جست‌وجوی استاد و پژوهشگر

• Geotechnical Engineering
• Environmental Geotechnology
• Unsaturated Soil Mechanics
• +۳ مورد دیگر

Dr. Rajesh Sathiyamoorthy is an Associate Professor in the Department of Civil Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur), specialising in Geotechnical Engineering. Since joining IIT Kanpur in 2011, he has built a strong research portfolio, secured competitive funding, and received numerous accolades for his contributions to environmental geotechnology and unsaturated soil mechanics. Education PhD, Indian Institute of Technology Bombay, India (2010) M.E., College of Engineering Guindy, Anna University, India (2002) B.E., Vellore Engineering College, University of Madras, India (2000) Research Interests Dr. Sathiyamoorthy’s research is centred on the behaviour of unsaturated soils, environmental geotechnics, and innovative ground-improvement techniques. His work employs advanced numerical and physical modelling to address challenges in landfill barrier design, soil–structure interaction, and performance of geosynthetically reinforced systems. A distinctive feature of his investigations is the integration of field-scale problems with controlled laboratory and centrifuge testing, coupled with state-of-the-art computational tools. Scientific Awards & Fellowships IEI Young Engineers Award 2013-2014 (Civil Engineering) – The Institution of Engineers (India) Talented Young Scientist Fellowship 2019 – Ministry of Science and Technology, China Distinguished Alumni Award 2019 (Academic & Research) – VIT University Best Paper Awards (Environmental Geotechnology) – Indian Geotechnical Society, 2019 & 2015 Best Paper Award – International Geotechnical Engineering Conference on Sustainability 2016 IGS Prof. A.V. Shroff Biannual Award 2013 – Indian Geotechnical Society International Geosynthetics Society Student Award 2012 – IGS, USA Best Paper Award 2010 – Geotextiles and Geomembranes Research Funding & Collaborations Dr. Sathiyamoorthy has successfully led multiple sponsored projects worth several crores of rupees from agencies such as SERB-DST, BRNS-DAE, Shanghai Council of Science & Technology, RDSO Lucknow, and PAAB-MHRD. These projects span centrifuge modelling of embankments on encased stone columns, hydro-mechanical behaviour of compacted soils, reliability-based assessment of landfill slopes, and field validation of stabilised subgrade materials for pavements. His international collaboration includes joint research at Tongji University, Shanghai, under the Talented Young Scientist Fellowship. Laboratories & Teams He heads experimental activities in the Geotechnical Engineering Laboratory at IIT Kanpur, where his group maintains specialised equipment for unsaturated soil testing, geosynthetic evaluation, and centrifuge modelling. The research team comprises doctoral scholars, master’s students, and project staff working on inter-related themes of environmental geotechnology and ground improvement.

• Machine Learning
• Artificial Intelligence
• Data Science
• +۵ مورد دیگر

Song Liu serves as Associate Professor in Data Sciences and AI within the School of Mathematics at the University of Bristol. His academic journey spans multiple continents with a BEng from Suzhou University, MSc from Bristol, and Doctor of Engineering from Tokyo Tech. Current research focuses integrate mathematical foundations with practical AI applications across engineering domains. His educational background demonstrates international expertise: BEng: Suzhou University MSc: University of Bristol Doctor of Engineering: Tokyo Tech Research centers on exponential family manifolds and graphical models , with significant contributions to score matching techniques for missing data and generative modeling. His work bridges theoretical statistics with real-world applications in structural health monitoring and power electronics, particularly through transfer learning frameworks for magnetic core loss prediction. Recent publications reveal increasing focus on Wasserstein gradient flows and differential parameter inference in high-dimensional spaces. Liu's publication trajectory shows consistent innovation in density estimation and generative modeling, with recent work (2023-2025) emphasizing practical implementations in engineering contexts. Key themes include score-based diffusion models, manifold learning applications, and novel approaches to divergence minimization using velocity fields and optimal transport theory. Award recognition includes: Outstanding Paper at ICML2025 3rd Place in MagNet Challenge 2023 (Outstanding Performance Award) Grant leadership includes the 2023-2024 project Using Machine Learning to Correct Probe Skew in High-frequency Electrical Loss Measurements as Co-Investigator, and the 2019 Joint Workshop Between JGI and ISM as Principal Investigator. His academic service extends to hosting international researchers like Ayaka Sakata (2023) and receiving competitive fellowships for boundary example simulation (2018-2020). While no formal lab structure is specified, his collaborative network spans electrical engineering (magnetic core loss projects) and structural analysis (offshore wind foundation monitoring).

Vinkle Srivastavمشاهده پروفایل

پژوهشگر

• Surgical Data Science
• 3D Medical Image Analysis
• Multi-modal Pre-training
• +۷ مورد دیگر

Vinkle Srivastav is a Research Scientist (Chargé de recherche R&D) at the CAMMA group, a collaborative research team between IHU Strasbourg and the University of Strasbourg, where he focuses on advancing surgical data science through novel computer vision and machine learning approaches. His work bridges the gap between clinical practice and artificial intelligence, developing methods for surgical video analysis, 3D medical imaging, and surgical workflow understanding. Education PhD in Computer Science (2018-2021) from University of Strasbourg, France. Thesis: "Unsupervised Domain Adaptation Approaches for Person Localization in the Operating Rooms." Master of Science in Computer Science (2014-2017) from Indian Institute of Technology, Delhi, India. Thesis: "Computerized evaluation of neurosurgery skills using image processing and computer vision techniques." Bachelor of Technology in Electronics and Communication (2007-2011) from Punjab Technical University, Jalandhar, India. Research Interests Vinkle's research spans surgical data science, with particular focus on multi-modal learning approaches for surgical computer vision. His work addresses fundamental challenges in medical AI including domain adaptation, self-supervised learning, and privacy preservation in clinical environments. He develops methods for 3D medical image analysis, multi-view human pose estimation in operating rooms, and surgical activity recognition. His recent work emphasizes multi-modal pretraining frameworks that leverage both visual and textual information to improve surgical workflow understanding. He also investigates scientific simulation techniques, particularly for therapeutic ultrasound applications, where physics-aware deep learning models can accelerate computational processes while maintaining accuracy. Publication Trends Vinkle's recent publications demonstrate a strong trajectory toward multi-modal surgical AI systems that integrate vision, language, and physics-based modeling. His work increasingly focuses on few-shot and zero-shot adaptation techniques to address the data scarcity problem in surgical AI. The publications reveal a progression from basic pose estimation to holistic surgical scene understanding, incorporating team communication analysis and surgical safety protocols. Scientific Awards IPCAI 2024 Best paper award (co-author) IPCAI 2019 Runner-up award in the bench-to-bedside category (co-author) Joint winner for the best paper award in the machine learning for CAI track, IPCAI 2025 Advising and Grants Vinkle actively mentors multiple PhD students and research interns at various levels, supervising thesis work on topics including large-scale multi-modality learning, holistic surgical scene analysis, and self-supervised video representation learning. He serves as Co-PI on two ITI-HealthTech projects: one focused on multi-modality learning for 3D medical imaging (2023), and another on physics-aware deep-learning approaches for therapeutic ultrasound simulation (2024). Laboratories and Teams Vinkle is a key member of the CAMMA research group at IHU Strasbourg, a collaborative team focused on computer-assisted medical modeling and analytics. He co-organizes the Surgical Data Science Summer School, an interdisciplinary program that brings together clinicians and computer scientists to develop AI-driven solutions with clinical impact. His work involves close collaboration with surgical teams at University Hospitals of Strasbourg and international partners including Johns Hopkins University and Technical University of Munich.

Seung Yeoun Leeمشاهده پروفایل

استاد

• Survival Analysis
• Gene-Gene Interactions
• Clinical Trials
• +۳ مورد دیگر

Seung Yeoun Lee is a Professor in the Department of Mathematics and Statistics at Sejong University, where he has been faculty since 1993. His research focuses on applying advanced statistical methodologies to biomedical problems, particularly in cancer research and survival analysis. He serves as Vice President of the Korean Statistical Society and previously served as President of the International Biometric Society Korean Region from 2016-2017. Education: Ph.D. in Statistics, University of Michigan (1990) M.S. in Statistics, Seoul National University (1986) B.S. in Statistics, Seoul National University (1984) Professor Lee's research primarily centers on survival analysis methodologies and their applications in biomedical research, with a particular emphasis on gene-gene interactions and clinical trial statistics. He has pioneered innovative approaches including the Cox model based unified MDR method for gene-gene interaction analysis for survival phenotypes. His work bridges mathematical statistics with practical medical applications, particularly in pancreatic cancer diagnostics and prognostics. The fingerprint analysis of his work shows strong connections to Dimensionality Reduction (94%), Pancreas Cancer research (89%), and Gene Interaction studies (64%). His recent publications demonstrate a clear trend toward interdisciplinary research combining traditional biostatistical methods with modern machine learning approaches. Many papers focus on survival prediction models, dimensionality reduction techniques, and applications to pancreatic cancer research. His work consistently involves international collaborations across statistics, oncology, and computational biology fields, reflecting the highly interdisciplinary nature of modern biomedical research. Professor Lee has made substantial contributions to biostatistical methodology with an h-index of 22, reflecting the impact of his 89 research publications. His work contributes to UN Sustainable Development Goals related to good health and well-being through advanced statistical approaches to medical research. His research group focuses on developing and applying advanced statistical methods to solve complex biomedical problems, with particular emphasis on cancer research and survival analysis. The interdisciplinary nature of his work suggests extensive collaborations with medical researchers, oncologists, and computational biologists across multiple institutions.

• Human-robot physical interaction
• Elderly people
• Smart-walker
• +۳ مورد دیگر

Ludovic Saint-Bauzel is a lecturer at Sorbonne University and head of the IRIS team at the Institute of Intelligent Systems and Robotics (ISIR). His work focuses on improving physical human-robot interaction for individuals with autonomy loss through disability or aging. Specializes in computational models of disabilities Develops user intent detection through sensor fusion Active in IFRH and Fedrha federations IEEE and True Life Lab member Research interests His research centers on Human-robot physical interaction with applications in Elderly care , Smart-walker development, and Walking exoskeleton systems. Key methodologies include: Sensor fusion (depth cameras, force sensors, IMUs) Adaptive robot control systems Pathological movement modeling Motor intent prediction algorithms Article trends show consistent focus on haptic communication (6/15), assistive robotics (9/15), and sensorimotor interaction (11/15) across 2013-2022 publications. Laboratory involvement : Leads the IRIS team at ISIR, part of Fedrha (Federation for Research on Disability and Autonomy) with 50+ research teams.

• Acoustic Signal Processing
• Blind Source Separation
• Digital Signal Processing
• +۶ مورد دیگر

Shoji Makino is a Professor at Waseda University's Graduate School of Information, Production and Systems. He has held academic and research positions at institutions such as the University of Tsukuba and NTT Communication Science Laboratories. His work spans acoustic signal processing, blind source separation, and adaptive filtering. Education: Ph.D., Tohoku University (1993.03) Mechanical Engineering, Tohoku University Graduate School of Engineering (1979.04–1981.03) Engineering, Tohoku University Faculty of Engineering (1975.04–1979.03) Research Interests: His research focuses on acoustic signal processing for speech and audio, including blind source separation (BSS) , beamforming , and adaptive filtering . He pioneered methods for solving permutation alignment in frequency-domain BSS and developed geometrically constrained ICA techniques. Scientific Awards: Hoko Award (2018.10, Hattori Hokokai Foundation) Outstanding Contribution Award of the Institute of Electronics, Information, and Communication Engineers (2018.06) IEEE Signal Processing Society Best Paper Award (2014.01) IEEE Fellow (2004.01) IEICE Achievement Award (1997.05) Committee Memberships: He has served as Chair of the IEEE CAS Society's Blind Signal Processing TC, General Chair of IEEE WASPAA2007, and Associate Editor of IEEE Trans. SAP. He is actively involved in EURASIP, APSIPA, and the Acoustical Society of Japan.

Vinh Nguyenمشاهده پروفایل

استادیار

• Advanced Manufacturing
• Industry 4.0
• Human-Robot-Machine Interaction
• +۵ مورد دیگر

Vinh Nguyen is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University, where he directs the Michigan Tech Center for AI and coordinates the NIST-PREP program. His research focuses on advanced manufacturing through Industry 4.0, human-robot-machine interaction, and physics-based/data-driven modeling. He has developed solutions for machining, additive manufacturing, metal forming, and robotic assembly to promote smart and sustainable manufacturing. Prior to joining Michigan Tech in 2022, he was a National Research Council Postdoctoral Fellow at NIST (2020–2022). Dr. Nguyen earned his PhD (2020), MS in Mechanical Engineering (2017), and MS in Electrical & Computer Engineering (2017) from Georgia Institute of Technology. He received dual bachelor’s degrees in Electrical and Mechanical Engineering from Rensselaer Polytechnic Institute (2014). His research portfolio spans Advanced Manufacturing Industry 4.0 and 5.0 Human-Robot Interaction Physics-Based/Data-Driven Modeling Industrial Automation based on his lab’s interdisciplinary focus on human-centric, resilient solutions. His recent publications address trends in Machine Learning for Manufacturing Autonomous Vehicle Sensors Hybrid Additive/Subtractive Manufacturing Augmented/Mixed Reality Interfaces Industrial Robot Diagnostics Material-Specific Machining with keywords spanning Robotics, Data Science, and Industrial Engineering.

Mehtaab Sawhneyمشاهده پروفایل

استادیار

• combinatorics
• probability
• analytic number theory
• +۱ مورد دیگر

Mehtaab Sawhney is a Clay Research Fellow and a tenure-track assistant professor at Columbia University specializing in combinatorics, probability, analytic number theory, and theoretical computer science. His academic journey began at the University of Pennsylvania where he enrolled in a Bachelor of Engineering in Computer Science (2016-2017), then continued at MIT where he earned a Bachelor of Science in Mathematics with Minor in Computer Science (2017-2020), followed by a Doctor of Philosophy in Mathematics (2020-2024) under the advisorship of Yufei Zhao. His research spans probabilistic combinatorics, random matrix theory, additive number theory, and theoretical computer science. Sawhney's work bridges theoretical mathematics with computational applications, focusing on random structures, additive combinatorics, and spectral properties of discrete objects. His publications demonstrate a strong interdisciplinary approach that connects number theory with probabilistic methods to solve complex combinatorial problems. The analysis of his publication record reveals a consistent focus on foundational mathematical structures with applications across multiple domains. His work on random graphs, additive bases, and arithmetic progressions has established him as a leading researcher in modern combinatorics, often collaborating with prominent mathematicians including Ashwin Sah, Yufei Zhao, and Vishesh Jain. His research output shows remarkable depth and breadth, with contributions to both pure mathematics and theoretical computer science. 2024 Clay Research Fellow 2021 Frank and Brennie Morgan Prize for Outstanding Research in Mathematics by an Undergraduate Student (joint with Ashwin Sah) Churchill Scholar 2020 Best Student Paper STOC 2021 (Joint with Ryan Alweiss, Yang Liu) Best Student Paper ITCS 2022 (Joint with Yang Liu, Ashwin Sah) 2023 Hartley Rogers Jr. Prize 2022 Charles W. and Jennifer C. Johnson Prize (joint with Ashwin Sah) NSF Graduate Fellowship Sawhney has established a robust research program with significant contributions across multiple mathematical disciplines. His frequent collaborations with top researchers worldwide indicate an active and influential research network. While specific advisees aren't listed in available information, his extensive publication record with numerous co-authors suggests active mentorship of junior researchers through collaborative projects.

Yoann Altmannمشاهده پروفایل

استاد

• Statistical signal processing
• Image processing
• Bayesian inverse problems
• +۵ مورد دیگر

Yoann Altmann is Professor in the School of Engineering & Physical Sciences at Heriot-Watt University and a member of the Institute of Sensors, Signals & Systems. Since 2024 he holds the Chair in Electrical, Electronic & Computer Engineering (EECE), directing a research programme that bridges statistical signal processing, computational imaging and quantum & neuromorphic sensing. Education & career: 2010 – Eng. degree (Electrical Engineering), ENSEEIHT, Toulouse, France 2010 – M.Sc. (Signal Processing), National Polytechnic Institute of Toulouse 2013 – Ph.D. (Signal & Communications), IRIT Laboratory, Toulouse 2014-2017 – Post-doctoral Research Fellow, Heriot-Watt University 2017 – Royal Academy of Engineering Research Fellow & Assistant Professor, HWU 2024 – promoted to Professor, School of Engineering & Physical Sciences, HWU Research interests: Prof. Altmann develops mathematical and algorithmic tools for Bayesian inverse problems, with emphasis on single-photon LiDAR, low-illumination imaging, neuromorphic computational sensing, variational inference and sparse reconstruction. His work combines principled statistical modelling with efficient computational schemes to enable imaging in extreme scenarios such as underwater scattering, photon-starved environments, quantum metrology and real-time 3-D scene reconstruction. Publication trends: Across 160 outputs (2011-2025) his recent articles reveal a clear trajectory toward integrating modern machine-learning paradigms—variational autoencoders, diffusion generative models, spiking neural networks—with rigorous physics-based forward models. Applications span quantum parameter estimation, multimode-fiber endoscopy, hyperspectral & Compton imaging, nuclear safeguards and cultural-heritage spectroscopy, demonstrating both methodological breadth and high-impact interdisciplinary deployment. Honours & recognition: Royal Academy of Engineering Research Fellowship – competitively awarded (2017) Grants & datasets: He has generated four open datasets supporting reproducible research in quantum sensing, variational autoencoders, underwater single-photon LiDAR and multispectral fluorescence imaging, reflecting sustained funding and commitment to open science. Continuous peer-review service for IEEE and Elsevier journals since 2013 underlines his standing within the signal-processing community. Labs & teams: He leads the Bayesian Imaging & Sensing Computing (BISC) group ( https://bisc.site.hw.ac.uk ) which hosts post-docs, PhD researchers and international visitors working on statistical machine-learning for imaging, sensing and quantum technologies.

• Biomechanics
• Biomedical Engineering
• Computational Modeling
• +۶ مورد دیگر

Lindsey Westover, PhD, PEng, serves as an Associate Professor in the Department of Mechanical Engineering and Associate Dean in the Faculty of Engineering at the University of Alberta. Her research and teaching activities are centered in the Biomedical Engineering program, with her laboratory located in the Donadeo Innovation Centre for Engineering (13-224, 9211 116 St, Edmonton, AB T6G 2H5). She maintains an active research profile while contributing to academic leadership through her deanship. Her educational background includes: 2018: Postdoctoral Fellowship in Rehabilitation Medicine, University of Alberta 2016: Ph.D. in Mechanical Engineering, University of Alberta 2011: M.Sc. in Mechanical Engineering, University of Calgary 2007: B.Sc. in Mechanical Engineering, University of Calgary Dr. Westover's research program spans biomechanics and biomedical engineering with emphasis on noninvasive assessment of biological structures, vibration analysis for percutaneous implants, joint biomechanics (ligaments and cartilage), spinal deformity analysis through asymmetry metrics, mechanical testing of biological tissues, and computational modeling of biological systems. Her work integrates laboratory experiments, computational methods, and in vivo studies to develop innovative diagnostic and therapeutic approaches. Analysis of her 15 most recent publications (2018-2020) reveals consistent focus on bone mechanics, implant stability, and symmetry analysis across orthopedics, audiology, and dentistry. Key themes include osseointegration evaluation using ASIST technology, pelvic/spinal deformity quantification, and computational modeling of biological structures. Her work appears in high-impact journals spanning engineering and clinical disciplines, demonstrating strong interdisciplinary collaboration. Scientific recognition includes: Nomination for Ear and Hearing 2018 Editor's Award for bone conduction device research Dr. Westover mentors graduate students through co-authorship on numerous publications and teaches core mechanical engineering courses including MEC E 451 (Vibrations and Sound), MEC E 390 (Numerical Methods), and MEC E 200 (Introduction to Mechanical Engineering). Her research is supported by collaborative grants with clinical partners and engineering colleagues. She leads biomechanics research within the Department of Mechanical Engineering, collaborating extensively with the Faculty of Rehabilitation Medicine and surgical departments. Her laboratory develops advanced testing systems like ASIST for implant stability evaluation across hearing devices and dental applications, while her computational work informs clinical approaches to scoliosis management and fracture reconstruction.

Brian Horsakمشاهده پروفایل

دانشیار

• Clinical biomechanics
• Gait analysis
• Musculoskeletal loading
• +۵ مورد دیگر

Brian Horsak is a Privatdozent (equivalent to Associate Professor) at the University of Vienna, affiliated with the Department of Sport and Human Movement Science within the Faculty of Sports Sciences. He serves as a member of the PI Panel/Supervisors for the Sport Science program at the Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences (VDS PhaNuSpo). His research group is located at the St. Pölten University of Applied Sciences with strong collaboration to the Centre for Sport Science and University Sports at the University of Vienna. Dr. Horsak's primary research interests focus on clinical (gait) biomechanics and interdisciplinary approaches between therapy and technology. His work is dedicated to understanding musculoskeletal loading of the lower extremities during locomotion and exercise, and developing assistive technologies such as machine learning algorithms and virtual reality applications to support patients and therapists during rehabilitation. Specific areas include: Clinical gait analysis and biomechanics Musculoskeletal loading in lower extremities Patellofemoral instability and joint biomechanics Machine learning applications for gait classification Virtual reality for rehabilitation Biomechanics of obesity and pediatric movement disorders 3D motion capture and markerless technology His recent publications demonstrate a strong trend toward integrating advanced computational methods with clinical biomechanics. Over the past five years, his work has increasingly focused on smartphone-based motion capture systems, deep learning applications for gait analysis, and virtual reality environments. His research bridges engineering technology and clinical rehabilitation, with particular attention to knee biomechanics, patellofemoral disorders, and movement analysis in pediatric obesity. Dr. Horsak teaches courses including 'Methods and Concepts of Biomechanics and Sports Informatics' and serves as supervisor for PhD students Bernhard Dumphart and Mark Simonlehner. His research group maintains strong collaborations with the St. Pölten University of Applied Sciences and appears to be involved in multiple interdisciplinary projects combining sports science, engineering, and clinical rehabilitation.

Jérémie DEQUIDTمشاهده پروفایل

پژوهشگر

• Soft Robotics
• Robotics Simulation
• Control Systems
• +۳ مورد دیگر

Jérémie DEQUIDT is a Researcher at Inria affiliated with Polytech Lille, University of Lille, serving as Thematic Group Facilitator for CO2 (Control and Scientific Computing) and Member of the Scientific Council. His offices are located at Inria Building A (Haute Borne) and Polytech Lille's Scientific City. His research spans soft robotics, simulation, control systems, and medical robotics. He develops modeling and simulation techniques for sensorimotor perception in soft robots and medical applications like prostate biopsy/brachytherapy using bio-inspired phantoms. His work integrates computer graphics, haptics, and deformable object robotics to advance medical interventions and robotic control systems. He supervises PhD students including Thomas Moupfouma (Sensorimotor perception in soft robotics through modeling and simulation) and Sizhe Tian (Simulation of needle insertion in bio-inspired active prostate phantom). As a core member of the DEFROST research team within CRIStAL laboratory (University of Lille/CNRS/Centrale Lille joint unit), he contributes to foundational research on deformable objects and robotics, with leadership roles in scientific governance and thematic coordination.

Alexei Sokolovمشاهده پروفایل

استاد

• Polymer Science
• Soft Materials Dynamics
• Glass Transition
• +۶ مورد دیگر

Alexei Sokolov is the Governor's Chair at the University of Tennessee and Oak Ridge National Laboratory (ORNL), with joint appointments as Professor of Chemistry and Physics. He leads the Soft Materials and Membranes group, focusing on dynamics of disordered systems, polymer science, and energy materials. His affiliations include the College of Arts & Sciences at UT and the Chemical Sciences Division at ORNL. Education highlights: M.S. in Physics, Novosibirsk State University (1981) Ph.D. in Physics, Russian Academy of Sciences (1986) Dr. Sci. (Habilitation) in Physics, Russian Academy of Sciences (1993) Research focuses on: Glass transition phenomena and polymer dynamics Biological macromolecules and nanocomposites Materials for clean energy technologies including polymer electrolytes Vitrimers, self-healing materials, and recyclable composites Recent publications emphasize ion transport mechanisms, solid-state electrolytes, and advanced polymer design for energy storage applications, reflecting cutting-edge work in materials innovation. Scientific awards and honors: Fellow, American Physical Society Fellow, American Association for the Advancement of Science Member, National Academy of Inventors Humboldt Fellowship Max-Planck Fellowship Leads research initiatives in soft materials at UT-ORNL, with current projects on polymer dynamics and nanocomposites. The Sokolov Group offers PhD and postdoctoral positions in these areas.

• Immunology
• Molecular Immunology
• Inflammation
• +۳ مورد دیگر

Irina Udalova serves as Professor of Molecular Immunology at the Kennedy Institute of Rheumatology, part of the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS) at the University of Oxford. She leads the "Genomics of Inflammation" research group, focusing on transcriptional regulation in inflammatory diseases through integrated genomic and immunological approaches. Her academic foundation includes a joint BSc/MSc in physics and mathematics and a PhD in molecular biology, both from the Moscow Institute of Physics and Technology. This unique interdisciplinary background underpins her innovative research methodology. Professor Udalova's work centers on transcriptional regulators controlling myeloid cell phenotypes in inflammation. Her laboratory combines functional genomics with classical immunology to dissect regulatory networks involving factors like IRF5 and type III interferons. Key discoveries include identifying IRF5 as a molecular switch for inflammatory macrophages and revealing anti-inflammatory properties of type III IFNs that specifically target neutrophil function. This research bridges fundamental mechanisms with translational applications in rheumatoid arthritis, obesity-related inflammation, and infectious diseases. Recent publications demonstrate evolving expertise in single-cell and spatial analysis techniques, particularly in COVID-19 lung pathology and sepsis immunology. Her work consistently emphasizes neutrophil-macrophage crosstalk and microenvironment-specific immune responses across inflammatory conditions. Through the University of Oxford, Professor Udalova actively supervises doctoral candidates in Cancer Sciences, Genomic Medicine and Statistics, and Interdisciplinary Biosciences programs. Her laboratory maintains strong collaborative networks and regularly recruits postdoctoral researchers for projects investigating transcriptional circuitry in inflammatory diseases. The "Genomics of Inflammation" group operates at the cutting edge of immunogenomics, utilizing advanced techniques to map regulatory landscapes in myeloid cells. Current work focuses on validating novel therapeutic targets for chronic inflammatory conditions and deciphering context-dependent immune responses in tissue microenvironments.

Jonathan Simonمشاهده پروفایل

استاد

• Neuroscience
• Auditory Neuroscience
• Speech Processing
• +۶ مورد دیگر

Jonathan Simon is a Professor in the Department of Electrical and Computer Engineering at the University of Maryland (UMD), with affiliations in Biology, the Institute for Systems Research (ISR), and the Brain and Behavior Institute. He holds joint appointments in multiple departments, reflecting his interdisciplinary research in neuroscience and engineering. Simon earned his B.S. in Physics from Princeton University and his Ph.D. in Physics from UC Santa Barbara, followed by postdoctoral work in theoretical relativity before transitioning to auditory neuroscience. His research focuses on auditory neuroscience, particularly how the brain processes speech and complex sounds using non-invasive techniques like magnetoencephalography (MEG). Key interests include the 'cocktail party problem'—selective attention in noisy environments—and the neural mechanisms underlying speech comprehension. He co-directs the KIT-Maryland Magnetoencephalography Center and the Computational Sensorimotor Systems Laboratory (CSSL). Simon’s work bridges computational neuroscience and engineering applications, such as improving hearing aids and cochlear implants. His recent studies investigate aging-related changes in auditory processing, neural plasticity from auditory-cognitive training, and post-stroke cognitive recovery. He has published extensively on cortical responses to speech, neural decoding of attention, and functional connectivity in aging and clinical populations. His research has been featured in journals like *Neuron*, *Nature Communications*, and *Proceedings of the National Academy of Sciences (PNAS)*, and he collaborates internationally on projects funded by grants from NIH, NSF, and industry partners.