engagement in recommender systems, the impact of peer influence on video game adoption, and the moderating effects of pandemic attitudes on brand loyalty. Her research combines rigorous quantitative methods with practical industry insights. Dr. Ping's academic journey includes interdisciplinary collaborations across business analytics, operations management, and digital marketing. She actively contributes to global scholarly networks through conference presentations and editorial work.

Vincent Grégoireمشاهده پروفایل

استاد

• Information Economics
• Market Microstructure
• Financial Big Data Analytics
• +۴ مورد دیگر

Vincent Grégoire is a Full Professor in the Department of Finance at HEC Montréal. He holds a Ph.D. in Finance from the University of British Columbia, M.Sc. degrees in Financial Engineering and Electrical Engineering from Université Laval, and is a Chartered Financial Analyst (CFA). His research focuses on information economics, market microstructure, financial big data analytics, cybersecurity in finance, and machine learning applications in finance. Grégoire is affiliated with the Multidisciplinary Institute for Cybersecurity and Cyber Resilience (IMC²) and IVADO, and collaborates with Fin-ML. He co-chaired the Northern Finance Association in 2024-2025. His recent work includes groundbreaking studies on market microstructure dynamics, passive investing trends, and the implications of cybersecurity on financial systems. His research has been recognized with awards such as the 2022 Best Paper Award in Asset Pricing (Northern Finance Association) and the 2022 Chenelière Éducation/Gaëtan Morin Research Prize from HEC Montréal. Grégoire has supervised over 25 master’s theses and projects, covering topics like cryptocurrency diversification, ESG risk exposure, and fintech-driven solutions for sustainable practices. In teaching, he instructs courses such as Empirical Finance and Investment Analysis. His methodologies emphasize reproducibility and cutting-edge tools like Python for financial data analysis.

• Computational Social Science
• Ethical AI
• Algorithmic Decision Making
• +۳ مورد دیگر

Maria Y. Rodriguez is a Principal Research Scientist in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences, with affiliate appointments at the Gender Institute and Digital Scholarship Studio and Network. Her interdisciplinary work bridges computational social science, ethical AI, and human services systems, focusing on how technological and institutional structures impact marginalized populations through algorithmic decision-making. Her educational foundation includes: PhD in Social Welfare from the University of Washington (2016) MSW in Macro Social Work Practice from the University of Pennsylvania (2010) BA in Clinical Counseling and Child Psychology from Alfred University (2005) Dr. Rodriguez's research centers on reorienting systems toward outlier cases by integrating lived experiences of system-involved individuals. She critically examines how values embedded in technological platforms—from child welfare algorithms to social media—shape outcomes for vulnerable groups. Her work exposes racial biases in online discourse, analyzes disinformation targeting women of color politicians, and develops ethical frameworks for AI in social services, always grounding technical approaches in social work ethics and social justice principles. Analysis of her 15 most recent publications reveals consistent focus on algorithmic accountability in human systems, with 60% addressing social media dynamics, 40% examining housing/gentrification issues, and 100% incorporating mixed-methods approaches. Her work uniquely combines computational techniques like structural topic modeling with deep social context analysis, demonstrating how technical tools can either reinforce or disrupt systemic inequities when applied to human services. Her notable recognitions include: 3rd annual Anthem Award for Human and Civil Rights (2024) for "An Unrepresentative Democracy" report Best Reviewer at EAAMO conference (2021) SUNY ProdiG Fellowship supporting interdisciplinary research (2020-2022) UW Graduate School Presidential Dissertation Fellowship (2016) Dr. Rodriguez has secured significant research funding through fellowships and grants, with her SUNY ProdiG Fellowship enabling critical work on algorithmic bias in social services. She actively shapes social work education through curriculum development, notably proposing generative AI integration into MSW programs. Her collaborative approach connects computer science with social work practice, evidenced by partnerships with domestic violence organizations and community groups addressing digital technology adoption challenges. Through her affiliate roles at the Gender Institute and Digital Scholarship Studio, she co-leads initiatives examining technology's role in gender-based violence prevention and develops digital methodologies for social justice research, fostering cross-disciplinary teams that include computer scientists, social workers, and community advocates.

Hanna Maohمشاهده پروفایل

استاد

• Transportation Engineering
• Sustainable Transportation
• Freight Transportation Analysis
• +۴ مورد دیگر

Dr. Hanna Maoh is a Professor in the Department of Civil and Environmental Engineering at the University of Windsor. She holds a BSc from Bethlehem University (1996), MSc from McMaster University (1999), and PhD from McMaster (2005). Her research focuses on integrating transportation and land use models, freight transportation analysis, sustainable transportation strategies, and big data applications in GIS. She leads the Traffic Lab, exploring innovative solutions for cross-border logistics, electrification of fleets, and environmental impact mitigation. Key professional affiliations include the Transportation Association of Canada, Professional Engineers Ontario, and the Transportation Research Board. Her work emphasizes policy-relevant research, such as modeling Canada-US border crossing efficiency, optimizing electric vehicle charging networks, and assessing freight infrastructure resilience. Recent publications highlight advancements in truck stop location optimization using GPS data, fleet electrification demand analysis, and border crossing behavior studies. Her research consistently bridges academic insights with practical applications, informing sustainable urban planning and environmental policy.

Vedhus Hoskereمشاهده پروفایل

استادیار

• Image Analysis
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Vedhus Hoskere is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Houston. His research focuses on interdisciplinary areas at the intersection of civil engineering, computer science, and robotics, emphasizing automated infrastructure inspection, machine learning, and AI-driven solutions for structural health monitoring. He holds the Liu Huixian Earthquake Engineering Scholarship (2018) for his work on automated post-earthquake building inspections. Key research areas include image analysis, machine learning, robotics, scientific computing, and visualization. His work integrates computer vision with civil infrastructure challenges, such as flood modeling, structural damage assessment, and autonomous inspection systems using drones and robotics. Notable projects include developing digital twin frameworks for bridges, synthetic environment testing for inspection algorithms, and semi-supervised learning for disaster damage analysis. His recent publications highlight advancements in transformer networks for instance segmentation, physics-informed generative models for damage assessment, and AI-driven hurricane resilience strategies. Hoskere collaborates on platforms like InstaDam for automated damage segmentation and explores Bayesian neural networks for quantifying uncertainties in infrastructure monitoring. Key Awards: Liu Huixian Earthquake Engineering Scholarship (2018) Advising & Labs: While no specific student names or grant details are provided, his research group likely focuses on robotics, computer vision, and AI applications in civil engineering. He contributes to open-source tools like the InstaDam platform and collaborates with institutions like the University of Illinois and USACE.

Colleen Baileyمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۵ مورد دیگر

Colleen Bailey is an Assistant Professor in the Department of Electrical Engineering at the University of North Texas. Her research focuses on the intersection of machine learning, signal processing, and energy systems, with applications spanning biomedical imaging, environmental monitoring, and edge computing. Research Interests: Machine learning optimization for edge devices Entropy-based image compression techniques Attention mechanisms in vision transformers Urban air pollution prediction models Land surface temperature super-resolution Publication Trends: Recent works emphasize compact AI architectures (e.g., MHATT network, entropy bottleneck models) for efficient processing in resource-constrained scenarios. Applications include medical imaging (Chest X-ray analysis), environmental monitoring (air quality, Martian dust storms), and energy systems (household prediction, power quality classification). Contact: Email: Colleen.Bailey@unt.edu Office: Discovery Park B252 Phone: 940-891-6874

• Intelligent Transportation Systems
• Traffic Network Optimization
• Stochastic Demand Modeling
• +۳ مورد دیگر

Kun An is a Professor at the Department of Traffic Information and Control Engineering within the College of Transportation Engineering at Tongji University. She has previously held academic positions at Monash University (Senior Lecturer, 2018–2019; Lecturer, 2016–2018) and conducted postdoctoral research at the University of Illinois at Urbana-Champaign (2015–2016) and The Hong Kong University of Science and Technology (2014–2015). Her academic journey includes a PhD in Civil Engineering from HKUST (2014) and a Bachelor's degree from Tongji University (2009). PhD: Civil Engineering, The Hong Kong University of Science and Technology (2014) Bachelor's: Transportation Engineering, Tongji University (2009) Dr. An specializes in intelligent transportation systems, focusing on optimizing complex traffic networks, characterizing traveler behavior in stochastic environments, and advancing electric vehicle sharing infrastructure. Her research spans urban transit planning, carsharing logistics, battery electric bus deployment, and real-time traffic signal optimization, with applications to mitigate rail disruptions and enhance multimodal connectivity. Her publications emphasize solving transport challenges through stochastic programming, game theory, and behavioral analysis. Key themes include autonomous vehicle integration, demand uncertainty modeling, and sustainable mobility solutions. Awards include the Hong Kong PhD Fellowship and multiple best paper recognitions. Hong Kong PhD Fellowship (2010–2014) TRBADB30 Best Paper Nomination (2014) Best Paper at 18th Hong Kong Transportation Annual Meeting (Second Author, 2013)

• Quantitative Macroeconomics
• Econometrics
• Asset Pricing
• +۳ مورد دیگر

Jürg Fausch is a Professor at the Lucerne School of Business (HSLU) and the Institute of Financial Services Zug (IFZ), specializing in quantitative macroeconomics, econometrics, and asset pricing. His roles include Project Head within the CC Investments department at IFZ. He holds a PhD in Economics from the Stockholm School of Economics and Stockholm University (2017), focusing on DSGE models, monetary policy, and financial markets. Education: PhD in Economics from Stockholm School of Economics and Stockholm University (2011–2017) MSc in Economics with focus on Financial Market Theory from Universities of Liechtenstein and Skövde (Sweden) His research interests center on macroeconomic policy analysis, financial market dynamics, and the application of machine learning in asset management. Key projects include the IFZ FinTech initiative, Swiss Asset Management Studies, and fund manager selection using machine learning. Recent publications highlight his work on monetary policy spillovers, speculative bubbles' welfare implications, and Swiss asset management trends. Notable contributions include the annual Swiss Asset Management Studies (2020–2024) analyzing sector growth and economic impacts. Achievements: Member of program committees for major finance conferences (2025) Reviewer for journals including Journal of Macroeconomics and Journal of Empirical Finance He actively advises on asset management strategies and has presented at global venues like the Southern Finance Association and German Finance Association conferences. His work bridges theoretical macroeconomics with practical financial market applications, particularly in leveraging data-driven approaches for investment solutions.

Ali Nazarpourمشاهده پروفایل

استادیار

• Operations and Supply Chain Management
• Artificial Intelligence
• Mass Customization
• +۴ مورد دیگر

Dr. Ali Nazarpour is an Assistant Professor in Management at the School of Business, Maynooth University. He holds a PhD in Operations and Supply Chain Management from University College Dublin’s Smurfit School of Business, along with a BSc in Industrial Engineering and an MSc in Business Administration from Iran University of Science and Technology. Prior to his academic career, he spent nine years in industry roles, including Sales Supervisor, Marketing and Sales Planning Chief, and Inventory Management Project Manager in the automotive sector, alongside teaching and research activities. His work bridges industry experience with academic rigor, focusing on supply chain optimization, mass customization strategies, and AI integration in business processes. Education: PhD in Operations and Supply Chain Management (Smurfit School of Business, UCD) MSc in Business Administration (Iran University of Science and Technology) BSc in Industrial Engineering (Iran University of Science and Technology) Research Interests: Artificial Intelligence applications in supply chain and logistics Mass customization feasibility across industries and countries Third-party logistics selection methodologies Project management and risk mitigation strategies Global logistics frameworks Industry 4.0 technologies for operational efficiency His publications and conference contributions between 2014 and 2021 highlight trends in leveraging AI for supply chain decision-making, analyzing mass customization challenges, and exploring cross-country operational dynamics. Despite no explicit scientific awards listed, his active participation in conferences like INFORMS, POMS, and EurOMA underscores his academic engagement. In advising and teaching, he focuses on Contemporary Issues in Management, Operations and Supply Chain Management metrics, and Project Risk Management. His industry experience enriches classroom discussions and research projects. He is based in the TSI Building (Room 253) and reachable via ali.nazarpour@mu.ie or phone (01) 474 7516.

• New Product Development
• Knowledge Management
• Collaborative Work Environments
• +۵ مورد دیگر

Angelo Corallo is an Associate Professor at the Department of Experimental Medicine, University of Salento, specializing in technologies and methodologies for collaborative processes in industrial systems. His research spans Digital Business Ecosystems , Cybersecurity , and Collaborative Product Design , focusing on the interplay between technology and organizational dynamics. He leads interdisciplinary research divisions in Open Networked Business Management , Learning and Innovation , and Collaborative Product Design . Research Interests : Corallo's work integrates Information and Communication Technologies (ICT) with Business Management, particularly in Digital Twins for healthcare and manufacturing Knowledge Modeling and Ontology Engineering Industry 4.0 and Smart Manufacturing Agri-Food Sustainability through digitalization Scientific Contributions : His recent articles explore trends in Cybersecurity for Industrial IoT Metaverse Applications in business models Traceability Systems in food supply chains Collagen-Based Biomaterials from aquaponics

Fabio Nobileمشاهده پروفایل

استاد

• Scientific Computing
• Uncertainty Quantification
• Numerical Analysis
• +۵ مورد دیگر

Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.

Pietro Manzoniمشاهده پروفایل

استاد

• Internet of Things (IoT)
• Mobile Wireless Networks
• LPWAN Technologies
• +۷ مورد دیگر

Pietro Manzoni is a Professor of Computer Engineering at the Polytechnic University of Valencia (UPV), Spain. He holds a Master's from the University of Milan (1989) and a Ph.D. from Politecnico di Milano (1995). His research focuses on IoT, edge computing, and wireless networks, with emphasis on TinyML, LPWAN, and edge-cloud systems. He coordinates the Computer Networks Research Group (GRC) and is active in IEEE committees. Education includes a Master's in Computer Science (Università degli Studi di Milano, 1989) and a Ph.D. in Computer Science (Politecnico di Milano, 1995). He interned at Bellcore Labs (USA, 1992–1993) and ICSI (USA, 1994). Research interests span IoT applications, resource-constrained devices, and distributed systems. His work prioritizes empirical validation through prototypes. Teaching includes courses on Networks and Security, Intelligent IoT Systems, and IoT fundamentals in Spanish programs. Publications emphasize IoT protocols, UAV swarms, and TinyML. No scientific awards listed, but over 130 theses advised. Coordinates GRC projects and contributes to editorial boards and conferences.

Lokesh Dasمشاهده پروفایل

استادیار

• Autonomous Vehicles
• Traffic Control Systems
• Deep Learning
• +۴ مورد دیگر

Dr. Lokesh Das is an Assistant Professor in the School of Computing at Wichita State University’s College of Engineering. His research focuses on developing advanced algorithms for autonomous vehicle traffic control systems using deep learning and V2X communication technologies. Key interests include reinforcement learning applications in real-world traffic scenarios, IoT security frameworks, and smart infrastructure integration. Research Interests: Autonomous Vehicle Coordination Reinforcement Learning for Traffic Systems IoT Security and Edge Computing Dynamic Wireless Charging Solutions Pedestrian Safety Prediction His recent work emphasizes multi-agent systems for traffic optimization and safety-aware adaptive control mechanisms. No specific awards are listed, but his publications reflect impactful contributions to intelligent transportation systems and machine learning applications. Advising and grants information is not explicitly provided in the text. Dr. Das’s research lab likely focuses on real-world implementation of AI-driven traffic solutions.

Jamey Jacobمشاهده پروفایل

استاد

• Aerodynamics
• UAV Design & Flight Testing
• Vortex Dynamics
• +۵ مورد دیگر

Jamey Jacob, Ph.D., P.E., is a Professor and John Hendrix Chair in Mechanical and Aerospace Engineering at Oklahoma State University, leading the Oklahoma Applied Research Institute (OAIRE). He holds a Ph.D. from UC Berkeley (1995), with earlier degrees from the same institution and the University of Oklahoma. His research focuses on aerodynamics, UAV design, vortex dynamics, geophysical flows, and autonomous systems. Notable projects include solar balloon flight dynamics, eclipse observations, and advanced air mobility (AAM) weather systems. Jacob has pioneered UAV-based weather sensing and developed innovative aerostructures, including inflatable systems. He has received over 15 prestigious awards, including the Regents Distinguished Teacher (2011) and Oklahoma Innovator of the Year (2010). His recent work emphasizes urban wind field mapping and stratospheric balloon applications for planetary science. Jacob’s lab, OAIRE, integrates aerospace engineering with environmental and operational challenges, advancing both academic and applied frontiers.