جستجوی استاد راهنمای دانشگاهی

Gulser Köksalمشاهده پروفایل

استاد

• Quality Engineering
• Statistical Process Control
• Robust Design
• +۶ مورد دیگر

Professor Gulser Köksal is a distinguished academic in the Department of Industrial Engineering at TED University. She previously served as Professor at Middle East Technical University (METU) from 1993 to present, holding significant administrative roles including Vice Rector (2017-2019) and Advisor to the Rector (2016-2017). Her academic journey includes positions as Associate Professor (1999-2006) and Assistant Professor (1993-1999) at METU, and Assistant at North Carolina State University (1988-1993). Her educational background includes a PhD in Industrial and Systems Engineering from North Carolina State University (1987-1992), an MS in Industrial Engineering from Middle East Technical University (1985-1987), and a BS in Industrial Engineering from the same institution (1980-1985). She is proficient in English at an advanced level (C1). Professor Köksal's research focuses on quality engineering, statistical process control, robust design methodologies, and data mining applications for quality improvement. Her work bridges theoretical statistical methods with practical industrial applications, particularly in multi-response optimization, desirability functions, and quality function deployment. She has made significant contributions to understanding inspection errors, rework processes, and their economic impacts on quality characteristics. Her publication portfolio demonstrates a consistent trajectory from foundational work in statistical process control to more recent applications of machine learning and neural networks in quality engineering. The research shows strong emphasis on practical industrial applications while maintaining theoretical rigor, particularly in handling categorical responses, uncertainty modeling, and multi-criteria decision making in product design. Professor Köksal has successfully supervised 64 theses across doctoral and master's levels, demonstrating her commitment to academic mentorship. Her research has been supported by numerous projects from TÜBİTAK, Erasmus, and Teknopark, totaling 21 projects. These projects span quality engineering, data mining applications, product design optimization, and strategic university governance. She has contributed to the academic community through extensive service, including participation in international conferences, workshops on quality engineering and higher education leadership, and collaborative research projects with institutions across Europe and China. Her work on university governance, particularly through the LEAD project, demonstrates her commitment to advancing academic leadership practices.

Elizabeth Akinyi Ondulaمشاهده پروفایل

استادیار

• Artificial Intelligence
• Reinforcement Learning
• Decision Theory
• +۳ مورد دیگر

Elizabeth Akinyi Ondula is an Assistant Professor of Computer Science at Winthrop University , with a Ph.D. in Computer Science from the University of Southern California where she was part of the Autonomous Networks Research Group under Prof. Bhaskar Krishnamachari. Her research focuses on AI systems operating under uncertainty , integrating reinforcement learning , decision theory , and control-theoretic perspectives to design reliable AI for high-stakes environments . Research Interests She specializes in: Evaluating AI decision-making under stochastic environmental and model variability Developing domain-specific frameworks for AI deployment in physical/social contexts Designing interpretable and safe AI through coordination structures and information pattern analysis Her work spans epidemic response systems (SafeCampus-RL, Classroom-MDP), multi-agent fairness frameworks (Fair-MARL), and agricultural optimization tools for Kenyan farmers. Teaching Roles (Fall 2025) CSCI 475: Software Engineering I CSCI/DIFD 141: Introduction to Web Application Design CSCI 245: Front End Web Development Her email is eondula@usc.edu .

Fotis Kopsaftopoulosمشاهده پروفایل

استادیار

• Structural Health Monitoring
• Aerospace Engineering
• Machine Learning
• +۷ مورد دیگر

Fotis Kopsaftopoulos is an Assistant Professor at the Department of Mechanical, Aerospace, and Nuclear Engineering (MANE) at Rensselaer Polytechnic Institute (RPI) , where he has been since 2017. His research focuses on intelligent aerospace and mechanical systems , particularly structural health monitoring (SHM) , stochastic modeling , and machine learning applications for damage detection and fault diagnosis. Education: Ph.D. in Mechanical Engineering & Aeronautics (2012), University of Patras Diploma in Mechanical Engineering & Aeronautics (2004), University of Patras Research Interests: Kopsaftopoulos develops data-driven SHM frameworks using stochastic time series models , Gaussian process regression , and guided wave propagation for aerospace structures. His work addresses damage detection under varying loads , rotor fault diagnosis in multicopters, and health monitoring of batteries and adhesively bonded joints . Scientific Awards: 2021 Class of 1951 Outstanding Teaching Award at RPI 2015 Most Practical SHM Solution for Aerospace Award (sponsored by Airbus) 1998–1999 Academic Scholarship of Excellence from the State Scholarship Foundation of Greece Teaching & Leadership: He has taught courses such as System Identification , Aerospace Structures and Materials , and Engineering Dynamics at RPI. He served as co-Editor for Structural Health Monitoring proceedings, Associate Editor for the Structural Health Monitoring Journal , and Topic Editor for Sensors Journal . He has advised 6 PhD, 14 MEng/MS, and 46 BS projects.

Marco Munderlohمشاهده پروفایل

پژوهشگر ارشد

• Video Coding
• Motion Detection
• Wave Field Synthesis
• +۶ مورد دیگر

Marco Munderloh is a research fellow at the Leibniz University Hannover under the Institute for Information Processing . His career spans advanced video coding, predictive maintenance, and photonic design, with a focus on low-bitrate aerial video compression and motion detection. Dipl.-Ing. in Computer Engineering (2004), Technical University of Ilmenau PhD (2015), Leibniz University Hannover His research interests include: Wave Field Synthesis (WFS) with patented applications in cinema sound systems Predictive maintenance using deep learning and Bayesian neural networks Inverse design for photonic integrated circuits and 3D nanostructures Region-of-interest (ROI) video coding and motion compensation Optical verification of construction materials Marco's recent publications (2025–2018) cover predictive maintenance, video compression, and photonic design. Key trends involve applying machine learning to industrial reliability, optimizing video codecs for aerial surveillance, and computational inverse design for optical devices. Scientific achievements include: Patent for WFS technology in sound reproduction Collaborations span institutions like IEEE, CIRP, EUSPEN, and journals such as APSIPA Transactions and Bautechnik . Contact: Marco.Munderloh@tnt.uni-hannover.de , Marco.Munderloh@web.de .

Amedeo R. Odoniمشاهده پروفایل

استاد

• Operations Research
• Applied Probability Theory
• Stochastic Processes
• +۱۰ مورد دیگر

Amedeo R. Odoni is a Professor of Aeronautics and Astronautics and Professor of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT) within the School of Engineering. He has been a faculty member since 1969, holding leadership roles including Head of the Systems Division (1991-1996) and Co-Director of the Operations Research Center (1985-1991). Currently, he serves as Lead Principal Investigator of the Future Urban Mobility research project at the Singapore-MIT Alliance for Research and Technology (SMART). His educational background includes: S.B. in Electrical Engineering and Computer Science from MIT (1965) S.M. in Electrical Engineering and Computer Science from MIT (1967) Ph.D. in Operations Research from MIT (1969) Professor Odoni's research focuses on applied probability theory, stochastic processes, and decision-making under uncertainty, with applications in airport planning, air traffic management, and urban service systems. His work bridges operations research and transportation engineering, emphasizing practical solutions for air transportation challenges including queuing theory, location theory, and probabilistic combinatorial optimization. Recent publications demonstrate a progression from foundational models to sophisticated network analyses addressing delay propagation, runway configuration optimization, and policy-oriented studies of aviation infrastructure. His distinguished honors include: Election to the National Academy of Engineering INFORMS Fellowship and Robert F. Herman Lifetime Achievement Award U.S. FAA National Award for Excellence in Aviation Education Multiple MIT teaching awards including the Capers and Marion McDonald Award Honorary Ph.D. from Athens University of Economics and Business As an advisor, Professor Odoni has supervised 41 Ph.D. students (several winning major dissertation awards) and served on over 100 thesis committees. His research has been supported by significant grants including leadership of the Future Urban Mobility project (Singapore National Research Foundation), the Global Airline Industry Center (Alfred P. Sloan Foundation), and the FAA's National Center of Excellence in Aviation Operations Research (NEXTOR). He maintains active collaborations with major international airports and civil aviation authorities through consulting assignments worldwide. He leads the Future Urban Mobility research group at SMART and has established influential research centers including NEXTOR and the Global Airline Industry Center, fostering global partnerships between academia, government, and industry to address critical challenges in air transportation systems.

Mohamed Sallakمشاهده پروفایل

دانشیار

• Reliability Engineering
• Risk Analysis
• Uncertainty Quantification
• +۵ مورد دیگر

Mohamed Sallak is an Associate Professor and HDR (Habilitation à Diriger des Recherches) at the University of Technology of Compiègne, where he works in the Department of Computer Engineering within the Heudiasyc Laboratory (UMR CNRS 7253), a joint research unit between UTC and CNRS. His research focuses on dependability assessment of systems under uncertainty, railway systems safety, and predictive maintenance. His primary research interests include: Dependability assessment of systems under uncertainty, particularly using RAMS analysis (Reliability, Availability, Maintainability, Safety) Railway systems safety, including safety studies, railway standards (EN 50126, EN 50128, EN 50129), and risk analysis Predictive maintenance for transportation systems Uncertainty quantification using belief functions theory and evidential networks Human reliability in railway supervision systems His publication record demonstrates consistent contributions to reliability engineering, with over 35 journal articles and 80 conference papers. His recent work shows a strong emphasis on applying advanced mathematical methods to reliability problems in transportation systems, particularly focusing on uncertainty representation and quantification. He frequently publishes in top journals in reliability engineering, risk analysis, and transportation systems, often collaborating with researchers from various French institutions. Dr. Sallak has supervised numerous PhD students whose research spans reliability analysis, predictive maintenance, railway systems safety, and uncertainty quantification. His students have worked on diverse applications including railway systems, transportation networks, and industrial machinery. He has secured significant research funding through multiple projects: Principal Investigator of ANR PRCE MAPSYD project (2018-2021): "Predictive Maintenance of transportation systems under incomplete and imprecise data" Principal Investigator of the regional project ODISYM (2016-2019): "Optimization of Availability of multi-state systems under uncertainty" Principal Investigator of ANR JCJC Récif project (2013-2017): "Belief networks for quantitative evaluation of dependability parameters of railway systems" Member of ANR PRCE DRIFT-HF project (2022-2026): "Digitalization – Risks, (U)ncertainties and Fragilities of Technologies in relation to the Human Factor" Member of the national project "Autonomous Freight Train (TFA)" (2018-2023) Member of the national project "Remote Driving on Rail (TC-Rail)" (2017-2021) He holds significant scientific responsibilities including being responsible for Topic 2 (Data with uncertainty, Bayesian Networks, Evidential Networks) of the International Scientific Coordination Network "Human-Machine Systems in Transportation and Industry (GDR I-HAMASYTI)" since 2014, co-convenor of the Working Group GT: Human-Machine systems of the GDR MACS, and member of the Scientific Council of both the Compiègne University of Technology and the Heudiasyc Laboratory.

• Optimal Stopping Theory
• Stochastic Processes
• Applied Probability
• +۳ مورد دیگر

Hugh Entwistle is a part-time lecturer at the School of Mathematical and Physical Sciences, Macquarie University. His research focuses on theoretical and applied aspects of stochastic processes and decision theory, particularly in optimal stopping problems. His work bridges probabilistic modeling with computational methods for dynamic optimization scenarios. Research Trends: His publications over the last three years demonstrate expertise in asymptotic analysis of stopping rules, dynamic programming approximations, and sequential decision frameworks. Common themes include maximizing reward functions under uncertainty, developing bounds for stochastic systems, and computational approaches to multi-stage stopping problems.

Andrea Carronمشاهده پروفایل

مدرس ارشد

• Model Predictive Control
• Learning-based Control
• Coverage Control
• +۷ مورد دیگر

Dr. Andrea Carron is a Senior Lecturer at ETH Zürich, affiliated with the Intelligent Control Systems group under Professor M. Zeilinger at the Department of Mechanical and Process Engineering. He holds a PhD in Information Technology from the University of Padova (2016) and was a Postdoc at ETH Zurich from 2016 to 2020. Education: B.S. and M.Sc. in Control Engineering (University of Padova, 2010 and 2012) Professional Roles: Senior Lecturer (ETH Zurich, 2022–present), Postdoc Fellow (ETH Zurich, 2016–2020) Research Interests: Andrea Carron's work focuses on Model Predictive Control (MPC) and Learning-based Control with safety guarantees. His research addresses challenges in Distributed Safe Learning , Coverage Control , and Autonomous Racing , utilizing Gaussian Processes and Stochastic Control frameworks. He has developed safety filters for racing vehicles, scalable MPC for mobility-on-demand systems, and Kalman-filter-enhanced GP regression techniques. Article Trends: Recent publications emphasize Autonomous Racing (ForzaETH Race Stack), Safe Learning for distributed systems, Gaussian Process applications in control, and Robust MPC under uncertainty. His work bridges machine learning and classical control theory, with applications in robotics and real-time systems. Teaching Activities: He has taught courses such as Signals and Systems and Advanced Model Predictive Control at ETH Zurich and Ashesi University since 2017. Course content includes discrete-time signal processing, system identification, and control algorithms.

• Chemical Engineering
• Optimisation
• Process Control and Simulation
• +۴ مورد دیگر

Research Fellow at Imperial College London's Department of Computing within the Faculty of Engineering, Gabriel D. Patrón focuses on stochastic optimization and control systems for water, energy, and food nexus applications. His work integrates economic incentives with technical systems optimization. PhD in Chemical Engineering (2023) from University of Waterloo MSc in Computing (2018) from Imperial College London BASc in Chemical Engineering (2017) from University of Toronto Research spans chemical engineering , optimization , process control , and carbon capture , with particular expertise in: Real-time optimization algorithms Economic model predictive control Uncertainty quantification in process systems Water-energy-food nexus applications Post-combustion CO2 capture Aquaculture process optimization Recent publications show publication trends in CO2 capture and water-energy-food nexus applications, with methodological contributions to real-time optimization and model predictive control . Key application areas include aquaculture systems and chemical looping combustion technologies. Associated with the Computational Optimization Group (COG) under Professor Calvin Tsay, Gabriel collaborates on projects involving stochastic optimization and energy systems at Imperial College London.

• Operations Management
• Management Accounting
• Demand Forecasting
• +۷ مورد دیگر

Hermann Jahnke is a Full Professor and Chair for Management Accounting and Operations Management at the Faculty of Economics, Bielefeld University. He has held academic positions at the University of the German Federal Armed Forces in Hamburg and Cologne University, and served as Dean of his faculty twice (1997-1998, 2015-2023). Education: Habilitation in Operations Management (Hamburg University), PhD in Statistics and Econometrics (Hamburg University) Affiliations: Chair of Business Administration, Controlling & Production Management; Bielefeld Graduate School of Economics and Management Research Focus: His work bridges managerial accounting, decision theory, and operational challenges in modern retail and manufacturing contexts. Key contributions include: Demand forecasting frameworks for e-grocery retailers using distributional regression Complexity analysis of discrete lot-sizing and scheduling problems Integration of internal/external accounting under IFRS for SMEs Strategic cost modeling and service operations management Investigations into crowd logistics and fast fashion supply chains Publications: Over 30 peer-reviewed articles in journals like Decision Sciences , European Journal of Operational Research , and International Journal of Forecasting , plus 3 German-language books on cluster analysis and operations management.

Pandu R. Tadikamallaمشاهده پروفایل

استاد

• Simulation Methodology
• Statistical Techniques in Management
• Quality Management
• +۳ مورد دیگر

Pandu R. Tadikamalla is Professor of Business Administration at the Katz Graduate School of Business, University of Pittsburgh, where he teaches statistical techniques, decision technologies, simulation, and total quality management. With over 45 publications and editorial roles at OMEGA and the American Journal of Mathematical and Management Sciences , he bridges academic rigor with corporate problem-solving through MBA Consulting Field Projects. His educational foundation includes a PhD and MS in Industrial and Management Engineering from the University of Iowa and a BS in Mechanical Engineering from Andhra University, India. This interdisciplinary background informs his research in simulation methodology, statistical management techniques, and quality/productivity systems. Tadikamalla's research spans supply chain optimization under carbon constraints, natural disaster relief logistics, and customer risk hedging. His recent publications demonstrate methodological innovation through bilevel programming for emergency allocation, evolutionary game theory for environmental governance, and agent-based models for digital branding—consistently applying operations research to sustainability and crisis management challenges. He has earned multiple teaching accolades including five Distinguished Professor Awards across EMBA programs and the Outstanding Professor of the Year for the MBA Class of 1986. His consulting work with multinational corporations translates theoretical frameworks into optimized inventory systems and supply chain solutions. As an active community leader, Tadikamalla serves as president of the Executive Committee and chairman/treasurer of the Board of Directors for S.V. Temple, demonstrating commitment to societal impact beyond academia.

• Data-Driven Decision-Making
• Industrial Statistics
• Reliability Engineering
• +۳ مورد دیگر

Qiuzhuang Sun is a Lecturer in the School of Mathematics and Statistics at the University of Sydney. His research focuses on data-driven decision-making, industrial statistics, and reliability engineering, with applications in healthcare, transportation, and sustainable urban systems. Alma Mater: Joint B.Eng. in Industrial Engineering and Computer Science from Shanghai Jiao Tong University; Ph.D. in Industrial Systems Engineering from National University of Singapore His research interests include: Data-Driven Decision-Making for healthcare, emergency response, and preventive maintenance Applied data science and machine learning for transportation data, clinical data, and megacity sustainability Reliability engineering with emphasis on anomaly detection, degradation modeling, and prognostic health management Recent publications highlight advancements in degradation modeling, robust optimization, and healthcare system design, reflecting interdisciplinary collaborations in industrial engineering, statistics, and biomedical research. His work aligns with the Faculty of Science's research strengths in Data and Decisions, Precision Health, and Sustainable Systems. Scientific accolades include: Finalist, IISE Data Analytics & Information Systems Case Study Competition (2024) First Place, INFORMS Blue Summit Supplies Data Mining Challenge (2023) Best Student Paper Award, INFORMS Society on Location Analysis (2023) Best Paper Award, INFORMS Conference on Quality, Statistics, and Reliability (2023) Winner, INFORMS QSR Industrial Data Challenge Competition (2022) ISE Magazine Featured Article (2020) Excellent Paper Award, Operations Research Society of China Reliability Chapter (2019) He supervises students in projects related to reliability engineering and data-driven decision-making. His work integrates statistical modeling, machine learning, and industrial systems optimization.

Francisco Jurado Melguizoمشاهده پروفایل

استاد

• Power Systems
• Renewable Energy
• Energy Storage
• +۳ مورد دیگر

Francisco Jurado Melguizo is a Professor in the Department of Electrical Engineering at Universidad de Jaén. His research focuses on power systems, renewable energy integration, and optimization algorithms, with over 520 JCR-indexed publications and 260 conference papers. He has led projects funded by Spanish Ministries and the European Commission and authored 8 books. PhD: Universidad Nacional de Educación a Distancia (UNED), 1999 His work spans distributed generation, energy storage, smart grids, and techno-economic analysis, often incorporating AI and metaheuristic optimization techniques. Recent projects include hybrid renewable systems, EV charging infrastructure, and grid resilience under climate uncertainties. He has received recognition as one of the most influential researchers globally by Stanford University and contributes to research groups focused on electrical technology and energy systems.

• Adaptive Software Architectures
• Smart Cyber-Physical Systems (CPS)
• Internet of Things (IoT)
• +۵ مورد دیگر

Tomáš Bureš is a Professor at the Department of Distributed and Dependable Systems , Faculty of Mathematics and Physics , Charles University . His work focuses on adaptive software architectures for smart cyber-physical systems (CPS) and IoT, leveraging machine learning (neural networks) for self-adaptation, edge-cloud systems, and model-driven development. Research Groups: Leader of the SmartArch research group. Projects: Involved in ExtremeXP , ESTABLISH , AFarCloud , FitOptiVis , Trust 4.0 , ASCENS , RELATE ITN , Q-ImPrESS . Publications span topics like performance regression testing, ML workflow optimization, confidentiality analysis under uncertainty, and edge-cloud continuum modeling. Key subfields include self-adaptive architectures, adaptation rules, component ensembles, and statistical methods in software engineering. His work bridges theoretical models (e.g., DEECo, SOFA) with practical tools (e.g., ExpEngine, Robin) for CPS and cloud applications. Co-Authors include P. Hnětynka, M. Abdullah, R. Heinrich, and others. Publications appear in venues like IEEE Transactions on Autonomic Computing , ACM/SPEC ICPE , and ECSA Workshops .

James Longمشاهده پروفایل

استاد

• Accounting
• Behavioral Accounting
• Financial Accounting
• +۷ مورد دیگر

James Long is a Professor of Accountancy at the Harbert College of Business, Auburn University, where he serves as Assistant Dean of Global Programs. His career spans academic leadership, research, and teaching excellence in accounting, financial wellness, and international business. Education: PhD in Accounting, Virginia Polytechnic Institute and State University (2009) MAcc in Accounting, Auburn University (2002) BSBA in Accounting, Auburn University (2001) Dr. Long's research focuses on how task timing affects judgment, decision-making, and performance in accounting. His work intersects behavioral accounting, audit quality, and emerging technologies like blockchain and artificial intelligence, particularly in educational contexts. His scholarly output includes publications in top-tier journals such as Accounting, Organizations & Society , Auditing: A Journal of Practice & Theory , and Journal of Accounting Education . Recent studies examine AI's role in accounting education, cryptocurrency accounting challenges, and interruptions in financial decision-making. Scientific Awards: Gerald and Emily Leischuck Endowed Presidential Award for Excellence in Teaching Alumni Undergraduate Teaching Excellence Award Harbert College's Lowder and McCartney Teaching Awards School of Accountancy Outstanding Research Award (three-time recipient) National awards for educational research articles (four-time recipient) Dr. Long has also contributed to professional practice, including a Fulbright Scholar stint in Hungary (2017), and prior industry roles as a Staff Auditor and Internal Audit Manager. He holds certifications in CIA, CMA, CPA, ABV, and CGMA, and is active in major accounting associations.