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Mingzhou YinView profile

Research Fellow

• Data-based modeling
• System identification
• Sparse learning theory
• +3 more

Dr. Mingzhou Yin is a postdoctoral researcher at the Institute of Automatic Control within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover, where he has been working since August 2024. He received his Doctor of Sciences degree from ETH Zurich in 2024 under the supervision of Prof. Roy S. Smith, with a dissertation titled 'Regularized and Nonparametric Approaches in System Identification and Data-Driven Control.' His research interests span data-based modeling and control, sparse learning theory, system identification using subspace and regularized methods, model predictive control, and periodic system theory. Dr. Yin has developed innovative approaches in low-rank matrix regression, Gaussian process-based control of nonlinear systems, and closed-loop identification frameworks. His work bridges theoretical advances with practical applications in energy-flexible buildings and aerospace systems. Dr. Yin has received significant recognition including the IEEE Control Systems Society Swiss Chapter Young Author Best Journal Paper Award and the Systems Identification and Adaptive Control Technical Committee Outstanding Student Paper Prize in 2023. His publications in IEEE Control Systems Letters, Automatica, and other top journals demonstrate his contributions to data-driven control theory. IEEE Control Systems Society Swiss Chapter Young Author Best Journal Paper Award (2023) Systems Identification and Adaptive Control Technical Committee Outstanding Student Paper Prize (2023) As an educator, Dr. Yin has supervised numerous student projects on data-driven predictive control, sparse learning algorithms, and closed-loop identification of networked systems. His teaching includes 'Data- and Learning-Based Control' exercises and previous TA roles for 'Robust Control and Convex Optimisation' and 'System Identification' courses.

Philip WaltherView profile

Professor

• Quantum Computing
• Quantum Information
• Quantum Cryptography
• +3 more

Philip Walther is a Professor at the University of Vienna's Faculty of Physics, specializing in Quantum Optics, Quantum Nanophysics, and Quantum Information. He leads cutting-edge research in quantum computing, cryptography, and photonic processing. University of Vienna – Faculty of Physics Harvard University (Postdoctoral Researcher, 2006–2008) University of Latvia (Visiting Researcher, 2024) His research encompasses quantum superposition, indefinite causal order, and secure quantum networks. Key projects include GRAVITES (gravitational interferometry) and Quantum Secure Networks Partnership. Publications span quantum machine learning, entanglement distribution, and photonic processors. Recent collaborations focus on quantum cryptography and European research networks. He holds a 2020 Fellowship from the Optical Society of America and distinctions for quantum logic gates and secure protocols. Quantum-enhanced photonic processors Secure metropolitan fiber networks Gravitational interferometry with entangled states Walther actively participates in academic events like the EPIQUE Annual Meeting and contributes to public engagement through media appearances, including Scientia Global and IEEE publications.

Vivekananda RoyView profile

Professor

• Bayesian Statistics
• Markov Chain Monte Carlo Methods
• High-Dimensional Data Analysis
• +2 more

Vivekananda Roy is a Professor in the Department of Statistics at Iowa State University . He earned his BS from R.K.M. Residential College (2001), an MS from the Indian Statistical Institute (2003), and a PhD from the University of Florida (2008). Education BS, R.K.M. Residential College (2001) MS, Indian Statistical Institute (2003) PhD, University of Florida (2008) Roy's research focuses on Bayesian statistics , Markov Chain Monte Carlo (MCMC) algorithms , and high-dimensional data analysis . His work includes theoretical analysis of MCMC convergence properties, Bayesian variable selection methods, and applications to spatial and generalized linear mixed models. Recent projects address computational challenges in ultra-high dimensional settings and geometric approaches to sampling algorithms. Key trends in his publications include advancements in MCMC diagnostics , Bayesian hierarchical modeling , and statistical computing tools for efficient posterior estimation. He has also contributed to practical implementations through software packages like geoBayes for geostatistical analysis. Contact: vroy@iastate.edu

• Machine Learning
• Object-Centric Learning
• Deep Learning
• +5 more

Rongzhen Zhao is a Doctoral Researcher at Aalto University's Department of Electrical Engineering and Automation, affiliated with the Robot Learning research group. Their work focuses on machine learning and object-centric learning, with recent publications in top-tier conferences like ECML and ICLR. Research interests include: Object-Centric Learning Slot Attention Mechanisms Spatiotemporal Processing Neural Network Architecture Design Brain-inspired AI IoT Middleware Development Recent trends in their publications highlight advancements in attention mechanisms, discrete representations for object recognition, and multi-scale fusion techniques. They also explore bio-inspired approaches for temporal data analysis and IoT systems. Research groups: Robot Learning Contact: Email: rongzhen.zhao@aalto.fi

• Statistics and Probability

Ersin Yılmaz serves as a Postdoctoral Researcher within the Department of Computer Science at Aalto University, conducting advanced research at the intersection of statistical theory and computational applications. His work contributes to the university's data science initiatives through methodological innovations in regression analysis. His research concentrates on statistical methodology development, particularly for complex data structures involving correlated errors and high-dimensional spaces. Key interests include shrinkage estimation techniques, kernel-based regression frameworks, and robust modeling approaches applicable to machine learning systems. This work bridges theoretical statistics with practical computational implementations in computer science contexts. Analysis of his publication record reveals a focused trajectory in advanced regression methodologies, specifically addressing estimation challenges in partially linear models with dependency structures. His work demonstrates technical rigor in mathematical statistics while maintaining relevance to contemporary data science applications, particularly in improving estimator efficiency under complex error dependencies. Scientific Awards: No scientific awards, fellowships, or honors were documented in the source material. Current information indicates no formal student supervision roles or grant-funded projects. The researcher operates within Aalto University's computer science infrastructure without specific lab or team affiliations mentioned in available records.

• Signal Processing
• Wireless Communication Systems
• Power Amplifier Modeling
• +4 more

María José Madero Ayora is a Professor at the Department of Signal Theory and Communications , Universidad de Sevilla , specializing in nonlinear system modeling and digital predistortion for wireless communication systems. Her research focuses on Volterra series applications in power amplifier linearization, microwave measurements , and machine learning techniques for signal processing. Principal Investigator for projects like Statistical Signal Modeling for Brain-Computer Interfaces (PID2021-123090NB-I00) Recipient of the Arftg Roger Pollard Student Fellowship in microwave measurement Her work spans 5G waveform linearization , I/Q modulator impairments , and thermal memory effects in RF amplifiers. Recent publications combine sparse Bayesian methods with Volterra models to address nonlinear distortion in OFDM and visible light communication systems. She has supervised doctoral theses and participated in international conferences across the U.S., Europe, and Asia.

Rishabh IyerView profile

Assistant Professor

• Computer Systems
• Operating Systems
• Networking
• +5 more

Rishabh Iyer is an Assistant Professor in the Electrical Engineering and Computer Sciences department at the University of California, Berkeley. His research focuses on developing techniques that enable engineers to build systems with well-understood performance and functionality. Iyer is affiliated with the Networked Systems Lab (NETSYS) at Berkeley and teaches courses such as CS 294-262: Performance Analysis and Optimization of Computer Systems. Dr. Iyer completed his PhD at EPFL in Switzerland and his bachelor's degree at IIT Bombay. His educational background includes: 2023, PhD, Computer Science, EPFL, Switzerland 2017, BTech, Electrical Engineering, IIT Bombay Iyer's research spans multiple areas in computer systems, with a particular focus on performance analysis and optimization. His work draws insights from operating systems, networking, computer architecture, and formal methods. He has developed techniques for creating performance interfaces that allow engineers to reason about system performance before deployment, focusing on areas such as packet processing applications, OS system calls, cryptographic libraries, and specialized hardware accelerators for deep learning. His research also includes work on kernel extensibility through frameworks like KFlex, which improves the flexibility of eBPF extensions in Linux while maintaining negligible performance overheads. Additionally, he has contributed to building reliable and provably correct systems, particularly in the domain of software-defined wide-area networks and network packet-processing applications. Dr. Iyer's publication record demonstrates a consistent focus on performance analysis, system reliability, and network function optimization. His recent work has centered on performance interfaces for various system components, kernel extensions, and validation techniques for software-defined networks. His research has practical applications, with several systems he has helped design being deployed in production at companies such as Meta and Alibaba. Dr. Iyer's contributions to the field have been recognized with several prestigious awards: ACM SIGOPS Dennis M. Ritchie Doctoral Dissertation Award (2023) Eurosys Roger Needham PhD Award Dimitris N. Chorafas Award As an educator, Dr. Iyer teaches courses related to computer systems and performance analysis. He has previously served as a teaching assistant for various computer science courses at EPFL and IIT Bombay. He actively seeks talented and ambitious students to join his research group, encouraging Berkeley students to contact him directly via email and prospective graduate students to mention his name on their Berkeley EECS Graduate Admissions applications.

Mihai Teodor LazarescuView profile

Associate Professor

• Artificial intelligence
• Capacitive sensors
• Embedded software
• +10 more

Dr. Mihai Teodor Lazarescu is an Associate Professor at the Department of Electronics and Telecommunications (DET), Politecnico di Torino , where he contributes to research and teaching activities. He is also a member of the PolitoBIOMed Lab (Biomedical Engineering Lab) and the Ambient Sensing and Processing research group. Scientific Affiliation: IEEE Member (2019-present) Editorial Roles: Guest Editor for SENSORS, ELECTRONICS, and ACM Transactions on Embedded Computing Systems His research interests focus on hardware acceleration for machine learning algorithms, particularly using FPGAs for data center and embedded applications. He works on high-level synthesis optimization flows, capacitive sensor design for indoor monitoring, and low-power embedded systems . His work intersects Internet of Things , Wireless Sensor Networks , and Machine Learning with applications in human localization, environmental monitoring, and industrial automation. Recent publications demonstrate expertise in neural network optimization , DSP resource sharing , and multi-FPGA allocation . His teaching spans Applied Electronics , Digital Electronic Design , and Embedded Systems Optimization across bachelor's and master's programs in Electronic Engineering and Computer Engineering . Patents: Noise cancellation for single-plate capacitive sensors Capacitive sensor for space change detection Projects: Scientific Manager for Horizon 2020-S2RJU project (2018)

• Circuit modeling
• Circuit simulation
• Kriging
• +9 more

Paolo Manfredi is a Full Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino, Italy. He actively contributes to the EMC Group (Electromagnetic Compatibility) and serves as an Associate Editor for journals including IEEE Journal on Multiscale and Multiphysics Computational Techniques and International Journal of Circuit Theory and Applications. His research focuses on uncertainty quantification in circuits, surrogate modeling , machine learning applications, and signal integrity analysis . He leads projects on compact dynamical modeling of complex systems and stochastic analysis of interconnects. Recent publications highlight advancements in active learning for PCB line uncertainty quantification, connector degradation impacts on microwave signals , and SPICE-compliant IC model compression , reflecting his expertise at the intersection of electrical engineering and data science . Scientific Awards: Best Paper Award EPEPS (2010, 2013) Premio Optime (2010) URSI Young Scientist Award (2011) Honorable Mention - IMS (2011) He supervises PhD students in projects spanning 5G/6G metasurfaces , power cable corrosion assessment , and neural network applications in circuit design.

Andrii ShalaginovView profile

Professor

• Internet of things (IoT)
• Machine learning
• Smart cities
• +4 more

Andrii Shalaginov is a Professor in Cyber Security at Kristiania University of Applied Sciences, School of Economics, Innovation and Technology. He leads the Smart Security Lab with a research focus on applying artificial intelligence for cybersecurity, detection of computer viruses, network attacks and protection of Internet of Things devices. Dr. Shalaginov holds a PhD in Information Security from the Norwegian University of Science and Technology (NTNU), where he worked with digital forensics at the Center for Cyber and Information Security (CCIS). His research spans multiple critical areas of modern cybersecurity with a particular emphasis on Internet of Things security , machine learning applications for threat detection, and digital forensics . He has developed expertise in intelligent malware detection systems, network security for smart environments, and forensic analysis of IoT devices. His work bridges theoretical computer science with practical security implementations for real-world systems. Analysis of his recent publication trends (2022-2025) reveals a strong focus on AI-enabled security solutions for IoT ecosystems, vulnerability detection frameworks, and resource-efficient security mechanisms for constrained devices. His research increasingly addresses the intersection of machine learning and practical security implementations, with growing attention to energy efficiency in security protocols and international data space security frameworks. Dr. Shalaginov has received professional recognition through: Nomination as Norwegian representative in EU COST Action CA17124 "DigForAsp" Nomination as Norwegian representative in EU COST Action CA22104 "BEiNG-WISE" Membership in the "Impact of Technology" Expert Group at the European Union Intellectual Property Office Observatory As head of the Smart Security Lab, Dr. Shalaginov oversees research projects focused on developing practical security solutions for emerging technologies. His GitHub activity demonstrates active development of open-source tools for malware analysis across multiple platforms (Linux, Windows, IoT) and implementation of machine learning approaches for security applications. His research group appears to focus on bridging the gap between theoretical security models and practical implementations for real-world systems.

Gilles BlanchardView profile

Professor

• Statistics
• Machine Learning
• Statistical Learning Theory
• +5 more

Gilles Blanchard is a Professor at Paris-Saclay University , affiliated with the Institute of Mathematics in Orsay. His research focuses on statistical learning theory , multiple testing , kernel methods , and high-dimensional statistics . Key Contributions: Decontamination of mutually contaminated models Novelty detection with semi-supervised learning Non-Gaussian component analysis (NGCA) for dimension reduction Scientific Awards: PhD thesis award from University of Paris 6 (2004) Google Scholar h-index of 49 with over 8,000 citations Recent Research Trends: False Discovery Rate (FDR) control in structured hypothesis testing Conformal prediction for link analysis Adaptive sampling in restless bandits Statistical learning on measure spaces His work bridges theoretical statistics with practical machine learning applications, including genome-wide association studies , persistent homology in topological data analysis , and random feature moments for compressive learning . He is involved in open-source software development like μTOSS for multiple testing standardization and has advised on projects related to flow cytometry and Hadrontherapy applications.

• Remote Sensing
• Machine Learning
• Geospatial Data Analysis
• +4 more

Dr. Emma Izquierdo-Verdiguier is a Researcher at the Institute of Geomatics within the University of Natural Resources and Life Sciences, Vienna (BOKU) . She specializes in machine learning algorithms for remote sensing data analysis , with particular focus on land surface phenology and cloud computing environments like Google Earth Engine. Her research spans two decades, including postdoctoral work at the University of Valencia (2017-2018) and University of Twente (2014-2017). She has developed Matlab toolboxes for nonlinear feature extraction and transitioned to JavaScript/Python-based cloud computing solutions for high-resolution image classification. Key methodologies involve kernel methods , multispectral image analysis , and data cube clustering . Recent publications focus on climate change impact assessment (2025), carbon flux modeling (2023), and soil organic carbon monitoring (2025), demonstrating her expertise in Earth Observation and machine learning integration . Her work frequently employs Google Earth Engine for continental-scale analyses. Scientific Awards: 2018 Travel Award sponsored by Remote Sensing 2017 Best Youth Oral Paper Award (X. Wu), ISPRS 2012 2nd Best Paper Award, IEEE IGARSS Student Competition

Aurélien GarivierView profile

Professor

• Machine Learning
• Statistics
• Sequential Decision Making
• +5 more

Aurélien Garivier is a Professor at Ecole Normale Supérieure de Lyon , affiliated with the UMPA and LIP laboratories. He serves as Deputy Director of UMPA and leads the Master 2 Fundamental Computer Science program at ENS Lyon. His research and academic activities span multiple domains within Machine Learning , Statistics , and Sequential Decision Problems . Local chair of COLT 2025 in Lyon Co-organizer of workshops on Graph Representation Learning and Reinforcement Learning Former director of the Department of Mathematics at the Faculty of Science and Engineering (2016-2018) He has contributed to comparative studies of programming languages for statistical tasks, such as the Baum-Welch algorithm speed analysis in R/Python/Matlab, and co-authored the Perfect Simulation of Processes with Long Memory (2015), which introduced CIAFTP algorithms for stationary processes with transition kernels. His leadership extends to organizing educational initiatives like the UT3-INSA-UT1 Big Data Challenges and Enter the World of AI exhibition. He is also co-host of the SciDoLySE group (Data Science in Lyon and Saint-Etienne).

• Distributed Algorithms
• Metric Graph Theory
• Theoretical Computer Science
• +3 more

Jérémie Chalopin is a Directeur de Recherche (DR CNRS) at LIS (Laboratoire d'Informatique et Systèmes) in Marseille, where he is a member of the DALGO research team. His position at CNRS is affiliated with Aix-Marseille University, one of France's largest multidisciplinary universities. As a senior researcher with habilitation to supervise research (awarded in 2020), he plays a significant role in the French theoretical computer science community. Chalopin's research primarily focuses on distributed algorithms and metric graph theory , with substantial contributions to understanding the theoretical foundations of distributed computing systems and the geometric properties of graphs. His work bridges theoretical computer science with practical applications in mobile agent systems, programmable matter, and network algorithms. He has developed fundamental results in areas including leader election, graph exploration, metric embeddings, and sample compression schemes for graph structures. Analysis of his recent publications reveals a strong trend toward interdisciplinary work combining distributed computing with geometric graph theory. His research on balls in graphs has applications in machine learning, while his work on programmable matter addresses cutting-edge challenges in distributed robotics. The consistent high-quality output in top theoretical venues demonstrates sustained research productivity across multiple subfields. As a habilitated researcher at CNRS, Chalopin supervises PhD students and participates in research projects, though specific details about current students or grants aren't provided in the source material. His long-standing collaboration with Victor Chepoi (appearing in numerous publications) represents a significant research partnership in metric graph theory. Chalopin is based at LIS in the TPR2 building (office 05.28, 5th floor) where he works as part of the DALGO team, which focuses on algorithms and theoretical computer science. His research environment provides strong connections to both the French and international theoretical computer science communities through collaborations with researchers across Europe and beyond.

• Quantum Information
• Quantum Computing
• Quantum Communication
• +5 more

Dr. Shahram Dehdashti is a researcher at the Chair of Theoretical Information Technology (Lehrstuhl für Theoretische Informationstechnik) at the Technical University of Munich (TUM), working under Prof. Boche. His research spans quantum information science, quantum communication, and quantum machine learning, with significant contributions to theoretical frameworks for future quantum networks and practical implementations in optical systems. His primary research domains include: Quantum Information and Communication Quantum Computing Algorithms Quantum Machine Learning Quantum Optics and Photonics Metamaterials and Transformation Optics Quantum Cognition He investigates quantum channel capacities, quantum-classical signal co-propagation, and quantum-enhanced feature spaces, while uniquely applying quantum probability models to cognitive decision-making processes. Analysis of his 2020-2025 publications reveals accelerating convergence between quantum computing and artificial intelligence, particularly in quantum federated learning for edge devices and quantum-inspired sentiment analysis. His work consistently bridges theoretical quantum information with practical optical implementations, while maintaining distinctive contributions to quantum cognition through experimental protocols for decision modeling. No scientific awards were documented in the source material. There is no evidence of student supervision or grant funding in the available information. Dr. Dehdashti operates within TUM's Chair of Theoretical Information Technology, which focuses on fundamental quantum information processing and theoretical aspects of next-generation communication systems.