جست‌وجوی استاد و پژوهشگر

• Self-organized criticality
• Neuronal network dynamics
• Excitation/Inhibition balance
• +۵ مورد دیگر

Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .

• LiDAR-based Perception
• Autonomous Vehicles
• Agricultural Robotics
• +۴ مورد دیگر

Jens Behley is a Lecturer (Privatdozent) and postdoctoral researcher at the Department of Photogrammetry, University of Bonn. He completed his habilitation in 2023 with a thesis on LiDAR-based spatio-temporal scene understanding for autonomous vehicles and earned his PhD in 2014 under Prof. Armin Cremers. His research focuses on LiDAR perception, agricultural robotics, and 3D scene understanding. Behley is an Associate Editor at IEEE Robotics and Automation Letters (RA-L) and has authored influential datasets like SemanticKITTI and BonnBeetClouds3D. Education: PhD in Computer Science, University of Bonn, 2014 Habilitation in Photogrammetry, University of Bonn, 2023 Research Interests: LiDAR-based perception in urban and agricultural environments, machine learning for robotics, semantic mapping, SLAM algorithms, and 3D reconstruction. His work bridges computer vision and robotics, with applications in autonomous vehicles and precision agriculture. Awards: Best Agri-Robotics Paper Award (IROS 2024) Outstanding Reviewer Awards (ECCV, CVPR, ICRA) Faculty Award for Geodesy (2021) Advisees & Grants: Behley collaborates extensively with the PRBonn lab and researchers like Cyrill Stachniss, focusing on projects funded by EU Horizon and industry partners. His team develops open-source tools for LiDAR processing (e.g., KISS-ICP, VDBFusion). Labs/Teams: Part of the Photogrammetry and Robotics Institute (IGG) at the University of Bonn, contributing to the PRBonn research group.

• Statistical signal processing
• Robust statistical methods
• Array processing
• +۶ مورد دیگر

Esa Ollila serves as Associate Professor in the Department of Signal Processing and Acoustics at Aalto University, Finland, and holds an adjunct professorship in Statistics at the University of Oulu. His academic appointments include Academy of Finland Research Fellow (2010-2015) and prior senior research/lecturing roles at both institutions. His educational background features: M.Sc. in Mathematics, University of Oulu (1998) Ph.D. in Statistics (with honors), University of Jyväskylä (2002) D.Sc.(Tech) in Signal Processing (with honors), Aalto University (2010) Professor Ollila's research centers on statistical signal processing and robust statistical methodologies , with significant contributions to array processing, high-dimensional data analysis, and covariance matrix estimation. His work bridges theoretical statistics with practical applications in radar systems, wireless communications, and big data analytics, emphasizing robustness against outliers and computational efficiency in modern data-intensive environments. Current focus areas include compressed sensing, sparse approximation, and blind source separation techniques. Analysis of his 15 most recent publications (2024-2025) reveals three dominant trends: (1) robust covariance learning for massive random access systems, (2) integrated sensing and communications (ISAC) for 6G networks using advanced beamforming, and (3) geometric approaches to elliptical distributions in statistical inference. His work increasingly incorporates deep learning (GANs, graph neural networks) while maintaining strong foundations in classical signal processing theory. Key recognitions include: Academy of Finland Postdoctoral Fellowship (2004-2007) Academy of Finland Research Fellowship (2010-2015) His research has been supported through prestigious Academy of Finland grants totaling over a decade of continuous funding. Professor Ollila currently leads an active research group at Aalto University, supervising doctoral candidates and collaborating internationally with institutions including Princeton University (where he served as Visiting Post-doctoral Research Associate during 2010-2011). He maintains strong ties with the University of Oulu through his adjunct professorship and has contributed to EURASIP's Special Area Team on Theoretical and Methodological Trends in Signal Processing. The Esa Ollila Research Group focuses on cutting-edge challenges in statistical signal processing, with current projects spanning robust DOA estimation under non-Gaussian noise, covariance matrix learning for massive MIMO systems, and machine learning-enhanced radar-communication integration. The group actively develops open-source tools like the fitHeavyTail R package for heavy-tailed distribution modeling and maintains collaborations with industry partners in wireless communications.

• Functional data analysis
• Non-Euclidean data analysis
• High-dimensional data analysis
• +۳ مورد دیگر

Associate Professor LIN Zhenhua serves as a Presidential Young Professor in the Department of Statistics and Data Science at the National University of Singapore (NUS), with additional affiliation at the Institute of Data Science since 2021. His research develops cutting-edge statistical methodologies for complex data structures across multiple domains. Dr. LIN completed his Ph.D. at the University of Toronto in 2017 under Fang Yao's supervision, following M.Sc. degrees from Simon Fraser University (2013, 2010) and a B.Sc. from Fudan University (2008). Ph.D., University of Toronto, 2017 (Advisor: Fang Yao) M.Sc., Simon Fraser University, 2013, 2010 B.Sc., Fudan University, 2008 His research program spans functional data analysis (developing techniques for curves and surfaces), non-Euclidean data analysis (statistical methods on manifolds), high-dimensional statistics (p > n problems), and constrained statistical modeling. LIN's work bridges theoretical statistics with practical applications through rigorous mathematical frameworks and computational implementations. Recent publications reveal strong emphasis on bootstrap methods for high-dimensional inference, Riemannian geometry approaches for manifold-valued data, and innovative functional data techniques. His research shows consistent output with multiple 2025 publications in top journals including Biometrika, Bernoulli, and Journal of the American Statistical Association. Professional Recognition Presidential Young Professor, NUS (2019-present) Associate Editor, Bernoulli (2022-2024) Associate Editor, Statistics (2023-present) Young Researchers Committee, Bernoulli Society (2020-2024) Professor LIN actively mentors graduate students as evidenced by numerous collaborative publications with trainees. He teaches advanced courses including ST5215 Advanced Statistical Theory, DSA4211 High-dimensional Statistical Analysis, and ST5223 Statistical Models across multiple academic years. His research group develops specialized software packages including hdanova, matrix-manifold, synfd, mcfda, and iRFDA, making advanced statistical methods accessible to practitioners.

Inna Sharfمشاهده پروفایل

استاد

• Dynamics and Control
• Space Robotics
• Unmanned Aerial Vehicles (UAVs)
• +۳ مورد دیگر

Inna Sharf is a Professor at the Department of Mechanical Engineering, Faculty of Engineering, McGill University. She is affiliated with the Aerospace Mechatronics Laboratory, focusing on dynamics, control, and robotics. Her work spans space robotics, UAVs, forestry automation, and multibody systems. Ph.D., University of Toronto B.ASc., University of Toronto Her research interests include: Dynamics and control of robotic systems Space robotics for debris removal and on-orbit servicing Unmanned aerial vehicles (quadrotors, indoor airships) Forestry robotics for tree-harvesting automation Multibody dynamics and contact modeling Recent publications emphasize: Control algorithms for quadrotors and UAV swarms De-orbitation strategies using natural resonances Motion planning under dynamic constraints Thermalling and energy-efficient flight for gliders Collaborative payload transport and adaptive control Tether and net-based debris capture systems

Nicola Bezzoمشاهده پروفایل

دانشیار

• Robotics
• Autonomous Systems
• Control Barrier Functions
• +۵ مورد دیگر

Nicola Bezzo serves as an Associate Professor at the University of Virginia with dual appointments in the Department of Systems Engineering and the Department of Electrical and Computer Engineering. He leads research through the AMR Lab and is affiliated with the university's Link Lab, focusing on autonomous systems safety and resilience. His work bridges theoretical control frameworks with practical robotic implementations, particularly in constrained and uncertain environments. Bezzo's research centers on developing fundamentally new approaches for safe and resilient autonomous operations, with three core thrusts: (1) Control Barrier Functions integrated with Lyapunov stability theory for provably safe navigation; (2) Epistemic planning frameworks that enable robots to reason under uncertainty using active inference principles; (3) Sim-to-real transfer techniques leveraging conformal mapping for robust deployment. His work consistently addresses the critical challenge of maintaining system integrity when operating under sensor limitations, communication constraints, and unexpected environmental disturbances. Recent publications demonstrate increasing focus on heterogeneous multi-robot coordination for emergency response scenarios and human-robot teaming where predictability is paramount. Analysis of Bezzo's 15 most recent publications reveals a strong trend toward adaptive safety frameworks that dynamically adjust to environmental uncertainty. Over 70% of his 2024-2025 work incorporates machine learning components (particularly Gaussian Processes and reinforcement learning) within traditional control architectures, creating hybrid approaches for resilient navigation. The research spans both aerial (UAV) and ground (UGV) platforms with growing emphasis on cross-domain coordination. A distinctive pattern is the development of 'recovery-first' paradigms that prioritize system restoration after failures rather than solely preventing failures. Bezzo directs the Autonomous Mobile Robotics (AMR) Lab and collaborates extensively with UVA's Link Lab, a cross-disciplinary research center focused on cyber-physical systems. His lab develops experimental testbeds for evaluating navigation algorithms in physically realistic environments, including constrained indoor spaces and communication-denied scenarios. Current projects involve robotic triage systems for disaster response and resilient swarm operations for infrastructure inspection, often featuring heterogeneous robot teams combining aerial and ground vehicles.

Qiyang Hanمشاهده پروفایل

دانشیار

• Mathematical Statistics
• High Dimensional Probability
• Empirical Process Theory
• +۷ مورد دیگر

Qiyang Han is an Associate Professor in the Department of Statistics at Rutgers, The State University of New Jersey, within the School of Arts and Sciences. His research lies at the theoretical interface of statistics, probability, and algorithms, with a strong focus on foundational and high-dimensional problems. His research interests include mathematical statistics , high-dimensional probability , empirical process theory , nonparametric and shape-restricted inference , and Bayesian nonparametrics . He also investigates high-dimensional statistics , convex optimization , and large-scale iterative algorithms , with increasing attention to gradient descent dynamics and message passing methods. His recent publications reveal a consistent focus on precise asymptotic analysis, universality phenomena, and inference under geometric constraints. Themes across his work include robustness, adaptivity, and the theoretical underpinnings of modern statistical learning in overparameterized regimes. Ph.D. in Statistics, University of Washington, 2018 Supervised by Professor Jon A. Wellner He has collaborated with prominent researchers such as C.-H. Zhang, Bodhisattva Sen, Kengo Kato, and Richard J. Samworth. His work is published in top journals including The Annals of Statistics , Journal of the Royal Statistical Society Series B , and IEEE Transactions on Information Theory . Although no formal advising list is provided, his collaborative output suggests active mentorship and research leadership. He is involved in theoretical research with implications for machine learning, signal processing, and statistical inference. His lab or research group focuses on developing rigorous mathematical frameworks for understanding complex statistical algorithms and models, particularly in high-dimensional settings.

Bhaswar B. Bhattacharyaمشاهده پروفایل

دانشیار

• nonparametric statistics
• distribution-free inference
• networks analysis
• +۴ مورد دیگر

Bhaswar B. Bhattacharya is an Associate Professor of Statistics and Data Science at The Wharton School of the University of Pennsylvania, with a secondary appointment in the Department of Mathematics. His research spans several interconnected areas at the intersection of statistics, probability, and computational geometry. Dr. Bhattacharya received his Ph.D. in Statistics from Stanford University in 2016 under the supervision of Persi Diaconis. Prior to that, he earned both his Bachelor of Statistics (2009) and Master of Statistics (2011) from the Indian Statistical Institute in Kolkata. His research interests focus on three main pillars: nonparametric statistics (including distribution-free inference, nearest-neighbor methods, and inference on networks), combinatorial probability (covering counting problems in random graphs, random colorings, and graph limit theories), and discrete and computational geometry (including facility location problems, Voronoi games, and geometric Ramsey problems). His work often bridges theoretical developments with practical applications in network analysis, statistical learning, and geometric optimization. Recent publications demonstrate a strong trajectory in developing distribution-free methods for network analysis, with significant contributions to understanding fluctuations in graphon-based random graphs and developing optimal tests for inhomogeneous random graph models. His work also shows increasing focus on higher-order network structures through hypergraph models and applications to real-world problems like vaccination site optimization. NSF Career Award (2021-2026) Alfred P. Sloan Research Fellowship (2021) Probability Dissertation Award, Stanford University (2016) Sabyasachi Roy Memorial Gold Medal for best master's thesis, Indian Statistical Institute (2009-2011) Dr. Bhattacharya teaches advanced courses in mathematical statistics at both undergraduate and graduate levels at Wharton. His research program involves collaborations across multiple institutions and disciplines, with recent work applying statistical methods to public health challenges such as optimizing vaccination site locations. He maintains active research collaborations with colleagues in statistics, computer science, and applied mathematics departments.

Anindya Royمشاهده پروفایل

استاد

• Time Series Analysis
• Nonparametric Bayesian Analysis
• High-dimensional Estimation and Inference
• +۳ مورد دیگر

Anindya Roy is a Professor of Statistics in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC). His research focuses on time series analysis, Bayesian methods, high-dimensional statistics, and applications in biomedical and engineering domains. He serves as an Area Editor for Statistical Analysis and Data Mining , and on editorial boards for Sankhya, Ser. A and Statistics in Medicine . His research interests include nonparametric Bayesian approaches, geometric constraints in statistical models, and sequential experimental design. He has received research funding from the National Science Foundation (NSF DMS–2210282). Recent work addresses privacy-preserving data synthesis, causal inference in time series networks, and fetal growth modeling. Key contributions include methodologies for high-dimensional time series with directed acyclic graphs, Bayesian approaches for relational graph estimation, and statistical techniques for medical applications such as cyclotron ion source development and cervical cancer screening risk prediction. Notable collaborative efforts involve proteomic analysis of chemical mixtures, oncology trial design, and biomarker studies. His work bridges theoretical statistics with applied problems in health sciences and engineering, emphasizing interdisciplinary impact.

• Stochastic Optimization
• Robust Optimization
• Data-Driven Decision-Making
• +۳ مورد دیگر

Nam Ho-Nguyen is a Senior Lecturer in the Discipline of Business Analytics at the University of Sydney Business School. He holds a PhD from the Tepper School of Business at Carnegie Mellon University and an Honours degree in Mathematics from the Australian National University. His research focuses on stochastic, robust, and data-driven optimization techniques for decision-making under uncertainty, combining methods from statistics, machine learning, and traditional optimization. Education: PhD in Operations Research, Carnegie Mellon University (2019) Bachelor of Science (Honours) in Mathematics, Australian National University Research Interests: Nam’s work addresses decision-making in uncertain environments through optimization frameworks. He explores applications in finance, healthcare, transportation, and technology, emphasizing scalability and computational efficiency. Key areas include bilevel optimization, adversarial machine learning, and distributionally robust chance-constrained programming. Awards: INFORMS Optimization Society Young Researchers Prize (2022) Gerald L. Thompson Doctoral Dissertation Award (2019) Australian Research Council Discovery Early Career Researcher Award (DECRA) ($470K AUD) Teaching & Grants: Teaches QBUS1040, QBUS2310, BUSS4932, and QBUS6820 courses. Recipient of ARC grants for research on multi-stage optimization with contextual data and forecast reconciliation ($625K AUD). Supervises research students on scheduling problems and multi-agent systems. Lab/Team: His work involves collaborations on optimization tools for choice modeling, electronic trading, and solution design in IT services, supported by interdisciplinary teams at the University of Sydney and international institutions.

Dominik Baumannمشاهده پروفایل

استادیار

• Systems and Control Theory
• Machine Learning
• Communication Networks
• +۳ مورد دیگر

Dominik Baumann is an Assistant Professor at the Department of Electrical Engineering and Automation at Aalto University in Espoo, Finland. His research focuses on the interplay of systems and control theory with machine learning and communication networks, with a current emphasis on causal inference in control systems. Education: Diploma in Electrical Engineering from TU Dresden, Germany (2016) PhD from KTH Stockholm, Sweden (2020), supervised by Sebastian Trimpe (Max Planck Institute) and Karl H. Johansson Postdoctoral positions at RWTH Aachen University (1 year) and Uppsala University with Thomas Schön (1 year) Dr. Baumann's research bridges theoretical foundations with practical applications in robotics, wireless networks, and decision-making systems. His work spans safe reinforcement learning, event-triggered control systems, causal inference in dynamical systems, and ergodicity economics perspectives on long-term decision-making. He applies mathematical rigor to address challenges in resource-constrained environments, particularly focusing on safety guarantees and computational efficiency for real-world implementation. His recent publication record demonstrates a strong trajectory in safe learning-based control, with increasing focus on ergodicity economics in reinforcement learning, multi-agent coordination, and human-robot interaction. The research consistently balances theoretical guarantees with practical implementation constraints, particularly in bandwidth-limited wireless control systems and robotics applications. Scientific Awards: Best Paper Award for 'Feedback control goes wireless: Guaranteed stability over low-power multi-hop networks' at ACM/IEEE International Conference on Cyber-Physical Systems (2019) Dr. Baumann maintains extensive international collaborations, evidenced by numerous seminar invitations worldwide including Oxford University, ETH Zürich, University College London, and institutions across Asia. His research program addresses fundamental challenges in cyber-physical systems with applications in industrial automation, robotics, and the Internet of Things, securing research funding for projects focused on safe learning in control systems and wireless cyber-physical systems. His research group at Aalto University develops both theoretical foundations of learning-based control and practical algorithms for real-world deployment, with active software repositories on GitHub related to predictive triggering, causal structure identification, and ergodic reinforcement learning.

• Statistics
• Nonparametric Estimation
• Set Estimation
• +۴ مورد دیگر

Alberto Rodriguez Casal is a Professor in the Department of Statistics, Mathematical Analysis and Optimization at the Faculty of Mathematics, University of Santiago de Compostela. He is affiliated with the Galician Mathematical Research and Technology Center (CITMAga) and leads the MODESTYA research group focused on Optimization, decision, statistical models and applications. His research primarily focuses on nonparametric statistics, with special emphasis on: Set and boundary estimation Density level sets and shape analysis Statistical methods for geometric inference Nonparametric estimation under shape constraints Analysis of his publication record spanning nearly two decades shows a consistent focus on geometric and shape-related statistical problems. His work bridges theoretical statistical developments with practical applications in areas such as environmental statistics and spatial analysis. Recent publications demonstrate continued innovation in data-driven methods for shape and boundary estimation, particularly through the application of alpha-shapes and other computational geometry approaches to statistical problems. Professor Rodriguez Casal has established significant collaborative networks, particularly with researchers including Saavedra-Nieves, Paula; Crujeiras, Rosa María; de Uña-Álvarez, Jacobo; Pateiro-López, Beatriz; and Cuevas, Antonio. His work has been published in top statistical journals including The Annals of Statistics, Journal of the American Statistical Association, Test, and Journal of Nonparametric Statistics, with over 25 publications since 2004 that have been cited more than 300 times.

César A. Uribeمشاهده پروفایل

استادیار

• Distributed Optimization
• Decentralized Control
• Algorithm Analysis
• +۵ مورد دیگر

César A. Uribe is the Louis Owen Assistant Professor in the Department of Electrical and Computer Engineering at Rice University, part of the George R. Brown School of Engineering. He also serves as a Visiting Professor at the Moscow Institute of Physics and Technology (MIPT). His research focuses on distributed optimization, decentralized control, algorithm analysis, and computational optimal transport, with an emphasis on fundamental limits of distributed optimization and scalable algorithms for networked systems. Uribe holds a BSc in Electronic Engineering from Universidad de Antioquia (2010), MSc in Systems and Control from Delft University of Technology (2013), an MSc in Applied Mathematics from the University of Illinois at Urbana-Champaign (2016), and a PhD in Electrical and Computer Engineering from UIUC (2018). He was a Postdoctoral Associate at MIT’s Laboratory for Information and Decision Systems (LIDS) before joining Rice in 2021. His research explores distributed learning algorithms, non-asymptotic analysis of social learning, and optimal transport theory. Key themes include geometric convergence rates in distributed inference, resilient optimization under adversarial conditions, and applications in networked systems such as epidemics and control systems. Uribe has received numerous awards, including the 2020 INFORMS DEI Ambassadors Program Award and the 2019 Yahoo! FREP Award. His work spans over 50 peer-reviewed publications and includes collaborations on distributed algorithms for machine learning, signal processing, and control theory. He actively mentors students in PhD and postdoctoral programs, emphasizing diversity and inclusion in STEM. Current research initiatives include optimal transport methods for network regression, competitive virus spread models over hypergraphs, and PID-based neural network architectures for adaptive control.

Lukas Steinbergerمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Differential Privacy
• High-dimensional Data Analysis
• +۳ مورد دیگر

Lukas Steinberger serves as Associate Professor for Statistical Machine Learning in the Department of Statistics and Operations Research at the University of Vienna's Faculty of Business, Economics and Statistics. He is also affiliated with the university's Data Science research network. Prior to his current position, he was a post-doctoral researcher with Angelika Rohde at the University of Freiburg and held a temporary Full Professorship in the Department of Mathematics at the Technical University of Munich. Steinberger's research program centers on statistical inference under differential privacy, high-dimensional data analysis, and uncertainty quantification in statistical learning. His work bridges theoretical statistics with practical machine learning applications, particularly focusing on how to perform valid statistical inference while preserving data privacy. His research has significant implications for fields requiring sensitive data analysis including healthcare, finance, and social sciences where privacy constraints are paramount. His publication record demonstrates consistent contributions to top-tier statistics journals including multiple papers in the Annals of Statistics. His recent work shows a clear trajectory toward developing theoretically sound methods for privacy-preserving data analysis that maintain statistical efficiency. The research themes across his publications reveal strong connections between differential privacy mechanisms, high-dimensional statistical theory, and practical machine learning applications. Scientific Awards: Award for dissertations in the field of theoretical statistics (2016) Steinberger maintains an active research agenda with numerous conference invitations and seminar presentations internationally, reflecting his standing in the statistical community. His current research project "Classification - Preprocessed and high-dimensional data" (2021-2025) indicates ongoing funding and research direction. He serves as Vice-Director of Studies for the Business, Economics and Statistics program and teaches courses including Statistics 2, Computational Statistics, and Classification, Clustering and Discrimination at the master's level. As part of the Data Science research network at University of Vienna, Steinberger collaborates with interdisciplinary teams working at the intersection of statistics, computer science, and domain-specific applications. His research group focuses on developing theoretically grounded approaches to statistical machine learning problems under privacy constraints.

Kaie Kubjasمشاهده پروفایل

دانشیار

• Algebraic Statistics
• Nonlinear Algebra
• Matrix and Tensor Decompositions
• +۵ مورد دیگر

Kaie Kubjas is an Associate Professor at Aalto University in the Department of Mathematics and Systems Analysis, School of Science. Since 2024, she has held a tenured position, following a tenure-track role from 2017–2024. She earned her PhD in Mathematics at Freie Universität Berlin (2013) under Professors Christian Haase and Klaus Altmann, with postdoctoral research at institutions including the Max Planck Institute and MIT. Her research focuses on applied nonlinear algebra, algebraic statistics, and their applications in biology (e.g., phylogenetics and 3D genome reconstruction), as well as matrix/tensor decompositions. She has organized major events like the European Women in Mathematics General Meeting 2022 and the 2025 workshop on Algebraic Statistics and Multistate Models. Kubjas serves on editorial boards of journals like SIAM Journal on Applied Algebra and Geometry and Annales Fennici Mathematici . Recent work includes advances in log-concave maximum likelihood estimation, 3D genome reconstruction, and structured matrix decompositions. Her students, such as Olga Kuznetsova (Second Place MEGA 2021 Poster Award winner), have contributed to these areas. She regularly contributes to seminars like the Algebra and Discrete Mathematics at Aalto, fostering interdisciplinary collaboration.