جست‌وجوی استاد و پژوهشگر

Katherine J. Kuchenbeckerمشاهده پروفایل

استاد

• Haptics
• Robotics
• Human-Computer Interaction
• +۳ مورد دیگر

Katherine J. Kuchenbecker is the Director of the Haptic Intelligence Department at the Max Planck Institute for Intelligent Systems in Stuttgart, Germany, and an Honorary Professor at the University of Stuttgart. She previously held a tenured position as an Associate Professor at the University of Pennsylvania. Her research focuses on haptic interfaces and sensing systems, enabling users to interact with virtual and distant objects through touch. She earned her Ph.D. in Mechanical Engineering from Stanford University and completed postdoctoral research at Johns Hopkins University. Her academic journey includes leadership roles such as co-chair of the IEEE Technical Committee on Haptics and associate editorships for major conferences. She has received numerous awards, including the NSF CAREER Award (2009), IEEE Academic Early Career Award (2012), and elevation to IEEE Fellow (2021). Her work spans applications in medical robotics, teleoperation, and human-robot interaction. Kuchenbecker’s research emphasizes translating haptic technology into real-world applications, such as surgical training, tactile feedback in virtual environments, and assistive devices. Her team’s contributions include innovations in wearable haptic devices and tactile sensing for robots. She frequently delivers keynote addresses and chairs international conferences, furthering the field’s global impact. Her publications highlight advancements in haptic feedback systems, surgical robotics, and biomimetic sensors. She also advocates for diversity and leadership in academia, serving as Spokesperson for the International Max Planck Research School for Intelligent Systems since 2017.

• Electron Microscopy
• In-situ TEM
• Machine Learning in Material Science
• +۶ مورد دیگر

Dr. Junbeom Park is a Staff Scientist in the Institute of Energy Technologies (IET-1) at Forschungszentrum Jülich, Germany, and a member of the In-situ Electron Microscopy (iEM) group . His work focuses on advanced electron microscopy techniques, data processing, and electrochemical material characterization. PhD in Chemical Engineering from Pohang University of Science and Technology (POSTECH), South Korea Current focus on in-situ TEM for low-temperature water electrolysis and solid-state battery materials Key skills: Python-based image processing, 4D STEM analysis, FIB/SEM, and machine learning for microscopy data Research Interests span electron microscopy , in-situ TEM , machine learning in material science , and nanoscale characterization . His work bridges fundamental structural analysis with applications in energy storage and conversion. Trends in Recent Publications emphasize quantitative in-situ liquid-phase TEM , electrochemical mechanism visualization , and automation-driven data analysis . Topics include solid electrolyte interfaces , metal electrodeposition , and nanoscale process optimization . Professional Roles : Chair of Materials Division, VeKNI (2024–Present) Member, K-TAG Europe (2025–Present) Session Chair at Europe-Korea Conferences (2024, 2025) Team Affiliations include collaboration with the iEM group at Forschungszentrum Jülich, specializing in environmental TEM and data-intensive material analysis .

• Process Monitoring
• Thermal Optimization
• Pharmaceutical Polymer Engineering
• +۶ مورد دیگر

Adrian Kelly is a Professor of Process Engineering at the University of Bradford's School of Engineering, part of the Faculty of Engineering & Digital Technologies. He holds roles including Placements Tutor for Engineering disciplines and has expertise in polymer processing, thermal optimization, and sustainable materials. His research focuses on process monitoring, pharmaceutical polymer engineering, nanocomposites, and recycling technologies. Kelly has co-founded the Centre for Pharmaceutical Engineering Science and is active in the Centre of Advanced Materials Engineering and the Centre for Polymer Micro and Nano Technology. Prof. Kelly earned a PhD in Mechanical Engineering with industrial sponsorship for rheometry research. He leads research on topics like extrusion processing, material recycling, and drug delivery systems. His work bridges academia and industry, emphasizing process analytical technologies (PAT) and sustainable manufacturing. His publications (2020–2025) explore advanced materials recycling, polymer composite development, and process optimization. Key trends include enhancing material recyclability, improving drug delivery via hot melt extrusion, and applying machine learning to process prediction. His research impacts fields from biomedical engineering to environmental sustainability through innovative material applications.

• Fluid Dynamics
• Dynamical Systems
• Uncertainty Quantification
• +۳ مورد دیگر

Associate Professor Sanjeeva Balasuriya is affiliated with the School of Computer and Mathematical Sciences at the University of Adelaide, within the Faculty of Sciences, Engineering and Technology. His research focuses on fluid dynamics, dynamical systems, uncertainty quantification, and mathematical biology. He has contributed to studies on Lagrangian coherent structures, stochastic sensitivity in flows, and biofilm expansion modeling. His work bridges applied mathematics with real-world phenomena, including environmental modeling and turbulence analysis. Recent publications explore topics like stochastic differential equations, optimal vector field reconstruction, and flow control strategies. He is eligible to supervise PhD and Master’s students in these areas. His research emphasizes interdisciplinary applications of mathematical techniques to complex fluid systems and nonlinear dynamics.

Georgios Dimitriadisمشاهده پروفایل

پژوهشگر ارشد

• Transient Astronomy
• Type Ia Supernovae
• Explosion Physics
• +۶ مورد دیگر

Dr. Georgios Dimitriadis is a Marie Curie Postdoctoral Fellow in the Department of Physics at Lancaster University . His research focuses on Transient Astronomy , particularly Type Ia supernovae explosion physics , progenitor systems , and thermonuclear transient populations . He participates in major surveys such as the Zwicky Transient Facility (ZTF) , 4MOST/TiDES , and La Silla Schmidt Southern Survey (LS4) . Research Fellow at Lancaster University Member of the Physics Department Specializes in Transient Astronomy and Observational Astrophysics His research investigates the diversity of thermonuclear explosions , including peculiar Type Ia supernovae and their impact on cosmological measurements. He combines observational data with theoretical input to study explosion mechanisms and environmental dependencies, aiming to understand white dwarf explosion pathways . Key areas include optical and infrared spectroscopy , transient survey design , and cosmological bias mitigation . The ZTF SN Ia DR2 series of 2025 publications highlights his work on color standardization , high-velocity spectral features , galaxy cluster effects , and dust distribution changes with redshift . These studies utilize large-scale surveys and advanced spectroscopic analysis. Scientific awards: Marie Curie Postdoctoral Fellowship Contact: g.dimitriadis@lancaster.ac.uk | Office: C063, Physics Building.

Xiang Wanمشاهده پروفایل

استادیار

• Partial Differential Equations
• Control Theory
• Numerical Analysis
• +۳ مورد دیگر

Xiang Wan is an Assistant Professor in the Department of Mathematics and Statistics at Loyola University Chicago (LUC), part of the College of Arts and Sciences. He holds a Ph.D. in Mathematics from the University of Virginia (2017), supervised by Dr. Irena Lasiecka, with prior degrees from Auburn University (M.S.) and Beijing Jiaotong University (B.Sc.). Before joining LUC in 2022, he held postdoctoral and visiting positions at Wayne State University, George Washington University, and Haverford College. His research focuses on applied mathematics, particularly Partial Differential Equations (PDEs), Control Theory, and Numerical Analysis. He explores qualitative properties of PDE systems (e.g., well-posedness, long-term behavior, control theory) and develops numerical methods for discrete schemes. His work is supported by the National Science Foundation (NSF) and institutional grants from LUC's College of Arts and Sciences and Office of the Provost. Recent research trends include studies on the SMGTJ equation, heat-structure interactions, and vibrational analysis of alkyne systems. He co-advises the LUC SIAM Student Chapter and maintains an active role in academic outreach. His professional webpage is hosted at xiangwanmath.github.io .

• Computational Mechanics
• Fluid-Structure Interaction
• Solid Dynamics
• +۱۲ مورد دیگر

Professor Antonio Gil is a faculty member at Swansea University within the Faculty of Science and Engineering , specifically in the Department of Civil Engineering . He graduated magna cum laude from the University of Granada (Spain) in 1999 and earned his PhD in computational analysis of non-linear structural membranes at Swansea University in 2005, winning the UK Society for Computational Mechanics best PhD paper in 2004. Education: Ingeniero de Caminos, Canales y Puertos (University of Granada, 1999); MSc in Computational Mechanics; PhD in Computational Mechanics (Swansea University, 2005) Research Interests span Computational Mechanics , Fluid-Structure Interaction , Finite Strain Modeling , and Soft Robotics , with a focus on physics-informed machine learning and multi-physics simulations. His work addresses challenges in electromechanical systems , cardiac mechanics , and high-strain dynamics . Recent Publications highlight advancements in SPH algorithms , topology optimization , and hyperelastic metamodels , reflecting his leadership in numerical methods and material modeling . These contributions have been recognized by prestigious awards including the Philip Leverhulme Award (2011) and the Olgierd Cecil Zienkiewicz Award (2016) . Scientific Awards: UK Philip Leverhulme Award (2011) Olgierd Cecil Zienkiewicz Award (2016) 1st National Award (Spanish Ministry of Education, 2000) Grants include coordination of the Erasmus Mundus PhD Program and leadership in H2020 Marie Curie ETN projects with budgets exceeding €6M. His supervision covers PhD projects in soft robotics , computational fracture , and MRI scanner design .

• Algorithm Design
• Dynamic Algorithms
• Graph Theory
• +۷ مورد دیگر

Gramoz Goranci is an Assistant Professor (tenure-track) of Algorithms in the Department of Computer Science at the University of Vienna. He leads research in algorithm design with strong interdisciplinary connections to optimization, graph theory, and machine learning. He is a member of the Research Group Theory and Applications of Algorithms and a board member of the Research Network Data Science. His research focuses on the design of fast dynamic algorithms for large-scale optimization problems, combining techniques from combinatorial data structures, algorithmic graph theory, numerical linear algebra, and metric embeddings. His work emphasizes both theoretical guarantees and practical efficiency. The recent publications highlight a consistent trend in dynamic and incremental graph algorithms, particularly in shortest paths, connectivity, flow, and facility location problems. His work increasingly bridges theoretical computer science with applications in machine learning and high-dimensional data. Keywords across publications include dynamic algorithms, graph sparsification, electrical flows, and optimization in metric spaces. FWF ESPRIT grant (supporting PostDoc Peter Kiss) He advises a growing team of researchers, including PostDoc Peter Kiss and PhD students Eva Szilagyi and Ali Momeni Mohammadabadi. His research is supported by independent funding and collaborations with leading institutions like ETH Zürich, University of Toronto, and UC Berkeley's Simons Institute. He has served on program committees of top conferences including FOCS, STOC, SODA, ICML, ESA, and ALENEX. He is actively involved in the academic community through seminars and talks at institutions such as ETH Zürich, University of Warwick, and Google Research. His team contributes to foundational work in dynamic graph algorithms and their applications in data science.

• Augmented Reality
• Robotics
• Human-Computer Interaction
• +۵ مورد دیگر

Ke Huo is a researcher focused on advanced interactions in Augmented Reality (AR) , Robotics , and Sensor Systems . His work bridges physical and digital environments through innovative tools like GhostAR , V.Ra , and iSoft , enabling intuitive authoring of context-aware applications. Research Interests : Augmented Reality (AR) systems for collaborative task planning Soft sensor technology with multimodal sensing Context-aware robotics and IoT integration 3D design ideation in mixed reality Autonomous driving decision-making frameworks Publication Trends : 2014–2024: 15+ papers on AR, sensor design, and human-robot collaboration Key venues: UIST , CHI , Sensors , ACM DIS Collaborations with Karthik Ramani, Yuanzhi Cao, and Sang Ho Yoon

• Cryptographic Hardware Design
• Homomorphic Encryption
• Approximate Computing
• +۴ مورد دیگر

Dr. Sunwoong 'Sunny' Kim is an Assistant Professor at the Rochester Institute of Technology (RIT), affiliated with the Kate Gleason College of Engineering and the Global Cybersecurity Institute. Previously, he held roles at the University of Washington Bothell (2019–2023) and completed postdoctoral research at the University of Illinois Urbana-Champaign (2017–2019) and Seoul National University (2016–2017). His expertise spans cryptographic hardware design, homomorphic encryption, and privacy-preserving systems, with a focus on FPGA-based accelerators. Education: Ph.D. (2016), M.S. (2012), B.S. (2010) in Electrical Engineering and Computer Science from Seoul National University, advised by Prof. Hyuk-Jae Lee. Research interests include custom hardware accelerators for cryptography, machine learning, and image/video compression. He explores approximate computing and privacy-preserving applications in embedded systems. Recent trends in his articles emphasize homomorphic encryption implementations, efficient cryptographic primitives, and FPGA-based solutions for secure computing. Scientific Awards: NSF CRII Grant (2021), RIT SURF (2024), RIT FEAD (2024), UW RRF Grant (2020), and multiple student scholarships. Advising & Grants: Supervised students including Wei He, Grant Bosworth, and James. Active in securing grants as PI/Co-PI (e.g., IITP Grant, FEAD Grant). Collaborates with labs like CryptoLab in South Korea. Labs/Teams: Leads research in cryptographic hardware acceleration and cybersecurity at RIT, with collaborations on FPGA-based secure systems and privacy-preserving AI.

• Signal Processing
• Radar Systems
• MIMO Technology
• +۵ مورد دیگر

Michael Zoltowski is the Thomas J. and Wendy Engibous Professor of Electrical and Computer Engineering at Purdue University's College of Engineering. He specializes in radar systems, signal processing, and wireless communication technologies. His research focuses on MIMO radar waveform design, OFDM transmission optimization, and adaptive beamforming techniques. He holds positions in committees such as the Engineering Named Professorships Committee and Faculty Awards Committee. His research interests include radar signal design, waveform diversity, and advanced OFDM systems. He has contributed extensively to topics like Doppler-tolerant radar ambiguity functions, bistatic radar detection, and millimeter-wave beamforming. His work integrates machine learning into radar systems and explores applications in UAV environments. Notable contributions include developing complementary sequence techniques for OFDM transmission, low-complexity detection algorithms in massive MIMO systems, and adaptive beamforming codebook designs. His publications span over two decades, with recent work focusing on high-resolution radar imaging and interference mitigation strategies. Zoltowski has advised numerous projects in radar technology and wireless communications but no specific student names are listed in the provided texts. His work is supported by institutional and external grants, though specific funding details are not disclosed here.

• Cancer
• Surgical Oncology
• Melanoma
• +۳ مورد دیگر

Associate Professor Jonathan Stretch is affiliated with the Department of Surgery at the University of Sydney's Central Clinical School. His research focuses on melanoma, surgical oncology, and cancer biology, with a particular emphasis on diagnostic accuracy, treatment outcomes, and genomic insights into melanoma subtypes. He collaborates extensively with multidisciplinary teams on clinical trials and translational research. Key research interests include sentinel node biopsy strategies, neoadjuvant therapies, and prognostic biomarkers for melanoma. His work spans surgical techniques, molecular diagnostics, and patient outcomes in high-risk melanoma cases. Recent studies address topics like PD-1 blockade efficacy, ultrasound surveillance, and polygenic risk associations with survival. Publications highlight advancements in melanoma management, including predictive models (e.g., MIA Calculator), therapeutic combinations, and genomic profiling. His contributions bridge clinical practice and research, aiming to improve melanoma diagnosis and treatment efficacy.

• Engineering Acoustics
• Acoustic Measurement
• Communication Acoustics
• +۴ مورد دیگر

Yasuhiro Oikawa is a Professor at the School of Fundamental Science and Engineering, Faculty of Science and Engineering, Waseda University. He holds a Doctor of Engineering degree from Waseda University and has been actively contributing to acoustics, signal processing, and optical measurement techniques. His research spans Sound field visualization using parallel phase-shifting interferometry Phase-aware audio signal processing algorithms Acoustic calibration and microphone sensitivity analysis Real-time sound event localization systems His recent publications focus on advanced time-frequency analysis, optical methods for sound measurement, and deep learning applications in acoustics. Key trends include Improving resolution in spectrogram-based signal processing Integration of physical models with neural networks Development of wearable acoustic sensor arrays Scientific awards recognizing his work include The Fumio Okano Best 3D Paper Awards CSS2018 Best Paper Award Acoustical Society of Japan Contribution Award Institute of Electronics, Information and Communication Engineers Human Communication Award He has served as a committee member for the Acoustical Society of Japan and is affiliated with organizations such as ACM, IEEE, and Acoustical Society of America. His research has been supported by collaborations with institutions like Technical University of Denmark and applications in robotics, museum exhibits, and consumer electronics.

• Biomedical Engineering
• Tissue Engineering
• Orthopedics
• +۷ مورد دیگر

Deva Chan is an Assistant Professor in Biomedical Engineering at Purdue University, with a courtesy appointment in Mechanical Engineering. She is affiliated with the Weldon School of Biomedical Engineering and focuses on interdisciplinary research at the intersection of biomechanics, medical imaging, and tissue engineering. Her work emphasizes cartilage mechanics, osteoarthritis progression, and the application of advanced imaging techniques for understanding musculoskeletal disorders. Research interests span the biomechanical and biochemical mechanisms underlying joint degeneration, including hyaluronic acid metabolism, cellular repair responses, and fibrosis. Methodologically, her lab employs finite element modeling, micro-CT imaging, and live-cell biomechanical systems to study tissue behavior under physiological and pathological conditions. Recent studies highlight her contributions to understanding how mechanical loading influences synovial biology, the role of hyaluronic acid in cartilage health, and the development of novel imaging modalities for non-invasive tissue characterization. Key projects include modeling knee osteoarthritis progression and developing pneumatically controlled cell-stretching devices for mechanobiology studies.

Daniel Roweمشاهده پروفایل

استاد

• Bayesian Statistics
• Computational Neuroscience
• Medical Imaging
• +۳ مورد دیگر

Dr. Daniel Rowe is a Professor of Data Science and Co-Director in the Department of Mathematical and Statistical Sciences at Marquette University. His research focuses on Bayesian statistics, computational neuroscience, and medical imaging, particularly in fMRI analysis. He has contributed extensively to improving fMRI signal processing techniques, including Bayesian approaches for brain activity mapping and parallel imaging reconstruction methods like GRAPPA and SENSE. His work emphasizes statistical methodologies to address challenges in neuroimaging, such as noise reduction, artifact correction, and the development of complex-valued analysis frameworks. Notable contributions include the Bayesian merged utilization of GRAPPA and SENSE (BMUGS) for enhanced fMRI detection and efficient fully Bayesian approaches for brain activity mapping. Dr. Rowe collaborates on interdisciplinary projects, including cardiac chamber modeling using Bayesian neural networks and studies on radiation-induced cardiotoxicity. His publications span high-impact journals like the Annals of Applied Statistics, Magnetic Resonance Imaging, and the Journal of the Royal Statistical Society.