Professor Yuliya Yurova serves as Professor of Research Methods and Decision Sciences at Nova Southeastern University's H. Wayne Huizenga College of Business & Entrepreneurship. She teaches graduate courses including QNT 5160 Analytical Modeling for Decision Making and QNT 5485 Data Mining and Predictive Analytics Fundamentals, leveraging her expertise in quantitative methodologies to prepare students for data-driven business environments. Her educational background includes a Ph.D. in Business Administration/Statistics from University of Illinois-Chicago, an M.S. in Computer Information Systems from Eastern Michigan University, and a B.A. in Mathematical Economics from Novosibirsky State University. This interdisciplinary foundation supports her integrative research approach spanning business analytics, marketing, and organizational behavior. Professor Yurova's research examines critical intersections of technology and human behavior in business contexts. She investigates how HR professionals adopt analytics, how mobile devices reshape retail experiences, and how organizational justice affects employee satisfaction - often through cross-cultural comparisons between US and Russian business environments. Her work consistently addresses practical challenges while employing rigorous quantitative methods. Her publication record demonstrates sustained scholarly impact with recent articles in journals including International Journal of Information Systems and Supply Chain Management (2022), Management Decision (2021), and Journal of Marketing Education (2021). Her research trajectory shows increasing focus on pandemic-era business adaptations and ambidextrous organizational learning. Best Paper in Track, Southwest Academy of Management 2017 Consistent publication in top-tier business journals since 2006 Research cited for practical HR analytics implementation frameworks Methodologically rigorous quantitative studies with real-world applications Professor Yurova actively bridges academic research and industry practice through studies of actual job market requirements, corporate technology adoption barriers, and retail consumer behavior. Her international collaborations, particularly with Russian institutions, provide unique comparative perspectives on business practices across different institutional environments. Students benefit from her practitioner-relevant research through coursework emphasizing analytical modeling and data mining applications.
جستجوی استاد راهنمای دانشگاهی
برای مسیر پژوهشیتان، استاد راهنما، استاد دانشگاه و پژوهشگر مناسب را پیدا کنید و با آنها ارتباط بگیرید.
نمایش ۵۷۱-۵۷۶ از ۵۷۶ استاد راهنما
اطلاعات پروفایلها از صفحات عمومی دانشگاهها گرفته شده و ممکن است بهروز نباشد.
Erin Vitale serves as Professor and Department Chair of Civil Engineering Technology at Alfred State College (State University of New York), leveraging 15+ years of industry experience to bridge academic theory with practical construction management applications. As an institutionally designated industry expert, she teaches on-campus and online courses while maintaining strong employer connections for student placement. Her educational foundation includes: Certified Professional Constructor (2003) from Stanford University M.S. in Civil Engineering - Construction Engineering and Management (University of California, Riverside) B.S. in Economics with Philosophy minor Undergraduate Civil Engineering coursework (California State University, Chico) Professor Vitale's expertise centers on Construction Engineering and Management, with specialized knowledge in project scheduling systems, construction cost estimating methodologies, and field engineering coordination. Her research interests emerge directly from industry practice, focusing on optimizing construction project delivery through advanced scheduling techniques and practical resource management. This practitioner-scholar approach ensures curriculum relevance to current industry demands. Her professional recognition includes the Certified Professional Constructor credential: Certified Professional Constructor (2003) As Department Chair, she actively mentors students toward career success in construction management, emphasizing her satisfaction in 'getting to know them and... when they get jobs they're excited about.' Her industry network facilitates direct placement opportunities, while her leadership shapes program development to meet evolving workforce needs in civil engineering technology.
Dr. Edward Kozłowski is a Professor at the Department of Quantitative Methods in Management within the Faculty of Management at Lublin University of Technology. He holds consultations every Wednesday from 10:00 to 12:00 in room 20 of the New Oxford Connecting Building and can be reached via email at e.kozlovski@pollub.pl or through his personal website. Dr. Kozłowski earned his academic credentials through a rigorous path: completing his Master's degree in 1996 from the Faculty of Mathematics and Physics at Maria Curie-Skłodowska University, his PhD in 2002 at the Systems Research Institute of the Polish Academy of Sciences in Warsaw with a thesis on 'Optimization of investment portfolio taking into account the cost of information,' and achieving habilitation in 2019 from the same institution. His research focuses on optimal adaptive control for stochastic systems with random horizon and modeling decision support and control systems under uncertainty. This work spans stochastic optimization, machine learning applications, statistical modeling, and decision theory under uncertain conditions. Dr. Kozłowski's approach consistently bridges theoretical mathematical frameworks with practical applications in management contexts. Analysis of his recent publications (2017-2020) reveals a strong trend toward interdisciplinary applications of quantitative methods. His work spans pandemic response modeling, manufacturing optimization, water resource management, transportation systems analysis, and building diagnostics. The integration of machine learning techniques with traditional statistical approaches has become increasingly prominent in his recent research. Dr. Kozłowski teaches courses in forecasting and simulations, descriptive statistics, and mathematical statistics, emphasizing practical applications of quantitative methods in management. His teaching philosophy aligns with the Department of Quantitative Methods in Management's postgraduate studies in Data Analysis, which focuses on teaching statistical methods as practical tools rather than theoretical constructs. His collaborative research network includes frequent partnerships with Tomasz Rymarczyk, Grzegorz Kłosowski, Anna Borucka, and Dariusz Mazurkiewicz across diverse application domains. This collaborative approach has resulted in numerous publications in reputable journals and conference proceedings.
Muhammad Mustafa Rafique is an Associate Professor in the Department of Computer Science at Rochester Institute of Technology's Golisano College of Computing and Information Sciences. His research specializes in optimizing large-scale computing systems, with focus areas including: High-performance computing (HPC) resource management Distributed deep learning acceleration Fault-tolerant cloud architectures GPU-accelerated checkpointing systems Serverless computing frameworks Dr. Rafique's work demonstrates consistent innovation in improving computational efficiency for containerized HPC workflows, multi-GPU scheduling, memory optimization, and distributed training pipelines. His publications frequently appear in premier IEEE/ACM conferences, reflecting contributions to systems performance engineering. While no awards or student advisees are mentioned in available materials, his research collaborations include co-authors from institutions worldwide, indicating active engagement in the high-performance computing research community. Current work explores emerging memory technologies like CXL and advanced containerization techniques for next-generation datacenters.
Ioana E. Marinescu is an Associate Professor at the University of Pennsylvania School of Social Policy & Practice, with secondary appointments in the Economics Department and the Wharton School of Business (BEPP). She serves as Director of the MS in Social Policy Program and is a Faculty Research Fellow at the National Bureau of Economic Research. Previously, she served as Principal Economist at the US Department of Justice Antitrust Division (2022-2024), where she helped integrate labor market analysis into the 2023 Merger Guidelines, the first version to explicitly address worker impacts. Her educational background includes a PhD in Economics, though specific details about her degrees and institutions are not provided in the given text. Dr. Marinescu's research spans several interconnected areas of economics and policy. She is particularly known for her pioneering work on labor market competition, monopsony power, and antitrust policy. Her scholarship links labor economics, antitrust, and technological change to study how market power and innovation affect wages, employment, and inequality. She has conducted significant research on the economic implications of artificial intelligence and policy tools such as safety nets and competition policy that can foster broad-based prosperity in an AI-driven economy. Her work on income support policies, including unemployment insurance, minimum wage, and universal basic income, has been influential in policy discussions. A notable finding from her research is that in labor markets where employers have more control over wages, increasing the minimum wage often results in a rise in employment. Dr. Marinescu publishes in top journals including the Review of Economic Studies and the Quarterly Journal of Economics, where she serves as an associate editor. Her research regularly appears in mainstream media outlets like the New York Times and the Economist. She has testified before Congress on labor market competition and the positive effects of universal basic income. Her work demonstrates consistent trends toward understanding market power dynamics in labor markets and developing policy solutions to address wage suppression and inequality. Faculty Research Fellow, National Bureau of Economic Research Principal Economist, US Department of Justice Antitrust Division (2022-2024) Member, Anthropic's Economic Advisory Council Co-creator, "Just Economics" podcast exploring policies and labor markets Dr. Marinescu has advised on significant policy initiatives, including the integration of labor market analysis into the 2023 Merger Guidelines. She provides expert insights on labor markets, antitrust, and AI to policymakers, journalists, and private-sector leaders. She has collaborated with researchers like José Azar and Hyeri Choi on various projects related to labor market concentration and power. While specific lab information isn't detailed in the provided text, her work appears to involve collaborations with the National Bureau of Economic Research and research teams at UPenn focused on labor economics and policy analysis.
Hongyu Zhang is a Professor and Dean of the School of Big Data and Software Engineering at Chongqing University, China, and an Honorary Professor at The University of Newcastle, Australia. Previously, he served as a Lead Researcher at Microsoft Research Asia and an Associate Professor at Tsinghua University, China. He received his PhD from the National University of Singapore in 2003. His academic journey spans prestigious institutions, combining industry research experience with academic leadership. Dr. Zhang's research interests focus on intelligent software engineering, software analytics, data-driven software engineering, software fault management, testing and debugging, and software maintenance and reuse. His work centers on improving software quality and productivity by mining and analyzing vast amounts of software data. Over the years, he has developed innovative methods that apply data mining, machine learning (including deep learning), and information retrieval techniques to extract knowledge from software data and solve complex software engineering problems. His research spans three major areas: intelligent programming (code search, code summarization, code generation), intelligent quality prediction (defect prediction, cloud failure prediction, performance prediction), and intelligent fault detection and diagnosis (log-based fault detection, crash-based fault localization, bug report analytics). His recent publications demonstrate a clear trend toward integrating large language models and deep learning techniques with traditional software engineering practices. The research spans intelligent programming assistance, code security, UI automation, distributed systems optimization, and performance analysis. His work increasingly focuses on practical applications of AI in software engineering, with emphasis on real-world impact in industrial settings, particularly in microservices, cloud systems, and large-scale software development environments. 8 ACM Distinguished Paper Awards Best Paper Award: How Long Will it Take to Mitigate this Incident for Online Service Systems? David Lorge Parnis Fellowship Senior Member of IEEE Distinguished Member of ACM Distinguished Member of CCF Fellow of Engineers Australia (FIEAust) Recognized in The Australian's Top Researchers special edition as leading researcher in Software Systems World's Top 2% Scientists (career-long) Dr. Zhang has successfully advised numerous PhD and Master's students who have gone on to prominent positions at leading technology companies and academic institutions worldwide. His research has been supported by significant grants including Australian Research Council Discovery Projects (as Lead CI) and multiple National Science Foundation of China projects. His work has made tangible impacts in industry, most notably through the Microsoft Developer Assistant project which received over 450K downloads in 2016. He leads research groups focused on intelligent software engineering and software analytics, with strong collaborations between Chongqing University, The University of Newcastle, and Microsoft Research. His teams develop practical tools for code intelligence, log analysis, and fault diagnosis that are deployed in real-world online service systems.