جستجوی استاد راهنمای دانشگاهی

• Robotics
• Electronics
• Autonomous Vehicles
• +۷ مورد دیگر

María Soledad Escudero Hernanz is a Full Professor in the Electronics Department at the University of Alcalá's School of Engineering. With extensive experience in both teaching and research, she has been actively contributing to the university community since at least 2005. Her research focuses on several cutting-edge areas within robotics and electronics, with particular emphasis on service robotics and transportation safety applications. Professor Escudero Hernanz is an active member of the 'Robótica de Servicios y Tecnologías para la Seguridad Vial - Service Robotics and e-Safety' research group, where she contributes to multiple research lines including autonomous driving systems, intelligent control for robotics, driver monitoring technologies, and mobile robot navigation. Her scholarly output shows a consistent pattern of research focused on practical applications of robotics and computer vision technologies. The analysis of her publications reveals a strong emphasis on transportation safety systems, with numerous projects related to driver monitoring, distraction detection, and advanced driver assistance systems. She has also made significant contributions to assistive robotics and multi-robot navigation systems. Professor Escudero Hernanz has been involved in numerous research projects funded by various entities including: Spanish Ministry of Economic Affairs and Digital Transformation Community of Madrid University of Alcalá Spanish Foundation for Science and Technology (FECYT) Her work demonstrates a strong commitment to both fundamental research and practical applications, particularly in the areas of transportation safety and assistive technologies. As an educator, she teaches Circuit Electronics and Digital Electronics across multiple engineering programs, contributing to the development of future engineers in various specializations. Her collaborative approach is evident through her long-standing research partnerships with colleagues at the University of Alcalá, particularly with Rafael Barea Navarro, María Elena López Guillén, and Luis Miguel Bergasa Pascual, with whom she has worked on multiple projects spanning more than a decade.

Elvira Albertمشاهده پروفایل

استاد

• Program Verification
• Program Optimization
• Blockchain
• +۷ مورد دیگر

Elvira Albert is a Professor in the Department of Computer Systems and Programming at the School of Computer Science, Complutense University of Madrid, Spain. She leads the COSTA research group, which specializes in formal methods for program optimization and verification, with a strong focus on blockchain technologies and smart contracts. She holds a Ph.D. in Computer Science. Her research encompasses program verification, static analysis, and compiler construction, particularly applied to blockchain ecosystems. The COSTA group has developed influential tools including circom (for arithmetic circuit compilation), CIVER (for circuit verification), EthIR (EVM decompiler), and superoptimizers such as GASOL and SuperStack. Recent publications (2019-2024) demonstrate her expertise in smart contract safety (e.g., SAFEVM), concurrency testing, and bytecode superoptimization using constraint solvers. Her work consistently bridges theoretical formal methods with practical tool development for the blockchain industry. Albert has secured substantial funding from the Ethereum Foundation for multiple projects (GASOL, GREEN, SOPA, FORVES series, ZK-ARCKIT, GREY). She serves as an area editor for Theory and Practice of Logic Programming (TPLP) since 2019. The COSTA group, under her leadership, maintains an active research agenda in blockchain verification, zero-knowledge proofs, and formally verified compilers. Current projects include ZK-ARCKIT for arithmetic circuit analysis and FORYU for formal semantics of Yul.

Eli Geffenمشاهده پروفایل

استاد

• Behavioral ecology
• Population genetics
• Vocal communication
• +۷ مورد دیگر

Prof. Eli Geffen is a Professor at the School of Zoology, Tel Aviv University, and serves as the Curator of Terrestrial Vertebrates at the Steinhardt Museum of Natural History. He received his PhD from the University of Oxford in 1990 and has authored more than 100 scientific papers throughout his distinguished career in behavioral ecology and population genetics. Prof. Geffen's research focuses on behavioral ecology as expressed in the social dynamics and vocalization of the rock hyrax, visual communication in chameleons, and evolution of sociality in canids and other carnivores. His work also encompasses population genetics, examining behavioral and environmental factors that influence dispersal decisions in carnivores and other vertebrates, as well as comparative studies on various behavioral aspects of birds and mammals. He utilizes samples from the Museum collections in his study of population genetics of wolves and jackals, dog and cat domestication, and the interaction between the changing environment and genetics in other carnivores. Prof. Geffen's recent publications demonstrate a strong focus on rock hyrax vocal communication and social behavior, carnivore ecology and conservation, and amphibian and reptile population genetics. His work often involves interdisciplinary approaches, combining behavioral observations with genetic analyses to understand evolutionary processes and ecological adaptations. Many of his studies examine the impact of environmental factors, including climate change and human activity, on animal behavior and population dynamics. Prof. Geffen has made significant contributions to understanding the behavioral ecology of rock hyraxes, particularly their complex vocal communication systems and social structures. His research on carnivores, including foxes, wolves, and leopards, has provided valuable insights for conservation efforts. Additionally, his work on amphibians, particularly the rediscovered Hula painted frog, has contributed to conservation biology in the region.

• Causal Machine Learning
• Mechanistic Machine Learning
• Dynamical Systems
• +۶ مورد دیگر

Niki Kilbertus is a Professor in the Department of Informatics at the Technical University of Munich and a group leader at Helmholtz AI (Helmholtz Munich). They are also affiliated with MCML, the Konrad Zuse School relAI, and the Munich Unit of ELLIS. Since 2024, they have been a member of the Junge Akademie and received the Leopoldina Prize for Young Scientists. In 2025, they were awarded an ERC Starting Grant and achieved tenure at TUM. Professor Kilbertus's research focuses on causal machine learning, mechanistic ML, dynamical systems, and AI for science. Their work spans theoretical foundations of causal inference and practical applications across scientific domains. They have made significant contributions to causal effect estimation, causal discovery in stochastic processes, learning differential equations, and fair machine learning. Their research often bridges computer science with physics, biology, and climate science, demonstrating the interdisciplinary nature of their work. Professor Kilbertus has published extensively in top machine learning venues including NeurIPS, ICML, and ICLR, with numerous publications in 2024-2025. Their recent work shows a strong trend toward causal discovery in continuous-time systems, intervention modeling, and physics-informed machine learning applications. Scientific Awards: Leopoldina Prize for Young Scientists (2024) ERC Starting Grant (2025) Professor Kilbertus actively supervises multiple PhD students and collaborates with researchers across institutions including Max Planck Institutes and Helmholtz centers. They serve as an Action Editor for TMLR and regularly review for major ML conferences. The research group is well-funded through the ERC grant and institutional support from TUM and Helmholtz AI, enabling active recruitment of new PhD students and postdocs. Based at Technical University of Munich and Helmholtz AI, Professor Kilbertus's team works at the intersection of theoretical machine learning and scientific applications, with particular strengths in causal reasoning for complex dynamical systems.

Olivier Serreمشاهده پروفایل

استاد

• theoretical computer science
• automata theory
• formal languages
• +۷ مورد دیگر

Olivier Serre is a CNRS Research Director working at the Institute for Research in Fundamental Computer Science (IRIF), a joint research unit of CNRS and University of Paris. Since September 2021, he serves as Deputy Scientific Director at the Institute for Information Sciences and Technologies (INS2I) at CNRS. His research focuses on theoretical computer science with particular emphasis on automata theory and formal verification frameworks. Dr. Serre completed his education at ENS Cachan (1999-2003) and earned his PhD in Computer Science from Paris-Diderot University (now University of Paris) in 2004. Following a postdoctoral position at RWTH Aachen, he joined LIAFA (now IRIF) as a research fellow in 2005 and became a CNRS Research Director in 2016. His academic journey reflects deep engagement with foundational theoretical problems and their practical verification applications. His research centers on automata theory and formal languages, with particular expertise in games and logic, infinite structures, and verification of higher-order functional programs. Dr. Serre has made significant contributions to collapsible pushdown systems, recursion schemes, and parity games, bridging theoretical foundations with practical applications in program verification. His work demonstrates exceptional depth in developing decision procedures for complex verification problems previously considered intractable. Dr. Serre's extensive publication record in premier theoretical venues shows consistent contribution to advancing the state of the art. His research trajectory reveals a clear evolution from foundational automata theory toward increasingly sophisticated systems involving higher-order computation and imperfect information, with notable breakthroughs in establishing decidability results for complex verification problems.

Yu Wangمشاهده پروفایل

استادیار

• Mathematical Control Theory
• Inverse Problems
• Stochastic Partial Differential Equations
• +۲ مورد دیگر

Yu Wang serves as Assistant Professor at Southwest Jiaotong University since 2023 and concurrently holds a Visiting Scientist position at Friedrich-Alexander-Universität Erlangen-Nürnberg's DCN-AvH Chair for Dynamics, Control, Machine Learning and Numerics. His academic foundation stems from a 2023 PhD in Mathematics at Sichuan University. Education background: PhD in Mathematics, Sichuan University (2023) Research expertise centers on mathematical control theory with rigorous focus on inverse problems for stochastic partial differential equations. His work develops theoretical methods to control systems governed by SPDEs and reconstruct unknown parameters from observational data under randomness. This niche intersects applied mathematics, stochastic analysis, and computational modeling, with potential applications in engineering systems subject to unpredictable disturbances. As an early-career faculty member, Dr. Wang actively builds research capacity through international collaboration (evidenced by his German affiliation) and seeks to establish a specialized group in stochastic control theory. Prospective students should possess advanced mathematical training to contribute to this technically demanding field.

Gianluca Breroمشاهده پروفایل

استادیار

• Market Design
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Gianluca Brero is an Assistant Professor of Information Systems and Analytics in the College of Business at Bryant University, specializing in AI-driven optimization of economic platforms including electricity trading, spectrum auctions, and supply chains to enhance efficiency and fairness. His academic credentials include: Ph.D. in Computer Science from University of Zurich (2020), advised by Sven Seuken Double Master's in Mathematics from Turin Polytechnic and Milan Polytechnic (Alta Scuola Politecnica) Bachelor's in Mathematics from Turin Polytechnic, all earned summa cum laude Dr. Brero's research integrates machine learning, reinforcement learning, and game theory to solve complex market design problems. His work develops novel algorithms for combinatorial auctions, sequential pricing mechanisms, and AI collusion mitigation, with direct applications in critical economic infrastructure. He emphasizes ethical AI deployment throughout his methodology. His publication trajectory shows consistent output in top AI/economics venues (AAAI, IJCAI, AAMAS, NeurIPS) from 2014-2025, with increasing focus on real-world platform optimization. Recent work (2021-2025) demonstrates growing impact in applying ML to economic mechanisms and collusion prevention. Key recognitions include: Jeanne and John Rowe Fellow in Data Science Top mathematics graduate at Turin Polytechnic As an educator, Dr. Brero teaches deep learning, AI ethics, and statistics at Bryant, building on prior teaching at University of Zurich and Brown University. His mentorship focuses on equipping students with both technical rigor and ethical frameworks for AI development. While specific grant details aren't public, his postdoctoral appointments and publication record indicate sustained research funding. Though no formal lab is established at Bryant, his collaborative network spans Brown, Harvard, University of Zurich, and Microsoft Research, providing students access to interdisciplinary research opportunities in market design.

• Partial Differential Equations
• Control Theory
• Quantum Control
• +۴ مورد دیگر

Karine Beauchard is a University Professor in the Department of Mathematics at École normale supérieure de Rennes (ENS Rennes), France. Her research focuses on the mathematical analysis and control of partial differential equations with applications to quantum systems and other physical phenomena. Her primary research interests include: Analysis and control of partial differential equations Interaction between geometry (Lie brackets) and analysis in control theory Bilinear control and quantum control (exact/approximate controllability, feedback stabilization of Schrödinger and Bloch equations) Control of degenerate parabolic equations of hypoelliptic type (Grushin, Heisenberg, Kolmogorov, Ornstein-Uhlenbeck) Inverse problems and asymptotic observers Professor Beauchard's research output shows a strong focus on controllability properties of various partial differential equations, particularly examining the minimal time requirements, geometric conditions, and quadratic obstructions to controllability. Her work bridges pure mathematical analysis with applications in quantum physics and engineering systems. Professor Beauchard completed her PhD in 2005 at Université d'Orsay with a thesis on the controllability and stabilization of the Schrödinger equation, supervised by Jean-Michel Coron. She obtained her HDR (Habilitation à Diriger des Recherches) in 2010 at ENS Cachan on the analysis and control of partial differential equations. She has published 44 papers spanning from 2005 to 2025, with recent work focusing on small-time controllability of Schrödinger equations, obstructions to controllability, and control of hypoelliptic equations. Her publications appear in prestigious journals including Annales de l'Institut Henri Poincaré, Journal of Differential Equations, and SIAM Journal on Control and Optimization. Professor Beauchard has collaborated with numerous researchers including F. Marbach, E. Pozzoli, P. Cannarsa, and J.-M. Coron, demonstrating her active engagement in the mathematical community. Her work has contributed significantly to the theoretical foundations of control theory for partial differential equations.

• Inverse Problems
• Partial Differential Equations
• Data Assimilation
• +۵ مورد دیگر

Tram Thi Ngoc Nguyen is a postdoctoral researcher at the Max Planck Institute for Solar System Research, working within the Department of Interiors of the Sun and Stars as part of the Max Planck Fellow Group Inverse Problems led by Thorsten Hohage. Her research focuses on applying advanced mathematical methods to solar and stellar physics. Her educational background includes: PhD in Technical Mathematics, University of Klagenfurt, Austria (2017-2020) MA in Applied Mathematics, Ho Chi Minh City University of Technology, Vietnam (2012-2015) BA in Mathematics and Informatics, Ho Chi Minh City University of Science, Vietnam (2004-2008) Dr. Nguyen specializes in nonlinear dynamic inverse problems , parameter identification for PDEs , and data assimilation , with current applications in helioseismology and asteroseismology. Her work bridges mathematical theory and observational solar physics through projects like GDC-SDO and PLATO, utilizing techniques from imaging and machine learning to model stellar interiors. Her scientific recognition includes: GIP prize on Inverse Problems for best PhD thesis in German-speaking countries (2020-2022) As a key member of the Max Planck Fellow Group Inverse Problems, she contributes to developing cutting-edge methodologies for interpreting solar oscillation data. Her research team collaborates on missions analyzing the Sun's interior structure through seismic waves, advancing our understanding of stellar dynamics and evolution.

Beomjoon Kimمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Humanoid Robotics
• +۳ مورد دیگر

Beomjoon Kim is an Associate Professor at the Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST). He directs the Humanoid Generalization (HuGe) lab, which focuses on creating general-purpose humanoids capable of efficient decision-making in complex environments. Education: Ph.D. in Computer Science from MIT CSAIL M.Sc. in Computer Science from McGill University B.Math in Computer Science and Statistics from University of Waterloo Professor Kim's research spans multiple areas of robotics and artificial intelligence, with a particular focus on humanoid robotics, task and motion planning, and robot learning. His work aims to bridge the gap between high-level task planning and low-level motion control, enabling robots to operate effectively in complex, real-world environments. He has made significant contributions to the field of geometric task and motion planning, developing novel algorithms that improve the efficiency and effectiveness of robot decision-making processes. Analysis of Professor Kim's recent publications reveals a strong focus on humanoid robotics, with particular emphasis on motion planning, object manipulation, and learning-based approaches. His work increasingly integrates deep learning techniques with traditional robotics algorithms, demonstrating a trend toward more data-driven approaches in robotics research. Many of his papers address the challenge of generalization in robotics, seeking to develop systems that can handle novel objects and environments without extensive retraining. Scientific Awards: Best Cognitive Robotics Paper award at ICRA 2017 Oral presentation (top 6% of accepted papers) at AAAI 2020 Oral presentation (top 6% of accepted papers) at AAAI 2019 Oral presentation (top 6% of accepted papers) at AAAI 2018 Spotlight presentation (top 3.5% of accepted papers) at NeurIPS 2018 Spotlight presentation (top 4% of accepted papers) at NeurIPS 2013 Plenary talk (top 12% of accepted papers) at CoRL 2020 Professor Kim actively mentors students at various levels, currently advising multiple Ph.D. and Master's students in the HuGe lab. His research is supported by grants that enable his team to pursue ambitious projects in humanoid robotics and AI. Through collaborations with institutions like MIT and industry partners, his work has significant impact on both academic research and practical applications of robotics technology. The HuGe lab, under Professor Kim's direction, has established itself as a leading research group in humanoid robotics. The lab maintains strong connections with other research institutions and regularly publishes in top-tier robotics and AI conferences. Current research directions include developing more efficient motion planning algorithms, improving robot manipulation capabilities, and exploring the integration of large language models with robotic systems.