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Zhou YingView profile

Associate Professor

• Seismology
• Geophysics
• Computational Seismology
• +3 more

Zhou Ying is an Associate Professor of Geophysics at Virginia Tech's College of Science, Department of Geosciences. Her research focuses on understanding seismic heterogeneities in the Earth's interior through finite-frequency wave propagation theory and diffractional tomography. Ph.D. in Geophysics from Princeton University (2005) Her research interests include: Seismic Wave Propagation in Heterogeneous Media Global and Computational Seismology Finite-Frequency Seismic Tomography 3-D Anelasticity (Q) Structure in the Mantle Seismic Anisotropy in the Upper Mantle and Transition Zone Core-Diffracted Waves and Core-Mantle Boundary Structure Signal Processing and Array Data Analysis Zhou's 15 most recent publications span seismology, geophysics, and unexpected interdisciplinary topics like molecular biology. Most studies focus on mantle discontinuities, finite-frequency tomography, and outer core dynamics, with specific attention to 410-km/660-km discontinuities and surface-wave sensitivity kernels. Current graduate advisee: Zhen Guo (Ph.D. candidate in Geophysics). Former advisees include Youyi Ruan (Ph.D. 2012), Kui Liu (Ph.D. 2014), Kai Deng (MS 2014), and Jing Xue (MS 2014). She teaches undergraduate courses in Physical Geology and Earthquake Seismology, and graduate courses in Theoretical Seismology, Advanced Seismology, and Seismic Data Analysis.

• Mathematical Biology
• Applied Mathematics
• Cancer Research
• +5 more

Dr. Marianna Cerasuolo is a Reader in Numerical Analysis and Scientific Computation at the University of Sussex's School of Mathematical and Physical Sciences. Her research focuses on mathematical and computational modeling for medical, biological, and environmental applications, including cancer dynamics, soil microbiome analysis, and sustainable agriculture. She has held positions at the University of Portsmouth and Rothamsted Research, and leads projects like the Horizon Europe-funded BIOservicES and the H2020 Diverfarming initiative. Her work integrates statistical, mathematical, and computational methods to address problems in oncology, ecology, and public health. Education: Advanced degrees in applied mathematics and mathematical biology (not explicitly listed). Research Interests: Cancer modeling, epidemiology, plant-environment interactions, bio-statistics, and computational biology. Her grants include the £6M BIOservicES (2023-2028) and a London Mathematical Society grant on microbial dynamics. She teaches courses like Machine Learning and Statistics for Health and Monte Carlo Simulations. Key contributions include developing the SusDiver app for agricultural sustainability and models predicting prostate cancer therapy efficacy.

Davide PrevitaliView profile

Assistant Professor

• Control Systems
• Black-Box Optimization
• Preference-Based Optimization
• +4 more

Davide Previtali is an Assistant Professor at the University of Bergamo's Department of Engineering. His research focuses on control systems, black-box and preference-based optimization, and lithium-ion battery technologies. He teaches the 'Data science and automation' course (6 CFU). Education: Master’s in Computer Science Engineering (2019), PhD in Engineering and Applied Sciences (2022), both from University of Bergamo Research interests include model predictive control, thermal systems optimization, regenerative braking systems, and integration of biological models with neural networks. Recent work emphasizes energy-efficient industrial processes and algorithms for preference-based optimization with convergence guarantees. Publications span industrial oven modeling, shrink tunnel control systems, and advanced optimization algorithms. He leads the Control systems and Automation Laboratory, focusing on practical applications of control theory in manufacturing and energy systems. Labs/Teams: Control systems and Automation Laboratory

Aleksandar JevremovićView profile

Professor

• Human-Computer Interaction (HCI)
• Cybersecurity
• Internet of Things (IoT)
• +6 more

Aleksandar Jevremović is a Full Professor at the Faculty of Informatics and Computing, Singidunum University (Belgrade, Serbia), and holds multiple academic and professional roles. He is the Serbian representative at the UNESCO IFIP Technical Committee on Human-Computer Interaction since 2018. He has served as Vice-Dean of his faculty (2015–2018) and held visiting professorships at institutions like Ss. Cyril and Methodius University (North Macedonia) and Tallinn University (Estonia). His research focuses on cybersecurity, IoT, AI, and e-learning innovation. Education and Affiliations: External Researcher at the Mathematical Institute of the Serbian Academy of Sciences and Arts Visiting Scholar at Cyprus Interaction Lab (Cyprus University of Technology) Alumni/Postdoc Researcher at Tallinn University's HCI Group Member of IEEE and the Informatics Association of Serbia Research Interests: Jevremović’s work spans cybersecurity (e.g., intrusion detection, secure IoT protocols), human-computer interaction (HCI), AI-driven education tools, and neurotechnological applications like EEG-based assessment systems. He emphasizes practical solutions for digital safety, such as children’s online protection and cryptographic key generation from biometric data. Grants and Projects: Member of the External Advisory Committee for the EU-funded ONTOCHAIN project (2022–2023) Mentor for training schools like AAPELE Training School and NET4Age-Friendly initiatives Trainer in IoT, cybersecurity, and health promotion programs across Europe Labs and Teams: He collaborates with interdisciplinary teams on projects like CASPER (Children Agents for Secure and Privacy Enhanced Reaction) and led the development of WIDE, a collaborative web development education platform.

Nicholas EvansView profile

Professor

• Speaker Recognition
• Biometrics
• Anti-Spoofing
• +7 more

Nicholas Evans is a Professor in the Digital Security department at EURECOM, where he teaches mathematical methods for engineers and speech and audio processing. He has been affiliated with EURECOM since October 2007 and leads research in speaker recognition, anti-spoofing, and biometric security. Previously, he was a Professor at the University of Wales Swansea (2002–2006) and taught at the University of Avignon (2006–2007). Research Interests: His work centers on biometric security, particularly in detecting spoofing and deepfake attacks in automatic speaker verification systems. Key areas include presentation attack detection, privacy-preserving voice technologies, and robust speech processing under real-world conditions. He actively contributes to advancing anti-spoofing countermeasures through large-scale datasets and challenge evaluations. The recent publications reflect a strong trend in developing and evaluating spoofing detection mechanisms, with a focus on real-world applicability, adversarial robustness, and multimodal analysis. His work spans from foundational feature engineering (e.g., Constant Q cepstral coefficients) to large-scale datasets like SpoofCeleb and ASVspoof, influencing both academic research and practical security systems. NeurIPS 2023 Scholar Award for 'StressID: a multimodal dataset for stress identification' Best Paper Award at IBERSPEECH 2022 Best System Award at IberSpeech 2018 Multiple Best Paper Awards (2016, 2017) Student Paper Award at WIFS 2013 Elected to IEEE Speech and Language Technical Committee (2013) Nicholas Evans has supervised numerous students and early-career researchers, many of whom are co-authors on his publications. He leads significant research initiatives such as the ASVspoof and SpoofCeleb challenges, which are supported by collaborative grants and institutional funding. His lab focuses on developing secure, privacy-preserving speech technologies with applications in biometrics and cybersecurity. He is a key member of international research teams and contributes to major challenges in voice privacy and spoofing detection, including the Voice Privacy Challenge and BIOSIG. His work involves close collaboration with institutions across Europe and Asia, and he maintains active profiles on Google Scholar, ResearchGate, and IEEE Xplore.

• Remote Sensing
• Synthetic Aperture Radar (SAR)
• Subsurface Imaging
• +7 more

Keith Morrison is a Professor at the University of Reading, affiliated with the School of Mathematical, Physical and Computational Sciences and the Department of Meteorology. His research focuses on advanced remote sensing techniques, particularly synthetic aperture radar (SAR), for environmental monitoring and subsurface imaging. Institution: University of Reading School: School of Mathematical, Physical and Computational Sciences Department: Department of Meteorology Research Focus: Radar Remote Sensing, Soil Moisture, Peatlands, Subsurface Scattering His research interests lie in the development and application of radar systems for Earth observation. He specializes in SAR-based methods such as tomographic profiling, interferometry, and virtual bandwidth SAR (VB-SAR) to study soil moisture dynamics, peatland hydrology, forest structure, and subsurface features. His work bridges physics, signal processing, and environmental science, enabling improved understanding of ecosystem processes through microwave remote sensing. The analysis of his recent publications reveals a strong focus on C-band and S-band SAR applications, particularly in explaining anomalous backscatter due to subsurface scattering in dry soils and peatlands. He has pioneered techniques like VB-SAR for centimeter-scale vertical profiling from space, contributing significantly to the accuracy of soil moisture and vegetation parameter retrievals. His work increasingly integrates laboratory experiments, field studies, and satellite data for robust validation. Although no formal awards are listed in the provided text, his sustained contributions to high-impact journals such as IEEE TGRS, Remote Sensing of Environment, and International Journal of Remote Sensing suggest recognition within the scientific community. Morrison has supervised and collaborated with numerous researchers, including Edwards-Smith, Zwieback, Andre, and Bennett. While formal student advising is not detailed, his role as a senior author and frequent collaborator indicates mentorship responsibilities. He has been involved in projects related to peatland monitoring, forest biophysical retrieval, and SAR-based change detection, likely supported by research grants from UK and European funding bodies. He contributes to major international conferences such as IGARSS, EUSAR, and IEEE RadarCon, demonstrating active engagement in the global radar and remote sensing community. His work supports future advancements in satellite-based environmental monitoring, particularly in carbon-rich ecosystems like peatlands and forests.

Per BækgaardView profile

Associate Professor

• Human-Computer Interaction
• User Experience
• Human-Centered Artificial Intelligence
• +7 more

Per Bækgaard is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), specializing in Cognitive Systems. He serves as Head of Study for Human-Centered Artificial Intelligence, leading research in human-computer interaction, user experience, eye tracking, and cognitive neuroscience. His work bridges AI and human cognition to create systems that enhance daily life and support meaningful tasks. PhD, MSc EE, Technical University of Denmark His research interests center on Human-Computer Interaction (HCI) , User Experience , and Human-Centered Artificial Intelligence , with strong emphasis on Eye Tracking , Cognitive Neuroscience , and Digital Media . He explores how digital systems can adapt to users’ cognitive states using physiological signals like pupil dilation and gaze patterns, aiming to improve learning, health, and decision-making. His work aligns with UN Sustainable Development Goals in health and education. The recent publications reflect a strong trend in integrating eye tracking and pupillometry with AI-driven adaptive systems , particularly in education and healthcare. Themes include generative AI in learning , trustworthy AI in supply chains , and digital micro-interventions for mental health . The interdisciplinary nature spans computer science, psychology, and biomedical engineering, showcasing a cohesive focus on human-centered technology evaluation. Scientific Awards: Best Paper Award, 26 Jun 2020 – for contributions to gaze interaction research Per Bækgaard actively supervises PhD students and leads multiple research projects, including those involving generative AI in education , digital phenotyping , and AI in nursing and mental health . He is the main supervisor for several PhD projects and a co-supervisor or examiner in others, demonstrating a strong commitment to academic mentoring. His grant involvement includes projects funded by DTU and collaborative research initiatives in digital health and AI. He is part of the Cognitive Systems group at DTU, contributing to interdisciplinary research in AI, neuroscience, and human factors. His team collaborates on projects involving real-time physiological monitoring, adaptive interfaces, and AI-mediated learning systems, positioning him at the forefront of human-centered AI research in Scandinavia.

• Control Systems
• Petroleum Engineering
• Machine Learning
• +3 more

Alexey Pavlov is a Professor of Petroleum Cybernetics at the Department of Geosciences and Petroleum, Norwegian University of Science and Technology (NTNU). He holds an MSc in Applied Mathematics from St. Petersburg State University, a PhD in Mechanical Engineering from Eindhoven University of Technology, and has industrial R&D experience from Statoil and Ford Motor Co. Education: MSc (Applied Mathematics, St. Petersburg State University), PhD (Mechanical Engineering, Eindhoven University) His research focuses on control systems for petroleum engineering applications, including nonlinear control theory, iterative learning control, and data-driven optimization methods. Publications reveal a strong emphasis on real-time drilling optimization, well integrity monitoring, and synchronization in networked systems. Recent work trends include machine learning integration for oil well monitoring, moment matching in model reduction, and extremum seeking control for multi-agent systems. Collaborations span institutions like Ford Motor Co., Statoil, and Eindhoven University of Technology. Current affiliations include the Department of Geosciences and Petroleum at NTNU. No scientific awards or advisee information is explicitly mentioned in the provided texts.

Thor Inge FossenView profile

Professor

• Marine craft hydrodynamics
• Motion control systems
• Navigation systems
• +7 more

Thor Inge Fossen is a Professor of Navigation and Marine Craft Control at the Department of Engineering Cybernetics, Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). He is a key scientist at the Norwegian Centre for Embodied AI (NCEI) and internationally recognized for his work in navigation systems, guidance systems, and control of marine vessels, aircraft, and drones. Professor Fossen holds a PhD in Engineering Cybernetics and an MSc in Marine Technology. His academic journey has led him to become a Fellow of AAIA, IEEE, and IFAC, reflecting his significant contributions to the field. His research spans several critical areas in marine and aerospace systems: Marine craft hydrodynamics and motion control Navigation, guidance, and control systems for marine craft, aircraft, and drones Cybersecurity of autonomous vehicles Sea-state estimation and wave analysis Attitude control and estimation Fossen's marine craft model, which is widely used in the industry Professor Fossen's publication record demonstrates a strong focus on adaptive control systems, particularly Line-of-Sight (LOS) guidance laws, with numerous papers on 3D path following for marine and aerial vehicles. His recent work (2023-2025) shows increasing integration of machine learning techniques with traditional control systems, particularly in areas like constrained control allocation using deep neural networks. There's also a growing emphasis on cybersecurity aspects of autonomous vehicle guidance systems. His scientific recognition includes: Fellow of the American Institute of Aeronautics and Astronautics (AAIA) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the International Federation of Automatic Control (IFAC) Professor Fossen has been actively involved in advising graduate students, with numerous PhD and MSc graduates. He has led significant research projects including the Marine Systems Simulator (MSS) and the Python Vehicle Simulator, which are widely used tools in the field. His current appointments include being a Study Program Coordinator for the Master's program in Cybernetics and Robotics at NTNU and a Key Scientist at the Norwegian Centre for Embodied AI. He leads research teams focused on embodied AI applications for marine systems, with particular emphasis on safe and secure autonomous operations in complex maritime environments. His work bridges theoretical control systems with practical marine applications, making significant contributions to both academic research and industry implementation.

Taehyung KimView profile

Associate Professor

• Power Electronics
• Motor Drives
• Automotive Engineering
• +3 more

Taehyung Kim is an Associate Professor at the University of Michigan-Dearborn in the Department of Electrical and Computer Engineering , College of Engineering and Computer Science. His research focuses on power electronics , motor drives , and electric/hybrid power systems for vehicles and aircraft , with an emphasis on renewable energy integration and fault-tolerant control . Education Ph.D., Electrical & Computer Engineering, Texas A&M University M.S., Electrical Engineering, Korea University B.S., Electrical Engineering, Korea University His research interests include energy conversion systems, power electronics for electric vehicles, evaluation and diagnosis of AC motors, and position sensorless control of permanent magnet motors. He leads the KIM Laboratory , which explores unmanned aerial vehicles (UAVs) , battery systems , and powertrain reliability . The 15 most recent articles (2024-2021) highlight his work on hybrid UAVs , fault detection algorithms , cost-effective converters , and powertrain optimization . These publications span power electronics , renewable energy integration , and electric propulsion systems , with applications in transportation electrification and industrial power systems . Scientific Awards NSF Mid Career Advancement Award, 2023 IEEE-IAS Prize Paper Award (2nd Place), 2012 Best Paper Award, IEEE Transportation Electrification Conference, 2021 Listed in "World Top 2% Scientists" (Stanford University, 2020-2024) Listed in Marquis Who’s Who in America Technical Program Co-Chair, 2009 IEEE Vehicle Power and Propulsion Conference Prof. Kim has advised numerous PhD and Master’s students , including Feng Zhou , Sreekanthreddy Chalapala , and Sahithya Parvathareddy . He has secured significant grants from the NSF , Department of Energy , and industry partners like Ford, focusing on smart monitoring , fault identification , and energy management for electrified systems. His lab’s facilities include advanced power electronics labs and hybrid powertrain testing environments .

Sujith VijayanView profile

Associate Professor

• Computational modeling
• Brain oscillatory dynamics in health and disease
• Development of stimulation and pharmacological therapies for brain disorders
• +7 more

Sujith Vijayan is an Associate Professor at the School of Neuroscience , part of the Virginia Tech College of Science . His research focuses on neural dynamics during sleep and active behavior , leveraging computational modeling , signal processing , and invasive/non-invasive recording to explore memory consolidation, emotional regulation, and therapeutic applications for neurological disorders. Research Interests include: Sleep-mediated memory processing Brain-computer interface (BMI) learning Pathological brain rhythms in PTSD and Parkinson’s Neural stimulation therapy development REM sleep emotional memory mechanisms His 15 most recent publications reveal a trajectory from anesthetic-induced neural oscillations (2013), through schizophrenia network analysis (2015), to targeted memory reactivation during sleep (2017). Key scientific contributions include the 2022 NSF CAREER award for BCI learning enhancement via sleep manipulation . Lab members include graduate students in Biomedical Engineering, TBMH, and Neuroscience programs, with alumni now at institutions like Barrow Neurological Institute and University of Arizona’s Sleep Lab.

• Graph Representation Learning
• Deep Learning
• Reinforcement Learning
• +7 more

Jian Tang is an Assistant Professor at HEC Montreal and the Montreal Institute for Learning Algorithms (MILA), as well as an Associate Professor at the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also affiliated with IVADO (Institut de valorisation des données) as a member. His research spans multiple institutions including collaborations with leading biology labs worldwide and access to extensive computational resources through industry partners. Ph.D. in Computer Science, Peking University (2009-2014) Visiting Ph.D. student, University of Michigan (2011.10-2013.8) B.S. in Mathematics, Beijing Normal University (2005-2009) Professor Tang's research focuses on the intersection of deep learning and graph theory, with particular emphasis on geometric deep learning, knowledge graph reasoning, and applications in drug discovery. His work bridges symbolic and neural approaches to create robust reasoning systems that can handle complex structured data. He has pioneered techniques in graph representation learning that have significantly advanced the field of molecular property prediction and protein design. His publication record shows a clear trajectory toward applying geometric deep learning to biological problems, with a growing emphasis on protein design, molecular conformation generation, and multi-omics analysis. Recent work demonstrates sophisticated integration of 3D geometry with deep learning architectures to model complex biomolecular interactions. Canada CIFAR Artificial Intelligence Chairs (CCAI Chair) Tencent AI Lab Rhino-Bird Gift Fund Amazon Faculty Research Award Microsoft-Mila collaboration grant National Research Council Canada (NRC) Collaborative Research and Development Grant Professor Tang actively mentors doctoral and master's students, with six recent graduates working on cutting-edge topics including graph neural networks for reasoning, protein design, and molecular representation learning. His research is supported by substantial funding from industry partners including Microsoft, Amazon, and Tencent, as well as government agencies like NRC. He collaborates extensively with biology labs worldwide, applying AI to solve real-world biomedical challenges. He leads a research group focused on geometric deep learning for drug discovery, with active projects in protein design using geometric-aware models and large language models for multi-omics analysis. The group has access to thousands of GPUs through industry collaborations, enabling large-scale experiments in molecular simulation and generative modeling.

• Chiral Recognition
• Enantiomeric Separations
• Ionic Liquids
• +5 more

Daniel W Armstrong is a Professor at the University of Texas at Arlington in the Department of Chemistry and Biochemistry. With over 35 years of experience, he is a pioneering figure in chiral recognition, enantiomeric separations, and the biological relevance of D-amino acids. His work has led to over 740 publications, 35 patents, and 560 invited seminars worldwide. Developed first chiral recognition mechanism by cyclodextrins First to use macrocyclic antibiotics as chiral selectors Synthesized most new ionic liquids (ILs) globally Created ultra-fast separation techniques now standard in analytical chemistry Contributed to FDA 1992 guidelines on chiral drug separation His research focuses on chiral separations, ionic liquids, and the role of D-amino acids in biological systems. He has commercialized over 30 HPLC and GC columns, transforming analytical chemistry and pharmaceutical analysis. Recent publications demonstrate continued innovation in chiral chromatography, biomarker analysis, and AI-driven separation optimization. His grants include industry collaborations with Merck, Alcon, and Sigma-Aldrich, emphasizing practical applications of his work. Scientific honors include multiple lifetime achievement awards, fellowships in the Royal Society of Chemistry and American Chemical Society, the Chirality Medal, and induction into the National Academy of Inventors. He has mentored over 100 PhD students, many from first-generation college backgrounds.

Mathieu FontaineView profile

Associate Professor

• Machine Listening
• Speech Enhancement
• Speaker Separation
• +9 more

Mathieu Fontaine is an Associate Professor in Machine Listening at Télécom Paris , affiliated with the LTCI Lab within the IDS Department (Information, Data, Signal). His research focuses on machine listening for speech and audio signal processing. PhD in Informatics (2019), Lorraine University Master in Applied and Fundamental Mathematics (2015), Poitiers University BSc in Fundamental Mathematics (2013), Rennes University Fontaine's research spans speech enhancement , speaker separation , source localization , and music source separation using heavy-tailed probabilistic models and deep Bayesian networks , with applications in augmented reality . He has expertise in Python , signal processing , and machine learning (80% proficiency). His recent publications (2024) include work on diffusion models for speech synthesis , room acoustics estimation from 3D meshes , robust audio scene analysis , and direction-aware speech processing . Earlier publications (2022-2023) explore flow-based NMF , alpha-stable representations , and adaptive beamforming in multiparty environments. Fontaine collaborates with the S2A team and ADASP group at LTCI Lab. His work integrates probabilistic modeling with deep learning to address challenges in real-world audio processing, including reverberation, noise, and complex acoustic environments.

Renaud PacaletView profile

Researcher

• Hardware Security
• Side-Channel Attacks
• Embedded Systems
• +7 more

Renaud Pacalet is a Researcher at Institut Mines-Télécom – Télécom Paris , affiliated with the Communications and Electronics (Comelec) Department and the System on Chip (LabSoc) research team under the Information Processing and Communication Laboratory (LTCI). His work spans hardware security, embedded systems, and software-defined radio (SDR) architectures. Current Research: Hardware security, side-channel attacks (power, timing, fault injection), RISC-V security analysis using gem5, FPGA scheduling for cloud data centers, and model-driven design methodologies. Past Research: Hardware acceleration for ray tracing, SDR front-end processing, SoC security, and memory bus protection (SecBus project). Teaching: Courses on Digital Systems, Computer Architecture, and Hardware Security at EURECOM, including lab sessions on side-channel attacks and fault analysis. Email: renaud.pacalet@telecom-paris.fr Contact: Télécom ParisTech, Campus SophiaTech, 450 route des Chappes 06410 Biot, France