Yuan Zhong is an Associate Professor of Operations Management at the University of Chicago Booth School of Business . He previously held positions as an Assistant Professor at Columbia University’s Department of Industrial Engineering and Operations Research and was a Postdoctoral Scholar at UC Berkeley’s Computer Science Department. Education: PhD in Operations Research, MIT (2012) MA in Mathematics, Caltech (2008) BA in Mathematics, University of Cambridge (2006) His research focuses on applied probability and stochastic system design , with applications in cloud computing , supply chain management , and e-commerce logistics . Recent work explores multi-period production systems and dynamic resource allocation in data centers and healthcare operations . Recent publications analyze cloud value chains , sparse graph design for delivery networks, and process flexibility in manufacturing. He has contributed to journals like Operations Research , Annals of Applied Probability , and Stochastic Systems . Scientific Awards: 2012 Kenneth C. Sevcik Outstanding Student Paper Award Best Student Paper Award at ACM Sigmetrics (2012) He teaches courses in business process fundamentals and queueing theory , with a future schedule including Operations Management: Business Process Fundamentals (2025–2026). No explicit student advising list was provided.
Rafael Villena Taranilla is a Professor at Camilo José Cela University (UCJC) and the International University of La Rioja (UNIR), teaching in master's programs on advanced teaching competencies, digital technology in teaching, and inclusive education. He concurrently serves as a primary school teacher, integrating practical classroom experience with academic research in educational technology. His academic credentials include: PhD in Education from the University of Castilla-La Mancha (Outstanding Cum Laude), focusing on Virtual Reality for Social Studies in Primary Education Master's degree in Educational Research and Innovation from the University of Castilla-La Mancha Bachelor's degree in Primary Education with specialization in History, Culture, and Heritage Villena Taranilla's research centers on emerging technologies in education, particularly virtual reality, augmented reality, educational robotics, and artificial intelligence applications for primary education. He investigates their impact on learning outcomes, historical thinking development, and teacher digital competence, with significant contributions to motivation studies and technology acceptance models in educational contexts. His recent publications (2022-2025) demonstrate a concentrated focus on virtual reality for history and social sciences education in primary schools, supported by rigorous meta-analyses and technology acceptance frameworks. The work shows progressive expansion into gamification for historical thinking, augmented reality for geometry instruction, and robotics for computational thinking, reflecting a cohesive interdisciplinary approach bridging education, cognitive science, and technology innovation. His scientific recognition includes: Second prize in the 'Thesis in three minutes' contest (UCLM, 2020) National finalist in the #HiloTesis contest (CRUE Universidades) Villena Taranilla has supervised numerous Master's theses and academic internships across educational innovation programs. He has secured research funding from prestigious entities including FECYT, La Caixa Foundation, and the University of Valencia, while contributing to teacher training initiatives as a digital competence tutor with Castilla-La Mancha's Regional Teacher Training Center. He is an active member of the Labintic research group at the University of Castilla-La Mancha, dedicated to critical ICT integration in teaching. His collaborative research extends to the University of Valencia, where he completed a 2024 research stay and has scheduled another for June-September 2025 under Dr. Pascual D. Diago Nebot's supervision.
Günter J. Hitsch is the Kilts Family Professor of Marketing at the University of Chicago Booth School of Business, where he has been a faculty member since 2001. His academic leadership extends to editorial roles as Co-Editor of the Journal of Quantitative Marketing and Economics and Associate Editor at Marketing Science and Management Science. Hitsch's educational journey includes an undergraduate degree from the University of Vienna (1995), followed by master's degrees in economics (1997, 1998), and a PhD in economics from Yale University (2001). This strong foundation in economics informs his approach to marketing research. His research program focuses on quantitative marketing and industrial organization, with particular emphasis on dynamic models of firm and consumer decision-making. Key areas include advertising effectiveness, pricing strategies, sequential learning and experimentation, and intertemporal consumer choice. Hitsch is pioneering in applying causal inference and machine learning to solve practical marketing problems such as optimal customer targeting. His work on dating and marriage markets demonstrates innovative application of economic theory to social phenomena. Hitsch's publication trajectory shows evolution from foundational work on consumer choice and switching costs to more recent applications of machine learning in marketing contexts. His research spans theoretical development and practical application, examining everything from private label demand during economic recessions to television advertising effectiveness across hundreds of brands. Co-Editor, Journal of Quantitative Marketing and Economics Associate Editor, Marketing Science Associate Editor, Management Science Hitsch's editorial leadership has significantly shaped the direction of quantitative marketing research. His commitment to methodological rigor and generalizable results ensures his work provides reliable inputs for both marketing practitioners and academic researchers. As an educator, Hitsch teaches advanced courses in quantitative marketing and business analytics, with scheduled courses for 2024-2026. He emphasizes that 'good marketing isn't fluffy,' challenging students to develop analytical approaches to marketing problems. His research philosophy prioritizes providing generalizable results that apply beyond specific case studies, serving as inputs for both practitioner decision-making and academic advancement.
Giovanni Compiani is an Associate Professor at the University of Chicago Booth School of Business, specializing in Marketing. His research bridges industrial organization and quantitative marketing, focusing on advanced econometric methods. PhD, MPhil, MA in Economics from Yale University BSc, MSc in Economics from Bocconi University Previous Assistant Professor at Haas School of Business His work explores unstructured data integration in demand estimation, consumer search behavior on online platforms, risk preferences in cryptocurrency markets, and time perception in behavioral economics. He has published in top journals including Journal of Political Economy , Marketing Science , and Review of Economic Studies . Recent publications emphasize machine learning applications in econometrics, equilibrium modeling of lotteries, and crypto mining's economic impact. His research portfolio spans demand analysis, structural modeling, and behavioral insights. Editor's Choice Award, The Review of Asset Pricing Studies (2024) Developed nonparametric demand estimation frameworks Advances dynamic model identification with instrumental variables Compiani teaches Data Science for Marketing Decision Making at Booth and maintains active collaborations with researchers across econometrics, computer science, and behavioral disciplines.
Víctor Dalmau is an Associate Professor in the Department of Information and Communication Technologies at Universitat Pompeu Fabra (UPF), Barcelona, where he has been a faculty member since 2007. He is affiliated with the Artificial Intelligence and Machine Learning Research Group, contributing to foundational and theoretical research in computer science. His research centers on the Theory of Constraint Satisfaction , a fundamental framework in computer science with applications in artificial intelligence, scheduling, logistics, and computer vision. His work integrates techniques from combinatorics, logic, database theory, universal algebra , and complexity theory . He also investigates proof complexity, computational learning , and schema mappings , focusing on the theoretical underpinnings of computational problems. Víctor Dalmau has been recognized with the prestigious Ramón y Cajal Fellowship and received the ICDT 2012 Best Paper Award for his contributions to database theory and logic. These honors reflect his impact in theoretical computer science. He has advised students and contributed to research grants, though specific names and projects are not detailed in the provided text. He is actively involved in the Artificial Intelligence and Machine Learning Group at UPF, fostering collaborative and interdisciplinary research in foundational aspects of AI and computation.
Represa Pérez, César is a faculty member at the University of Burgos , affiliated with the School of Engineering . He has contributed extensively to computer science, parallel computing, and embedded systems through research articles, educational materials, and conference presentations. Education: Doctorate in Parallel & Hybrid Programming (2002). Research Areas: Parallel computing (MPI/CUDA), 3D virtual labs, sensor technology, Android applications, and embedded systems design. Key Collaborations: Co-authored works with José María Cámara Nebreda, Pedro L. Sánchez Ortega, and others on technical education and hardware-software integration. Recent Trends: Focus on smartphone-driven sensors, additive manufacturing monitoring, and educational tools for engineering students.
Jose Enrique Adsuara Fuster is a Researcher at the Department of Computer Science and Artificial Intelligence within the School of Engineering at Universitat de Valencia. He earned his PhD in 2017 with the thesis 'Improved numerical methods for elliptic problems in astrophysics' supervised by Dr. Miguel-Ángel Aloy Toras and Dr. Pablo Cerdá-Durán. He belongs to the ERI Image Processing Laboratory (IPL) and focuses on numerical methods for astrophysical simulations. His research bridges computational mathematics and astrophysics, specializing in iterative solvers like the Scheduled Relaxation Jacobi method and their equivalence to classical algorithms. His work emphasizes improving convergence rates for elliptic PDEs relevant to astrophysical phenomena. No scientific awards or student supervision details are listed in available sources. The three articles since 2016 show a consistent focus on numerical analysis for astrophysical applications, particularly elliptic PDEs and iterative methods. His publications appear in computational physics journals, with collaborations within the IPL group.
Enrique Benavent López is an Associate Professor at the Universitat de València, affiliated with the Department of Statistics and Operations Research. His academic career has been dedicated to operations research, particularly in the area of arc routing and combinatorial optimization. Research Interests: Operations Research and Mathematical Programming Capacitated Arc Routing Problem (CARP) Rural Postman Problem (RPP) and its variants Vehicle Routing with Turn Penalties Integer Programming and Polyhedral Combinatorics Heuristic and Metaheuristic Algorithms His research spans theoretical algorithm development and practical applications in logistics and transportation. He has made significant contributions to the formulation and solution of hard optimization problems on networks, particularly through cutting-plane methods and branch-and-cut algorithms. Publication Trends: His work over the past four decades shows a consistent focus on arc routing problems. Early work addressed the quadratic assignment problem and basic postman problems, while later research concentrated on capacitated, windy, and mixed variants with additional constraints. The publications demonstrate a shift from exact methods to hybrid heuristics, reflecting broader trends in the field. His most cited works involve lower bounds, cutting planes, and branch-and-cut for CARP. Scientific Contributions: Developed foundational lower bounds for the Capacitated Arc Routing Problem Introduced exact algorithms based on cutting planes and branch-and-cut Contributed to the polyhedral analysis of arc routing problems Advanced heuristic methods for windy and mixed networks Pioneered work on turn penalties in directed rural postman problems Collaborations and Advising: He has collaborated extensively with leading researchers such as Ángel Corberán, José María Sanchis, and José-Manuel Belenguer. While specific students are not listed, his long-standing research activity suggests he has supervised PhD and Master's students in operations research. He has contributed to major reference works in arc routing and is frequently cited in the literature. Laboratories and Research Groups: He is part of a strong research group in combinatorial optimization at the Universitat de València, focusing on routing and scheduling problems. This group has produced influential work in both theoretical and applied aspects of operations research.
Jésica De Armas is an Associate Professor at the Department of Economics and Business, Universitat Pompeu Fabra (UPF) in Barcelona, Spain. She holds a prestigious Ramon y Cajal Fellowship from the Spanish Ministry of Science and Innovation, recognizing her outstanding research contributions. Her academic career spans multiple institutions, with previous postdoctoral positions at the Open University of Catalonia (UOC) and the University of La Laguna (ULL). PhD in Computing (Cum Laude) from University of La Laguna (2012) Computer Engineering degree with special award for best academic record Ramon y Cajal Fellowship recipient (2022) PhD Extraordinary Award from University of La Laguna (2012) PhD Award from Spanish Association of Artificial Intelligence (AEPIA) (2014) Her research focuses on developing and applying optimization techniques to solve complex real-world problems. She specializes in combinatorial optimization, metaheuristics, and machine learning approaches applied to logistics, transportation, and health care systems. Her work bridges theoretical advancements with practical implementations, particularly in vehicle routing, scheduling, and resource allocation problems. She has established herself as a leading researcher in optimization for social good, with significant contributions to humanitarian logistics and health care delivery systems. Dr. De Armas's publications reveal a strong trajectory in operations research with applications spanning multiple domains. Her research shows a clear evolution from theoretical optimization methods toward increasingly complex real-world applications, particularly in health care and social services. Her most recent work demonstrates a sophisticated integration of simulation, machine learning, and optimization techniques to address challenges in refugee resettlement, home care services, and public infrastructure planning. The consistent publication in high-impact journals reflects her growing influence in the field of operations research. Ramon y Cajal Fellowship (2022) Best Paper Award from Energies (2019) Luis Azcárraga Award from ENAIRE Foundation (2017) PhD Award from Spanish Association of Artificial Intelligence (2014) PhD Extraordinary Award from University of La Laguna (2012) HPC-Europa2 Fellowship (2011) Dr. De Armas has successfully secured multiple competitive research grants and has advised numerous students across various academic levels. She has served as Principal Investigator for projects including REASON (2024-2027) on optimizing health care delivery in rural areas, and previously led the EPHoCaS project (2020-2022) focused on sustainable home care services for the elderly. Her advising portfolio includes three PhD students to completion and numerous master's and undergraduate thesis students, demonstrating her commitment to mentoring the next generation of researchers. Her research has led to tangible industry applications, particularly in vehicle routing optimization where her algorithms have been implemented by companies as key business tools. Dr. De Armas maintains an active international research network with collaborations spanning institutions in Edinburgh, Nottingham, Cosenza, Boulder, Hamburg, and Montreal. Her work on the ROAR-NET research network demonstrates her leadership in advancing optimization algorithms research across European institutions. She has also contributed significantly to the academic community through conference organization, including the International Conference on Computational Logistics (ICCL2022) and the Metaheuristics International Conference (MIC 2017).
Eduard Ayguade Parra is a Full Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), Faculty of Computer Science of Barcelona (FIB). He is a leading researcher in the UPC PM - Programming Models group and maintains a significant affiliation with the Barcelona Supercomputing Center (BSC-CNS), a premier national supercomputing facility. His primary research interests lie in High-Performance Computing (HPC) , with a deep focus on parallel and distributed architectures , programming models (especially task-based models like OmpSs), multicore and multiprocessor systems , and compilers for high-performance architectures . His work bridges hardware and software to optimize performance for complex computational problems. The trends in his recent publications highlight a sustained and evolving research program. He is actively advancing task-based programming for distributed memory and hybrid systems, exploring FPGA acceleration for key HPC kernels like SpMV, and innovating in memory system design, including active compute memory and hybrid memory object placement. His research also extends into applying AI techniques to hardware reliability and creating high-quality datasets for computer vision evaluation. HiPEAC Paper Award 2024 Professor Ayguade has been instrumental in securing and leading numerous competitive and non-competitive R&D+i projects, often funded by national and European programs. He has advised a significant number of doctoral students, whose theses cover topics such as task-based programming, FPGA acceleration, HPC compilers, and machine learning for systems. His collaborations are extensive, with frequent co-authorship with prominent figures at UPC and BSC-CNS, such as Jesús Labarta and Mateo Valero. His research has led to advancements in runtime systems, compiler technology, and FPGA-based acceleration. He is a core member of the UPC PM - Programming Models research group and his work is deeply integrated with the resources and mission of the Barcelona Supercomputing Center (BSC-CNS), one of Europe's leading institutions in supercomputing.
Maria Luisa Gil Gómez is a researcher at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture and the Barcelona Higher Technical School of Telecommunications Engineering. She holds a PhD in Computer Science and leads research in parallel computing, high-performance computing (HPC), and programming models. Her work focuses on optimizing computational workflows, fault tolerance in distributed systems, and leveraging multi-GPU architectures for scientific simulations. She has contributed to projects like the Programming Models group (PM) at UPC and collaborates with the Barcelona Supercomputing Center (BSC-CNS). Affiliations: UPC Department of Computer Architecture, BSC-CNS, PM Research Group Research Interests: Parallel Programming Models, HPC, GPU Computing, Cellular Automata, Education Technology Her research spans over 147 documented activities, including journal articles, conference presentations, and funded projects. Notable contributions include studies on BCI in music education, distributed algorithms for ARM-based clusters, and the OpenCAL++ framework for parallel cellular automata simulations. She has also explored educational methods integrating creativity and game-based learning in engineering.
Ramon Alvarez-Valdés Olaguibel is a Professor in the Department of Statistics and Operations Research at the Universitat de València. His academic work is centered on operations research, with a focus on project scheduling and optimization under resource constraints. His research interests include Operations Research , Mathematical Programming , Project Scheduling , and Heuristic Algorithms . His work applies advanced mathematical techniques to model and solve complex scheduling problems, particularly in resource-limited environments. The two most recent articles highlight his contributions to the theoretical foundations of project scheduling, particularly through polyhedral analysis and the development of heuristic methods. These works reflect a strong trend in discrete optimization and combinatorial scheduling , with applications in industrial planning and operations management. There are no recorded scientific awards in the provided data. He has collaborated extensively with José Manuel Tamarit Goerlich on key publications. No information is available about grants or student advising. No details about labs, research teams, or current projects are available in the given text.
Consuelo Parreño Torres is a Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, University of Valencia, Spain. She holds a permanent academic position and is an active researcher in operations research and optimization, particularly in maritime logistics. University: University of Valencia School: Faculty of Mathematics Department: Department of Statistics and Operations Research Email: consuelo.parreno@uv.es She earned her PhD in 2020 with a thesis on improving container terminal efficiency through models and algorithms for pre-marshalling and stowage problems, supervised by Dr. Ramón Álvarez Valdés and Dr. Rubén Ruiz García. Her research focuses on operations research, mathematical programming, and combinatorial optimization , with specific applications in container terminal logistics . She develops advanced algorithmic solutions such as integer programming, constraint programming, and matheuristics to solve complex scheduling and optimization problems in port operations. Her work addresses real-world challenges like minimizing crane times and optimizing container rearrangement. Her recent publications, primarily in journals like European Journal of Operational Research and Computers & Operations Research , show a consistent trend in solving the pre-marshalling and stowage problems using exact and heuristic methods. These works reflect a deep engagement with computational optimization and algorithm design for dynamic logistical environments. Consuelo Parreño Torres is a member of the research group PROMEDyA (Prediction and Optimization under uncertainty: dynamic stochastic models and applications), which focuses on developing models for uncertain and dynamic systems. She collaborates extensively with researchers such as Ramón Álvarez-Valdés, Rubén Ruiz, and Celia Jiménez-Piqueras. While no formal students are listed, her collaborative work indicates a strong advisory and mentoring role within her research team. There is no mention of major grants or scientific awards in the provided text.
María Isabel Alfonso Galipienso is a full-time University Professor in the Department of Computer Science and Artificial Intelligence at the University of Alicante, Spain, and a member of the University Institute of Computer Research (IU of Computer Research). She has held significant administrative roles, including Director of her department from 2012-2016 and Sub-director from 2005-2012. Education PhD in Computer Engineering, University of Alicante (2001) Graduate in Computer Science, Polytechnic University of Valencia (1991) Research Interests Her research spans Software Engineering , Artificial Intelligence , Constraint Satisfaction Problems , and Educational Technology . She focuses on developing scheduling algorithms, agile software processes, and innovative pedagogical approaches for teaching computer science and engineering courses, often integrating real-world projects and collaborative learning techniques. Across her publications and projects, a clear trend emerges toward bridging theoretical AI methods with practical software engineering education . Her early 2000s journal articles introduced hybrid CSP-based scheduling techniques, while later conference papers and books translated these insights into iterative, student-centered curricula and group-based learning frameworks. Scientific Awards No specific scientific awards or fellowships are recorded in the provided documents. Advising & Grants Directed or co-directed 1 undergraduate/master’s thesis in the last five years. Participated as collaborator in 8 competitive public research projects (2012-2016), funded by Spanish Ministries of Economy and Education, focusing on robotics, SLAM, and vision-based systems. Contributed to one technology-transfer project developing a research portal for accounting and business administration entities (2004-2005). Labs & Teams She is affiliated with the IU of Computer Research at the University of Alicante, collaborating in multidisciplinary teams that integrate robotics, computer vision, and educational innovation.
Leticia Lorenzo Picado is a Professor in the Department of Statistics and Operational Research at the Faculty of Economic and Business Sciences, University of Vigo, Spain. She is affiliated with the Interuniversity Research Center CITMAga and is based on the Vigo campus. Her research focuses on game theory and operations research with particular emphasis on cost allocation problems and spanning tree models. Her primary research interests include Game Theory, Operations Research, Cost Allocation Problems, Spanning Tree Models, Multi-issue Allocation Situations, and Cooperative Games. Her work bridges theoretical game theory with practical applications in resource allocation and network optimization problems. Analysis of her publication record shows consistent research output since 2004, with a strong focus on minimum cost spanning tree problems, multi-issue allocation situations, and various allocation rules. Her work demonstrates a clear trajectory from foundational theoretical contributions to more complex applications involving multiple sources and constraints. Lorenzo Picado has established a significant research profile with 15 publications since 2004, primarily in top operations research journals including European Journal of Operational Research, Naval Research Logistics, and Annals of Operations Research. Her research has been cited 113 times across 66 documents, indicating substantial impact in her field. She maintains an active research collaboration network, most notably with Gustavo Bergantiños Cid (12 joint publications), Silvia Lorenzo-Freire (6 joint publications), and other researchers in game theory and operations research. Her work connects to broader research in game theory, economics, and operations research as evidenced by citations across 23 different serials.
.jpg)