جستجوی استاد راهنمای دانشگاهی

Yann Disserمشاهده پروفایل

استاد

• Combinatorial Optimization
• Online Algorithms
• Graph Theory
• +۶ مورد دیگر

Professor Yann Disser is a faculty member in the Department of Mathematics at TU Darmstadt since 2021. He previously held an Assistant Professor (tenure-track) position at TU Darmstadt (2016-2021), a PostDoc position at TU Berlin (2012-2016), and a Visiting Professor role at Augsburg University (2015). His research spans Combinatorial Optimization , Online Algorithms , Graph Exploration , Computational Complexity , and Robust Optimization . Current position: Professor (W2), TU Darmstadt Previous: Assistant Professor (2016-2021), TU Darmstadt PostDoc & Habilitation: TU Berlin (2012-2016) His research focuses on algorithmic approaches to optimization problems, including: Combinatorial Optimization for problems like Steiner trees and knapsack variants Online Algorithms with applications to scheduling and transportation Graph Exploration by mobile agents and complexity bounds Computational Complexity of linear programming pivot rules Network Flows and geometric reconstruction Recent publications analyze incremental maximization with greedy methods, lower bounds for active-set methods , and universal circuit designs . His work appears in top venues like IPCO , ESA , and SODA . He advises a team of researchers including David Weckbecker, Farehe Soheil, and Alexander Birx. Current and past advisees often focus on algorithmic theory, with placements at institutions like HPI Potsdam and Merck.

Abraham Punnenمشاهده پروفایل

استاد

• Operations Research
• Discrete Optimization
• Combinatorial Optimization
• +۵ مورد دیگر

Abraham Punnen is a Professor of Operations Research at the Department of Mathematics, Simon Fraser University (SFU), Surrey. He holds a Ph.D. in Operations Research from the Indian Institute of Technology, Kanpur (1990). His research focuses on discrete optimization, combinatorial optimization, and their applications in transportation, logistics, healthcare, and satellite systems. He has supervised numerous graduate students and postdoctoral fellows, contributing to impactful projects like satellite downlink scheduling and fiber optic network design. Education: Ph.D. in Operations Research, Indian Institute of Technology, Kanpur (1990). Research Interests: Operations Research, Combinatorial Optimization, Scheduling, Transportation Logistics, Healthcare Optimization, Satellite Mission Planning, and Network Design. Grants/Projects: Completed projects include satellite downlink scheduling, ferry scheduling, and fiber optic network design. He collaborates with industries on optimization challenges. Students/Advising: Supervised over 20 M.Sc., Ph.D., and postdoctoral researchers, many of whom now work in academia, industry, and consulting.

• Digital Circuits and Systems
• Energy Efficiency
• Smart Cities
• +۱۳ مورد دیگر

Enrico Macii is a Full Professor at the Politecnico di Torino, affiliated with the Interuniversity Department of Regional and Urban Studies and Planning (DIST) and the Department of Control and Computer Engineering (DAUIN). He leads the Electronic Design Automation (EDA) research group and holds key roles as Scientific Advisor for the Politecnico-STMicroelectronics partnership and Scientific Contact for the European Chips Joint Undertaking. Research Interests: His work spans digital circuits and systems, energy efficiency, smart cities, Industry 4.0, and smart manufacturing. He focuses on embedded and cyber-physical systems, low-power design, neuromorphic computing, AIoT, and sustainable urban development. Recent Publications: His recent research demonstrates strong trends in edge AI, neuromorphic computing, and smart energy systems. Articles highlight innovations in low-power hardware acceleration, federated learning, physics-informed AI, and digital twin applications for urban and industrial systems. There is a clear emphasis on deploying AI efficiently on constrained devices and integrating physical models with machine learning. J. William Fullbright Fellowship (1993) Best paper award IEEE European Design Automation Conference (1996) Best paper award ACM/IEEE Great Lakes Symposium on VLSI (2008) DAC Service Award (2014) IEEE Fellow (2006) DATE Fellow (2014) Advising and Grants: He has supervised over 25 PhD students in computer engineering, AI, and urban systems. His research is funded by major EU programs (Horizon 2020, PNRR, KDT JU), national (PRIN, FAR), and regional grants, as well as industrial contracts with STMicroelectronics, Michelin, and Cefriel. He leads numerous high-impact projects in smart manufacturing, energy efficiency, and digital twins. Labs and Teams: He is a core member of the EDA Group, an interdepartmental research team at Politecnico di Torino focusing on VLSI-CAD, bioinformatics, smart cities, and Industry 4.0. He also contributes to IAM@PoliTo (Integrated Additive Manufacturing) and leads multiple EU and national research consortia.

• Combinatorial Optimization
• Integer Programming
• Quadratic Programming
• +۴ مورد دیگر

Cédric Soutil is a Researcher at the Conservatoire National des Arts et Métiers (CNAM) , affiliated with the CEDRIC Laboratory. His work spans combinatorial optimization , integer programming , quadratic programming , and algorithm design , with a focus on solving complex optimization problems in scheduling and graph theory. Recent publications highlight his expertise in non-separable and non-convex quadratic integer programming , knapsack problems , and online computation . His research trends emphasize mathematical reformulations , upper bound algorithms , and optimization models for real-world applications like horse race scheduling and hydrogen production . He has collaborated extensively with researchers such as A. Houdayer , D. Quadri , and P. Tolla , contributing to over two decades of academic output in operations research and combinatorial optimization .

Shahin Kamaliمشاهده پروفایل

دانشیار

• Algorithms Design
• Algorithms Analysis
• Online Algorithms
• +۹ مورد دیگر

Shahin Kamali is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. Previously, he served as an Assistant Professor at York University (2022-2024) and the University of Manitoba (2017-2022), where he continues to hold an Adjunct Professor position. He completed his Ph.D. in Computer Science at the University of Waterloo in 2014 and was a postdoctoral fellow at MIT's CSAIL lab from 2015-2017. Dr. Kamali received his B.Sc. from the University of Tehran and his M.Sc. from Concordia University, both in Computer Science. He has earned teaching certifications including the Kaufman Teaching Certificate Program from MIT and the Certificate in University Teaching from the University of Waterloo. His research focuses on algorithms' design, analysis, and limitations, with particular emphasis on online problems such as bin packing, paging, list update, and k-Server. His work extends to big-data applications of algorithms in data compression, graph partitioning, resource allocation in the cloud, and algorithmic aspects of blockchain technology. His recent publications (2023-2025) demonstrate a strong focus on learning-augmented algorithms, online computation with predictions, and novel applications in blockchain and graph theory. His research has been supported by multiple NSERC grants since 2010, with recent projects investigating models, applications, and limitations of online algorithms. He has successfully supervised numerous graduate students and serves on various committees related to algorithms conferences and academic governance. University of Waterloo Doctoral Thesis Completion Award (2014) University of Waterloo Mathematics Graduate Experience Award (2008) Dr. Kamali teaches advanced courses in algorithms and data structures and is scheduled to teach Design and Analysis of Algorithms (EECS 3101) in Fall 2025 and Advanced Data Structures (EECS 4101/5101) in Winter 2026. He is actively involved in organizing major conferences, including CCCG and WADS 2025 at York University.

Ciriaco D'Ambrosioمشاهده پروفایل

پژوهشگر

• Combinatorial Optimization
• Heuristics
• Metaheuristics
• +۴ مورد دیگر

Ciriaco D'Ambrosio is a Research Fellow at the Department of Mathematics, University of Salerno, specializing in combinatorial optimization and its applications to wireless sensor networks. He teaches courses in operations research and maintains regular reception hours for students on Tuesdays (3:00-5:00 PM) and Wednesdays (4:00-5:00 PM), conducted both in-person and via Microsoft Teams. Education: PhD in Computer Science, University of Salerno (2015) - Thesis: models and algorithms for coverage in Wireless Sensor Network Laurea cum laude in Computer Science, University of Salerno (2011) Research Focus: D'Ambrosio specializes in combinatorial optimization, developing heuristics, metaheuristics, and math-heuristics for mixed integer linear programming problems. His work addresses challenging optimization problems in wireless sensor networks, particularly network lifetime maximization under coverage, connectivity, and interference constraints. His research bridges theoretical computer science with practical applications in sensor network design, seismic monitoring systems, and resource allocation problems. His methodology often combines exact approaches with sophisticated heuristic techniques to solve computationally difficult problems. Publication Trends: Analysis of D'Ambrosio's publications (2017-2025) reveals a progression from foundational work on sensor network lifetime problems toward increasingly sophisticated algorithmic approaches for combinatorial optimization. His recent work shows expansion into seismic monitoring applications while maintaining strong focus on knapsack problem variants and network optimization. His publications appear in high-quality journals including Soft Computing, Computers & Operations Research, and Networks, demonstrating both theoretical rigor and practical relevance of his research. Professional Activities: Member of the Italian Operations Research Society (AIRO) Associate Editor for Soft Computing, A Fusion of Foundations, Methodologies and Applications Active collaborator with researchers including Andrea Raiconi, Raffaele Cerulli, and Francesco Carrabs Research Infrastructure: D'Ambrosio works within the Department of Mathematics at University of Salerno's Fisciano Campus (Building F2, Room 040). His research contributes to the university's growing expertise in computational optimization and has practical applications in environmental monitoring systems like SEISMONOISY.

Alessandro Arlottoمشاهده پروفایل

دانشیار

• Applied Probability
• Stochastic Modeling
• Stochastic Dynamic Programming
• +۴ مورد دیگر

Alessandro Arlotto is an Associate Professor of Business Administration at Duke University’s Fuqua School of Business , with secondary appointments in the departments of Mathematics and Statistical Science. His research focuses on applied probability, stochastic modeling, and optimization , with applications to management and economics. He holds a Ph.D. from the University of Pennsylvania (2012) and joined Duke the same year. His work has been recognized with the National Science Foundation’s CAREER award. Education : Ph.D. (2012), M.A. (2009) in Mathematics from the University of Pennsylvania; M.S. (2007), B.S. (2004) in Mathematics from the University of Turin, Italy. Research : Explores dynamic resource allocation, stochastic processes, and sequential decision-making. Recent work includes studies on ballot design impacts, inventory management, and strategic service network routing. Teaching : Instructors courses like Stochastic Models, Foundations of Business Analytics, and Quantitative Business Analysis. Grants : Principal Investigator on NSF-funded projects including “CAREER: The effects of centralized and decentralized sequential decisions” (2016–2021) and a conference on Probability Theory and Combinatorial Optimization (2015–2016). His publications emphasize rigorous mathematical approaches to real-world problems, with a focus on minimizing regret in decision-making frameworks and optimizing resource allocation under uncertainty.

• Algorithms and Complexity
• Parameterized Algorithms
• Approximation Algorithms
• +۷ مورد دیگر

Matthias Mnich is a Professor and Head of the Institute for Algorithms and Complexity at Hamburg University of Technology (TUHH), within the School of Electrical Engineering, Computer Science and Mathematics. He also serves as Deputy Dean International, reflecting his leadership in academic administration and international collaboration. He is a principal investigator at the Helmholtz Graduate School for the Structure of Matter, further emphasizing his interdisciplinary impact. His research lies at the intersection of theoretical computer science and practical algorithm design, focusing on parameterized algorithms , approximation algorithms , combinatorial optimization , scheduling , and algorithmic game theory . His work often bridges theoretical guarantees with real-world applications in energy systems, quantum computing, and logistics. The recent publications (2023–2025) highlight his sustained excellence in top-tier venues such as FOCS, ICALP, ESA, STACS, and journals like Mathematical Programming and ACM Transactions on Algorithms . These works explore foundational problems in vector bin packing , integer programming , graph algorithms , and kernelization , while also applying algorithmic techniques to microgrid energy optimization and quantum algorithm engineering . He is deeply embedded in the theoretical computer science community, having served on program committees of major conferences including: STACS 2023 ESA 2024 FOCS 2023 ICALP 2024 IJCAI 2019–2025 AAAI 2018 SWAT 2018 He has successfully supervised several PhD students to completion, including Matthias Kaul , Roland Vincze , and Alexander Göke , many of whom have taken postdoctoral positions at institutions like the University of Bonn and University of Augsburg. His current research projects include PATTERN (2025–2031) , Hamburg Quantum Computing (2024–2029) , and Kernelization for Big Data , indicating long-term funding and strategic research directions. He leads the Institute for Algorithms and Complexity (E-11) , fostering a research environment focused on high-impact algorithmic research.

Martin Schmidtمشاهده پروفایل

استاد

• Mathematical Optimization
• Energy Systems
• Mixed-Integer Nonlinear Optimization
• +۳ مورد دیگر

Martin Schmidt is a Full Professor (W3) for Nonlinear Optimization at the Department of Mathematics, Trier University, since 2019. He has held leadership roles in research training groups and international committees, focusing on mathematical modeling and optimization of energy systems, gas networks, and market equilibria. His work bridges mixed-integer nonlinear optimization , bilevel optimization , and robust methods with applications to real-world energy challenges. Education: PhD in Mathematics (2013), Diplom in Mathematics (2008), both from Leibniz University Hannover. Editorial Roles: Editorial Board member of Journal of Optimization Theory and Applications and Optimization Letters , Associate Editor for OR Spectrum and EURO Journal on Computational Optimization . His research integrates complex physical systems (e.g., gas transmission networks) with game-theoretic models to analyze energy markets. Recent publications emphasize robust optimization , decomposition techniques , and machine learning integration in bilevel frameworks. Awards highlight his contributions to gas market feasibility , linear bilevel optimization , and practical applications in energy systems. Collaborations span institutions like Universidad Zaragoza, Sapienza University, and Forschungszentrum Jülich.

Philipp Baumannمشاهده پروفایل

استاد

• Operations Research
• Optimization
• Machine Learning
• +۵ مورد دیگر

Prof. Dr. Philipp Baumann is a Professor at the University of Bern, affiliated with the Group for Business Analytics, Operations Research and Quantitative Methods. His research focuses on optimization algorithms, clustering techniques, and operations research applications. He has contributed to advancements in constraint-based clustering, fault-tolerant facility location, and fair-capacitated clustering, leveraging mathematical programming and heuristic methods. His work bridges theoretical optimization and practical applications in industries like printing and healthcare. Research interests include combinatorial optimization, algorithm design for machine learning, and large-scale data mining. Notable contributions include the MPFCC algorithm for fair clustering and FT-KMEANS for fault-tolerant systems. His recent publications emphasize scalable solutions for real-world scheduling and resource allocation challenges. No scientific awards are explicitly mentioned. He advises students on thesis guidelines (Bachelor and Master levels) but no specific advisees are listed. His research group collaborates on projects involving mathematical programming in operations and finance, with applications to production scheduling and portfolio optimization.

• Combinatorial Optimization
• Operations Research
• Stochastic Programming
• +۵ مورد دیگر

Hanyu Gu is a Senior Lecturer in the School of Mathematical and Physical Sciences at the University of Technology Sydney (UTS), part of the Faculty of Science. He holds a PhD in Power Engineering and Automation from Shanghai Jiao Tong University (1999) and has extensive industry experience in telecommunications, airline optimization, and mining. His research focuses on combinatorial optimization, decomposition methods, stochastic programming, and machine learning applications. Notable awards include second place in the 2020 ROADEF competition. He collaborates with institutions like the UTS Transportation Research Centre and has contributed to projects such as optimisation engines for airline management and underground mining algorithms. Current research explores hybrid algorithms, Bayesian optimisation, and scheduling under uncertainty. Education: Bachelor in Industrial Automation, Shanghai Jiao Tong University (1994) Master in Control Theory and Application, Shanghai Jiao Tong University (1997) PhD in Power Engineering and Automation, Shanghai Jiao Tong University (1999) Industry Experience: ZTE (1999–2001): Senior Wireless Communication Engineer CTI, Melbourne (2007–2011): Airline Management Optimisation Researcher NICTA (2011–2013): Underground Mining Optimisation Researcher Grants: ARC Linkage Project LP0883855 (2008–2012): Developed optimisation tools for transportation crewing, valued at $840,000. Research interests span decomposition methods for large-scale problems (e.g., airline scheduling), stochastic programming for resource sharing, and hybridisation of mathematical programming with constraint programming. Recent work includes Bayesian optimisation for knapsack problems and relax-and-solve algorithms for project scheduling. His articles frequently address optimisation in logistics, healthcare, and transportation, emphasizing practical industry applications and algorithmic innovation. Awards: Second place in the ROADEF 2020 competition for maintenance planning solutions. Advising & Grants: Supervises Masters and PhD students in operations research and optimisation. Collaborates with Ausgrid, UGL, and ANC on optimisation projects (e.g., employee training timetabling, logistics). Active in the Optimisation Group of UTS Transportation Research Centre, he bridges academic research with real-world challenges in scheduling, logistics, and resource management. Ongoing efforts include advancing metaheuristics and integrating machine learning with traditional optimisation techniques.

Ward Romeijndersمشاهده پروفایل

استاد

• Operations Research
• Management Science
• Stochastic Programming
• +۷ مورد دیگر

Prof. Ward Romeijnders is a Full Professor in the Department of Operations Management & Operations Research at the University of Groningen. He holds leadership roles in academic organizations such as Secretary of the Stochastic Programming Society (COSP), Associate Editor of Mathematical Methods of Operations Research, and Board member of LNMB. His research focuses on stochastic programming, optimization under uncertainty, and applications in energy, logistics, healthcare, and finance. He has led projects like the NWO VIDI grant 'Discrete Decision Making under Uncertainty' (2023-2027) and previously the NWO VENI project on planning under uncertainty. Education: Doctorate in Operations Research (details unspecified). Research Interests: Stochastic optimization, risk-averse decision-making, integer programming, and applications in societal challenges. Recent publications span algorithmic advancements in Benders decomposition, robust optimization, and error-bound theories. He has received multiple awards, including the Gijs de Leve Prize (2015-2017) and Willem R. van Zwet Award (2016). As a teacher, he instructs advanced courses on stochastic programming and optimization under uncertainty. Grants: NWO VENI (2017-2020), NWO VIDI (2023-2027). Editorial Roles: Mathematical Methods of Operations Research, Euro WG on Stochastic Optimization.

Yuri Faenzaمشاهده پروفایل

دانشیار

• Discrete Optimization
• Operations Research
• Mathematical Programming
• +۴ مورد دیگر

Yuri Faenza is an Associate Professor in the Department of Industrial Engineering and Operations Research at Columbia Engineering, with affiliations to the Data Science Institute (DSI) and Foundations of Data Science Center. His research bridges Discrete Optimization, Operations Research, and Computer Science, focusing on algorithmic theory and applications to Market Design (particularly School Choice) and Machine Learning. Key research areas include Knapsack , Matching , and Extended Formulations , with methodological work on polytope structures, greedy algorithms, and stochastic or semi-random models. His recent publications emphasize stable matching, optimization under uncertainty, and the intersection of discrete mathematics with data science. Scientific awards include the NSF CAREER award Meta Research Award . He has served on program committees for major conferences like ALGA, EC, APPROX, and IPCO, and is an Associate Editor for journals including Mathematical Programming and Discrete Optimization . Prior to Columbia, he held postdoctoral positions at the University of Brussels, EPFL, and University of Padua.

Robert D. Kleinbergمشاهده پروفایل

استاد

• Algorithms
• Theoretical Computer Science
• Economic Aspects of Algorithms
• +۵ مورد دیگر

Robert D. Kleinberg is a Professor of Computer Science at Cornell University's Department of Computer Science and a member of the field of Information Science. He received his Ph.D. from MIT in 2005 and previously worked at Akamai Technologies designing internet content delivery networks. His research bridges theoretical computer science and practical applications in electronic commerce, networking, and information systems. Research Interests: Kleinberg's work focuses on the design and analysis of algorithms, with special emphasis on economic aspects of algorithms, online learning, random processes in networks, and applications to electronic commerce and information retrieval. His research combines theoretical rigor with real-world impact, exploring how algorithmic principles can optimize complex systems. Publication Trends: Kleinberg's recent publications demonstrate a consistent focus on online optimization, algorithmic fairness, network design, and learning algorithms. His work frequently appears in top-tier venues spanning theoretical computer science (STOC, FOCS), machine learning (NeurIPS, COLT), and networking (NSDI). The publications reflect interdisciplinary approaches combining computer science, economics, and applied mathematics. Awards and Honors: Microsoft Research New Faculty Fellowship Alfred P. Sloan Foundation Fellowship NSF CAREER Award Best Paper Award at ACM EC 2014 Advising and Academic Leadership: Kleinberg actively mentors Ph.D. students and postdoctoral researchers, with current advisees including Raunak Kumar, Princewill Okoroafor, and Tegan Wilson. He has supervised numerous graduates who now hold academic positions worldwide. His teaching includes core algorithm courses (CS 4820, CS 6820) and specialized topics at both undergraduate and graduate levels.

• Mixed-Integer Optimization
• Combinatorial Optimization
• Discrete Mathematics
• +۳ مورد دیگر

Timm Oertel is a Professor in the Department of Data Science at Friedrich Alexander University Erlangen-Nuremberg (FAU), holding the Chair of Analytics & Mixed-Integer Optimization. His office is located in Room 03.344 at Cauerstraße 11, Erlangen, and he can be contacted via email at timm.oertel@fau.de or phone at +49 9131 85-67313. His research focuses on mixed-integer optimization, combinatorial optimization, and discrete mathematics, with significant contributions to sparse solutions in lattices and semigroups, integer Carathéodory rank, knapsack polyhedra, and parametric integer optimization. His work bridges theoretical computer science, operations research, and discrete geometry, emphasizing structural properties of integer solutions and algorithmic efficiency. Professor Oertel's publication record (2013-2025) reveals consistent trends in theoretical integer programming, with recent work (2020-2025) concentrating on sparsity patterns, approximation in algebraic structures, and complexity bounds. He frequently collaborates with leading researchers including Iskander Aliev, Robert Weismantel, and Joseph Paat, publishing in top venues like Mathematical Programming and SIAM Journal on Optimization. His research demonstrates deep connections between combinatorial geometry and optimization theory.