جست‌وجوی استاد و پژوهشگر

• Industrial Engineering and Management
• Lean Production Systems
• Operations Management
• +۵ مورد دیگر

Rui Manuel Sá Pereira Lima is an Associate Professor at the Department of Production and Systems within the School of Engineering at the University of Minho, Portugal. He serves as a Senior Researcher with PhD and Coordinator of the Research Group on Industrial Engineering and Management (Lean Production Research Group) at the Algoritmi Centre. His academic leadership extends to chairing the Project-Approaches in Engineering Education Association (PAEE) and serving on the steering committee of the Active Learning in Engineering Education (ALE) network. Dr. Lima's research spans Industrial Engineering and Management with focus on Production Management, Lean and Agile Project Management, Lean Healthcare, Lean Services, and Operations Management. His educational research concentrates on Project-Based Learning (PBL), University-Business Cooperation, and applying project management concepts to improve learning. He has delivered over 60 workshops across 30+ higher education institutions in Europe, Asia, South America, and Africa on Active Learning and PBL for teachers. His publication portfolio reveals strong trends in healthcare operations management, Lean methodologies across various sectors, and innovative engineering education approaches. Recent work shows growing emphasis on digital transformation, Industry 4.0 integration with traditional lean practices, and extended reality applications in educational settings. h-index 20 with 191 publications including 38 in Q1/Q2 journals Editorial board member for Production journal, Production Engineering Archives, and European Journal of Engineering Education Member of Consultative Committee on UNESCO Research Centre UCPBL Member of Academic Committee of College Doctoral Tordesillas in Production Engineering Dr. Lima has supervised 9 PhD students (co-supervised) with 6 currently under supervision, and over 80 MSc students while evaluating more than 40 MSc exams. His research projects involve international collaboration with universities across eight countries in Europe, Asia, and South America, focusing on hospital operations improvement, business process modeling, and Lean project development with industry partners. He has served as course coordinator for the integrated master's in industrial engineering and management and as an elected member of the School Council at the University of Minho's School of Engineering. His laboratory affiliations include the Industrial Engineering and Management (IEM) R&D Group and the Lean Production Systems Laboratory (LPSL) at the Algoritmi Centre, where his team develops practical applications of Lean methodologies across healthcare, manufacturing, and educational contexts. His current work emphasizes the integration of digital technologies with traditional Lean approaches to address contemporary operational challenges.

Shivam Guptaمشاهده پروفایل

دانشیار

• Procurement
• Supplier Management
• Innovation Management
• +۱ مورد دیگر

Shivam Gupta is an Associate Professor of Supply Chain Management and Analytics at the University of Nebraska-Lincoln's College of Business. He joined the university in 2018 as an Assistant Professor and was promoted to his current rank in 2024. His office is located in HLH 511K, Lincoln, NE. Education: Ph.D. in Management Science, The University of Texas at Dallas (2011-2016) M.Tech. in Civil Engineering, Indian Institute of Technology Kanpur (2009-2011) B.Tech. in Civil Engineering, Indian Institute of Technology Kanpur (2003-2007) His research expertise spans procurement systems, supplier relationship management, agricultural operations optimization, and incentive mechanism design. He develops analytical models to address challenges in supply chains, focusing on both theoretical frameworks and practical applications in agricultural markets. His work integrates game theory, optimization techniques, and economic analysis to solve complex operational problems. Gupta's publications demonstrate consistent focus on mechanism design and operational challenges, with recent work emphasizing dynamic incentive structures, procurement optimization, and agricultural policy impacts. His articles appear in premier journals including Management Science, Operations Research, and Production and Operations Management. Honors: First Place, INFORMS Public Sector OR Best Paper Competition (2015) Finalist, POMS SCM Student Paper Competition (2016) He teaches undergraduate and graduate courses in business analytics, including SCMA 350: Business Analytics/Information Analysis and SCMA 855: Prescriptive Analytics.

Justine Sherryمشاهده پروفایل

دانشیار

• Networked Systems
• Middleboxes
• Congestion Control Algorithms
• +۵ مورد دیگر

Justine Sherry is the A. Nico Habermann Associate Professor of Computer Science at Carnegie Mellon University (CMU). Her research focuses on networked systems across software and hardware layers, particularly middleboxes, congestion control, FPGA-based acceleration, and Internet fairness. Affiliations: Computer Science Department (CSD) , CyLab , SNAP Lab . Academic Rank: Associate Professor Her recent work explores radical shifts in datacenter architectures via SmartNICs, hardware-software co-design for network functions, and database proxy innovation using eBPF. She leads research on Internet fairness , denial-of-service mitigation , and high-speed intrusion detection through systems like Pigasus. Key scientific contributions span 15 recent publications at venues like SIGCOMM, OSDI, and VLDB, addressing topics such as: Mathematical fairness in congestion control KERNEL bypass strategies in FPGA SmartNICs BBR algorithmic unfairness Wireless latency optimization (Zhuge) Heterogeneous hardware performance analysis Open-source security infrastructure Major awards include: Sloan Research Fellowship VMWare Systems Award NSF grants Intel Outstanding Researcher Award Early Career Diamond from UW Engineering She mentors students in systems research, including Ranysha Ware , Nirav Atre , and Hugo Sadok , while collaborating with institutions like Google, Facebook, and DARPA.

Weina Wangمشاهده پروفایل

استادیار

• Applied Probability
• Stochastic Systems
• Reinforcement Learning
• +۷ مورد دیگر

Weina Wang is an Assistant Professor in the Computer Science Department at Carnegie Mellon University, joining in Fall 2018. Her research lies at the intersection of applied probability , stochastic systems , and reinforcement learning , focusing on decision-making in large-scale systems with applications to computing resource orchestration, data privacy, and graph statistics. She has received prestigious awards including the NSF CAREER Award (2022) , ACM MobiHoc Best Paper (2022) , and ACM SIGMETRICS Rising Star Research Award (2023) . PhD in Electrical Engineering, Arizona State University (2016) Bachelor’s in Electronic Engineering, Tsinghua University (2009) Her recent publications span restless bandits , queueing theory , and attributed graph alignment , reflecting her dual focus on fundamental limits and algorithmic solutions. Notable collaborations include work on privacy-preserving data routing , phase-aware scheduling , and erasure-coded servers for heterogeneous traffic. She has advised PhD students Jalani Williams , Tuhinangshu Choudhury , and Yige Hong . Her research has been recognized with best paper awards and grants like the NSF CAREER . She also contributes to professional societies, recently joining the INFORMS Applied Probability Society council . Her teaching includes courses like Probability and Computing and Fundamentals of MDPs and Reinforcement Learning .

• Data Structures and Algorithms
• Robotics
• Artificial Intelligence
• +۴ مورد دیگر

Mustafa AKSU is a Lecturer at Ahi Evran University, Faculty of Engineering and Architecture, Department of Computer Engineering. He holds a Doctorate (2016) from İnönü University in Computer Hardware, a Master's (2004) in Electrical and Electronics Engineering from Kahramanmaraş Sütçü İmam University, and a BSc (1998) in Computer Science Engineering from Kocaeli University. Current Position: Doctor Öğretim Üyesi (Lecturer) since 2022 Previous Role: Full-time Teaching Staff at Kahramanmaraş Sütçü İmam University (2001-2022) His research focuses on Data Structures and Algorithms , with significant contributions to skip ring/skip list innovations and their applications in robotics, artificial intelligence, and image processing. Key interests include: Algorithm optimization Machine learning applications Image segmentation techniques Mobile system development Renewable energy simulations Publications showcase expertise in data structures, robotics, and energy systems, with a recent 2024 paper on COVID-19 prediction algorithms and a 2023 book chapter on robotic simulation importance . Collaborations span institutions including İnönü University, University of Turku, and Gaziantep University. As a thesis advisor, he has guided three Master's students in 2024. Professional experience includes department chairmanship (2011-2014) and extensive teaching roles since 2001.

Rodrigo Fonsecaمشاهده پروفایل

دانشیار

• distributed systems
• networking
• operating systems
• +۵ مورد دیگر

Rodrigo Fonseca is an Associate Professor with Tenure in the Computer Science Department at Brown University's School of Engineering. He received his PhD from UC Berkeley in 2008 under Ion Stoica, focusing on execution tracing for distributed systems. His educational background includes a PhD in Computer Science from UC Berkeley (2008). Fonseca's research focuses on distributed systems, networking, and operating systems, with particular interest in understanding complex system behavior. His work spans distributed tracing and debugging (X-Trace, Pivot Tracing), network management in datacenters (2DFQ, Planck), power management in mobile systems (Quanto, Application Modes), and software-defined networking (Participatory Networking, Simon). His research integrates theoretical insights with practical implementations that have influenced both academic and industrial systems. His publication record shows a strong focus on distributed system instrumentation, with recurring themes of causal tracing, resource management, and network monitoring. The most recent publications demonstrate continued innovation in data-aware indexing structures, in-network packet analysis, and context propagation techniques for distributed systems. Fonseca has received significant recognition including an NSDI Test of Time Award for X-Trace, an NSF CAREER Award, and a Best Paper Award at SOSP for Pivot Tracing. His work has been consistently published in top-tier venues including SIGCOMM, NSDI, SOSP, and OSDI. He has mentored numerous graduate students, with five PhD graduates as of 2019 who have gone on to positions at Google, Microsoft, Apple, and MPI-SWS. His research has been supported by funding from the National Science Foundation, Google, Intel, and Microsoft Research, where he also spent visiting periods. He is actively involved in the research community, having served on program committees for major conferences and co-organizing workshops including HotCloud and the New England Networking and Systems Day. In 2019-2020, he served as PC co-chair for HotNets and General Chair for SoCC'2020.

• Transport Engineering
• Traffic modelling
• Microscopic simulation
• +۷ مورد دیگر

Dr. Tuo Mao serves as a Research Fellow at the Data Science Institute within the Faculty of Engineering and Information Technology at University of Technology Sydney (UTS). He also holds visiting scientist status at the Intelligent Mobility group at Data61 CSIRO. With a PhD from University of New South Wales (UNSW), Dr. Mao has established himself as a senior engineer specializing in transportation systems research. Dr. Mao's research interests span Transport Engineering with particular expertise in traffic modelling, microscopic simulation, and traffic signal control and management. His work focuses on vehicle infrastructure integration (VII) systems within intelligent transport systems (ITS), with specific applications in motorway modelling, coordinated ramp metering optimization, and V2I communication wireless connection system modelling. He has developed significant expertise in applying genetic algorithms to traffic signal control plan optimization and specializes in machine learning applications, particularly reinforcement learning, for transportation challenges. His recent publications (2023-2025) demonstrate a clear progression toward AI-driven transportation solutions, with emphasis on digital twins for passenger flow prediction, connected vehicle data analysis for railway safety, and micro-mobility pattern analysis. These works reveal consistent application of machine learning techniques to both recurrent and non-recurrent traffic conditions, with practical implementations across Australian transportation networks. EV Charging as a Service (2024-2026) - Australian Renewable Energy Agency UTS Smart Campus for energy saving (2022-2023) - UTS Central Research Budget V2X freight and emergency vehicle priority trial (2021-2022) - iMOVE CRC Safety and risk evaluation of Train Level Crossings (2021) - Transport for NSW Ai-RAP Automation for Australian Road Safety (2020-2022) - iMOVE CRC Dr. Mao actively contributes to the Future Mobility Lab at UTS, where his team develops integrated approaches to transportation challenges through collaboration with government agencies including Transport for NSW and research consortia like iMOVE CRC. His work bridges theoretical optimization methods with practical transportation solutions across Australian contexts, with particular focus on Sydney's transportation infrastructure.

Eren Çilمشاهده پروفایل

استاد

• Service Operations Management
• Queuing Theory
• Design and Contract Theory
• +۵ مورد دیگر

Eren Çil is a Professor of Operations and Business Analytics and the Robert J. and Leona M. DeArmond Research Scholar at the Lundquist College of Business, University of Oregon. He serves as Academic Director for the Oregon MBA Program and is affiliated with Undergraduate Programs. PhD in Operations Management, Northwestern University (2010) MS in Industrial Engineering, Koc University, Turkey (2006) His research focuses on service operations management, queuing theory, design and contract theory, and strategic pricing under competition. Recent work examines healthcare capacity allocation, product trial structuring, and closed-loop supply chain incentives. Publications appear in Management Science , Production and Operations Management , and Operations Research . Key trends across his 15 most recent articles include applications of queueing theory to healthcare and urban mobility systems, optimization of closed-loop supply chains for sustainability, and game-theoretic models for strategic pricing in competitive markets. Robert J. and Leona M. DeArmond Research Scholar Recognition for Top Research (2018) Çil advises graduate students in operations management and collaborates with research teams at the Lundquist College. His grant activities focus on sustainability incentives and service system optimization, with industry applications in healthcare, retail, and aerospace.

Antonio Filieriمشاهده پروفایل

دانشیار مهمان

• Static analysis
• Privacy
• Automated test generation
• +۵ مورد دیگر

Antonio Filieri is a Senior Applied Scientist at Amazon Web Services (AWS) and holds a Visiting Associate Professor position at the Department of Computing, Imperial College London. Previously, he was a tenured Associate Professor at Imperial College London (2022-2024) and Assistant Professor (2016-2022), and served as Assistant Professor at the University of Stuttgart between 2013 and 2015. His academic career spans over a decade with significant contributions to software engineering research. Dr. Filieri's research focuses on formal mathematical methods for software design, verification, self-adaptation, and security. His primary research areas include static analysis, privacy, and automated test generation for security; exact and approximate methods for probabilistic program analysis; control theory for adaptive software; quantitative verification and model checking; and runtime-efficient and incremental verification. His work bridges theoretical foundations with practical applications in industry settings, particularly in cloud computing and security domains. His recent publications demonstrate a strong focus on probabilistic methods for software analysis, security testing, and performance modeling. The research trends show increasing integration of formal methods with machine learning techniques, particularly in test oracle generation and neural network analysis. There's also a clear emphasis on scalability and practical applicability of verification techniques to real-world systems like serverless computing and microservices architectures. Dr. Filieri has received numerous prestigious awards for his contributions: Best Student Paper Award (2025) for 'Robust Probabilistic Model Checking with Continuous Reward Domains' ACM Distinguished Paper Award (2023) for 'Sibyl: Improving Software Engineering Tools with SMT Selection' Best Paper Award (2022) for 'Enhancing Performance Modeling of Serverless Functions via Static Analysis' Best Artifact Award (2017) for 'Self-adaptive video encoder: comparison of multiple adaptation strategies made simple' Most Influential Paper Award (awarded at SEAMS 2025) for 'Software Engineering Meets Control Theory' ACM SigSoft Distinguished Paper Award (2011) for 'Run-time Efficient Probabilistic Model Checking' Dr. Filieri has advised several PhD students including Donato Clun (2024), Runan Wang (2024), and Xiaotong Ji (expected 2025). His advising focuses on probabilistic program analysis, automated testing, and security verification. His research has been supported by significant grants from both academic and industry sources, enabling collaborations across multiple institutions and contributing to advancements in software engineering practices. While specific lab information isn't prominently featured in the provided materials, Dr. Filieri's work suggests strong connections with research groups focused on formal methods, software verification, and adaptive systems at both Imperial College London and AWS. His research often involves interdisciplinary collaboration between theoretical computer science and practical software engineering challenges.

• stochastic approximation
• reinforcement learning
• sampling
• +۴ مورد دیگر

Sergey Vladimirovich Samsonov is an Associate Professor at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE), where he also serves as Head of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis within the Institute of Artificial Intelligence and Digital Sciences. He is affiliated with the Department of Big Data and Information Retrieval and the Joint Department of the A.A. Kharkevich Institute for Information Transmission Problems of the Russian Academy of Sciences. Samsonov began his tenure at HSE in 2018 and has accumulated 7 years of scientific and teaching experience. His educational background includes a PhD from HSE (2024) and a Bachelor's degree in Applied Mathematics and Computer Science from Lomonosov Moscow State University (2017). Samsonov's research focuses on stochastic approximation, reinforcement learning, sampling techniques, Markov chain Monte Carlo (MCMC) methods, and multivariate statistics . His work bridges theoretical mathematics with practical machine learning applications, particularly in developing algorithms with strong theoretical guarantees. He has made significant contributions to understanding convergence properties of stochastic algorithms and developing variance reduction techniques. Analysis of his recent publications reveals a consistent focus on the mathematical foundations of machine learning, particularly in stochastic approximation methods, reinforcement learning theory, and generative modeling. His work often combines rigorous theoretical analysis with practical applications, demonstrating expertise in both pure mathematics and applied machine learning. The publication venues (including top conferences like NeurIPS, ICLR, and AISTATS) reflect the high impact and quality of his research in the machine learning community. Young Scientist Badge (December 2024) Letter of gratitude from the First Vice-Rector of HSE (March 2023) Letter of gratitude from the Faculty of Computer Science at HSE (September 2021) Rector's personal allowance (2022-2023) Numerous bonuses for high-impact publications (2021-2027) Best Teacher award (2024-2025, 2022, 2020) Segalovich Scientific Prize (2022) National Prize 'Leaders in AI - 2024' Samsonov teaches advanced courses including Markov Chains, Sampling and Generative Modeling, and Matrix Computations. His laboratory work focuses on developing stochastic algorithms for machine learning applications. He has been involved in HSE's collaboration with Sber, which has resulted in 19 successfully implemented AI projects since 2021. His research has gained significant recognition, with multiple papers accepted at top-tier conferences including 12 papers presented at NeurIPS in recent years.

• Applied Stochastic Process
• Optimization
• Operations Research

Hiroaki Mohri is a Professor at Waseda University's School of Commerce, where he has been serving since 2023 after previously holding positions as Associate Professor (2001-2022) and Assistant Professor (1999-2001). He earned both his Master of Science and Doctor of Engineering degrees from Tokyo Institute of Technology. His academic career includes visiting professorships at the University of Bonn Research Institute for Discrete Mathematics (2005-2007). Waseda University, Professor (2023-present) Waseda University, Associate Professor (2001-2022) Waseda University, Assistant Professor (1999-2001) University of Bonn, Visiting Professor (2005-2007) Mohri's research spans applied mathematics with a focus on stochastic processes, optimization, and operations research. His work demonstrates strong interdisciplinary connections between mathematical theory and practical applications in network reliability, financial securities, and infrastructure management. He has made significant contributions to cooperative game theory applications in network design problems and reliability engineering. His publication record shows a consistent research trajectory with recent work focusing on catastrophic failure models, network reliability evaluation, and damage analysis from multiple shock types. Mohri has successfully bridged theoretical mathematics with practical applications in infrastructure management, political conflict analysis, and financial systems. His research demonstrates how mathematical modeling can address complex real-world problems across diverse domains. Mohri is an active member of several professional organizations including the Mathematical Association of America, Game Theory Society, Japan Society for Industrial and Applied Mathematics, and the Operations Research Society of Japan. He has led numerous research projects including 'An Interdisciplinary Study on the Effect of Non-Military Engagement in Preventing Escalation of Conflicts' and 'International involvement in escalating conflicts: Mediation theory and the case of the former Soviet Union.' Additionally, he has organized international student exchange programs to former Soviet Union countries, focusing on international relations, logistics, and multicultural societies.

• Operations Research
• Probability and Stochastic Modeling
• Optimization
• +۵ مورد دیگر

Michael Veatch is a Professor of Mathematics at Gordon College in the School of Science, Technology and Health. Holding a Ph.D. from MIT with prior industry experience in defense logistics, he bridges theoretical operations research with practical humanitarian applications. His educational background includes: B.A. from Whitman College M.S. from Rensselaer Polytechnic Institute Ph.D. from Massachusetts Institute of Technology Dr. Veatch specializes in applying probability models and optimization techniques to humanitarian logistics and queueing networks. His research spans pandemic vaccine allocation strategies, gift-in-kind donation systems for organizations like World Vision, and airport congestion management during disaster relief operations. He investigates how faith-based values influence operational decisions in Christian relief organizations through collaborations with Wheaton College and MIT. His work uniquely integrates mathematical rigor with real-world humanitarian challenges, particularly in crisis response scenarios. Analysis of his publication record reveals a strategic evolution from theoretical queueing network research toward increasingly applied humanitarian logistics work. His recent publications demonstrate sophisticated optimization frameworks addressing urgent global health challenges like pandemic response, while maintaining strong theoretical foundations in stochastic modeling and dynamic programming. The interdisciplinary nature of his work connects mathematics, operations research, public health, and ethical decision-making. Dr. Veatch has made significant contributions through his textbook Linear and Convex Optimization: A Mathematical Approach (Wiley, 2021) designed for mathematics majors. He developed an industry-focused course through the Preparation for Industrial Careers in Mathematical Sciences program and contributes to vocational guidance for mathematics students. His active research collaborations include: International Vaccine Allocation with MIT researchers Gift-in-Kind Acceptance Strategies for World Vision Informed Compassion project on faith-based operational decisions Disaster airport scheduling using Haiti earthquake data