جست‌وجوی استاد و پژوهشگر

Raoul de Charetteمشاهده پروفایل

استاد

• Visual Scene Understanding
• Computer Vision
• 3D Reconstruction
• +۳ مورد دیگر

Raoul de Charette is a Research Director in computer vision at Inria Paris, leading the Astra-Vision group within the ASTRA team. His academic journey includes a PhD from Mines Paris (2012) and Habilitation (HDR) in 2022, with research stints at Carnegie Mellon University (2011), Mines Paris (2013), and the University of Makedonia (2014). His educational background comprises: PhD from Mines Paris (2012) Habilitation (HDR) (2022) De Charette's research centers on robust and interpretable visual scene understanding , spanning 3D scene reconstruction, domain adaptation, material recognition, and physics-grounded vision foundation models. His work integrates physical principles and synthetic data to enhance model robustness in real-world scenarios like autonomous driving and urban environments. Key contributions include uncertainty-aware 3D scene completion (PaSCo), material extraction from single images (Material Palette), and prompt-driven domain adaptation (PODA). Recent publications reveal a strategic shift toward vision-language integration, material-centric scene understanding, and foundation models that minimize labeled data dependency. His group pioneers physics-informed approaches to improve interpretability and resilience against environmental challenges like adverse weather conditions. Key scientific recognition includes: Best Paper Honorable Mention at EGSR 2025 for MatSwap ELLIS Membership PR[AI]RIE-PSAI Fellowship De Charette actively mentors four PhD students—Fatima Balde, Mohammad Fahes, Ivan Lopes, and Tetiana Martyniuk—often in industry collaborations with Valeo.ai and Kyutai. He secures funding through fellowships and industry partnerships, regularly opening PhD positions (including a 2025 opening for Physics-Grounded Vision Foundation Models). As an area chair for CVPR, ECCV, WACV, and IROS, he shapes the field through conference leadership and co-organizing initiatives like the African Computer Vision Summer School. He directs the Astra-Vision group within Inria Paris' ASTRA team, driving interdisciplinary research at the intersection of computer vision, machine learning, and physics-based modeling for real-world deployment in robotics and intelligent transportation systems.

Anna Gautierمشاهده پروفایل

استادیار

• Multi-agent Systems
• Planning under Uncertainty
• Game Theory
• +۳ مورد دیگر

Anna Gautier is an Assistant Professor in the Department of Computer Science at Chalmers University of Technology, affiliated with the Division of Data Science and AI. Her research focuses on multi-agent systems, planning under uncertainty, and game theory with applications in robotics. She holds a PhD from the University of Oxford (2023), an MSc in Applied Mathematics from the London School of Economics, and dual undergraduate degrees (BA in Mathematics and BS in Computer Science) from Washington University in St. Louis. Her work centers on risk-aware planning for multi-agent systems using Markov Decision Processes and mechanism design , with key applications in multi-robot coordination and human-robot interaction where trust dynamics and risk behavior are critical. Recent publications demonstrate advancements in contingency planning for autonomous vehicles, resource allocation under constraints, and quadratic voting mechanisms, bridging theoretical foundations with practical robotics challenges. She is recognized with the following honors: ELLIS Society Member (2025) Digital Futures Postdoctoral Fellow at KTH Royal Institute of Technology Dr. Gautier serves as co-chair for the ECAI 2025 Demonstration Track and is establishing her research group at Chalmers with plans to recruit PhD students. Her work builds on prior contributions to the WASP-Nest Project (PerCorSo) and Vinnova Center (TECoSA) focusing on trustworthy autonomous systems.

• Robot Vision
• Robot Learning
• Computer Vision
• +۴ مورد دیگر

Joni-Kristian Kämäräinen serves as Professor of Signal Processing within the Computing Sciences department at Tampere University, where he leads research in the Vision Group. Previously, he held faculty positions at LUT University's School of Engineering Science for five years before joining Tampere University in 2012 (tenured 2017, promoted to full professor in 2020). His academic journey includes a postdoctoral fellowship at the University of Surrey's Center of Vision, Speech and Signal Processing under Josef Kittler. His research centers on robot vision and robot learning , with significant contributions to computer vision and machine learning. Key focus areas include visual place recognition, RGB-D tracking, color constancy, and anthropometric measurements. His group maintains strong industry collaborations with Huawei, Nokia Technologies, and Business Finland-funded projects. His publication portfolio shows a clear trajectory toward real-world robotic applications, with recent work emphasizing visual place recognition under varying conditions (2022-2024), depth-aware video processing (2023-2024), and reinforcement learning for industrial manipulators (2023-2025). The 2023 textbook Koneoppimisen perusteet (Machine Learning Fundamentals) demonstrates his commitment to education. Expert Statement for Finnish Parliament (2022) on AI solutions Contributor to Finnish Roadmap: Robots and the Future of Welfare Services (2021) Featured in YLE Uutiset (2018), Aamulehti (2021), and multiple technical press outlets He has supervised 20 PhD students since 2007, including Vivienne Huiling Wang (2025), Samu Koskinen (2025), and Fatemeh Shokollahi Yancheshmeh (2024), with alumni placed at Aalto University, Ericsson AB, and Huawei. His group receives funding from the Academy of Finland, EU Horizon 2020, Business Finland, Huawei, and Nokia Technologies. The Vision Group operates from Tampere University's Hervanta Campus, maintaining close ties with industrial partners through applied research projects.

• Machine Learning
• Computational Materials Science
• Partial Differential Equations
• +۷ مورد دیگر

Yaohua Zang is a Professor at the Technical University of Munich , affiliated with the Department of Data-driven Materials Modeling . Their research bridges computational materials science, machine learning, and mathematical modeling, focusing on advanced methods for solving partial differential equations (PDEs), inverse problems, and optimal control in complex systems. Research Interests include: Physics-informed neural operators for PDEs Stochastic generative modeling for materials design Optimal control in robotics and dynamic systems Bayesian inference and uncertainty quantification High-dimensional numerical analysis Adversarial and weak formulation-based neural networks Recent work highlights physics-aware neural operators (DGenNO), weak adversarial networks for inverse problems, and applications in elastography and robotic assembly. Their scientific contributions span PDEs, materials science, and computational control. Labs & Teams : Part of the Chair of Data-driven Materials Modeling, located in Garching b. München, Germany.

David Ginsbourgerمشاهده پروفایل

استاد

• Uncertainty Quantification
• Spatial Statistics
• Gaussian Process Modeling
• +۷ مورد دیگر

Prof. Dr. David Ginsbourger is a Professor of Statistical Data Science at the University of Bern , leading the Uncertainty Quantification and Spatial Statistics Group within the Institute of Mathematical Statistics and Actuarial Science. He has held visiting roles at institutions like the Isaac Newton Institute (Cambridge, UK) and actively collaborates across engineering , geosciences , and medicine . University of Bern (2021-present) Idiap Research Institute (2015-2020) Swiss Academy of Sciences (elected member, 2025) Education : PhD in Applied Mathematics, École des Mines de Saint-Etienne (2009) Double Graduate Diploma, École des Mines de Saint-Etienne & Berlin Technical University (2005) Master’s in Applied Mathematics, Jean Monnet University & École des Mines de Saint-Etienne (2005) Licence in Mathematics, Joseph Fourier University (2002) David’s research focuses on uncertainty quantification , Gaussian process modeling , Bayesian optimization , and design of experiments . His work spans theoretical developments (e.g., kernel design, excursion set estimation) and applications in climate science , medical diagnostics , and engineering . Recent collaborative projects address inverse problems in hydrogeology , autonomous ocean sampling , and high-impact weather forecasting . Publications highlight trends in adaptive experimental design , kernel methods for equivariant models , and uncertainty quantification in multidisciplinary contexts . Key themes include excursion set estimation , Bayesian optimization , and spatial distributional modeling . Awards & Memberships : Elected member, Swiss Academy of Sciences (2025) Elected member, International Statistical Institute (2023-) Member, ELLIS Society (2024-) Long-term member, Swiss Mathematical Society Advising & Collaboration : David has advised numerous PhD and master’s students, including Athénaïs Gautier , Cédric Travelletti , and Mickael Binois . He has led projects at Idiap Research Institute and collaborates with institutions like the Oeschger Center for Climate Change Research and the Center for Artificial Intelligence in Medicine . Labs & Teams : He founded the Uncertainty Quantification and Optimal Design group at Idiap (2015-2020) and currently leads research at the University of Bern , integrating with multidisciplinary initiatives in climate change and infectious diseases .

• Reliability Engineering
• Mechanical Systems Maintenance
• Safety Integrity Level (SIL) Assessment
• +۳ مورد دیگر

Robert Pilch is a Professor at the Department of Machine Design and Operation within the Faculty of Mechanical Engineering and Robotics at AGH University of Science and Technology in Kraków. His research focuses on reliability engineering, preventive maintenance strategies, and safety integrity level (SIL) assessment of complex technical systems. Research Interests: Reliability assessment of technical systems Preventive maintenance optimization Adaptive maintenance strategies Network systems reliability Safety integrity level (SIL) modeling Simulation-based failure prediction Scientific Awards: No specific awards mentioned in provided materials. Contact: pilch@agh.edu.pl

• Mechanical Engineering
• Structural Mechanics
• Mathematics Education
• +۳ مورد دیگر

Zoya Mikhailovna Shanina is an Associate Professor at the Department of Mathematics, Faculty of Mechanical Engineering, Zaporizhzhia Polytechnic National University. With over 37 years of academic experience since 1988, she holds the academic title of Associate Professor and the degree of Candidate of Technical Sciences. Her work bridges mechanical engineering, structural analysis, and mathematics education methodology. Dr. Shanina earned her higher education at Dnipropetrovsk Construction Institute, Mechanical Faculty in 1975, graduating with honors in mechanics with a specialization in mechanical engineering. She defended her Candidate's thesis in 1987 at Kharkiv Institute of Agricultural Mechanization on 'Research and justification of toothed working bodies operating in the conditions of southern Ukraine'. Her research spans two primary domains: structural mechanics and mathematics education methodology. With over 50 scientific publications including one patent, her work demonstrates consistent focus on mechanical systems, particularly agricultural machinery dynamics and crane structural properties. She has made significant contributions to understanding soil-tool interactions in agricultural implements and dynamic behavior of crane systems under various loading conditions. Honorary certificate of the Ministry of Education and Science of Ukraine (2000) Badge 'Excellence in Education of Ukraine' (2005) ZNTU Badge 3rd degree 'For impeccable work' (2009) Honorary certificate of the Zaporizhzhia City Council (2011) Honorary certificate of the Zaporizhzhia Regional Council (2015) University Badge 2nd degree 'For impeccable work' (2023) As an active member of the university staff conference, Dr. Shanina has mentored numerous students in Higher Mathematics while maintaining a robust research agenda focused on practical engineering applications in agricultural machinery and industrial lifting equipment.

• Artificial Intelligence
• Multi-Agent Path Finding
• Robotics
• +۴ مورد دیگر

Jiří Švancara is a Lecturer at the Department of Theoretical Computer Science and Mathematical Logic (KTIML) within the Faculty of Mathematics and Physics at Charles University in Prague. His academic career focuses on Artificial Intelligence, particularly Multi-Agent Path Finding (MAPF), where he has established himself as a prominent researcher with numerous publications in top-tier conferences. His research interests span across Multi-Agent Path Finding , Robotics , Algorithm Design , and Constraint Satisfaction Problems . Švancara has made significant contributions to the field of MAPF, developing novel approaches for large-scale maps, temporal uncertainty handling, and efficient solving methods. His work bridges theoretical foundations with practical applications in robotics and transportation systems. Analysis of his recent publications reveals a strong focus on improving the scalability and robustness of MAPF algorithms. His research explores graph pruning techniques for large maps, handling temporal uncertainty in path execution, and comparing different objective functions for optimization. He has also investigated applications of MAPF in autonomous intersections and train routing systems, demonstrating the practical relevance of his theoretical work. Best Paper Award for 'Multi-agent Path Finding on Real Robots: First Experience with Ozobots' at IBERAMIA 2018 Švancara actively teaches multiple courses including Introduction to Artificial Intelligence, Propositional and Predicate Logic, and Algorithms and Data Structures. His teaching spans both theoretical foundations and practical implementation, with students engaging in programming assignments that reflect current AI challenges. His research collaborations are extensive, with frequent co-authorship with Roman Barták and other researchers in the field, indicating strong integration within the international AI research community. His laboratory work involves practical implementations of MAPF algorithms, including testing on real robots as evidenced by several publications describing experiments with Ozobots and other robotic platforms. This hands-on approach connects theoretical research with tangible robotic applications, providing valuable validation for his algorithmic contributions.

Lu Ganمشاهده پروفایل

استادیار

• Robot Perception
• Robot Learning
• Autonomous Navigation
• +۷ مورد دیگر

Lu Gan is an Assistant Professor at the Daniel Guggenheim School of Aerospace Engineering at the Georgia Institute of Technology, where she joined in January 2024. She leads the Lu's Navigation and Autonomous Robotics (Lunar) Lab and serves as core faculty for the Institute for Robotics and Intelligent Machines (IRIM) at Georgia Tech. Dr. Gan earned her educational degrees from prestigious institutions: B.S. in Automation from the University of Electronic Science and Technology of China (2013), M.S. in Control Engineering from Beihang University (2016), and both M.S. and Ph.D. in Robotics from the University of Michigan, Ann Arbor (2021, 2022). Prior to her appointment at Georgia Tech, she completed a two-year postdoctoral fellowship at the Graduate Aerospace Laboratories of the California Institute of Technology (GALCIT) and the Center for Autonomous Systems and Technologies at Caltech. Her research focuses on robot perception, robot learning, and autonomous navigation, exploring the integration of computer vision, machine learning, estimation, probabilistic inference, kinematics, and dynamics to develop autonomous systems for ground, air, and space applications. Her work addresses challenges in highly unstructured environments under harsh sensing conditions for tasks including search and rescue, daily housework, and scientific exploration. Dr. Gan's recent publications demonstrate strong trends in applying advanced machine learning techniques, particularly graph neural networks and multi-modal fusion approaches, to robotics challenges. Her work spans multiple subfields including legged locomotion, semantic scene understanding, contact perception, and thermal imaging applications for robotic systems operating in challenging environments. DAAD AInet Fellowship (2023) Rising Star in Data Science, University of Chicago (2022) National Graduate Scholarship of China (2015) IROS Best Paper Award on Agri-Robotics (Finalist, 2023) Dr. Gan actively mentors students in the Lunar Lab, with current advisees working on cutting-edge robotics research. She serves as an Associate Editor for IEEE Transactions on Robotics (T-RO) and IEEE Robotics and Automation Letters (RA-L), and is Associate Co-Chair of the IEEE RAS Technical Committee for Computer & Robot Vision. Her lab has secured various research grants supporting their work in robotics and autonomous systems. The Lunar Lab maintains strong collaborations with leading institutions and researchers in the robotics community, hosting seminars and participating actively in major conferences like ICRA. The lab's research directions include physics-informed learning for robotics, multi-modal 3D scene understanding, and heterogeneous multi-robot active SLAM.

Robin Chhabraمشاهده پروفایل

استادیار

• Robotics
• Soft Robotics
• Artificial General Intelligence
• +۵ مورد دیگر

Dr. Robin Chhabra is an Assistant Professor at the Department of Mechanical, Industrial, and Mechatronics Engineering, Toronto Metropolitan University. Specializing in robotics and mechatronics, his research focuses on soft robotics, embodied artificial intelligence, and space exploration technologies. Canada Research Chair in Cognitive Soft Space Robotics (2018-2024) Director of the ELIXIR Lab (Embodied Learning and Intelligence for eXploration and Innovative soft Robotics) His research integrates geometric mechanics , neuroscience , and artificial general intelligence to develop damage-tolerant, self-aware robotic systems for space applications. Key contributions include: First-of-its-kind meta-package for legged robot morphologies Singularity-free control algorithms for space manipulators Neuromorphic soft robotics prototypes (e.g., silicone inchworm robots) Recent publications emphasize multi-robot coordination , Lie group-based sensor fusion , and biologically inspired control systems for planetary exploration. Awards include: Canada Research Chair in Autonomous Space Robotics and Mechatronics Early Career Discovery Launch Supplement At the ELIXIR Lab, Chhabra mentors graduate students in developing robots capable of creative problem-solving during space disasters like orbital debris encounters or legged locomotion failures. His work spans robotics, control theory, and space systems engineering.

• Cyber-Physical Systems
• Control Theory
• Optimization
• +۴ مورد دیگر

Geir Dullerud serves as the W. Grafton and Lillian B. Wilkins Professor of Mechanical Science and Engineering at the University of Illinois Urbana-Champaign, holding affiliate appointments in Computer Science and Electrical and Computer Engineering within the College of Engineering. He is a core faculty member at the Coordinated Science Laboratory and actively contributes to the Center for Autonomy initiative. Education Ph.D. in Information Engineering, Cambridge University, England, 1994 M.A.Sc. in Electrical Engineering, University of Toronto, Canada, 1990 B.A.Sc. in Engineering Science, University of Toronto, Canada, 1988 Research Focus : Professor Dullerud pioneers theoretical frameworks for cyber-physical systems, specializing in networked control architectures , stochastic hybrid systems , and optimization-driven machine learning . His work bridges formal verification with practical implementations in robotics and autonomous systems, emphasizing security-aware control and privacy-preserving algorithms for distributed environments. Recent breakthroughs integrate reinforcement learning with classical control theory to address scalability challenges in complex CPS. Publication Trends : Analysis of his 15 most recent publications (2021-2024) reveals three dominant threads: (1) Machine learning control (60% of works) focusing on reinforcement learning convergence and safety guarantees; (2) Formal verification (25%) for stochastic hybrid systems using model reduction; (3) Cybersecurity applications (15%) including anonymous networks and privacy-preserving mechanisms. This evolution demonstrates strategic expansion from foundational control theory into AI-integrated CPS. Scientific Recognition IEEE Fellow (2008) and ASME Fellow (2011) NSF CAREER Award (1999) W. Grafton and Lillian B. Wilkins Professorship (2016) Xerox Faculty Research Award (2005) Multiple plenary/keynote invitations (IWFASA 2019, CAV 2017, IWOTA 2016) Academic Leadership : Dullerud has advised numerous graduate students while securing sustained funding through NSF grants and industry partnerships. His Willett Faculty Scholar designation (2002-present) and Technical Committee leadership in IEEE Control Systems Society underscore institutional impact. Current teaching includes advanced courses in nonlinear systems (ECE 528) and convex control methods (ME 561), consistently ranked excellent by students. Research Infrastructure : He leads projects within UIUC's Coordinated Science Laboratory, leveraging resources from the Center for Autonomy to develop testbeds for cooperative robotics and networked control systems. His group maintains active collaborations with Stanford, KTH Stockholm, and industry partners in autonomous vehicle development.

Deepak Vasishtمشاهده پروفایل

استادیار

• Wireless Networking
• Satellite Networks
• Mobile Computing
• +۷ مورد دیگر

Deepak Vasisht is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), with affiliate appointments in Electrical and Computer Engineering and the Coordinated Sciences Laboratory. He leads the rural connectivity thrust at the Center for Digital Agriculture and directs a research group focused on next-generation mobile computing and wireless networking systems. Dr. Vasisht earned his Ph.D. in Computer Science and Engineering from MIT in 2019, advised by Professor Dina Katabi, following an S.M. in Computer Science from MIT in 2015 and a B.Tech. in Computer Science and Engineering from IIT-Delhi in 2013, where he received the President of India Gold Medal for highest CGPA across all departments. His research spans four primary thrusts: Low Latency Satellite Connectivity for near-realtime Earth observation; Machine Learning for Next Generation Networks focusing on robust and verifiable ML models; Localization and Sensing using radio signals for applications in smart homes, healthcare, and indoor navigation; and Rural Connectivity and Data-driven Agriculture for digital farming solutions. His work has resulted in numerous high-impact publications at top venues including SIGCOMM, MobiCom, NSDI, and MobiSys. Dr. Vasisht's research has demonstrated significant trends toward space-Earth connectivity, with multiple papers on satellite networking in recent years, alongside continued innovation in wireless sensing and privacy protection systems. His team has developed novel approaches for satellite traffic scheduling, constellation-aware medium access, and radar-based localization. NSF CAREER Award (2023) Best Community Contribution Paper Award at ACM MobiCom (2024) Systems Research Award by VMWare (2023) ACM SIGCOMM Doctoral Dissertation Award (2020) Multiple Best Paper Awards across top conferences List of Teachers Ranked as Excellent by Students at UIUC (2021-2024) Dr. Vasisht has successfully advised numerous Ph.D. students including Jay Shenoy, Zikun Liu, Bill Tao, and Emerson Sie, with several receiving prestigious fellowships such as the Qualcomm Innovation Fellowship and Rambus Computer Engineering fellowship. His research is generously funded by NSF (including CAREER and RINGS awards), USDA, Microsoft, Cisco, and IBM, supporting his work on satellite networks, wireless sensing, and rural connectivity solutions. He leads the Connected Systems Lab at UIUC, which has developed influential systems including FarmBeats (now a Microsoft product) and innovative approaches to satellite networking, RF privacy, and in-body communication.

• Reinforcement Learning
• Neural Networks
• Probabilistic Rules
• +۴ مورد دیگر

Christopher Child serves as a Lecturer in Computer Science at City, University of London, a position held continuously since February 2008. His academic foundation includes a PhD in Computer Science from City, University of London (2011), an MSc in Cognitive Science from the University of Birmingham (1993-1994), and undergraduate studies in Computer Science at the same institution (1990-1993). His research portfolio demonstrates deep expertise in artificial intelligence with three core pillars: reinforcement learning systems for game AI, computer vision applications for hand pose estimation, and neural network implementations for clinical text analysis. Early work established foundational contributions to Q-learning and behavior tree integration (QL-BT, 2013), while recent publications explore cutting-edge applications of non-Euclidean geometry in video games (2025) and ClinicalBERT for medical classification (2024). His methodology consistently bridges theoretical AI with real-time practical implementations, particularly evident in stereo vision systems for hand depth recovery. Publication trends reveal an evolution from pure reinforcement learning (2004-2013) toward interdisciplinary applications combining computer vision, gaming, and healthcare. His 21 verified works show strong institutional continuity at City, University of London with frequent collaboration across computer science and medical domains. While no specific laboratory affiliation is documented, his work on the Extreme AI Personality Engine (2016) and Kinect motion recognition toolkit suggests active development of experimental systems for human-AI interaction.

Rajesh P.N. Raoمشاهده پروفایل

استاد

• Computational Neuroscience
• Artificial Intelligence
• Brain-Computer Interfaces
• +۴ مورد دیگر

Rajesh P.N. Rao is the CJ and Elizabeth Hwang Professor in the Paul G. Allen School of Computer Science & Engineering and Department of Electrical & Computer Engineering at the University of Washington (UW), Seattle. He is also co-Director of the Center for Neurotechnology (CNT), Adjunct Professor in the Bioengineering department, and faculty member in the Neuroscience Graduate Program at UW. University of Washington, Seattle - CJ and Elizabeth Hwang Professor Co-Director - Center for Neurotechnology Adjunct Professor - Bioengineering department Neuroscience Graduate Program - Faculty member Paul G. Allen School of Computer Science & Engineering - Professor His research spans computational neuroscience, brain-computer interfaces, artificial intelligence, and the Indus script. He also studies classical Indian painting and explores the computational principles underlying the brain's remarkable ability to learn, process and store information. Recent publications include: 2024 - Sensory-Motor Theory of the Neocortex in Nature Neurosci 2024 - Dynamic Predictive Coding in PLoS Comp Bio 2024 - Active Predictive Coding in Neural Comp 2023 - Recursive Neural Programs in PNAS Nexus 2019 - BrainNet: A 'Social Network' of Brains in Nature Scientific Reports Dr. Rao has received numerous prestigious awards and fellowships: Guggenheim Fellowship IEEE Fellow award Fulbright Scholar award NSF CAREER award ONR Young Investigator Award Sloan Faculty Fellowship David and Lucile Packard Fellowship for Science and Engineering He is actively involved in advising and research initiatives, directing the Neural Systems Laboratory located in the Paul G. Allen Center for Computer Science and Engineering.

• Control Systems
• Structural Systems Theory
• Biomedical Engineering
• +۵ مورد دیگر

Sérgio Pequito is an Associate Professor in the Department of Electrical and Computer Engineering at Instituto Superior Técnico, University of Lisbon, and a principal investigator at the Evolutionary Systems and Biomedical Engineering Lab (Institute for Systems and Robotics). His research bridges structural control systems theory with applications in neuroscience and biomedicine. Education: PhD in Electrical and Computer Engineering from Carnegie Mellon University and Instituto Superior Técnico (2014); MSc (2009) and BSc (2007) in Mathematics from Instituto Superior Técnico Research focuses on understanding large-scale systems through structural/parametric analysis, with applications to brain dynamics, personalized medicine, and control of complex networks. Key themes include fractional-order systems, distributed consensus, and brain-machine interfaces. Recent publications analyze fractional-order dynamical networks, controllability metrics, and privacy-preserving control algorithms, reflecting his expertise in both theoretical and applied domains. Keywords: Control Systems, Structural Theory, Biomedical Engineering, Neuroscience. Scientific recognition includes the 2016 O. Hugo Schuck Award (Theory Category) and a 2009 IEEE Conference on Decision and Control Best Student Paper Finalist. He leads interdisciplinary research teams and collaborates on neural engineering projects, particularly in epilepsy treatment and cortical activity modeling.