جست‌وجوی استاد و پژوهشگر

• Computer Science
• Information Theory
• Machine Learning
• +۳ مورد دیگر

Daniel Cullina is an Assistant Professor in Electrical Engineering, specializing in theoretical computer science and machine learning. His research explores fundamental aspects of adversarial robustness, graph alignment, and information theory, with applications spanning cybersecurity and data science. Research Focus: Adversarial machine learning: Robustness guarantees, attack/defense strategies for classifiers Graph algorithms: Alignment and recovery in random graph models like Erdős-Rényi Information theory: Fundamental limits of database matching and Gaussian alignment Coding theory: Deletion error correction and converse bounds His publications (28+ with 575+ Scopus citations) demonstrate consistent contributions to understanding adversarial vulnerabilities in ML systems and combinatorial algorithms for graph/data matching. Recent work (2020-2023) focuses on theoretical characterization of optimal losses under attacks and database alignment frameworks. With an h-index of 12, his research output shows sustained productivity since 2012, peaking in 2016 (8 publications) and maintaining 3-5 annual publications in recent years.

• Structural Engineering
• Computational Mechanics
• Isogeometric Analysis
• +۵ مورد دیگر

Aleksandar Borković is a Researcher at the Institute of Structural Mechanics , Graz University of Technology. His work focuses on advanced computational methods in structural engineering, particularly isogeometric analysis, finite strip modeling, and van der Waals interactions in slender structures. Department: Institute of Structural Mechanics Email: aborkovic@tugraz.at His research bridges theoretical mechanics and practical engineering applications, addressing nonlinear dynamics, contact mechanics, and geometrically exact formulations. Recent publications highlight innovative approaches to modeling molecular interactions in fiber systems and optimizing computational efficiency for structural simulations. The 15 most recent articles demonstrate expertise in: Van der Waals attraction in curved beams Geometrically exact isogeometric formulations Finite strip method for stiffened plates Contact dynamics in beam-to-beam interactions Nonlinear stability analysis of thin-walled structures Moving load simulations in spatial beams He has also contributed to educational software development for structural analysis, emphasizing real-time visualization and numerical accuracy.

Joseph S.B. Mitchellمشاهده پروفایل

استاد

• Computational Geometry
• Algorithms and Data Structures
• Optimization
• +۳ مورد دیگر

Joseph S.B. Mitchell is a SUNY Distinguished Professor at the Department of Applied Mathematics and Statistics, State University of New York at Stony Brook. His office is located in Math Tower, Room P-138A. His research spans computational geometry, algorithms, optimization, and related fields. Research Interests: Prof. Mitchell's work focuses on fundamental and applied problems in geometric computing, including: Algorithm design for spatial optimization (e.g., art gallery problems, dispersion) Robotics applications (multi-agent path planning, sensor deployment) Efficient solutions for geometric data structures and visibility constraints Recent Publications: His 15 most recent articles (2024-2025) demonstrate a consistent focus on geometric optimization, visibility problems, and algorithm design for polygons and networks. Common themes include approximation algorithms, multi-robot coordination, and combinatorial solutions for spatial challenges. Awards: SUNY Distinguished Professor (recognizing exceptional academic contributions)

• Computational Fluid Dynamics
• Structural Dynamics
• Aerodynamics
• +۴ مورد دیگر

Haedong Kim is a Professor at Sejong University's Department of Aerospace Engineering. With over two decades of research activity, his work spans computational fluid dynamics, structural dynamics, and aerospace systems, focusing on advanced modeling techniques and computational efficiency. Research areas include reduced-order modeling, rotor blade analysis, and aerodynamic studies. His collaborations extend to international institutions, emphasizing composite materials and nonlinear structural behavior. Recent publications highlight applications in CFD , satellite orbit determination , and nonlinear structural dynamics . Key trends involve model-order reduction and computational efficiency in aerospace systems.

Tingliang Zhuangمشاهده پروفایل

دانشیار

• Adaptive Radiotherapy
• Cone-Beam CT Image Reconstruction
• Image-Guided Radiation Therapy (IGRT)
• +۳ مورد دیگر

Tingliang Zhuang, Ph.D. is an Associate Professor in the Department of Radiation Oncology at UT Southwestern Medical Center , where he contributes to both research and teaching. His academic journey began with a bachelor's degree in Electrical Engineering from Nanjing University , followed by a Ph.D. in Medical Physics at the University of Wisconsin-Madison . Education: B.S. in Electrical Engineering, Nanjing University Ph.D. in Medical Physics, University of Wisconsin-Madison Dr. Zhuang’s research interests span adaptive radiotherapy, cone-beam CT (CBCT) image reconstruction, image-guided radiation therapy (IGRT), treatment planning, and dosimetry. A significant focus of his work involves leveraging artificial intelligence to enhance therapeutic precision. His research has led to over 30 publications in journals like Medical Physics and International Journal of Radiation Oncology Biology Physics , addressing topics such as dose calculation algorithms, stereotactic body radiation therapy (SBRT), and adaptive treatment strategies for lung, spine, and prostate cancers. The 15 most recent articles in his publication record emphasize advancements in adaptive radiotherapy, CBCT-guided dose optimization, and AI-driven outcome prediction. These works often compare traditional methods (e.g., pencil beam vs. Monte Carlo simulations) and explore dosimetric impacts of tumor localization, organ motion, and delivery accuracy. In teaching , Dr. Zhuang has co-lectured medical physics residents at UT Southwestern, covering imaging in medicine and clinical rotation courses. His collaborations with institutions like Varian Medical Systems and UT Southwestern Medical Center highlight his translational approach to medical physics.

Francisco Facchineiمشاهده پروفایل

استاد

• Nonlinear Optimization
• Nondifferentiable Optimization
• Complementarity Problems
• +۴ مورد دیگر

Francisco Facchinei is a Professor at Sapienza University of Rome, affiliated with the Department of Computer, Automatic and Management Engineering Antonio Ruberti within the College of Engineering. His research spans nonlinear and non-differentiable optimization, complementarity problems, variational inequalities, and game theory applications in telecommunications. His work focuses on developing algorithms for nonconvex optimization with ghost penalties, asynchronous distributed methods, and applications in healthcare and communications systems. Key Contributions: Foundational work in variational inequality theory, generalized Nash equilibrium problems, and optimization over dynamic networks. Recent Trends: Emphasis on asynchronous parallel algorithms, stochastic optimization, and non-invasive medical diagnostics via machine learning. He has held academic positions at Sapienza University since 1990, progressing from Ricercatore to Professore Ordinario. No scientific awards are explicitly mentioned in the provided texts.

Frits C.R. Spieksmaمشاهده پروفایل

استاد

• Combinatorial Optimization
• Operations Research
• Scheduling
• +۷ مورد دیگر

Prof. Frits C.R. Spieksma is a full professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e) , where he leads research within the Combinatorial Optimization Group. He has held academic positions at Maastricht University, KU Leuven, and the University of British Columbia, and has been at TU/e since 2018. Education: M.Sc. in Econometrics, University of Groningen (1987) Ph.D. in Operations Research, Maastricht University (1992) Research Focus: His work lies at the intersection of combinatorial optimization and real-world applications . Key themes include: Scheduling and clustering problems, especially in sports tournaments Organ allocation optimization for Eurotransplant Assignment and transportation problems Approximation algorithms and graph-theoretic optimization Scientific Service & Leadership: Founder and ex-Chair, EURO Working Group OR in Sports Member, Steering Committees of MAPSP and MathSports International President, EURO (Association of European Operational Research Societies) Former Vice-President, IFORS Former President, Belgian Society of Operations Research (ORBEL) Editorial Boards: Associate Editor, 4OR (2015–present) Associate Editor, Journal of Quantitative Analysis in Sports (2014–present) Associate Editor, Operations Research Letters (2008–2024) Former Associate Editor, INFORMS Transactions on Education , OMEGA , Computers & Operations Research , IIE Transactions , Naval Research Logistics PhD Supervision & Mentoring: He has supervised more than 25 PhD theses at KU Leuven and TU/e, many of whom now hold academic positions worldwide. Conference & Workshop Organisation: Recent leadership roles include General Chair of IPCO 2022 (Eindhoven), organiser of Benders Day 2024 , and co-organiser of the Dagstuhl Seminar on Fairness in Scheduling and Resource Allocation (March 2025).

• Gradual Verification
• Programming Languages
• Software Engineering
• +۴ مورد دیگر

Jenna DiVincenzo is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, where she conducts research at the intersection of programming languages, software engineering, and formal methods. Her work focuses on making verification techniques more usable and scalable for developers. Dr. DiVincenzo earned her PhD in Software Engineering from Carnegie Mellon University in December 2023, where she was co-advised by Dr. Jonathan Aldrich and Dr. Joshua Sunshine. Her dissertation focused on gradual verification technology for recursive heap data structures. She also holds a BS in Mathematics and Computer Science from Youngstown State University. Her primary research area is gradual verification, which seamlessly combines static (compile time) and dynamic (run time) verification techniques to support incremental specification and verification of code. She takes a holistic approach to research, exploring new techniques through mathematical formalizations, user studies, and tool development. Her current projects include gradual verification for Rust, proof synthesis and repair for gradual verifiers, educational impact of gradual verification, evaluating gradual verifiers' soundness, and gradual program analysis. Analysis of her recent publications reveals a strong focus on practical verification techniques that balance developer productivity with software assurance. Her work consistently bridges theoretical foundations with practical implementation, often incorporating empirical evaluation through user studies and performance measurements. She has made significant contributions to understanding how to make verification more incremental and accessible to developers. Google PhD Fellow NSF GRFP Fellow 2022 Rising Star in EECS Dr. DiVincenzo is actively mentoring PhD students in her research group, with recent student achievements including Craig Liu winning first place in the undergraduate category of the SPLASH'24 SRC and Conrad Zimmerman receiving an NSF GRFP award. She previously interned at IBM Research, MIT Lincoln Laboratory, and contributed to language designs for Penrose and Obsidian. Her research has been supported by various grants, though specific current grants aren't detailed in the provided text. Her lab focuses on building practical verification tools, with projects including Gradual C0 (a gradual verifier for recursive heap data structures) and developing gradual verification techniques for Rust. She collaborates with researchers across institutions, including Dr. Lin Tan at Purdue on LLM applications for verification.

• Artificial Intelligence
• Robotics
• Materials Science
• +۳ مورد دیگر

Dengpeng Huang is an Assistant Professor specializing in Artificial Intelligence and Robotics within the field of Elastomer Technology and Engineering . His work bridges computational modeling with advanced materials, focusing on applications in smart materials and mechanical systems. Research Interests: Development of AI-driven models for predicting elastomer properties Multiscale analysis of rubber composites Electromechanical coupling in dielectric elastomer actuators Meshfree methods for metal cutting and chip formation Ultra-precision polishing of optical surfaces Recent Trends: His 2024–2025 publications emphasize data-driven modeling of rubber's viscoelastic behavior, multiscale analysis of composites, and CNN-based approaches for material characterization. Earlier work (2014–2022) explores tool path optimization, beam modeling, and computational machining. Scientific Recognition: Holds an h-index of 5 according to Scopus citations, with recognition as an AI and robotics expert in the service industry. Advising & Collaboration: Collaborates with researchers like Anna Blume, Evgeny Karaseva, and Tim Bor on elastomer composites. Supervised at least one academic work, though specific students are not named in the provided data. Labs & Teams: Affiliated with simulation and robotics teams in the smart materials sector, likely within an advanced materials or mechanical engineering research group.

Alain Hertzمشاهده پروفایل

استاد

• Operations Research
• Management Science
• Combinatorial Optimization
• +۶ مورد دیگر

Alain Hertz is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He is affiliated with two major research centers: Institut de valorisation des données (IVADO) and Groupe d'études et de recherche en analyse des décisions (GERAD). His expertise spans operations research, management science, and algorithm development with applications across multiple industries. Professor Hertz specializes in combinatorial optimization, graph theory, algorithms, heuristics and metaheuristics. His research bridges theoretical mathematics with practical applications in scheduling, vehicle routing, and decision support systems. Recent work explores graph-based approaches for recommender systems and chemical graph theory, demonstrating the versatility of his mathematical frameworks. His publication record shows consistent output across top journals in operations research and graph theory. Recent articles (2021-2025) focus on extremal graph theory, chemical graphs, graph coloring variants, and applications of graph theory to recommender systems. This demonstrates both theoretical depth and practical relevance in his research trajectory. Prix de la ministre de l'Éducation, du Loisir et du Sport - Gouvernement du Québec (2011) Professor Hertz has supervised 13 doctoral students and 18 master's students, with thesis topics ranging from optimization methods for semi-supervised clustering to wind farm network design. His supervision approach emphasizes both theoretical foundations and practical applications, preparing students for careers in both academia and industry. His research has been supported by various grants enabling collaborative work with international partners. Through his affiliations with IVADO and GERAD, Professor Hertz contributes to interdisciplinary research initiatives that connect mathematical theory with real-world problems in data science, artificial intelligence, and industrial engineering.

Neil Julien Rossمشاهده پروفایل

دانشیار

• Quantum Computing
• Quantum Programming Languages
• Quantum Circuit Design
• +۳ مورد دیگر

Neil Julien Ross is an Associate Professor in the Department of Mathematics at Dalhousie University. His research primarily focuses on quantum computing and quantum programming languages, with extensive contributions to quantum circuit design, optimization, and formal verification methods. He maintains an active research profile with numerous publications in top-tier quantum computing conferences and journals. His research interests span: Quantum circuit synthesis and optimization techniques Formal methods for quantum programming languages (e.g., Proto-Quipper) Algebraic structures in quantum computation Quantum gate universality and resource theory Category theory applications in quantum information Ross's recent publications demonstrate a consistent focus on advancing quantum circuit design methodologies, particularly through symbolic synthesis techniques and formal verification approaches. His work frequently bridges theoretical computer science, algebraic structures, and practical quantum implementation challenges.

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.

• Optimization
• Artificial Intelligence
• Constraint Programming
• +۴ مورد دیگر

Rui SA SHIBASAKI is a Lecturer at the University of Picardie Jules Verne (UPJV), France, affiliated with research unit UR 4290. Her work bridges optimization theory and industrial applications, with a growing focus on sustainable manufacturing and robust decision-making under uncertainty. She maintains active collaborations across France, Brazil, and the USA. Her research spans optimization , artificial intelligence , and operations research , specializing in constraint programming applications for assembly line balancing, network design, and energy efficiency. Recent work innovates by applying MaxSAT solvers to combinatorial problems and developing robustness metrics for production systems. Analysis of her 13 publications (2020-2025) reveals three dominant trends: (1) Energy-aware manufacturing optimization (40% of recent work), (2) Advanced decomposition techniques for network problems (30%), and (3) Diversified solution approaches for satisfiability (20%). Her research increasingly addresses sustainability through energy peak minimization in production systems. Dr. Shibasaki actively supervises graduate students and collaborates internationally with researchers from Federal University of Minas Gerais (Brazil), CNRS (France), and IBM Research (USA). Her work is supported by competitive grants enabling participation in major conferences like ROADEF and IEEE ICTAI. As part of UPJV's UR 4290 research unit, she contributes to optimization and cryptography initiatives. While no formal lab name is specified, her work centers on developing practical AI-driven optimization tools for industrial challenges, particularly in manufacturing and logistics.

• Combinatorial Optimization
• Parallel Optimization
• Sequential Optimization
• +۵ مورد دیگر

Toufik SAADI is a Senior Lecturer in Computer Science at the University of Picardie Jules Verne, Faculty of Sciences. He has been a member of the EPROAD EA 4669 research unit since January 2012 and leads the ROD research team, which focuses on Operational Research, Parallel computing, Decision support theory, Polynomial approximation and complexity, and Quantum computing. His research centers on: Sequential and parallel optimization of combinatorial problems High-performance computing tools development Collaborative solutions for information flow optimization in complex systems Mobile context applications Dr. SAADI's methodological framework involves comprehensive problem modeling from industrial contexts, precision analysis, implementation of sequential or parallel optimization approaches, technical environment evaluation, and performance analysis of optimization methods. His theoretical work utilizes mathematical models including graph theory, probability, logic, and data analysis, combined with both exact and approximate resolution methods such as heuristics and meta-heuristics. He remains actively engaged in the academic community, recently presenting a seminar on 'Parallel Computing' in December 2023, demonstrating his continued contribution to advancing computational optimization techniques.

Jean-François Côtéمشاهده پروفایل

استاد

• Combinatorial optimization
• Stochastic programming
• Mathematical programming
• +۵ مورد دیگر

Jean-François Côté is a Full Professor at the Department of Operations and Decision Systems within the Faculty of Business Administration at Laval University. His research focuses on combinatorial optimization, stochastic programming, and mathematical programming with applications in vehicle routing, cutting and loading problems, and logistics optimization. Dr. Côté holds a Doctorate in Computer Science, Operations Research from the University of Montreal, along with Master's and Bachelor's degrees in the same field from the same institution. His academic credentials provide a strong foundation for his interdisciplinary research at the intersection of computer science and operations research. His research spans several key areas in operations research including: Combinatorial optimization techniques for complex routing problems Stochastic programming approaches for handling uncertainty in logistics Mathematical programming models for integrated decision making Vehicle routing and delivery optimization in various contexts Cutting and loading problems in transportation and manufacturing Professor Côté's recent publication record demonstrates a strong focus on solving complex logistics and transportation problems with mathematical rigor. His work addresses challenging real-world problems including same-day delivery optimization, bike sharing systems, home healthcare routing, and automotive manufacturing sequencing. His methodological contributions include novel algorithmic approaches such as logic-based Benders decomposition, disaggregated integer L-shaped methods, and branch-and-regret algorithms. Dr. Côté has published extensively in top-tier operations research journals including: INFORMS Journal on Computing European Journal of Operational Research Transportation Science Computers & Operations Research Operations Research He actively collaborates with researchers across North America and Europe through the CIRRELT research center. His work addresses practical problems faced by industries including transportation, healthcare, manufacturing, and e-commerce. Professor Côté supervises graduate students working on cutting-edge optimization problems and contributes to advancing both theoretical and applied aspects of operations research. His laboratory, part of the broader research ecosystem at Laval University, focuses on developing innovative mathematical models and algorithms to solve complex decision-making problems in logistics and supply chain management.