جست‌وجوی استاد و پژوهشگر

• Work and Organizational Psychology
• Human Factors
• AI and Robotics in Human Systems
• +۴ مورد دیگر

Luca Pietrantoni is a Full Professor of Work and Organizational Psychology at the Department of Psychology 'Renzo Canestrari', University of Bologna. He coordinates the 'Human Factors, Risk and Safety' research group and actively participates in EU Horizon projects like SESTOSENSO and TUPLES, focusing on AI and robotics in human systems. His research spans mental health promotion in workplaces, cognitive ergonomics, and sustainable commuting. Education: PhD in Health Psychology (University of Florence, 2000) Postdoctoral fellowship in Social Psychology (University of Parma) Psychology Degree (University of Padua, 1995 with top honors) Research Interests: Human factors in AI/robotics integration Workplace mental health interventions Climate change adaptation strategies Cognitive ergonomics in manufacturing Grants & Projects: Principal Investigator in Horizon Europe projects (SESTOSENSO, TUPLES) Coordinator of H-WORK (mental health in SMEs/public workplaces) EU-funded studies on exoskeletons, coastal safety, and cyclist safety Labs/Teams: Human Factors, Risk and Safety research group.

Yaron Shaposhnikمشاهده پروفایل

دانشیار

• Stochastic Dynamic Optimization
• Machine Learning Applications
• Business Analytics
• +۵ مورد دیگر

Yaron Shaposhnik is an Associate Professor of Information Systems and Operations Management at the Simon School of Business, University of Rochester. His research focuses on stochastic dynamic optimization, machine learning applications, and business analytics. He holds degrees from MIT (Operations Research) and the Technion–IIT (Industrial Engineering and Information Systems Engineering). Education: PhD in Operations Research, MIT MS in Industrial Engineering, Technion–IIT BS in Information Systems Engineering, Technion–IIT Research interests include developing decision support tools leveraging analytics to improve operations, with a focus on healthcare systems, financial lending, and scheduling algorithms. His work bridges optimization theory and practical applications, emphasizing interpretable machine learning models. Teaching includes predictive analytics using Python, PhD-level optimization courses, and seminars on data analytics in operations management. He advises PhD students and has industry experience as a research intern at Brigham and Women’s Hospital and software engineer at Mind CTI.

Deepak Guptaمشاهده پروفایل

استاد

• Production Scheduling
• Supply Chain Optimization
• Energy Efficiency
• +۲ مورد دیگر

Dr. Deepak Gupta is Professor and Department Chair of Industrial and Manufacturing Engineering at Wichita State University's College of Engineering. As Graduate Coordinator, he leads research in industrial systems optimization and sustainable manufacturing. His research develops computational models for production scheduling, supply chain management, and energy efficiency. Recent publications focus on Industry 4.0 applications in logistics, including dynamic vehicle routing algorithms and machine scheduling optimizations under uncertainty. Sustainability integration in manufacturing systems is a growing thematic focus. Earlier foundational work established energy efficiency frameworks through projects like the Energy Efficiency Knowledge Center (EEKC) and maintenance models for industrial equipment.

Katherine Watmoughمشاهده پروفایل

دانشیار

• Library and Information Science
• Academic Librarianship
• User Services
• +۴ مورد دیگر

Katherine Watmough serves as Associate Librarian (equivalent to Associate Professor) at Thompson Rivers University Library, coordinating Borrower Services and acting as liaison for Adventure Studies, Agricultural Sciences, Animal Health Technology, Chemistry, Culinary Arts, Geology, Physics, Tourism Management, Trades & Technology, and Veterinary Technology. B.A. in History & English with First Nations Studies Minor M.L.I.S. with IT Management concentration B.C. Provincial Instructor's Diploma TESL level II eligible Her research centers on practical library operations including resource sharing models, staffing optimization, and innovative services like the 'Library of Things'. She critically examines neoliberalism's impact on academic libraries and actively integrates Indigenous knowledge frameworks through committee collaborations. Analysis of her 2010-2019 publications reveals consistent focus on user service enhancement, with recurring themes in staff training methodologies, extended-hour student engagement strategies, and collaborative collection development approaches across diverse academic disciplines. Watmough demonstrates professional leadership through conference presentations at national events including CAUT Librarian Conference and Access Services Conference, frequently partnering with committees like the Indigenous Knowledge Standing Committee to shape library policies and donation protocols.

• Computational Sustainability
• Cloud Computing
• Edge Computing
• +۳ مورد دیگر

Ivona Brandic is a Full Professor of High Performance Computing Systems at TU Wien's Institute of Information Systems Engineering, leading the HPC Research Group. She specializes in Computational Sustainability, Cloud/Edge Computing, and Quantum-Classical Systems. Her roles include Head of the Computational Sustainability Research Unit and membership in TU Wien's Faculty Council. She teaches courses such as AI/ML in Climate Change and Hybrid Quantum-Classical Systems. Her research focuses on sustainable IT, energy-efficient systems, and hybrid quantum-classical workflows. Projects include computational sustainability initiatives funded by the Austrian Science Fund (FWF) and industry partnerships like the Virtual Shepherd project. She has contributed to over 50 publications, emphasizing edge computing, quantum algorithms, and HPC optimization. Notable contributions include developing frameworks like RIGOLETTO for hybrid scientific workflows and FRESCO for edge offloading. Her work bridges theoretical advancements with practical applications in environmental monitoring and energy efficiency.

Fantahun M. Defershaمشاهده پروفایل

استاد

• Manufacturing system analysis
• Flexible and cellular manufacturing systems
• Reconfigurable manufacturing systems
• +۷ مورد دیگر

Fantahun M. Defersha is a Full Professor in the Department of Mechanical and Industrial Engineering at the University of Guelph, Ontario, Canada. He holds a PhD in Mechanical Engineering from Concordia University (2006) and has over 28 years of academic experience, including roles as Area Head in Mechanical Engineering. His research focuses on manufacturing systems optimization, cellular manufacturing, supply chain modeling, meta-heuristics, and parallel computing applications. Education: B.Sc. Mechanical Engineering (1995), Addis Ababa University MEng. Mechanical Engineering (2000), University of Roorkee (IIT Roorkee) PhD Mechanical Engineering (2006), Concordia University Research Interests: Manufacturing system analysis, flexible/cellular manufacturing systems, reconfigurable manufacturing systems, supply chain optimization, meta-heuristics, parallel computing, and additive manufacturing sustainability. His work integrates computational methods like genetic algorithms and machine learning to solve complex industrial problems. Publications: Over 50 peer-reviewed journal articles, emphasizing sustainable manufacturing, optimization algorithms, and industrial systems. Recent work includes hybrid machine learning approaches for additive manufacturing and cloud-based digital twin systems. Honors: Campaign for a New Millennium Graduate Scholarship (2004–2005) Concordia University International Tuition Fees Remission Award (2004–2005) Concordia University Graduate Fellowship (2004–2005) Teaching & Advising: Taught over 25 courses, including Optimization in Engineering, Discrete Event Simulation, and Manufacturing Systems Design. Actively advises graduate students in mechanical and industrial engineering. Current research funding includes NSERC grants for Industry 4.0 integration and digital twin technologies. Labs/Teams: Leads research on smart manufacturing systems, digital twins, and sustainable production processes through collaborations with industry partners like AVL Manufacturing Inc.

Yalçın Açıkgözمشاهده پروفایل

دانشیار

• Employee Recruitment
• Job Search Behavior
• Applicant Decision-Making
• +۶ مورد دیگر

Dr. Yalçın Açıkgöz is an Associate Professor at Appalachian State University's Department of Psychology. He holds a Ph.D. from Middle East Technical University (2016), an M.A. from Appalachian State University (2013), and a B.S. from Turkish Military Academy (2004). His research focuses on employee recruitment dynamics, applicant decision-making, social media applications in HR, and workplace professionalism. He leads a research lab with active projects on safety analytics and ethical AI in selection processes. Research Interests: Employee recruitment strategies, social media in talent acquisition, applicant reactions to modern selection tools, and occupational safety analytics. His work bridges organizational behavior, industrial-organizational psychology, and data-driven HR practices. Publications Trends: Recent work emphasizes safety analytics frameworks, AI ethics in hiring, and SDG alignment in organizational practices. His research often involves collaborations with industry partners to address real-world challenges in workplace safety and recruitment innovation. Awards: None explicitly listed. Advising & Grants: Mentors students in I-O psychology programs; no grant details provided. Lab Activities: Maintains an active research lab exploring cutting-edge topics in recruitment technology and occupational health analytics.

Nikolay Simakovمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• Machine Learning
• Scientific Software Optimization
• +۳ مورد دیگر

Dr. Nikolay Simakov is a Computational Scientist at the University at Buffalo's Center for Computational Research (CCR), joining in 2012. He holds a PhD in Computational Chemistry from Carnegie Mellon University, alongside BA and MA degrees in Applied Physics and Mathematics from Moscow Institute of Physics and Technology. Research Interests: High-Performance Computing (HPC), Machine Learning, Scientific Software Optimization, Data Analytics for Large Datasets, and Simulation of Biomolecular Systems Teaching: Taught graduate courses like EAS509 Statistical Learning and Data Mining-II and CDA501 Introduction to Data Driven Analysis, mentoring undergraduate interns at CCR Key Contributions: Developed Slurm Simulator for HPC scheduling, contributed to XDMoD/ACCESS Metrics for resource auditing, optimized GPU-based Poisson equation solvers, and advanced multi-rheology geophysical flow modeling Technical Focus: Performance monitoring of HPC systems, node sharing analysis, and ARM architecture benchmarking His work bridges computational infrastructure optimization and biological system modeling, with publications spanning HPC resource management, biomolecular simulations, and parallel computing frameworks.

Waguih ElMaraghyمشاهده پروفایل

استاد

• Manufacturing Engineering
• Industrial Engineering
• Systems Engineering
• +۳ مورد دیگر

Waguih ElMaraghy is a Professor at the University of Windsor's Faculty of Engineering, specializing in the Department of Mechanical, Automotive, and Materials Engineering. He is a renowned academic with notable contributions to manufacturing systems, reconfigurable robotics, and Industry 4.0 technologies. ElMaraghy holds prestigious fellowships from the Canadian Academy of Engineering and the Society of Manufacturing Engineers (SME), and actively serves on professional boards like the Canadian Engineering Accreditation Board. His research focuses on advanced manufacturing paradigms, including digital twin frameworks, supply chain resilience, and cognitive manufacturing systems. He has pioneered work on adaptable assembly lines and fault detection systems, integrating machine learning into manufacturing processes. His cross-disciplinary efforts extend to biomedical applications, such as using deep learning for breast cancer biomarker identification. ElMaraghy has been honored for over three decades of volunteer service to Professional Engineers Ontario and the American Society of Mechanical Engineers (ASME). His work on sustainable supply networks and reconfigurable machinery has shaped modern industrial practices, particularly in automotive framing systems for vehicles like GO trains. Awards: Ontario Volunteer Service Award, SME College of Fellows, Canadian Academy of Engineering Fellow Key Roles: Former Head of Industrial and Manufacturing Systems Engineering, Board Member of Canadian Engineering Accreditation Research Themes: Industry 4.0, Digital Twins, Supply Chain Resilience, Biomedical Applications of AI

Sreoshi Banerjeeمشاهده پروفایل

پژوهشگر ارشد

• Mechanism Design
• Cooperative Game Theory
• Queueing Theory
• +۷ مورد دیگر

Sreoshi Banerjee is a Research Fellow and postdoctoral researcher at the QSMS Research Centre within the Faculty of Economic and Social Sciences at Budapest University of Technology and Economics (BME). She is currently on the 2024 academic job market and previously held a visiting postdoctoral fellowship at the University of Rochester (NY), collaborating with Prof. William Thomson. Her PhD in Quantitative Economics was completed at the Indian Statistical Institute (Kolkata) under the late Prof. (HAG) Manipushpak Mitra, with additional research at the Economics and Planning Unit (EPU), Delhi, under Prof. Debasis Mishra. Education includes: B.A. Economics from Shri Ram College of Commerce (SRCC), Delhi University M.A. Economics (specialization in World Economy) from Jawaharlal Nehru University (JNU), CITD PhD in Quantitative Economics from the Indian Statistical Institute (ISI), Kolkata Her research interests span mechanism design , cooperative game theory , queueing theory , and applications to environmental economics , welfare economics , and income inequality . Recent work emphasizes sequencing problems, strategyproofness, and fair allocation mechanisms in multi-server systems. Her job market paper explores the Shapley and serial solutions in such contexts. Publications and working papers focus on theoretical economics with practical implications for fairness, sustainability, and policy. Current research collaborations include Prof. Christian Trudeau (on scheduling and divisible jobs) and Prof. Christopher Stapenhurst (marine plastics and income redistribution). No scientific awards explicitly mentioned. She has supervised seven master’s theses on Risk Management/ESG topics and taught courses on Price Forecasting (MSc Finance) and cooperative game theory workshops. Active in the QSMS Research Centre, she frequently organizes and participates in seminars and academic events.

Naveen Gargمشاهده پروفایل

استاد

• Algorithms Design
• Approximation Algorithms
• Combinatorial Optimization
• +۳ مورد دیگر

Naveen Garg is the Usha Hasteer Chair Professor in the Computer Science and Engineering Department at IIT Delhi. He earned his Ph.D. in 1994 and joined IIT Delhi as an Assistant Professor in 1997 after postdoctoral research at MPI-Informatik, Saarbruecken. He coordinates the UQ-IITD Academy of Research (UQIDAR). His research focuses on algorithms design, approximation algorithms for NP-hard problems, combinatorial optimization, and graph theory. He advises students on research projects and mentors through the department's structured programs. Contact: naveen@cse.iitd.ac.in. Research Interests: Approximation algorithms for NP-hard problems Combinatorial optimization Graph theory applications Algorithmic game theory Publications: Over 40 papers in top venues, focusing on network optimization, scheduling algorithms, and facility location problems. Active in conferences like ICALP and APPROX/RANDOM.

Feng Juمشاهده پروفایل

دانشیار

• Stochastic Modeling
• Additive Manufacturing
• Healthcare Systems
• +۴ مورد دیگر

Dr. Feng Ju is an Associate Professor and Program Chair of Industrial Engineering at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He joined ASU in 2015 and holds additional roles as a Senior Global Futures Scientist at the Julie Ann Wrigley Global Futures Laboratory. His research focuses on stochastic modeling, optimization of production systems, additive manufacturing, healthcare delivery systems, and battery management for electric vehicles. He is affiliated with IEEE, IISE, and INFORMS, and serves as an associate editor for multiple journals. Dr. Ju has received numerous awards, including the Dr. Hamed K. Eldin Outstanding Early Career IE Award and SME Outstanding Young Manufacturing Engineer Award. He advises students in Industrial Engineering and collaborates on projects funded by NIST, Boeing, and NSF. Education: Ph.D. Industrial and Systems Engineering from University of Wisconsin-Madison; M.S. Electrical and Computer Engineering from UW-Madison; B.S. Electrical and Computer Engineering from Shanghai Jiao Tong University. Additional training includes a visiting scholar position at Carnegie Mellon University’s Robotics Institute. Research Interests: Stochastic modeling of production systems, semiconductor manufacturing, healthcare logistics, machine learning in additive manufacturing, and battery management systems. His work emphasizes real-time control, simulation optimization, and smart manufacturing integration. Awards: Over 15 honors including Best Paper Awards at IEEE CASE, IISE Transactions, and NIST competitions. Mentored students in winning hackathons and competitions, including ASME-CIE Hackathon 2021 and Tesla Factory collaborations. Service: Organized conferences including IEEE CASE 2019 and served as track chair for IISE Annual Conference 2019. Active in editorial roles for IISE Transactions and IEEE Robotics and Automation Letters. Labs: Leads the Manufacturing and Service Automation Lab, focusing on interdisciplinary research in smart manufacturing, healthcare systems, and sustainable production.

Dinard van der Laanمشاهده پروفایل

استادیار

• Dynamic Programming
• Markov Decision Processes
• Optimization of Queueing Networks
• +۳ مورد دیگر

Dinard van der Laan is an Assistant Professor at the Department of Operations Analytics within the School of Business and Economics at Vrije Universiteit Amsterdam. He holds a PhD from Leiden University (2003). His research focuses on dynamic programming, Markov decision processes, optimization of queueing networks, and dynamic pricing, contributing to UN Sustainable Development Goals through operations research applications. Key research interests include decision rule engineering, stochastic optimization, and queueing network control. His work applies to resource allocation, server systems, and dynamic pricing strategies. Recent publications address parallel server assignment, call center control, and auction mechanisms. Teaches courses including Operations Research and Thesis supervision. Engaged in academic activities such as peer reviewing for Operations Research , visiting research at the University of York (2016–2018), and presenting on sequential assignment algorithms at international conferences.

Mieczysław Wodeckiمشاهده پروفایل

استاد

• Discrete Optimization
• Scheduling Problems
• NP-hard Problems
• +۷ مورد دیگر

Professor Mieczysław Wodecki is affiliated with the Faculty of Information and Communication Technology at Wrocław University of Science and Technology, where he is a member of the Department of Telecommunications and Teleinformatics. He holds a DSc and PhD and is actively engaged in research and teaching. Research Interests: His work centers on discrete optimization, scheduling problems, and methods for solving NP-hard problems. He applies parallel computing and probabilistic models to challenges in project management and automation in construction. His research bridges theoretical computer science with industrial engineering applications, particularly in manufacturing and production systems. The recent publications highlight a strong focus on cyclic job shop and flow shop scheduling, with an emphasis on parallel algorithms and metaheuristics. These works contribute to the optimization of manufacturing processes, especially in cyclic and flexible production environments. Scientific Contributions: While no specific awards are listed, his extensive publication record in reputable journals such as Computers & Industrial Engineering and Bulletin of the Polish Academy of Sciences: Technical Sciences reflects sustained scholarly impact. Advising and Grants: Information on students advised or research grants received is not available in the provided text. Laboratories and Teams: He collaborates closely with researchers including W. Bożejko, J. Pempera, and A. Gnatowski, contributing to a research group focused on discrete systems and scheduling optimization. His work appears in special issues dedicated to discrete systems, indicating active participation in thematic research initiatives.

Andrzej Kasprzakمشاهده پروفایل

استاد

• Machine Learning
• Computer Science
• Network Optimization
• +۳ مورد دیگر

Professor Andrzej Kasprzak is a faculty member at the Faculty of Information and Communication Technology, Wrocław University of Science and Technology, serving as Head of the Department of Systems and Computer Networks. His research spans machine learning, network optimization, and data stream processing, with applications in telecommunications, healthcare, and industrial systems. University: Wrocław University of Science and Technology School: Faculty of Information and Communication Technology Department: Department of Systems and Computer Networks Email: andrzej.kasprzak@pwr.edu.pl Kasprzak’s research focuses on integrating machine learning with network optimization, particularly in elastic optical networks and data stream environments. His work addresses challenges in multi-agent pathfinding, fake news detection, and 3D printing automation, emphasizing practical experimentation systems. Recent publications highlight trends in ensemble learning for real-time data streams, domain adaptation in AI systems, and innovative optimization algorithms for network traffic and scheduling problems. These contributions bridge theoretical advancements with industrial applications. As Head of Department, Kasprzak leads initiatives in systems and computer networks, fostering interdisciplinary collaboration in artificial intelligence and telecommunications. His career reflects a commitment to advancing computational methodologies in complex environments.