Find Academic Supervisors

• Health Policy
• Health Insurance Reform
• Affordable Care Act
• +13 more

JoAnn C Volk is a Research Professor at Georgetown University's McCourt School of Public Policy and the founder and co-director of the Center on Health Insurance Reforms (CHIR). She leads research initiatives on federal and state health insurance regulations, with a strong focus on the Affordable Care Act, health insurance marketplaces, and private insurance policy. Research Interests: Her work spans health policy, insurance regulation, Medicaid, CHIP, Medicare, employer-sponsored coverage, and health care quality. She investigates the impact of short-term plans, health care sharing ministries, and federal rules on insurance market stability and consumer protection. Publication Trends: Her recent publications analyze emerging threats to individual insurance markets, regulatory changes, and state-level innovations in health insurance marketplace design. The research emphasizes policy implications, market dynamics, and consumer risk. Scientific Awards: No specific awards listed in the provided text. Advising and Grants: While no formal students or grants are listed, her leadership at CHIR involves directing research teams, authoring policy papers, and providing technical assistance to policymakers, suggesting active mentorship and grant-supported research activities. Labs and Teams: She co-directs the Center on Health Insurance Reforms (CHIR), a key research hub focused on health insurance policy analysis and technical support for federal and state stakeholders.

Paul D. SchedlView profile

Professor

• Developmental Biology
• Gene Expression Regulation
• Chromatin Structure
• +7 more

Paul D. Schedl is a Professor in the Department of Molecular Biology at Princeton University, where he leads the Schedl Lab focused on understanding gene expression and early development in Drosophila melanogaster . His research investigates how genetic and epigenetic mechanisms regulate developmental pathways, including sex determination, chromatin architecture, and embryonic polarity. Position: Professor of Molecular Biology Institution: Princeton University Department: Department of Molecular Biology Lab: Schedl Lab Contact: pschedl@princeton.edu | 609-258-4979 His research centers on three major developmental systems in Drosophila : (1) The Sex-lethal gene in sex determination, examining how pathway choice, commitment, and differentiation are regulated through transcriptional and post-transcriptional mechanisms; (2) Chromatin structure and regulation of the Abd-B gene via cis-regulatory domains, Polycomb/Trithorax group proteins, and boundary elements like Fab-7 and Fab-8; and (3) mRNA localization in oocyte polarity, particularly the role of the orb gene in establishing anterior-posterior and dorsal-ventral axes. The 15 most recent publications reflect a consistent focus on chromatin boundaries, insulator function, gene regulation, and mRNA localization. Key themes include the functional dissection of boundary elements (e.g., Fab-7, Fab-8), the role of CTCF and GAGA factors in chromatin organization, the autoregulatory function of orb in oocyte specification, and the mechanisms of transcriptional quiescence in germ cells. The work spans molecular genetics, developmental biology, and epigenetics, often combining genetic screens with biochemical and imaging approaches. No scientific awards are explicitly listed in the provided texts. Dr. Schedl actively mentors graduate students and postdoctoral researchers, many of whom co-author his publications. His lab investigates fundamental questions in developmental gene regulation, including zygotic genome activation, germline-soma distinction, and chromatin domain organization. Research is supported by ongoing projects exploring the roles of insulator proteins, CPEB family members, and signaling pathways in cell fate determination. The Schedl Lab is a key contributor to understanding how chromatin architecture and RNA regulatory networks orchestrate development in Drosophila , serving as a model for higher eukaryotes.

• Migrant Workers
• Labor Market
• Division of Labour
• +4 more

Ruben Timmerman is a Researcher at the Criminology Department of the Erasmus School of Law, Erasmus University Rotterdam. His work focuses on migrant labor dynamics, crimmigration policies, and ethical challenges in criminological research within the Dutch context. Dr. Timmerman's research centers on systemic exploitation in labor markets, particularly examining how migrant workers experience social exclusion, flexibilization, and deteriorating working conditions. His scholarship critically analyzes the political economy of labor migration and its intersection with criminal justice systems. His recent publications (2023-2025) demonstrate consistent focus on EU migrant worker vulnerabilities in the Netherlands, removal policies, and ethical tensions in critical criminology research. Key trends include documenting growing labor market divides and challenging institutional ethics frameworks that constrain critical scholarship. Scientific Awards: No scientific awards mentioned in the provided text. Dr. Timmerman serves as consultant to Rotterdam and The Hague municipalities on migrant labor policy (2023-2024) and peer reviews for Journal of Ethnic and Migration Studies and Tijdschrift voor Criminologie. No student advising or grant activities are documented in the source material. No dedicated research labs or formal research teams are referenced in the available information.

• Medical Image Analysis
• Computer Vision
• Biomedical Imaging
• +6 more

Constantino Carlos Reyes-Aldasoro is a Professor in the Department of Electrical and Electronic Engineering within the School of Mathematics, Computer Science and Engineering at City, University of London. With an active research career spanning over two decades, he maintains a prolific publication record with recent contributions in 2025 across multiple disciplines including medical image analysis, computer vision, and biomedical engineering. His research interests focus on Medical Image Analysis, Computer Vision, Biomedical Imaging, Cancer Research, and Image Processing. His work bridges engineering and medical sciences, developing computational methods for biomedical applications. He has made significant contributions to tumor microenvironment modeling, vascular analysis, and electropalatography studies. Analysis of his recent publications reveals a strong emphasis on deep learning applications in medical imaging, with particular focus on cancer diagnostics, vascular analysis, and neuroimaging. His research demonstrates consistent methodological innovation in image processing techniques applied to biomedical problems, with publications spanning from fundamental algorithm development to clinical applications. Professor Reyes-Aldasoro has mentored numerous researchers, with several co-authors appearing consistently across multiple publications as likely students and postdoctoral researchers including C. Karabağ, J.A. Solis-Lemus, and M.A. Ortega-Ruiz. His work demonstrates interdisciplinary collaboration across engineering, computer science, medicine, and biology, reflecting the increasingly interconnected nature of modern biomedical research. He maintains active research programs with recent publications indicating ongoing projects in AI implementation in medical imaging, tumor modeling, and cardiovascular analysis.

Ricardo Munoz MartinView profile

Professor

• Cognitive Translation Studies
• Translation and Interpreting Studies
• Translation Technology
• +3 more

Ricardo Muñoz Martín is a Full Professor at the Department of Interpretation and Translation, University of Bologna (Italy). His academic career spans institutions including Universidad de Las Palmas de Gran Canaria, University of Granada, and University of California. He specializes in Cognitive Translation & Interpreting Studies, with expertise in translation technology, empirical research methods, and multilingual communication cognition. PhD in Hispanic Linguistics, University of California, Berkeley Diplomatura in Translation & Interpreting, Universidad de Granada Research interests integrate cognitive science with translation studies, focusing on process analysis, AI-assisted translation, and biometric metrics like heart rate variability. His work explores the indivisibility of translation acts and the evolution of empirical methodologies in the field. Recent publications emphasize quantitative empirical frameworks, cognitive load analysis in collaborative translation, and AI integration in interpreter training. Grants from Italy, Spain, Poland, and China support his research on translation technology and cognitive effort measurement. Co-director of the MC2 Lab (Cognitive Translation Summer School) Principal investigator in grants like "Big Sistah" (remote worker wellbeing) and "Attention, emotions and translation" He actively participates in international conferences, including keynote speeches at the 7th TTI Conference and panels at the 11th EST Congress.

Julia GluesingView profile

Research Professor

• Business and Organizational Anthropology
• Global Collaboration and Networks
• Innovation Diffusion
• +5 more

Julia Gluesing is a business and organizational anthropologist who serves as Research Professor in the Department of Industrial and Systems Engineering at Wayne State University and as Adjunct Faculty in the Global Executive Track Ph.D. Program. She is also Adjunct Professor of Anthropology and has held leadership roles such as Associate Director of the Institute for Information Technology and Culture (IITC) and Co-Director of the Global Executive Track Ph.D. in Industrial and Systems Engineering. Education: Ph.D. in Cultural Anthropology, Wayne State University, 1995 M.A. in Communication, Michigan State University, 1985 B.A. in French and Russian, University of California, Davis, 1971 (Phi Beta Kappa) Diplôme de Français Avancé, Université de Montpelier, France Research Interests: Her scholarship centers on culture and identity, global collaboration and networks, innovation diffusion, and qualitative methodology. She integrates insights from anthropology, international business, economics, finance, and political science to understand how engineering organizations operate across cultures and geographies. Publication Trends: Across more than 60 peer-reviewed works, Julia’s research has evolved from foundational ethnographic studies of global virtual teams to cutting-edge analytics combining text mining, social network analysis, and anthropological theory. Recent themes include innovation governance, environmental decision-making, and ethical challenges in globally networked organizations. Awards & Honors: Best Paper Award, International Management Division, Academy of Management (2010) Most Innovative Session Award, Organizational Behavior Division, Academy of Management (2000) Election to Phi Kappa Phi and Phi Beta Kappa honor societies Grants & Sponsored Research: Julia has served as Principal Investigator or Co-PI on six NSF grants totaling well over $2 million, including the flagship “Digital Diffusion Dashboard” project ($426k, 2005–2010) and the Michigan Alliance for Clinical and Translational Science evaluation ($1.8 million segment; total $20 million). Industry partners have included Ford, GM, Chrysler, Visteon, Motorola, Procter & Gamble, Bosch, and many others. Teaching & Advising: She teaches doctoral courses such as Qualitative Research Design and Methods, Global Leadership of Technical Organizations, and Management of Technology Change, and advises leadership projects in the Ford Engineering Management Master’s Program (EMMP). While specific advisee names are not listed, she mentors executives in both the Global Executive Track Ph.D. and EMMP cohorts. Labs & Teams: Julia founded Cultural Connections, Inc. , a global research and consulting firm, and co-founded the Collaborative Innovation Networks (COINs) Conference series, serving as local organizer for COINsDetroit 2017. She maintains active affiliations with the American Anthropological Association, EPIC, ION, and the Society for Applied Anthropology.

Yuning DingView profile

Researcher

• Natural Language Processing
• Educational Technology
• Automatic Essay Scoring
• +3 more

Yuning Ding is a PhD Student and Research Assistant in the junior research group 'EduNLP' at the Research Center CATALPA (Center of Advanced Technology for Assisted Learning and Predictive Analytics), FernUniversität in Hagen, since January 2022. Her work focuses on Natural Language Processing applications for educational technology, specifically developing systems for automatic essay scoring and generating formative feedback for learners and summative feedback for teachers. Her educational background includes: M.Sc. in Applied Cognitive and Media Science with Specialization in Cognition & Artificial Intelligence at University of Duisburg-Essen (2017-2019) B.Sc. in Applied Cognitive and Media Science at University of Duisburg-Essen (2014-2017) B.A. in Communications at University of International Relations, Beijing (2009-2013) Ding's research centers on leveraging NLP to enhance writing education through AI-driven assessment and feedback systems. Her work bridges computational linguistics and pedagogy, with particular emphasis on argument mining, cohesion analysis, and cross-lingual content scoring. She investigates how transformer models and multi-task learning can improve the reliability and educational value of automated writing evaluation, while addressing critical issues like fairness and adversarial vulnerability in scoring systems. Her research demonstrates how NLP can provide actionable insights for both students and educators in writing development. Analysis of her publication trends reveals a strategic progression from foundational work on content scoring and error analysis toward sophisticated integrated systems. Recent work emphasizes multimodal feedback generation, argument-cohesion integration, and cross-lingual transfer, with increasing focus on real-world implementation challenges including fairness, robustness, and user experience. Her research spans multiple languages and educational contexts, reflecting a commitment to globally applicable educational technology. Within CATALPA, Ding actively collaborates across disciplines through the center's vibrant knowledge-sharing culture. She participates in project presentations and colloquia that facilitate cross-pollination of ideas between computer science, linguistics, and educational theory. Her work on the DARIUS corpus and FEAT-writing system demonstrates tangible contributions to educational resource development and interactive learning environments.

Niels SeidelView profile

Researcher

• Adaptive Learning Environments
• Learning Analytics
• Self-Regulated Learning
• +4 more

Niels Seidel is a computer scientist and researcher at FernUniversität in Hagen, where he serves as the Lead of project APLE II at the CATALPA research center and as an alternate/deputy member of the CATALPA executive board. He works within the Faculty of Mathematics and Computer Science, focusing on the development of adaptive personalized learning environments for higher education. His work bridges computer science and educational technology, with particular emphasis on supporting self-regulated learning, reading comprehension, and assessment activities across diverse student populations. Seidel's research interests span multiple interconnected domains in educational technology. His primary focus is on Adaptive Learning Environments , where he designs, develops, and evaluates systems that support learners in self-regulated learning, reading, and assessment. His work in Learning Analytics involves analyzing and visualizing learning behavior at individual, group, and organizational levels while accounting for learner diversity. He has made significant contributions to Video-Based Learning , examining how video content can be structured and presented to optimize learning outcomes. His research increasingly incorporates Artificial Intelligence to create more responsive and personalized educational experiences, as evidenced by his recent work on generative AI applications for evaluating self-regulated learning skills. His publication record shows a clear trajectory toward increasingly sophisticated adaptive learning systems. Early work focused on foundational aspects of video-based learning and interaction design patterns, while recent publications demonstrate sophisticated integration of AI, learning analytics, and adaptive techniques. His research consistently addresses practical challenges in distance education while contributing to theoretical frameworks in educational technology. The 2024-2025 publications reveal particular emphasis on self-regulated learning assessment, reading comprehension support, and the application of generative AI in educational contexts. As an academic advisor, Seidel has supervised numerous bachelor's, master's, and diploma theses since 2018, mentoring students working on diverse projects related to educational technology. His current leadership roles include serving as spokesman for the Working Group Learning Analytics within the SIG Educational Technology of the German Informatics Society since 2021. He has secured funding for multiple projects, including the Google.org-funded Theresienstadt explained project and the BMBF-funded Life Long Learning Open Operating Platform (L³OOP). Seidel leads the APLE II project at CATALPA research center, which aims to develop domain-independent adaptive personalized learning environments for higher education. His work leverages the research infrastructure at FernUniversität in Hagen, particularly the Moodle-based learning management system, to implement and test innovative educational technologies with large student cohorts in real-world settings.

Katrina SluisView profile

Associate Professor

• Computational Culture
• Networked Images
• Digital Photography
• +6 more

Katrina Sluis is Associate Professor and Head of Photography & Media Arts at the Australian National University's School of Art & Design within the College of Arts & Social Sciences. She co-convenes the Computational Culture Lab and serves as Chair of the Research Committee. Previously, she was Senior Lecturer and founding Co-Director of the Centre for the Study of the Networked Image at London South Bank University, and held the inaugural position of Senior Curator (Digital Programmes) at The Photographers' Gallery in London, where she now continues as Adjunct Research Curator. Her educational background includes a PhD from the University of Sunderland, MA from University of the Arts London, PGCHE from London South Bank University, and BFA (Hons) from the University of New South Wales. Sluis serves on the Editorial Advisory Boards of Photographies (Routledge) and Media:Art:Write:Now (Open Humanities Press), and is a Board Member of PhotoAccess, the ACT's center for photography. She advises Serpentine Galleries on Future Art Ecosystems 2 & 3, and has served on international juries for the P3 Post-Photography Prototyping Prize, Organ Vida Festival, and Brighton Photo Fringe's OPEN20 SOLO award. Sluis' research focuses on the politics and aesthetics of art and photography in computational culture, examining social circulation, automation, and cultural value. As a curator and educator, she has worked extensively with museums and galleries to develop digital strategies, programming, and pedagogy. Her current work addresses emerging paradigms of human-machine curation in response to the massive intensification of global image production and circulation. Funded by the Swiss National Science Foundation, her project 'Curating Photography in the Networked Image Economy' explores the transit of data analytics and AI imaginaries into museums. Her recent publications reveal a strong focus on the intersection of photography, artificial intelligence, and network culture. Key themes include machine vision's photographic theory, generative AI's impact on photographic style, the cultural value of photographic documentation in the age of big data, and the transformation of museum practices in the networked image economy. Her work consistently bridges technical developments in computer vision with critical cultural analysis. Editorial Advisory Board, Photographies (Routledge) Editorial Advisory Board, Media:Art:Write:Now (Open Humanities Press) Board Member, PhotoAccess Incorporated Advisor, Serpentine Galleries Future Art Ecosystems Peer Assessor, Creative Australia International jury member for P3 Post-Photography Prototyping Prize Funded by the Swiss National Science Foundation, her project 'Curating Photography in the Networked Image Economy' explores the transit of data analytics and AI imaginaries into museums, while an AHRC-funded research strand examines computational reproduction's impact on the art object. She is registered to supervise higher degree research students and co-convenes the Computational Culture Lab, which investigates how network culture transforms the production and circulation of images and cultural objects. Her research has generated 267 citations with an h-index of 4 according to Scopus.

Nikolaos E. PanagiotouView profile

Researcher

• Text Mining
• IoT/Smart-Cities
• Spatio-temporal Analysis
• +3 more

Nikolaos E. Panagiotou is a Research Engineer at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, where he has been working since March 2014. He is also a Ph.D. candidate in Real-time Data Analysis in Massive Streams and Social Media at the same institution under the supervision of Prof. D. Gunopulos. He is an active member of the KDDLAB research group at the University of Athens, participating in EU research projects including INSIGHT and VaVeL. His educational background includes: MSc. with Distinction in Artificial Intelligence, Learning from Data from the School of Informatics, University of Edinburgh (2012-2013) BSc. in Informatics and Telecommunications from the Department of Informatics and Telecommunications, University of Athens (2007-2011) Dr. Panagiotou's research focuses on real-time analysis of high-volume data streams with particular emphasis on text and sensor streams, including news and social media content. His current research interests include: Text Mining: event detection, first story detection, open information extraction IoT/Smart-Cities: sensor analysis, heterogeneous sensor aggregation, real-time visualization Spatio-temporal Analysis: congested road-segments, travel time estimation, OSM His work bridges theoretical research with practical applications in smart city technologies and real-time data processing systems. His publication record demonstrates a strong focus on smart city applications, real-time data processing, and information extraction from diverse data sources. The research shows progression from foundational work on stream mining and event detection to sophisticated applications in urban mobility and news monitoring systems. His most recent work continues to advance techniques for handling sparse data environments and real-time analytics, with particular emphasis on travel time estimation and hybrid analytical approaches. Dr. Panagiotou has been involved in significant EU-funded projects: INSIGHT project: Implementation of real-time analysis modules and visualization engine for the smart city of Dublin VaVeL project: Design of integration schemes and development of real-time analysis modules using spatio-temporal data He has developed multiple demonstration systems including the News Monitor for real-time news analysis and various smart city monitoring dashboards. His technical expertise spans real-time data processing, stream mining, spatio-temporal analysis, and visualization systems, with applications in urban mobility and news monitoring. His collaborative work with researchers across Europe demonstrates his integration into the international research community in data science and smart city applications.

Adrian StaubView profile

Professor

• Psycholinguistics
• Eye movement research in reading
• Language comprehension
• +4 more

Adrian Staub is a Professor in the Department of Psychological and Brain Sciences at the University of Massachusetts Amherst. He directs the UMass Eyetracking Laboratory and focuses on psycholinguistics, particularly the cognitive processes underlying language comprehension and production. His work frequently employs eye-tracking methodologies to investigate syntactic parsing, word recognition, and predictability effects in reading. PhD in Psychological and Brain Sciences (2008), University of Massachusetts Amherst Research Interests Staub's research spans psycholinguistic theory, eye movement analysis during reading, and methodological issues in psychological science. He studies how readers process syntactic structures, recognize words, and integrate predictability information. His recent work includes investigations of function word errors in reading, statistical power in language research, and the theoretical foundations of the replication crisis in psychology. Recent Publications His publications examine phenomena such as the 'Sentence Superiority Effect,' the effects of predictability on lexical processing, and methodological challenges in eye-tracking research. These studies span diverse topics including cross-linguistic comparisons (e.g., Italian, French, Chinese), computational modeling of reading processes, and theoretical debates in psycholinguistics. Academic Roles Since January 2023, Staub has served as Editor-in-Chief of the Journal of Memory and Language . He has also acted as guest editor for a special issue on eye movements in reading at 50. His teaching includes graduate seminars on the replication crisis and undergraduate courses on the psychology of reading. International Collaborations Staub has held visiting appointments at institutions including the Labex EFL Project in Paris, the University of Salzburg, and the University of Milan-Bicocca. These roles reflect his engagement in international research networks focused on reading cognition and eye-tracking methodologies.

Kevin J LiangView profile

Researcher

• Deep Learning
• Computer Vision
• 3D Computer Vision
• +5 more

Dr. Kevin J Liang is a Research Scientist at Meta Platforms, Inc. , specializing in Deep Learning , Computer Vision , and 3D Reconstruction . He earned his PhD in Electrical & Computer Engineering from Duke University in 2020, with a dissertation on Deep Automatic Threat Recognition for Airport X-Ray Baggage Screening . His research focuses include: 3D Computer Vision (ICON, Fast3R) Few-Shot Learning (Sylph, HyperMix) Federated Learning (WAFFLe) Object Detection (EgoTracks, Self-Supervised Methods) Recent publications demonstrate his leadership in Egocentric Vision (Ego-Exo4D) and Transformer Applications (GliTr). He has received numerous awards including the E Bayard Halsted Fellowship (2017) and Summa cum laude (2015), and serves on program committees for major conferences like NeurIPS and CVPR . As an educator, he developed and taught tutorials for Duke University's Machine Learning School and Coursera courses, covering TensorFlow, PyTorch, and foundational ML concepts for over 600 students.

Agata KliberView profile

Associate Professor

• Economics
• Finance
• Econometrics
• +6 more

Agata Kliber , PhD, is an Associate Professor at the Department of Applied Mathematics , Institute of Informatics and Quantitative Economics , Poznań University of Economics and Business . Her work bridges Financial Economics and Econometrics , with a focus on cryptocurrency markets , energy economics , and financial risk management . Research Themes: Financial market volatility, sovereign risk dynamics, cryptocurrency hedging properties, oil price impacts on inflation and sustainable transport, and econometric modeling (GARCH, MIDAS, NARDL). Awards: No specific awards mentioned in available data. Collaborations: Regularly collaborates with Barbara Bedowska-Sojka's research group and international scholars like Pavel Rezac and Krzysztof Echaust. Recent Publications (2022-2025) analyze: Interdependencies between energy commodities and stock markets Asymmetric effects of oil prices on inflation expectations Safe-haven properties of cryptocurrencies during crises Volatility and liquidity forecasting models Impact of pandemics on sovereign risk and fintech-bank relationships Role of Bitcoin in crisis economies like Venezuela Her methodological expertise spans Bayesian inference , stochastic volatility , and multi-criteria decision-making frameworks . ResearchGate profile shows 94 publications and 25,489 reads , indicating significant scholarly engagement.

Iro ArmeniView profile

Assistant Professor

• Computer Vision
• 3D Scene Understanding
• Built Environment
• +5 more

Iro Armeni is an Assistant Professor in the Civil and Environmental Engineering Department at Stanford University's School of Engineering. She leads the Gradient Spaces research group, focusing on the intersection of civil engineering, architecture, and machine perception to design and construct data-driven environments across physical and digital space. Her educational background is highly interdisciplinary: PhD in Civil and Environmental Engineering with Minor in Computer Science from Stanford University (2020), Postdoctoral Researcher at ETH Zurich (2023), MSc in Computer Science from Ionian University (2013), MEng in Architectural Engineering from University of Tokyo (2011), and Diploma in Architectural Engineering from National Technical University of Athens (2009). Before academia, she worked as an architect and consultant for both private and public sectors. Dr. Armeni's research focuses on developing quantitative and data-driven methods that learn from real-world visual data to generate, predict, and simulate new or renewed built environments with humans at the center. She is particularly interested in creating gradient spaces that blend 100% physical (real reality) to 100% digital (virtual reality) using Mixed Reality. Her work spans computer vision, 3D scene understanding, semantic mapping, and their applications in the built environment. Her recent publications demonstrate significant contributions across multiple venues including CVPR, ECCV, SIGGRAPH, and ISPRS Journal, with research themes centered around 3D scene understanding, appearance transfer, scene synthesis, SLAM in dynamic environments, and semantic mapping. Her work shows a consistent trajectory toward creating sustainable, inclusive, and adaptive built environments that support current and future physical and digital needs. She has received prestigious awards including the ETH Zurich Postdoctoral Fellowship, Google PhD Fellowship, and MEXT Scholarship. Her teaching includes graduate courses such as Designing for Gradient Spaces (CEE342), Computer Vision for the Built Environment (CEE247C), and AI Applications in AEC (CEE329), reflecting her interdisciplinary approach to integrating machine perception with civil engineering applications.

• Medical Imaging
• Radiology
• Artificial Intelligence in Medicine
• +6 more

Joseph Yuan-Chieh Lo is a Professor in Radiology at Duke University, with additional appointments as Professor in the Department of Electrical and Computer Engineering and Professor of Biomedical Engineering. He is also a Member of the Duke Cancer Institute. His academic career spans multiple disciplines within medical imaging and computational medicine. Dr. Lo's research focuses on the intersection of radiology, artificial intelligence, and medical physics. His primary areas of interest include medical imaging, particularly digital breast tomosynthesis, computational phantoms, and the application of machine learning to improve cancer detection and diagnosis. His work has significantly contributed to the development of computer-aided detection systems, virtual imaging trials, and methods for improving the accuracy of breast cancer screening. His recent publications demonstrate a strong emphasis on applying artificial intelligence to radiology, with particular attention to breast cancer detection, lung cancer screening, and the development of computational models for medical imaging analysis. His research group has produced numerous papers on improving annotation efficiency, multi-disease classification, and interpretable AI models for clinical applications. Dr. Lo has mentored numerous students and researchers, as evidenced by the extensive list of co-authors on his publications. His collaborative work spans multiple institutions and departments, reflecting the interdisciplinary nature of his research. His technical expertise includes the development of computational phantoms (XCAT), virtual clinical trials, and methods for de-identification of medical imaging data. His work bridges the gap between engineering, computer science, and clinical medicine, with practical applications in improving cancer screening and diagnosis.