جست‌وجوی استاد و پژوهشگر

Simon Leglaiveمشاهده پروفایل

استادیار

• Audio Signal Processing
• Machine Learning
• Speech Enhancement
• +۷ مورد دیگر

Simon Leglaive is a tenured Assistant Professor at CentraleSupélec in Rennes, France, and a researcher in the AIMAC team at the IETR laboratory (CNRS UMR 6164). He holds a PhD in Audio Signal Processing from Télécom Paris (2017), and previously served as a postdoctoral researcher at Inria Grenoble Rhône-Alpes. His academic affiliations include CentraleSupélec, Télécom Paris, and CNRS, with research collaborations across institutions including Inria, GIPSA-lab, and Sorbonne University. His research focuses on the intersection of signal processing and machine learning, particularly for audio and speech applications. Key areas include probabilistic generative models, deep learning, low-dimensional representation learning, and weakly or semi-supervised learning. His work enables advanced multimodal machine perception tasks such as speech enhancement, audiovisual speech analysis, and human mesh recovery. He teaches machine learning, deep learning, and audio signal processing at CentraleSupélec. The 15 most recent publications highlight a strong trend in deep generative modeling, especially using variational autoencoders (VAEs) and masked autoencoders, for tasks like speech enhancement, source separation, emotion recognition, and human pose recovery. There is a clear emphasis on disentangled and interpretable latent representations, multimodal fusion, and unsupervised or weakly supervised methods. Applications span from speech technology to computer vision, reflecting a cross-disciplinary approach centered on fundamental representation learning. Coordinator and Principal Investigator, DEGREASE project (ANR-23-CE23-0009) Partner, DEESSE project (ANR-24-CE23-2229) Elected member, IEEE AASP Technical Committee (2023–2025) Organizing committee, CHiME-7 UDASE challenge (2023) Conference area chair, ICASSP (2024, 2025), WASPAA (2023, 2025) Simon Leglaive advises multiple PhD students and has served on the IETR laboratory council since 2022. His academic service includes extensive reviewing for top journals and conferences in signal processing and machine learning. His research is supported by competitive grants from the French National Research Agency (ANR). He is actively involved in collaborative research through the AIMAC team at IETR and partnerships with Inria and industry labs.

Oliver Matteمشاهده پروفایل

دانشیار

• Quantum Physics
• Mathematical Physics
• Spectral Theory
• +۳ مورد دیگر

Oliver Matte is an Associate Professor at the Department of Mathematical Sciences, Aalborg University, within The Faculty of Engineering and Science. His expertise lies in mathematical physics, focusing on operator analysis in relativistic quantum systems and spectral theory. Education: He holds a Diploma in Physics (2001), Dr. rer. nat. in Mathematics (2004) from Johannes Gutenberg-Universität Mainz, and a Habilitation in Mathematics (2011) from Ludwig-Maximilians-Universität München, specializing in mathematical analysis of operators in relativistic quantum physics. Research: His work centers on rigorous mathematical approaches to quantum field theories, including the Nelson model, Feynman-Kac formulas, and spectral analysis in infinite-dimensional systems. Key themes include ground state properties, path measures, and asymptotic behavior in relativistic settings. Publications: With 27 research outputs, his recent work explores renormalized models, multi-polaron systems, and applications of Feynman-Kac formalism. His 2022 paper on path measures in the renormalized Nelson model has received significant attention (9 Scopus citations).

• Movement Disorders
• Myoclonus
• Tremor
• +۵ مورد دیگر

Dr. Jan Willem Elting is a researcher at the University of Groningen's Faculty of Medical Sciences, specializing in movement disorders and neurological conditions. His work focuses on understanding the mechanisms and developing diagnostic tools for conditions such as myoclonus, tremor, dystonia, and cerebral autoregulation disorders, contributing to several UN Sustainable Development Goals related to health and well-being. His primary research interests include: Myoclonus and other movement disorders Tremor analysis and classification using advanced technologies Cerebral autoregulation and brain blood flow dynamics Application of machine learning in neurological diagnosis Dystonia and deep brain stimulation efficacy Neurophysiological testing methodologies Dr. Elting's recent publications demonstrate a strong trend toward integrating machine learning techniques with traditional neurological assessment methods. His work bridges computational approaches and clinical neurology, particularly in the classification and diagnosis of movement disorders. He has made significant contributions to understanding benign idiopathic myoclonus as a distinct clinical entity and exploring viral associations with movement disorders in children. His research employs various methodologies including accelerometry, near-infrared spectroscopy, and neurophysiological testing to quantify movement abnormalities. His scientific achievements have been recognized with: Publication Price 2018 for established researchers (awarded March 2019) Top 5 best publications in 2018 by the Journal of the American Society of Nephrology Dr. Elting actively contributes to academic mentoring, currently serving as co-supervisor for PhD student Celina Brunsch. His research has received significant attention, being picked up by 9 news outlets, blogged about by 2 sources, and referenced by multiple Bluesky users and Mendeley readers. He has presented his work at numerous academic conferences, focusing on tremor quantification using accelerometry in Parkinson's disease patients. His work is conducted within research groups focused on movement disorders and neurological diagnostics, collaborating with experts in neurology, machine learning, and clinical research. His fingerprint analysis shows strong expertise in myoclonus (100%), tremor (80%), cerebral autoregulation (64%), autoregulation (47%), brain blood flow (47%), subarachnoid hemorrhage (46%), dystonia (46%), and near-infrared spectroscopy (44%).

• Data Mining
• Process Mining
• Knowledge Representation
• +۵ مورد دیگر

Antonella Guzzo is an Assistant Professor (ING-INF/05) at the Faculty of Engineering, University of Calabria, affiliated with the Department of Electronic Informatics and Systems (DEIS). Her research spans Data Mining, Process Mining, Federated Learning, and Knowledge Representation, with applications in Cyber Security and Bioinformatics. Laurea (2000) and Ph.D. (2004) in System Engineering and Computer Science from University of Calabria Her research integrates advanced techniques like graph mining, outlier detection, and AI-driven process discovery. She contributes to projects like TOCAI.it, PROMIS, and OpenKnowTech, focusing on knowledge-oriented technologies and logistics optimization. Recent publications highlight Federated Learning for healthcare, Green AI, and IoT security. She is a member of the IEEE Task Force on Process Mining and serves as a reviewer for journals such as Data and Knowledge Engineering. Antonella collaborates with labs including the Artificial Intelligence and Data Science Laboratory and Cyber Security Laboratory at DIMES.

Astrid Lambrechtمشاهده پروفایل

استاد

• Quantum Physics
• Condensed Matter Physics
• Theoretical Physics
• +۳ مورد دیگر

Prof. Astrid Lambrecht serves as Chairwoman of the Board of Directors at the Jülich Research Centre, combining administrative leadership with her academic role as a Professor. Her research focuses on quantum vacuum phenomena, including Casimir effects in diverse geometries and materials. She explores antimatter physics, particularly antihydrogen's gravitational behavior in experiments like GBAR, and investigates quantum reflection mechanisms. Her work bridges theoretical predictions with high-precision measurements, involving collaborations across condensed matter, optics, and gravitational physics. Key contributions include studies on Casimir interactions in electrolytes, surface-mediated quantum effects, and the development of novel experimental setups for probing short-range forces. Her interdisciplinary approach integrates quantum field theory, nanoscale engineering, and precision instrumentation.

David DiVincenzoمشاهده پروفایل

استاد

• Quantum Computing
• Superconducting Qubits
• Quantum Communication
• +۳ مورد دیگر

Prof. David DiVincenzo serves as Director of the Institute for Theoretical Nanoelectronics (PGI-2) and the JARA Institute for Quantum Information (PGI-11) at Forschungszentrum Jülich. His research focuses on quantum computing, superconducting qubits, quantum error correction, and many-body localization. He has contributed to foundational work on quantum hardware design, noise mitigation, and theoretical frameworks for scalable quantum systems. Key research themes include the development of superconducting qubit architectures, analysis of quantum chaos effects, and exploration of topological states in semiconductor materials. His work bridges theoretical insights with practical applications in quantum communication and fault-tolerant computing.

• Air Transport
• Aviation Management
• Airline Business Models
• +۱۲ مورد دیگر

Professor Zheng Lei is a Professor of Aviation at Swinburne University of Technology, School of Engineering, where he serves as Chair of the Department of Aviation within the Faculty of Science, Engineering and Technology. He holds a PhD in Air Transport and Tourism Management from the University of Surrey and has previously held academic roles at the University of Surrey and Cranfield University. He also serves as a Visiting Professor at the Civil Aviation University of China and is the founder and President of the Institute for Aviation Research, a think tank focusing on aviation in China. His research expertise lies in airline business models, air transport market analysis, regulation and policy, and aviation’s role in regional competitiveness. His work emphasizes real-world impact, advising airlines, airports, regulators, and aviation authorities globally. His fields of research include transportation, logistics, supply chains, and urban and regional planning. The 15 most recent publications reflect a consistent focus on aviation economics, policy, and digital transformation. Key themes include aviation resilience during the pandemic, market liberalization, airline competition, digital technologies like AI and blockchain, service quality assessment via NLP, and sustainable connectivity. His work employs advanced econometric, modeling, and data analytics methods to inform policy and industry practice. Professor Lei has secured significant external research funding from government and industry, including grants from the Department of Foreign Affairs and Trade, Department of Infrastructure, and Civil Aviation Safety Authority, focusing on hydrogen in aviation, alternative fuels, and advanced air mobility. He actively supervises PhD and Master’s students on topics such as hydrogen adoption, autopilot liability, advanced air mobility, airport security, and pilot success prediction, demonstrating a strong commitment to training the next generation of aviation professionals. He has led or contributed to numerous research projects and contracts, including 'Hydrogen to the Skies' and 'Regional AAM Surrogate Trial,' working with partners such as Textron Systems and government agencies. His leadership in both academic and applied research underscores his role as a key figure in contemporary aviation studies.

Vojislav Krstićمشاهده پروفایل

استاد

• Quantum Physics
• Condensed Matter Physics
• Nanotechnology
• +۳ مورد دیگر

Prof. Dr. Vojislav Krstić is a faculty member at the Institute of Condensed Matter Physics at Friedrich-Alexander University Erlangen-Nürnberg (FAU). His research focuses on experimental investigations of novel low-dimensional fermion systems in solids, including 2D layered materials (e.g., graphene, black phosphorus), helical semiconductor nanostructures, and quantum nanowires. He explores electronic and optical properties related to quantized, symmetry-breaking, and topological states, aiming to leverage these functionalities for future technologies. His work involves targeted device design, material growth, and physical/chemical refinement to create new electronic states. Key systems under study include: Quantum nanowire electronics with wide-bandgap semiconductors 2D layered materials and their interaction phenomena Helical 3D nanostructures in parity-time (PT)-breaking regimes Contact: vojislav.krstic@fau.de | Phone: +49 9131 85-28431

Qiang Liuمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Probabilistic Graphical Models
• +۸ مورد دیگر

Qiang Liu is an Associate Professor in the Department of Computer Science at the University of Texas at Austin. He leads the Statistical Learning & AI Group, focusing on probabilistic graphical models, variational and Monte Carlo inference, deep reinforcement learning, and kernel methods with applications in crowdsourcing, computer vision, and bioinformatics. NSF CAREER Award recipient Co-organized workshops at ICML 2019 and NIPS 2013 Specializes in rectified flow for generative modeling and optimal transport His recent work includes fast inference via straight trajectories in ODE models and certifiable robustness in neural architectures. Publications span conferences like NeurIPS, ICML, ICLR, and JMLR.

Victoria Kostinaمشاهده پروفایل

استاد

• Information Theory
• Coding Theory
• Wireless Communications
• +۴ مورد دیگر

Victoria Kostina is a Professor of Electrical Engineering and Computing and Mathematical Sciences at the California Institute of Technology (Caltech). Her research bridges information theory , coding theory , and wireless communications , with a focus on the nonasymptotic regime to understand fundamental limits of delay-constrained systems. Bachelor's, Moscow Institute of Physics and Technology Master's, University of Ottawa PhD, Princeton University Her work explores the intersection of random processes , concentration of measure , and control theory to develop theoretical frameworks for modern communication systems. She has pioneered techniques for finite blocklength information theory and feedback communication , analyzing stability and performance in stochastic environments. Victoria's publications span channel coding , source-channel coding , and control with communication constraints , with a focus on practical applications in real-time systems . Her research has advanced the understanding of data compression , error exponents , and quantization in nonasymptotic scenarios. Notable scientific recognitions include: Princeton Electrical Engineering Best Dissertation Award (2013) NSF CAREER Award (2017) J. K. Wolf ISIT Student Paper Award Finalist (2021) She actively supervises students in the Computing and Mathematical Sciences (CMS), Control and Dynamical Systems (CDS), and Electrical Engineering (EE) PhD programs and leads the development of the SPECTRE Toolbox for finite blocklength information theory.

Vladimir Goncharoffمشاهده پروفایل

مدرس

• Signal Processing
• Radio Communication
• Speech Analysis
• +۲ مورد دیگر

Vladimir Goncharoff is a Lecturer in the Department of Electrical and Computer Engineering at the University of Illinois at Chicago (UIC). His educational background includes a Ph.D. and M.Sc. in Electrical Engineering from Northwestern University (1983, 1980), a B.Sc. in Electrical Engineering from UIC (1979), and an Associate of Science from William Rainey Harper College (1976). His research focuses on: Analog/Digital Signal Processing : Speech enhancement, spectral analysis, pitch detection, and watermark recovery. Radio Frequency Circuits : Design principles, educational methodologies, and practical applications. Acoustic Systems : Biomedical acoustics and voice communication technologies. Goncharoff's publications emphasize speech algorithms, RF circuit education, and signal reconstruction. Recent works transition toward pedagogical materials, including textbooks on radio frequency circuits (2015–2016) and signal processing (2018). Awards & Honors: UIC Silver Circle Award for Excellence in Teaching (1988, 2000, 2003, 2006, 2009) UIC College of Engineering Harold Simon Award (1985) No information is available regarding grants, student advising, or laboratory affiliations.

Mher Safaryanمشاهده پروفایل

پژوهشگر

• Optimization theory and algorithms
• Machine learning
• Federated learning
• +۷ مورد دیگر

Dr. Mher Safaryan is a postdoctoral researcher at the Institute of Science and Technology Austria (ISTA), affiliated with Prof. Dan Alistarh's research group since 2022. Previously, he held postdoctoral positions at King Abdullah University of Science and Technology (KAUST) from 2019-2022 and served as a research technician there from 2016-2019. He earned his Ph.D. in Mathematics from Yerevan State University in 2018 under Prof. Grigori Karagulyan's supervision. Education: Ph.D. in Mathematics (2018, YSU) Past Affiliations: KAUST (2016-2022), Neural Magic/Red Hat (industrial secondment) His research focuses on optimization theory and algorithms for machine learning , particularly in developing communication/computation/memory-efficient methods for large-scale training and federated learning. Key contributions include LDAdam (low-dimensional gradient statistics optimization), GradSkip (accelerated local gradient methods), and Unified Scaling Laws for compressed representations. He has published in top venues like NeurIPS, ICML, ICLR, TMLR, and The Journal of Geometric Analysis. Scientific Awards: Marie Skłodowska-Curie Fellowship (MSCA COFUND IST-BRIDGE) His work bridges machine learning optimization with mathematical foundations from his earlier research in real harmonic analysis. Current collaborations include Prof. Dan Alistarh (ISTA), Dr. Alexandre Marques (Neural Magic), and Prof. Peter Richtárik (KAUST).

Henrique De Melo Ribeiroمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Machine Learning
• Biomedical Signal Processing
• +۴ مورد دیگر

Dr Henrique De Melo Ribeiro is a Research Fellow II at the Brunel Innovation Centre (BIC) within the College of Engineering, Design and Physical Sciences at Brunel University, UK. His research spans multidisciplinary domains including artificial intelligence, biomedical signal processing, and edge computing. PhD in Computer Science (Artificial Intelligence), University of West London MSc in Software Engineering, University of West London Key research areas include: AI applications in construction waste management Biomedical signal analysis for voice classification Edge computing for real-time ECG monitoring He contributes to projects like RECONSTRUCT (circular construction systems) and SWAG (wearable assistive garments). Collaborations include co-authors such as Dr Evelyne El Masri and Dr Fabrizio Ceschin.

• Geometric Galois Actions
• Operads
• Finite Type Invariants of Knots
• +۵ مورد دیگر

Vasily Dolgushev is a Professor in the Department of Mathematics at Temple University, where he maintains his office in Wachman Hall. His research program bridges several sophisticated areas of modern mathematics, with a particular focus on the deep connections between algebraic structures and geometric phenomena. His primary research interests include geometric Galois actions, operad theory, and finite type invariants of knots. Much of his recent work centers on the Grothendieck-Teichmueller group (GT), GT-shadows, and their connections to the absolute Galois group of rational numbers. His investigations explore how these structures relate to embedding spaces, the theory of motives, and Grothendieck's dessins d'enfants (child's drawings). As a US-based participant in the International Research Network for Arithmetic & Homotopic Galois Theory, he contributes to cutting-edge research at the intersection of number theory and topology. Analysis of his recent publications reveals a consistent focus on computational and theoretical aspects of GT-shadows, with increasing attention to their action on combinatorial structures related to Galois theory. His work demonstrates the power of operadic methods in understanding deep arithmetic phenomena, connecting seemingly disparate areas of mathematics through innovative structural frameworks. Dolgushev actively mentors graduate students, including Jingfeng Xia who completed a master's thesis on GT-shadows for the gentle version of the Grothendieck-Teichmueller group. He has established significant collaborative relationships with researchers including Ivan Bortnovskyi, Jacob Guynee, K.Q. Le, A.A. Lorenz, and Geoffrey Schneider. His technical contributions include developing software packages for working with GT-shadows and their action on child's drawings, demonstrating his commitment to making advanced mathematical concepts computationally accessible. His presentations at institutions including Bar-Ilan University, University of Minnesota, Penn, and University of Angers reflect his active engagement with the international mathematical community.

Evgeni Ilichevمشاهده پروفایل

پژوهشگر

• Quantum Computing
• Superconducting Qubits
• Josephson Junctions
• +۵ مورد دیگر

Prof. Dr. Evgeni Ilichev is a Scientist in the Quantum Systems Research Department at the Leibniz Institute of Photonic Technology (Leibniz-IPHT), where he leads the Quantum Circuits work group. His research focuses on quantum computing, superconducting qubits, and quantum metrology, with particular emphasis on developing devices for axion detection and quantum standards. Dr. Ilichev's research interests span multiple areas of quantum physics and superconductivity: Quantum computing and quantum information processing Superconducting qubits and quantum circuits Josephson junctions and SQUIDs Quantum metrology and standards Axion detection and dark matter research Microwave photonics and quantum sensing His recent publications demonstrate a strong focus on developing quantum devices for fundamental physics research and practical applications. Dr. Ilichev's work bridges theoretical concepts with experimental implementations, particularly in the areas of quantum computing hardware and quantum-limited detection. His research has produced significant results in quantum phase slips, axion detection technologies, and quantum metrology standards, with publications in top journals including Nature, Physical Review Letters, and IEEE Transactions on Applied Superconductivity. Notable achievements include: Observation of quantized current steps due to AC coherent quantum phase slips published in Nature (2022) Development of superconducting qubit networks for single microwave photon detection for galactic axion search Research on Josephson voltage and current standards on a single chip Dr. Ilichev collaborates extensively with researchers across multiple institutions, contributing to advancements in quantum technology and fundamental physics. His work on NbN-Al hybrid technology has opened new pathways for quantum device fabrication, while his investigations into noise mechanisms in superconducting devices have provided critical insights for improving quantum computing hardware.