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Jithma P. AbeykoonView profile

Assistant Professor

• Hematology
• Medical Oncology
• Lymphoma Biology
• +7 more

Jithma P. Abeykoon, M.D. is an Assistant Professor of Medicine and Oncology at Mayo Clinic College of Medicine and Science , serving as a Senior Associate Consultant in the Division of Hematology within the Department of Internal Medicine . With expertise in lymphomas , histiocytic cancers , and DNA damage repair mechanisms , he bridges basic science , translational research , and clinical trials to develop novel therapies for hematologic malignancies. Education : MD from University of Kansas School of Medicine, BS in Biochemistry/Gerontology from Kansas State University, Hematology/Oncology Fellowship at Mayo Clinic Research Focus : DNA damage repair pathways in blood cancers, replication stress in lymphoma, Waldenstrom macroglobulinemia biology, and targeted therapies for histiocytic neoplasms Clinical Contributions : Pioneered CRM1 inhibitor + salicylate combination therapy, conducted investigator-initiated phase 1 trials for relapsed lymphoma Scientific Awards : Merit Award, Conquer Cancer Foundation of ASCO Robert A. Kyle Career Development Award Mayo Brothers Distinguished Fellowship Award Young Investigator Draft Award Alpha Omega Alpha Honor Society Publications : Over 80 peer-reviewed works focusing on lymphoma, Waldenstrom macroglobulinemia, histiocytic disorders, and DNA repair mechanisms Professional Roles : Co-chair of Mayo-University of Alabama Histiocytosis Working Group, editorial board member for Frontiers in Medicine and Biomolecules

Johnathan VotionView profile

Assistant Professor

• Machine Learning
• Robotics
• Control Systems
• +3 more

Johnathan Votion is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Texas at San Antonio (UTSA), affiliated with the Klesse College of Engineering and Integrated Design. His work spans machine learning, robotics, and control systems, with a focus on multi-agent coordination, sensor networks, and risk-aware optimization. Education: Ph.D. in Electrical Engineering from UTSA (2016) Research trends in his publications emphasize autonomous systems , data reliability , and uncertainty propagation , including applications in UAV trajectory planning , multi-robot collaboration , and nonholonomic control . His work integrates machine learning with engineering systems to address complex, real-world constraints.

Miguel A. Carreira-PerpinanView profile

Professor

• Machine Learning
• Optimization
• Computer Vision
• +12 more

Miguel A. Carreira-Perpinan is a Professor in the Department of Computer Science and Engineering at the School of Engineering, University of California, Merced. He leads research in machine learning with a focus on optimization and applications to computer vision , speech processing , sensor networks , and robotics . His work bridges theoretical advancements with practical implementations, particularly in deep neural nets , tree-based models , and nonlinear embeddings . PhD in Computer Science (2001), University of Sheffield, UK MSc in Computer Science (1991), Technical University of Madrid, Spain Research Interests Machine Learning Algorithms Optimization Techniques Deep Neural Networks Computer Vision Applications Speech and Sensor Network Processing Computational Neuroscience Scientific Contributions Tree Alternating Optimization (TAO) for decision trees and tree-based models Learning-Compression (LC) algorithm for neural network compression Method of Auxiliary Coordinates (MAC) for deeply nested systems Elastic Embedding for dimensionality reduction Binary Autoencoders for information retrieval Advising and Funding Advises PhD students in machine learning and optimization NSF Grant #2007147 ($425,000, 2020-2025): "The TAO algorithm: principled, efficient optimization of decision trees, forests, tree-based neural nets, and beyond" Labs and Teams Leads the Machine Learning Lab at UC Merced (Lab SE2-213B) Collaborates with teams in computational neuroscience and speech processing Develops open-source tools in GitHub repository for optimization and learning

Mohammad HajiaghayiView profile

Professor

• Algorithms
• Approximation Algorithms
• Fixed-Parameter Algorithms
• +7 more

Mohammad Hajiaghayi is the Jack and Rita G. Minker Professor of Computer Science at the University of Maryland, College Park, with appointments in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and as an affiliated Professor at the Robert H. Smith School of Business. He is also a Research Affiliate at MIT CSAIL and a Permanent Member of DIMACS at Rutgers. Education: Ph.D. in Computer Science, MIT (2005) M.Sc. in Computer Science, University of Waterloo (2001) B.Sc. in Computer Engineering, Sharif University of Technology (2000) Research Interests: Professor Hajiaghayi's work focuses on the design and analysis of algorithms, particularly approximation algorithms, fixed-parameter tractability, and algorithmic game theory. His research extends to combinatorial optimization, network design, computational geometry, and the theoretical foundations of big data and streaming algorithms. He has made significant contributions to bidimensionality theory and hardness of approximation. Scientific Awards: ACM Fellow (2018) IEEE Fellow (2020) AAAS Fellow (2023) EATCS Fellow (2020) Guggenheim Fellow (2019) Blavatnik National Awards Honoree (2020) Washington Academy of Science Distinguished Career Awardee (2023) University of Waterloo Alumni Achievement Medal (2022) EATCS Nerode Prize (2015) ONR Young Investigator Award (2011) Advising and Grants: He has mentored over 30 PhD and Master's students, many of whom now hold prominent positions in academia and industry. His research is supported by prestigious grants including an NSF CAREER Award, an ONR Young Investigator Award, multiple Google Faculty Research Awards, and funding from Northrop Grumman, AT&T, DARPA, and AFOSR. Labs and Teams: He co-leads the BigDND (Big Dynamic Network Data) project with Erik Demaine at MIT, focusing on large-scale network data analysis.

• Algorithms
• Complexity Theory
• Theoretical Computer Science
• +5 more

Jesper Nederlof is an Associate Professor in the Algorithms and Complexity group at the Department of Information and Computing Sciences, Faculty of Science, Utrecht University. His research focuses on designing efficient algorithms for computationally hard problems, particularly in the areas of parameterized complexity, graph algorithms, and NP-complete problems. He received his M.Sc. in Applied Computing Science from Utrecht University in 2008 and his Ph.D. from the University of Bergen in 2011 with the thesis 'Space and Time Efficient Structural Improvements of Dynamic Programming Algorithms' under supervision of Pinar Heggernes. Nederlof's research interests span multiple areas of theoretical computer science, with a particular focus on designing algorithms for NP-complete problems with small exponential worst-case run time. His work extends to algorithmic game theory, information theory, representation theory, approximation algorithms, and online algorithms. He has made significant contributions to parameterized complexity, particularly in developing algorithms parameterized by structural graph parameters like treewidth and cutwidth. His publication record shows a consistent output of high-quality research in top theoretical computer science venues. His recent work demonstrates trends toward tighter bounds for exponential-time algorithms, improved space complexity, and connections between different complexity hypotheses like ETH. Many papers focus on structural parameters of graphs to develop more efficient algorithms for fundamental problems like Hamiltonian cycle, Steiner tree, and subset sum. EATCS-IPEC Nerode Prize (2023) WG best paper award (2020) Nederlof has been involved in teaching courses on algorithms, (non)-linear optimization, graph theory, (vector) calculus, modeling, and management and product development. His research has been supported by various grants including an NWO open competition project during his postdoctoral period and an EU ERC Starting Grant for the project 'Finding Cracks in the wall of NP-Completeness' (2020-2025). As a member of the Algorithms and Complexity group at Utrecht University, Nederlof collaborates with researchers working on foundational aspects of computing, contributing to the group's reputation in theoretical computer science research.

Daniel KarapetyanView profile

Assistant Professor

• Combinatorial Optimization
• Artificial Intelligence
• Data Science
• +17 more

Daniel Karapetyan is an Assistant Professor at the School of Computer Science , University of Nottingham, and a member of the Computational Optimisation and Learning Lab . His research spans Artificial Intelligence , Data Science , Operational Research , and Optimization , with applications in transportation , logistics , satellite mission planning , and access control . Education: PhD in Computer Science, Royal Holloway, University of London (2007–2010) BSc/MSc in Computer Science, Bauman Moscow State Technical University (2001–2007) His research interests focus on the automated design of heuristic algorithms , multi-objective optimization , and parameter tuning for machine learning and combinatorial problems. He integrates Large Language Models (LLMs) and hyperparameter optimization into practical systems, addressing real-world challenges in airport sequencing , TV ad scheduling , and electric vehicle range prediction . Notable scientific contributions include the CORS Practice Prize (2014) for ferry scheduling work and pioneering fixed-parameter algorithms in security-related optimization problems. His publications emphasize combinatorial optimization , Markov Chain methods , and automated algorithm configuration . Teaching roles include module convener for COMP3008: Knowledge Representation and Reasoning , personal tutoring for COMP1002 , and supervision of COMP2002: Software Engineering Group Project . He also offers PhD supervision in areas like AI for optimization , interpretability in ML , and emergency department coordination .

György EignerView profile

Professor

• Control Theory
• Medical Informatics
• Biomechanics
• +7 more

György Eigner is a Professor at Obuda University, specifically at the John von Neumann Faculty of Informatics, where he serves as dean. He is affiliated with the Physiological Controls Research Center in Budapest, Hungary. His academic career includes leadership roles as president of the AI Transition Committee and as a senator on the University Council broad. Dr. Eigner's work bridges engineering and medical applications, focusing on control systems for physiological processes. Dr. Eigner earned his PhD in Applied Informatics from Obuda University in 2017, following his Biomedical Engineer MSc from Budapest University of Technology and Economics (2011-2013) and Mechatronics BSc from Obuda University (2006-2011). His educational background reflects his interdisciplinary approach that combines engineering principles with medical applications. Dr. Eigner's research focuses on applying control theory and engineering principles to physiological systems. His work spans several key areas: Development of control systems for diabetes management, including artificial pancreas algorithms Tumor growth modeling and control through anti-angiogenic therapy Medical device development, particularly for respiratory support systems Application of machine learning in healthcare monitoring and prediction Robotics for educational and medical applications Industry 4.0 implementations in manufacturing and healthcare settings His recent publications demonstrate a clear trend toward practical implementations of theoretical control systems. The research shows increasing focus on real-world applications of control theory in medical contexts, particularly in personalized patient care systems. His work on the Mass Ventilation System during the COVID-19 pandemic exemplifies this practical orientation. Additionally, there's a growing emphasis on integrating Industry 4.0 technologies with traditional medical systems, as seen in his work on digital twins and real-time locating systems for healthcare applications. Dr. Eigner has received several notable awards for his contributions to the field: Young Researcher of the Year Award (2019) Dean's commendation (2018) IEEE SMCS - Outstanding Contribution Award (2016) IEEE SMC 2016 - Best Conference Paper Finalist As a professor and dean, Dr. Eigner has advised numerous students on research projects related to control systems and medical applications. His research has been supported by various grants focused on developing innovative medical technologies and control systems. He has collaborated extensively with researchers across multiple disciplines, including medical professionals, engineers, and computer scientists, to develop practical solutions for healthcare challenges. Dr. Eigner leads research activities at the Physiological Controls Research Center at Obuda University. This center focuses on developing control systems for physiological processes, with particular emphasis on diabetes management, tumor growth control, and respiratory support systems. The center works closely with medical institutions to ensure that theoretical developments translate into practical clinical applications. Recent projects include the development of the Mass Ventilation System for pandemic response and the PlatypOUs mobile robot platform for STEM education.

• Artificial Intelligence
• Machine Learning
• Robotics
• +5 more

Raffaello Camoriano is a Fixed-term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Polytechnic University of Turin. He is also an Affiliated Researcher at the Istituto Italiano di Tecnologia. His academic work spans multiple roles including serving as an Editorial Board Member for MACHINE LEARNING journal (since 2024) and Associate Editor for IEEE ROBOTICS AND AUTOMATION LETTERS (since 2022). He is an active member of IEEE Robotics and Automation Society (since 2024) and ELLIS - European Laboratory for Learning and Intelligent Systems (since 2022). Dr. Camoriano's research focuses on the intersection of artificial intelligence, machine learning, and robotics. His work particularly emphasizes robot learning, incremental/lifelong learning, structured learning, and kernel methods. His research has significant applications in medical robotics, particularly in laser-based skincare procedures and prosthetic control systems. He has developed innovative approaches for federated learning, point cloud processing for robotics, and trajectory generation for humanoid robots. His work bridges theoretical machine learning with practical robotics applications, addressing challenges in resource efficiency, adaptation to changing environments, and human-robot interaction. His recent publications demonstrate a strong trajectory in advancing federated learning techniques, with particular emphasis on resource-efficient personalization and handling heterogeneous data. He has made significant contributions to robotics applications including upper-limb prosthetics using high-density sEMG, autonomous navigation using point clouds, and medical robotics for laser procedures. His work consistently combines theoretical machine learning advances with concrete robotics applications, showing expertise in both domains. Best PhD Thesis Award of the Computational Intelligence Society Italy Chapter 2017 Dr. Camoriano actively supervises PhD students in the Intelligenza Artificiale program, including Stephany Ortuno Chanelo, Andrea Protopapa, and Leonardo Iurada. He has led research projects including the ECML-PKDD 2023 International Workshop 'Adapting to Change: Reliable Learning Across Domains' and the International Workshop 'Learning Meets Model-based Methods for Manipulation and Grasping' (2023). His teaching portfolio includes Fundamentals of Artificial Intelligence, Machine and Deep Learning, and Algorithms and Programming across multiple engineering programs at Polytechnic University of Turin.

• Machine Learning
• Online Learning
• Computational Game Theory
• +3 more

Andrea Celli is an Assistant Professor in the Department of Computing Sciences at Bocconi University, where he is a member of the theory group and the Bocconi Institute for Data Science and Analytics. He previously spent one year as a postdoctoral researcher with the Core Data Science team at Meta (London). He obtained his Ph.D. in Computer Science from Politecnico di Milano, where he was advised by Nicola Gatti, and was a Visiting Researcher at Carnegie Mellon University working with Tuomas Sandholm. His educational background includes: Ph.D. in Computer Science from Politecnico di Milano, advised by Nicola Gatti Visiting Researcher at Carnegie Mellon University with Tuomas Sandholm Postdoctoral researcher with the Core Data Science team at Meta (London) Andrea Celli's research focuses on problems at the intersection of computer science, machine learning, and economics, with particular interest in computational problems arising from strategic interactions in online settings where learning processes and incentives are fundamentally intertwined. His work spans machine learning theory, online learning algorithms, computational game theory, and algorithmic economics. His research has been published in top venues including NeurIPS, ICML, STOC, and EC, with a best paper award at NeurIPS 2020. Analysis of his recent publications shows a strong focus on online learning algorithms with constraints, bandit problems, game-theoretic aspects of machine learning, and algorithmic game theory. His work often bridges theoretical foundations with practical applications in economics and decision-making systems. Key themes include no-regret learning, Bayesian persuasion, knapsack problems in online settings, and equilibrium computation in extensive-form games. His scientific achievements include: Best paper award at NeurIPS 2020 ERC Starting Grant (2024) for the project PLA-STEER MUR-PRIN grant (2022) Andrea Celli actively mentors students and postdocs, currently supervising Riccardo Poiani, Sarah Sachs, and Martino Bernasconi as postdocs, and Annalisa Barbara, Emanuele Coccia, Davide Drago (now at Bending Spoons), and Antonio Preiti (now at Amazon) as MSc students. His research is supported by competitive grants including an ERC Starting Grant and a MUR-PRIN grant, indicating strong recognition of his work's potential impact. He is also a member of the ELLIS Society, connecting him to a broader European network of excellence in AI research. He is affiliated with the theory group and the Bocconi Institute for Data Science and Analytics at Bocconi University, contributing to a vibrant research environment focused on theoretical foundations of data science and their applications.

Antonio OrvietoView profile

Professor

• Deep Learning
• Optimization Theory
• Sequence Modeling
• +7 more

Antonio Orvieto is a Professor and Principal Researcher at the Max Planck Institute for Intelligent Systems and ELLIS Institute Tübingen, where he leads the Deep Models and Optimization research group. He is also a lecturer at the University of Tübingen and faculty for the CLS, ELLIS, and IMPRS-IS PhD Programs. His research focuses on improving the efficiency of deep learning technologies through theoretical understanding of optimization dynamics and innovative neural network architectures. Dr. Orvieto earned his PhD from ETH Zürich under the supervision of Prof. Dr. Thomas Hofmann and Dr. Aurelien Lucchi. Prior to his PhD, he obtained his master's degree in Robotics, Systems, and Control from ETH. His educational background also includes undergraduate studies at Universita' degli studi di Padova in Italy. During his academic journey, he gained research experience at DeepMind (UK), Meta (US), MILA (CA), INRIA (FR), and HILTI (LI). Dr. Orvieto's research spans two main areas: understanding the intricacies of large-scale optimization dynamics and designing innovative architectures and powerful optimizers capable of handling complex data. His work particularly focuses on decoding patterns in sequential data, with applications in biology, neuroscience, natural language processing, and music generation. His theoretical approach to deep learning has led to significant contributions in understanding recurrent neural networks, transformers, and optimization methods. His recent publications reveal a strong focus on sequence modeling, optimization theory, and the theoretical foundations of deep learning, with particular emphasis on improving training efficiency and model performance. Schmidt Sciences AI2050 Early Career Fellow Dr. Orvieto actively mentors PhD students and leads a vibrant research group at the Max Planck Institute for Intelligent Systems. His Deep Models and Optimization group includes PhD students Destiny Okpekpe, Felix Sarnthein, Diganta Misra, Sajad Movahedi, and Wenjie Fan, among others. He is deeply involved in doctoral education as faculty for multiple PhD programs including CLS, ELLIS, and IMPRS-IS. His teaching includes the course "Nonconvex Optimization for Deep Learning" at the University of Tübingen. The Deep Models and Optimization research group investigates the interplay between optimizers and architectures in deep learning, with a focus on developing new networks for long-range reasoning. The group's mission is to design new optimizers and neural networks to accelerate technology and scientific discovery, with a strong theoretical foundation in optimization theory. They strongly believe that deep learning will revolutionize science and technology, and they aim to make powerful deep learning solutions accessible to scientists and engineers regardless of resource limitations.

• Commutative Algebra
• Algebraic Topology
• Algebraic Combinatorics
• +3 more

Marko Radovanović serves as an Associate Professor in the Department of Algebra and Logic at the Faculty of Mathematics, University of Belgrade, where he maintains active teaching and research programs. His current responsibilities for the 2024/2025 academic year include Advanced Concepts of Elementary Mathematics, Discrete Mathematics for MLR majors, Algebra 1 (First Direction), Algebra 3 (M direction), and Selected Chapters in Algebra at the Master's level. He is based at Studentski trg 16, 11000 Belgrade, with contact email markor@matf.bg.ac.rs. His research spans two interconnected domains: Commutative Algebra & Topological Interactions : Investigating cohomology rings of Grassmann/flag manifolds through algebraic topology, combinatorics, and geometry Structural Graph Theory : Developing structure theorems for theta/wheel-free graphs and related algorithmic applications Recent publications (2017-2024) reveal consistent methodology: applying algebraic structures to topological problems (e.g., computing topological complexity of Grassmannians) while using combinatorial decompositions to characterize complex graph families. His collaborative work with Prvulović, Petrović, and Vušković demonstrates significant contributions to both algebraic topology and combinatorial graph theory through high-impact publications in the Journal of Combinatorial Theory Series B and Proceedings of the Royal Society of Edinburgh.

• Systems and Control Theory
• Differential-Algebraic Equations
• Data-Driven Modelling
• +3 more

Jan Heiland is an Assistant Professor at Otto von Guericke University Magdeburg and a Researcher at the Max Planck Institute for Dynamics of Complex Technical Systems. His work focuses on computational methods in systems and control theory, particularly for differential-algebraic equations (DAEs) and data-driven modeling of fluid dynamics. Research Interests: Systems and Control Theory, Differential-Algebraic Equations, Data-Driven Modelling, Flow Control, Fluid Dynamics, Reduced-Order Modeling Publication Trends: Recent work emphasizes low-dimensional approximations of nonlinear systems using machine learning techniques (e.g., polytopic autoencoders, convolutional autoencoders) and robust control methods for Navier-Stokes equations. Key themes include model reduction, uncertainty quantification, and computational approaches to large-scale dynamical systems. Teaching: He teaches numerical methods for partial differential equations and scientific computing at TU Ilmenau and OVGU Magdeburg.

Yacine Ait-SahaliaView profile

Professor

• High-Frequency Financial Econometrics
• Stochastic Volatility Models
• Jump Processes
• +4 more

Yacine Ait-Sahalia is the Otto A. Hack 1903 Professor of Finance and Economics at Princeton University , affiliated with the Department of Economics and the Bendheim Center for Finance. His research focuses on high-frequency financial econometrics, stochastic volatility modeling, jump processes, and continuous-time asset pricing models. Research Interests include: High-Frequency Data Analysis Stochastic Volatility and Jump-Diffusion Models Nonparametric and Parametric Estimation Financial Market Microstructure Portfolio Optimization Under Jumps Term Structure of Interest Rates Recent Articles explore volatility-uncertainty disconnects, non-standard errors in high-frequency data, market making dynamics, and spectral analysis of asset returns. His work often bridges statistical theory with financial applications. Scientific Awards include: 1998 Cornerstone Research Award 2003 Dennis J. Aigner Award Michael J. Brennan Award for Best Paper in Review of Financial Studies Grants from the National Science Foundation (NSF) have supported his research. He has also authored influential books like High Frequency Financial Econometrics and co-edited the Handbook of Financial Econometrics .

Daniel LokshtanovView profile

Professor

• Theoretical Computer Science
• Discrete Mathematics
• Algorithmic Graph Theory
• +7 more

Daniel Lokshtanov is a Professor and Vice Chair at the Department of Computer Science at the University of California, Santa Barbara (UCSB) , with a visiting professor affiliation at the University of Bergen . He is renowned for his work in Theoretical Computer Science and Discrete Mathematics , particularly focusing on Algorithmic Graph Theory and Parameterized Complexity . Key roles: Professor (UCSB, since 2020), Vice Chair (UCSB), Visiting Professor (University of Bergen) Research focus: Kernelization, Graph Minors, Exact Algorithms, Treewidth, Subexponential Algorithms Research Trends : His recent publications (2022-2020) demonstrate expertise in applying Parameterized Complexity to Graph Algorithms , including work on Unit Disk Graphs , Graph Reconfiguration , and Subexponential Time Algorithms . Topics span Graph Contraction , Obstacle Removal , and Kemeny Rank Aggregation . Scientific Awards : Outstanding Young Researcher Meltzer Award Best ESA Paper Award (2015) Advising : While not actively seeking new PhD students, he supervises MS students at UCSB and has mentored numerous advisees through Parameterized Algorithms research. He co-organizes the Inter-Collegiate Programming Contest at UCSB and the Norwegian Informatics Olympiad for high school students.

• Control Systems Theory
• Fractional-Order Systems
• Brain Dynamics
• +5 more

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 Institute for Systems and Robotics. He previously held faculty positions at Uppsala University (2022-2024) and Technische Universiteit Delft (2020-2022), and was a faculty member at Rensselaer Polytechnic Institute (2017-2020). His academic journey includes a PhD in Electrical and Computer Engineering from Carnegie Mellon University and a Doctorate in Electrical and Computer Engineering from Universidade de Lisboa. Pequito's research focuses on understanding the global qualitative behavior of large-scale systems through structural and parametric descriptions, with applications spanning neuroscience, biomedicine, and control theory. His work leverages dynamical systems, control theory, and artificial intelligence to develop new analysis tools for brain dynamics aimed at personalized medicine and improved bidirectional brain interfaces. His specific interests include fractional-order systems, structural systems theory, network controllability, and neurotechnology applications for epilepsy treatment and brain-computer interfaces. His recent publications reveal a strong emphasis on applying fractional calculus to model brain dynamics, developing control frameworks for neurotechnology, and advancing structural systems theory for large-scale networks. His work demonstrates a consistent trajectory toward bridging theoretical control systems with practical biomedical applications, particularly in understanding and controlling brain dynamics for therapeutic purposes. 2016 O. Hugo Schuck Award in the Theory Category by the American Automatic Control Council 2009 Best student paper finalist in the 48th IEEE Conference on Decision and Control 2023 Royal Swedish Academy of Engineering Sciences 100 List 2023 European Laboratory for Learning and Intelligent Systems (ELLiS) Research Scholar Multiple Trustees Faculty Achievement awards from Rensselaer Polytechnic Institute Pequito actively mentors numerous PhD and Master's students across multiple institutions, with research topics spanning cyber-neural systems, structural control theory, fractional-order optimization, and neurotechnology applications. His laboratory work focuses on developing theoretical frameworks for large-scale system control while maintaining strong connections to practical applications in biomedical engineering and neuroscience, particularly in developing new diagnostic tools and treatment strategies for neurological diseases through control systems theory.