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Jun BaiView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Medical Image Analysis
• +2 more

Jun Bai is an Assistant Professor in the Department of Computer Science at the University of Cincinnati's College of Engineering and Applied Science. His research focuses on Machine Learning, Deep Learning, Medical Image Analysis, AI-driven diagnostics for cancer and diseases, and drug discovery. He holds a Ph.D. in Computer Science and Engineering from the University of Connecticut (2023), an M.S. in Computer Science from the University of Dayton (2019), and an M.S. in Interdisciplinary Studies in Education (2015). His work emphasizes applying AI to healthcare challenges, such as robust mammogram classification, 3D biomedical image registration, and peptide generation for drug discovery. Recent studies include hybrid transformer models for medical imaging and weakly-supervised systems for prostate cancer diagnosis. His computational methods span molecular dynamics simulations and graph neural networks. Despite his prolific research output, no specific grants, advising roles, or lab affiliations are explicitly listed in the provided data. Contact: Rhodes Hall 891, Cincinnati, OH | Email: baiju@ucmail.uc.edu

Viktor Sowinski-MydlarzView profile

Lecturer

• Cyber Security
• Data Analytics
• Machine Learning
• +3 more

Dr. Viktor Sowinski-Mydlarz is a Lecturer in Computer Science and Applied Computing at the School of Computing and Digital Media, London Metropolitan University. He also serves as a post-doctoral researcher at the Cyber Security Research Centre. His research focuses on hybrid frameworks combining semantic and machine learning methods for data analysis, with recent projects involving cloud computing, bioinformatics (e.g., 3D modeling of Coronavirus variants), and cyber security threat intelligence. He teaches programming, software engineering, ethical hacking, computer networking, and cloud computing. His academic background includes a PhD in hybrid data processing frameworks. He has contributed to research funded by Lloyds Banking Group and Innovate UK, including Urban Data Space (air quality analysis) and Microsoft Azure Cluster (NHS prescription data statistics with Machine Learning). Dr. Sowinski-Mydlarz's work emphasizes software integration, containerization, and cloud-based data analytics orchestration. His publications span threat intelligence, cyber security policy validation, and AI-driven hybrid platforms.

Flora ParrottView profile

Research Fellow

• Subterranean Geography
• Sensory Studies
• Art-Geography Interfaces
• +3 more

Flora Parrott is a Research Fellow in the Department of Geography at Royal Holloway, University of London, working on the ERC-funded project THINK DEEP: Novel Creative Approaches to the Underground. As a practice-based researcher, she investigates subterranean spaces through sculpture, textiles, and collaborative projects, exploring themes of darkness, sensory experience, and geologic time. Her research examines how underground environments inform our understanding of surface structures and behaviors, focusing on concepts of the unknown, shelter, and deep time. Projects include artistic explorations of cave systems, interdisciplinary collaborations with geologists, and investigations into material practices that bridge art and geography. Dr. Parrott holds a BA in Fine Art Printmaking from Glasgow School of Art (2004), MA from the Royal College of Art (2009), and completed her PhD at Royal Holloway (2023). Awards include Art Council England Grants, Leverhulme Residency, Henry Moore Institute Award, and TECHNE Scholarship. She maintains an active art practice and exhibits internationally. Her educational background includes Foundation Studies at Michaelis School of Fine Art, University of Cape Town. She teaches at the Royal College of Art and University for the Creative Arts, and serves as Trustee for Open School East.

Gabrielle DureposView profile

Associate Professor

• organizational history
• critical historiography
• history of management thought
• +8 more

Gabrielle (Gabie) Durepos is an Associate Professor in the Department of Business and Tourism at Mount Saint Vincent University in Halifax, Nova Scotia. She holds a BCOMM from Mount Allison University, an MA from Lancaster University, and a PhD in Business Administration from Saint Mary's University. She joined Mount Saint Vincent University in 2014 after teaching at Saint Mary's University (2006-2009) and St. Francis Xavier University (2009-2014). Her research focuses on organizational history, critical historiography, the history of management thought, methodology, and gender and diversity. Durepos has developed a critical organizational history approach called ANTi-History to reveal the role of history in liberationary politics. She has published extensively in journals such as Management & Organizational History, Journal of Management History, Critical Perspectives on International Business, and Organization. Durepos has co-authored the book ANTi-History: Theorizing the Past, History, and Historiography in Management and Organizational Studies and co-edited both the Sage Encyclopedia of Case Study Research and the SAGE Major Work on Case Study Methods in Business Research. Her research examines critical approaches to case study methodology and the contribution of feminist, poststructuralist, and other critical perspectives to organizational history. Her current research projects include a critical Organizational History of a Provincial Museum Complex in Nova Scotia and she serves as co-investigator on a major Social Sciences and Humanities Research Council grant studying the development of management thought in Canada. Her recent publications focus on archival silences, contextual approaches to historical research, and the application of ANTi-History methodology. ANTi-History: Theorizing the Past, History, and Historiography in Management and Organizational Studies (2012) Sage Encyclopedia of Case Study Research (2010) Case Study Methods in Business Research (2013) Durepos teaches courses in Communications Management, Gender and Management, Macro and Micro Organizational Behaviour, Industrial Relations, and Introduction to Business. She has received multiple research grants including a SSHRC Institutional Grant and University Council for Research Grants. Her methodological contributions to historical organization studies have positioned her as a leading scholar in critical approaches to management history.

• Urban Anthropology
• Gender Studies
• Labor Economics
• +7 more

Assoc Professor Swee Lin Ho is a distinguished sociologist and anthropologist at the National University of Singapore's Department of Sociology within the Faculty of Arts and Social Sciences. Joining NUS in 2012 after four years as a Korea Foundation Research Fellow and Assistant Professor at Catholic University of Korea, she maintains active research and teaching roles with office AS1 04-29 at 11 Arts Link, Singapore. Her academic journey began with undergraduate studies in Philosophy at NUS, followed by extensive professional experience as auditor, financial journalist, and Vice President in business development across China, Hong Kong, Japan, and the UK. She later earned an M.A. in Comparative Culture (Sociology) from Sophia University (Tokyo), followed by M.Sc. and Ph.D. in Social Anthropology at University of Oxford. Professor Ho's research focuses on neoliberal transformations of urban economic life across East Asia, examining how flexible labor patterns reshape self-identity, gender dynamics, money management, and urban space. Her work spans ethnographic studies on Japanese love hotels, extramarital experiences, women's drinking practices, and K-pop idolization, extending to current investigations of global classical music and food industries. She analyzes how 'creativity' and 'passion' co-option in creative sectors impacts economic security, having conducted fieldwork with violin-makers in Italy and Asian musicians in Europe. Her publications reveal consistent engagement with gender precarity in Japanese corporate culture, workplace harassment legislation, and pandemic-era consumption shifts. Key thematic clusters include gendered labor markets, urban spatial transformations, and cultural globalization's impact on intimate relationships. 2020 Book Grant by AmorePacific Foundation for Pride and Ambivalence: The Ascent of East Asia in Western Classical Music Professor Ho mentors students through ethnographic field research methods while examining emergent corporate cultures and global Asian popular culture flows. Her multilingual capabilities (English, Mandarin, Cantonese, Japanese, Korean, Malay, French, Italian, Spanish) enable deep cross-cultural analysis of financialization patterns and intimacy commercialization across Asia.

• Stochastic Analysis
• Mathematical Finance
• Option Pricing
• +5 more

Blanka Horvath is a Lecturer at King's College London and Honorary Lecturer at Imperial College London's Department of Mathematics (Faculty of Natural Sciences). Her research focuses on stochastic analysis and mathematical finance, particularly in numerical methods, machine learning applications, and volatility modeling (e.g., SABR and rough volatility models). She holds a PhD from ETH Zurich (2015), a Diplom in Mathematics from the University of Bonn, and an MSc in Economics from the University of Hong Kong. She has organized major conferences such as the SIAM MMF 2017 mini-symposium and co-organized the Rough Volatility Meeting at Imperial College. Her honors include the 2019 Risk Rising Star Award and the 2024-25 LMS Emmy Noether Fellowship. She collaborates with institutions like UBS, The Alan Turing Institute, and Quantennium LTD. Her teaching includes courses on numerical methods in finance and Python/R programming. She supervises PhD and MSc students in areas like rough volatility, machine learning, and quantitative finance. Recent work explores quantum GANs for option pricing and regime detection using Wasserstein distances.

Michael NussbaumView profile

Professor

• Quantum Statistics
• Mathematical Statistics
• Asymptotic Methods
• +3 more

Michael Nussbaum is a Professor of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. He holds a Ph.D. (1979) and Dr. Sc. (1990) from the Academy of Sciences Berlin (Germany). His research focuses on quantum statistics, applying mathematical statistics to analyze quantum experiments and developing asymptotic methods for hypothesis testing, equivalence theory of statistical experiments, and nonparametric models. Key contributions include work on quantum Chernoff bounds, Gaussian approximation of quantum models, and asymptotic equivalence between statistical experiments. Recent research emphasizes quantum hypothesis testing, low-rank quantum state estimation, and asymptotic equivalence with quantum Gaussian white noise. Supported by an NSF Grant (DMS-1915884), his work bridges operator algebras, quantum information, and noncommutative probability. Publications span foundational topics like nonparametric regression, spectral density estimation, and functional empirical processes. He teaches courses such as Statistical Theory and Application in the Real World and supervises graduate research. His lab explores interdisciplinary applications of asymptotic statistical methods in quantum engineering and communication technologies.

• Digital Manufacturing
• Sustainability Engineering
• Additive Manufacturing
• +5 more

Professor Jonathan Corney holds the Chair of Digital Manufacture in the School of Engineering at the University of Edinburgh. His research focuses on advanced manufacturing technologies, including digital twin applications, smart factory optimization, sustainability engineering, and additive manufacturing. He leads projects addressing human factors in industrial environments, predictive analytics for production processes, and intellectual property challenges in modern manufacturing systems. His academic background encompasses mechanical engineering with specialization in CAD/CAM systems, hydroforming technology, and patent analysis for design innovation. Corney has pioneered methods like the Economic and Environmental Impact Assessment for Sustainability (EENIAS), and developed decision support frameworks for energy-efficient scheduling and garment reprocessing in circular economies. Key research themes include: Smart factory design through real-time worker movement analysis Cyber-physical systems for supply chain optimization Machine learning applications in manufacturing process control Human-centric automation and safety protocols His recent work emphasizes predictive modeling using spatio-temporal graph networks, digital twin integration in assembly processes, and sustainable manufacturing practices. Over 150 peer-reviewed articles demonstrate his contributions to near-net-shape manufacturing, intellectual property management, and crowdsourced design methodologies.

Linda N. GroatView profile

Professor

• Architectural Theory
• Environment-Behavior Studies
• Design Methods
• +6 more

Linda N. Groat is a Professor of Architecture at the University of Michigan's Taubman College of Architecture and Urban Planning, where she has made significant contributions to architectural research and education. Her work bridges academic theory and professional practice, with recognition in national media outlets including U.S. News & World Report, The New York Times, NPR, and Women in Higher Education. Her research interests include architectural and environment-behavior theory, design methods, environmental meaning, empirical aesthetics, place theory, gender and minority issues in architectural education, and participatory processes in design. Groat's foundational work in these areas has had a significant impact on design studies research across academic and professional domains. Groat's publications reveal a strong focus on architectural research methodology and the theoretical underpinnings of design practice. Her work spans from foundational texts like Architectural Research Methods to more specialized explorations of games in architecture and environmental meaning. Her research demonstrates consistent engagement with both theoretical frameworks and practical applications in architectural design and education. Sarah Goddard Power Award (1998) Architectural Education Award (2023) James Haecker Distinguished Leadership Award (2018) Professor Groat has served on approximately 60 dissertation committees (including at least 30 as chair or co-chair) during her extensive career mentoring doctoral students. Her special focus in doctoral education has been on research design and methods, and their integrative application across architectural specialties. She teaches core courses in Taubman College's doctoral program as well as electives in the Master of Architecture program, including Research for Design Practices, Alternative Practices, and Theorizing Place. Her co-authored book Architectural Research Methods has been translated into Chinese and Persian, demonstrating its international impact.

Juhi JangView profile

Professor

• Fluid Dynamics
• Gas Dynamics
• Kinetic Theory
• +4 more

Professor Juhi Jang is a mathematician specializing in analysis and partial differential equations (PDEs) with applications to fluid dynamics, gas dynamics, kinetic theory, and astrophysics. She holds the rank of Professor in the Department of Mathematics at the University of Southern California (USC), where she has been since 2020. Prior to this, she served as an Associate Professor at USC (2015–2020) and UC Riverside (2014–2015), and as an Assistant Professor at UC Riverside (2010–2014). Her research focuses on the mathematical analysis of fluid and gas dynamics, singularities in PDEs, and gravitational collapse. Notable contributions include work on the Einstein-Euler system, hydrodynamic limits from kinetic equations, and nonlinear stability of expanding stars. Educationally, Jang earned a B.Sc. (summa cum laude) in Mathematics from Seoul National University (2000), followed by a M.Sc. (2004) and Ph.D. (2007) in Mathematics from Brown University. Her honors include the Frontiers of Science Award (2023), Simons Fellowship (2019–2020), and NSF CAREER grant (2014–2020). She has also organized international summer schools on mathematical fluids and served on editorial boards for journals like SIAM Journal on Mathematical Analysis and Kinetic and Related Models. Her research interests span: fluid dynamics, gas dynamics, kinetic theory, plasma physics, and mathematical physics. Key areas include moving boundary problems in compressible fluids, stability of gravitational systems, and singularity formation in fluid models. She has authored over 50 peer-reviewed articles, with recent work appearing in Annals of PDE , Archive for Rational Mechanics and Analysis , and Inventiones Mathematicae . Jang’s teaching spans graduate courses in PDEs, real analysis, and topology, alongside undergraduate calculus and topology. She has advised numerous graduate students and postdocs, contributing to the training of the next generation of mathematical analysts.

• Time Series Econometrics
• Financial Econometrics
• Volatility Modeling
• +3 more

Paolo Santucci de Magistris is a Professor of Econometrics at the Department of Economics and Finance of Luiss University (Rome) since February 2018 and previously served as Head of the Department from 2021 to 2024. He held roles as Associate Professor and Assistant Professor at Aarhus University (Denmark) from 2013 to 2018, with prior postdoctoral research at the University of Padova and CREATES. His education includes a PhD from the University of Pavia and visiting research at Northwestern University’s Kellogg School of Management. His research focuses on time series econometrics, financial econometrics, and energy economics, with emphasis on volatility modeling, liquidity risk, and climate impacts. Notable contributions include work on liquidity coverage in financial markets, cointegration models, and the interplay between wind energy and CO2 emissions. His publications span top journals like the Journal of Financial Economics and Journal of Econometrics , exploring topics ranging from stochastic volatility to climate policy analysis. Ongoing projects include research on option price dynamics, energy market connectedness, and risk-neutral density fitting via the RNDfittool MATLAB application. Prof. Santucci de Magistris collaborates internationally with institutions like CREATES and has contributed to policy-relevant studies on energy transition and financial stability. His methodological innovations include Bayesian econometric techniques and tools for analyzing financial risks embedded in derivatives markets.

Christopher GilliamView profile

Assistant Professor

• Signal Processing
• Radar Systems
• Sampling Theory
• +5 more

Dr. Christopher Gilliam is an Assistant Professor in Applied Signal Processing at the University of Birmingham's Department of Electronic, Electrical and Systems Engineering. He holds an MEng (1st Class Hons) in Electrical & Electronic Engineering (2008) and a Ph.D. in Signal Processing (2013), both from Imperial College London. Prior to joining Birmingham in 2022, he was a Postdoctoral Fellow at The Chinese University of Hong Kong (2013–2017) and a Research Fellow at RMIT University, Australia (2017–2022). Research Interests: Sensor signal processing, radar imaging, sampling theory, motion estimation, quantum navigation, and medical imaging. Labs: Microwave Integrated Systems Laboratory (MISL). Committees: Member of IEEE Signal Processing Society and APSIPA Technical Committees. His work focuses on advancing signal processing techniques for radar systems, navigation, and medical imaging. Recent research highlights include drone-based SAR imaging, motion correction in MRI, and fusion of classical/quantum sensors for inertial navigation. He is actively supervising PhD students and contributes to projects sponsored by DSTG. Publications span radar SLAM, probabilistic navigation algorithms, and deep learning-driven medical imaging solutions. His research bridges theoretical signal processing with practical applications in autonomous systems and healthcare.

Tony LindebergView profile

Professor

• Computer Vision
• Computational Neuroscience
• Medical Image Analysis
• +4 more

Tony Lindeberg is a Professor of Computer Science—Computational Vision at KTH Royal Institute of Technology, affiliated with the Division of Computational Science and Technology. He teaches the course Image Analysis and Computer Vision (DD2423). His research focuses on scale-space theory, early vision, and computational modeling of biological and auditory vision systems. Key contributions include theories on receptive fields, time-causal spatio-temporal models, and feature detection algorithms. Research interests span computational neuroscience, medical image analysis, and spatio-temporal recognition. Lindeberg has pioneered work on scale-invariant image features, affine transformations, and Galilean diagonalization for motion analysis. He is the author of the foundational book Scale-Space Theory in Computer Vision (1993). His work bridges computer vision and biological vision systems, with applications in gesture recognition, dynamic texture analysis, and neural networks. He leads the Vision Lab and Computational Brain Science Lab at KTH, emphasizing theoretical rigor and practical algorithms for visual perception tasks.

Yi-Zhuang YouView profile

Associate Professor

• Condensed Matter Physics
• Quantum Entanglement Dynamics
• Topological Phases
• +4 more

Yi-Zhuang You is an Associate Professor in the Department of Physics at the University of California, San Diego (UCSD). He holds a Ph.D. from Tsinghua University (2013). His research focuses on theoretical investigations of correlated topological phases, quantum entanglement dynamics, and machine learning applications in many-body systems. Key areas include deconfined quantum criticality, symmetry-protected topological (SPT) phases, and the interplay between topology and quantum matter. His work bridges condensed matter physics and high-energy physics, exploring topics such as topological responses in gauge theories, entanglement holography, and quantum machine learning. Recent studies involve machine learning-driven approaches to quantum state preparation, symmetry discovery, and tomographic reconstruction of quantum systems. His contributions span theoretical frameworks for understanding topological transitions, fractionalization in lattice models, and the role of symmetry in quantum critical phenomena. Notable research highlights include the study of symmetric mass generation as a deconfined quantum criticality mechanism, the application of classical shadow tomography for efficient quantum state estimation, and the development of algorithms for self-similar dynamics modeling. His publications frequently intersect with experimental proposals for observing topological phases in materials like graphene and iridates. Dr. You’s affiliations include the UCSD Physics Department, with collaborations extending to institutions globally. His research is supported by grants focusing on quantum information, topological materials, and machine learning applications in physics. While no specific awards are listed, his work has been widely cited in high-impact journals across condensed matter and theoretical physics.

• Control Theory
• Applied Mathematics
• Computational Mechanics
• +7 more

Goong Chen is a Professor at Texas A&M University (TAMU) within the College of Arts & Sciences. His research focuses on Control Theory, Applied Mathematics, and interdisciplinary applications in computational mechanics, fluid dynamics, and quantum systems. He holds a Ph.D. from the University of Wisconsin (1977) and a B.S. from National Tsing-Hua University (1972). His research interests span computational biomechanics, forensic modeling, nanofluidics, and mathematical physics. Recent work includes modal analysis of animal motion, crash mechanics of aircraft, and forensic reconstruction of disasters. He has contributed to theoretical advancements in PDEs, nonlinear dynamics, and quantum computing. Chen’s articles address cutting-edge topics like thermoelastic plates, Volterra equations, and DFIM systems. He has pioneered numerical methods for complex systems using OpenFOAM and finite element techniques. His computational models have been applied to real-world scenarios such as submarine implosions and chemical warfare forensics. No academic awards are explicitly listed, but his extensive publication record reflects significant contributions to applied mathematics and engineering. He leads research teams focused on interdisciplinary challenges in computational science and engineering.