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Markus NaglerView profile

Assistant Professor

• Labor Market Economics
• Innovation Economics
• Microeconometrics
• +2 more

Markus Nagler serves as a tenure-track junior professor (W1) for Labor Market and Innovation Economics at Friedrich-Alexander University Erlangen-Nuremberg (FAU), where he is affiliated with the Department of Business, Economics, and Social Sciences. His research employs microeconometric methods to address critical questions in labor market dynamics and innovation processes, contributing significantly to both academic literature and policy discussions. His educational background includes: B.Sc. in Economics from Ludwig Maximilian University of Munich (2012), with a stay at Universitat Pompeu Fabra Barcelona M.Sc. from University College London (2013) Ph.D. from Ludwig Maximilian University of Munich (2018) Professor Nagler's research interests center on labor market economics and innovation economics, with particular focus on how technological change, policy interventions, and economic shocks affect labor markets and innovation processes. His work frequently employs experimental and quasi-experimental methods to establish causal relationships, bridging theoretical economic models with empirical evidence. He examines topics including remote work arrangements, the diffusion of general purpose technologies, patent systems, and the effects of economic recessions on educational outcomes. His publication record demonstrates consistent scholarly output in top economics journals, with research spanning labor economics, innovation economics, and education economics. His interdisciplinary approach connects economics with sociology, education, and policy studies, particularly through his involvement in the university's 'Work in Transition' research focus. As part of the 'Work in Transition' research focus at FAU, Professor Nagler collaborates with colleagues from economics, sociology, and psychology through the Interdisciplinary Center for Labor Market and Working World (LASER). This strategic partnership with the Institute for Employment Research (IAB) and the Federal Employment Agency focuses on developing empirically informed labor market policy solutions to contemporary challenges.

Sayan BanerjeeView profile

Associate Professor

• Probability Theory
• Stochastic Processes
• Interacting Particle Systems
• +9 more

Sayan Banerjee is an Associate Professor in the Department of Statistics and Operations Research at the University of North Carolina at Chapel Hill, within the College of Arts and Sciences. His research lies at the intersection of probability theory, stochastic processes, and network science, with applications in operations research and statistical physics. Research Interests: His work focuses on interacting particle systems, ergodicity of diffusions, stochastic networks, probabilistic couplings, and dynamic random graphs. He also investigates random walks in random environments, random matrices, and network sampling and ranking algorithms. These areas reflect a deep engagement with both theoretical probability and its real-world applications in complex systems. Recent Publication Trends: His recent articles (2023–2025) reveal a strong focus on the analysis of infinite particle systems (e.g., Atlas models), dynamic network evolution under various delay regimes, convergence properties of machine learning algorithms like Stein Variational Gradient Descent, and structural properties of random graphs. The keywords span probability, operations research, network science, and computational statistics, indicating interdisciplinary impact. Subfields include rank-based diffusions, local weak convergence, load balancing, flocking dynamics, and network centrality. Scientific Awards: NSF CAREER Award (DMS-2141621) Grants and Advising: He is the principal investigator on the NSF CAREER award DMS-2141621 and a co-PI on the NSF RTG grant DMS-2134107, supporting research training in probability and stochastic processes. While no formal list of students is provided, his role as a Ph.D.-granting faculty member in a top statistics department implies active graduate student mentorship. His prior postdoctoral fellowship at the University of Warwick under Prof. Wilfrid Kendall highlights strong international collaboration. Laboratories and Research Teams: Though no named lab is mentioned, his extensive collaborative network—including researchers like A. Budhiraja, S. Bhamidi, P. Dey, and M. Olvera-Cravioto—indicates leadership in a vibrant research group focused on stochastic modeling and network analysis at UNC.

• Aviation Network Development
• Airport Connectivity
• Transportation Logistics and Supply Chains
• +7 more

Tommy Cheung is a Senior Lecturer and Department Chair in the Department of Aviation, School of Engineering at Swinburne University of Technology, Australia. His research focuses on aviation network development, global airport connectivity, blockchain applications in air travel, and data-driven route planning. He holds a PhD in Computer Engineering from the University of New South Wales, Sydney, and has prior academic experience at Hang Seng University of Hong Kong and the Hong Kong University of Science and Technology. PhD in Computer Engineering, University of New South Wales, Sydney, Australia Former Assistant Professor, Hang Seng University of Hong Kong (2014–2018) Former academic, HKUST, Department of Electrical and Electronic Engineering 12+ years in advanced manufacturing and supply chain management in Hong Kong and Shenzhen Dr. Cheung's research interests span aviation network resilience, airport connectivity indices, smart technologies in air travel, and sustainable aviation. He investigates how global disruptions like pandemics and geopolitical conflicts affect air transport systems. His work integrates machine learning, Bayesian modeling, and spatial econometrics to analyze aviation networks and optimize operations. His recent publications reveal a strong trend in applying data analytics and AI to aviation policy, route planning, and service quality. Topics include flight delay forecasting, blockchain integration, self-connection optimization, and pilot remuneration. His work often involves large-scale datasets and interdisciplinary methods, contributing significantly to air transport policy and network modeling. Dr. Cheung has secured industry research funding, including a major grant from Textron Systems Australia for the Regional AAM Surrogate Trial. He actively supervises PhD and Master’s students on topics such as hydrogen in aviation, pilot fatigue monitoring, and advanced air mobility. His leadership extends to curriculum and departmental strategy as Chair of Aviation. He is involved in several research teams and collaborations, particularly in intelligent transportation systems and aviation innovation. His lab and project work integrate UAVs, IoT, and AI for infrastructure-less environments, contributing to next-generation mobility solutions.

• Orthopaedic Surgery
• Biomechanics
• Joint Replacement
• +10 more

Professor David Murray is a leading academic and clinician in orthopaedic surgery at the University of Oxford , affiliated with the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS) within the Medical Sciences Division . He is also a Consultant Orthopaedic Surgeon at the Nuffield Orthopaedic Centre, where he specializes in knee and hip joint replacements and leads a multidisciplinary research team. His educational background is highly interdisciplinary: he earned a degree in Engineering Science from Cambridge University , winning the Wright Prize , and later qualified in medicine, training at Cambridge, London, and Oxford. He is a Fellow of the Royal College of Surgeons (FRCS) and holds a Doctorate (MD) from Cambridge. MA, University of Cambridge MD, University of Cambridge FRCS (Orth), Royal College of Surgeons of England Professor Murray’s research bridges clinical orthopaedics and engineering, with a strong focus on knee and hip arthroplasty . His team has pioneered the development of the Oxford Partial Knee , a mobile-bearing implant used worldwide. His work integrates biomechanical studies (using gait analysis, computer modelling, and X-ray techniques) with clinical trials and patient-reported outcome measures . His group runs the Knee Arthroplasty Trial (KAT) , the largest multicentre randomized study of knee replacements in the UK. His recent publications demonstrate sustained innovation across orthopaedic biomechanics, implant design, AI in radiology, and clinical outcomes. Themes include mobile-bearing implants , minimally invasive techniques , finite element analysis , and machine learning for surgical assessment. The breadth of his work spans engineering, clinical medicine, and data science. His contributions have been recognized with prestigious awards: British Research Society Prize European Research Society Prize Professor Murray leads a research group that collaborates with the Department of Engineering Science and Professor Ray Fitzpatrick’s team in the Institute of Health Sciences. His team has contributed to national audits of hip replacements and continues to shape best practices in joint surgery. He has secured significant research funding to support large-scale trials and biomechanical studies, advancing both surgical techniques and implant longevity. His laboratory and clinical team, known as the Oxford Orthopaedic Engineering Centre , integrates engineering principles with surgical innovation. The group is involved in multiple studies including UTMoSt , SPAARK , and KAT , focusing on improving implant performance, surgical precision, and patient recovery.

Aravind SrinivasanView profile

Professor

• Algorithms
• Probabilistic Methods
• Data Science
• +10 more

Aravind Srinivasan is a Distinguished University Professor and Professor of Computer Science at the University of Maryland, College Park, with additional appointments in UMIACS (University of Maryland Institute for Advanced Computer Studies) and AMSC (Applied Mathematics & Scientific Computation). He is an Amazon Scholar since 2019, applying his expertise in algorithms to cloud computing challenges at AWS. Dr. Srinivasan earned his B.Tech in Computer Science & Engineering from IIT Madras (1989) under Prof. C. Pandu Rangan, followed by a Ph.D. in Computer Science from Cornell University (1993) under Prof. David B. Shmoys. He completed postdoctoral work at the Institute for Advanced Study (Princeton) and DIMACS (Rutgers) from 1993-94. His research spans algorithms, probabilistic methods, data science, network science, and machine learning , with applications across multiple domains. He has pioneered work at the intersection of algorithms and AI, focusing on interpretability and explainability. His research in health includes computational epidemiology, cancer genomics, pharmacology, organ exchange, and medical devices. In the Internet economy, he works on E-commerce, digital marketing, cloud optimization, crowdsourcing, and social networks. He is a leading researcher in algorithmic fairness, developing methods to systematically incorporate probabilistic, per-user/demographic fairness in AI systems. His work also extends to networking algorithms, distributed computing, and sustainable growth applications including energy networks. His recent publications reveal a strong trend toward integrating algorithmic fairness with practical applications in healthcare, resource allocation, and sustainability. The research shows increasing focus on probabilistic methods for fair decision-making in online environments, with applications ranging from kidney exchange programs to pandemic response planning. His work consistently bridges theoretical computer science with real-world impact. Professional Recognition: Elected Fellow of six professional societies: ACM, IEEE, AMS, AAAS, EATCS, and SIAM Member of Academia Europaea (the Academy of Europe) Distinguished University Professor of the University of Maryland (2020) Dijkstra Prize, Danny Lewin Award, and Distinguished Career Award in Computer Science Dr. Srinivasan has advised an extensive portfolio of students at all levels, from high school to postdoctoral researchers. His current PhD students include Sharmila Duppala, Kishen Gowda, and Sanna Madan (co-advised with Eytan Ruppin). His graduated PhD students have gone on to faculty positions (Brian Brubach at Wellesley, Bo Han at George Mason), industry leadership roles (Tom DuBois as CTO of Maven/Salesforce), and research positions at major tech companies. His research has been funded by Adobe, Amazon, Google, NSF, IARPA, and other government agencies, focusing on applications in cloud computing, epidemiology, and fair resource allocation. He serves as Associate Editor for ACM Transactions on Algorithms and previously served as Editor-in-Chief (2014-2020), and as Managing Editor of Theory of Computing (2006-2019).

• Mathematical Biology
• Mechanobiology
• Biological Physics
• +7 more

Dr Pascal Buenzli is an Associate Professor in Mathematical Biology at the School of Mathematical Sciences, Faculty of Science, Queensland University of Technology (QUT). He has been a faculty member at QUT since 2017, progressing from Lecturer to Associate Professor by 2024. He was previously a Lecturer at Monash University and a Research Assistant Professor at The University of Western Australia. He was awarded an ARC DECRA Fellowship from 2013 to 2017, highlighting his research excellence. Education: PhD in Theoretical Physics (with distinction), École Polytechnique Fédérale de Lausanne (EPFL) MSc (Dipl. Phys. ETH), Swiss Federal Institute of Technology Zurich (ETH Zurich) His research lies at the intersection of applied mathematics and biological systems, with a focus on mathematical modeling of tissue growth and remodeling. His work particularly emphasizes mechanobiology, stochastic processes, and the role of physical constraints such as curvature in cell and tissue behavior. He employs computational and theoretical frameworks to interpret experimental data and uncover underlying biological mechanisms. The recent publications highlight a strong trend in modeling tissue dynamics, particularly in curved and confined geometries, bone remodeling, and the integration of data-driven methods like equation learning with traditional modeling. His work spans reaction-diffusion systems, discrete-to-continuum modeling, and applications in tissue engineering and regenerative medicine. Scientific Awards: ARC DECRA Fellow (2013–2017) Dr Buenzli actively supervises PhD students in areas such as bone mechanobiology, equation learning, and tissue modeling. He has secured competitive research funding, including the ARC DECRA. He teaches undergraduate courses in engineering mathematics and advanced calculus. He is a member of several professional societies, including the Australian Mathematical Society, ANZIAM, MBSIG, ESB, and ISB. He leads the Mathematics in Medicine research group at QUT, focusing on tissue growth and biomedical engineering applications.

Lars Edvin BruView profile

Professor

• Educational Psychology
• Social and Emotional Competence in Education
• Teacher-Student Relationships
• +3 more

Lars Edvin Bru is a Professor in Educational Psychology at the Centre for Learning Environment , University of Stavanger. His research focuses on the interplay between social-emotional competencies , academic engagement , and mental health in educational settings . He has led randomized controlled trials (RCTs) on interventions promoting student well-being, including stress management and anti-bullying programs. His work emphasizes teacher support as a critical factor in shaping students' growth mindset , emotional well-being , and academic outcomes . He has extensively studied the psychosocial learning environment , motivational climates , and barriers to educational attainment , particularly among vulnerable populations such as first-generation immigrants and students with learning difficulties . Key research trends from his publications include: Longitudinal analysis of social-emotional learning and academic performance Impact of teacher-student relationships on student retention Interventions for stress reduction and emotional regulation in schools Interdisciplinary studies linking education , health psychology , and chronic disease management Bru collaborates on projects involving multidisciplinary educational consultation and simulation training for healthcare professionals. He has contributed to understanding treatment burden in chronic conditions like heart failure and breast cancer , highlighting the role of support systems in both educational and clinical contexts.

Alexey MilekhinView profile

Academic

• Theoretical Physics
• Quantum Gravity
• Quantum Information
• +3 more

Alexey Milekhin is a Postdoctoral Scholar and Research Associate in Theoretical Physics at the Institute for Quantum Information and Matter (IQIM) within the Division of Physics, Mathematics, and Astronomy at the California Institute of Technology (Caltech). His research focuses on foundational questions in quantum gravity, black hole physics, and quantum information theory, with particular emphasis on holography, entanglement entropy, and wormhole dynamics. He explores theoretical frameworks connecting quantum systems to gravitational phenomena, including studies of Sachdev-Ye-Kitaev (SYK) models, operator growth, and measurement-induced phase transitions. Key research interests include: Entanglement and tensor networks in holographic systems Scrambling and thermalization in quantum many-body systems Traversable wormhole solutions in quantum gravity Applications of SYK models to de Sitter space and black hole microstates Recent work highlights the universality of scar states in holographic systems and investigates entropy fluctuations in Hawking radiation without replica techniques. His contributions bridge high-energy physics, condensed matter theory, and quantum information science.

L. MahadevanView profile

Professor

• Movement Theory
• Soft Matter
• Biological Physics
• +5 more

Lakshminarayanan Mahadevan, known as L. Mahadevan, is the Lola England de Valpine Professor of Applied Mathematics at Harvard University, with joint appointments as Professor of Organismic and Evolutionary Biology and Professor of Physics. He is a core faculty member of the School of Engineering and Applied Sciences (SEAS), where he leads interdisciplinary research spanning mathematics, physics, and biology through the Mahadevan group. His research explores the physics of living and non-living matter across scales, from molecular to planetary systems. Key interests include soft matter physics, biological morphogenesis, fluid dynamics, elasticity, and movement theory. Using experiments, theory, and computation—termed "Soft Math"—his work uncovers universal principles in complex systems, with recent focus on active matter, nematic systems, and tissue mechanics. Applications range from brain folding and snake locomotion to battery design and robotic systems. 2025 publications reveal intense activity in active matter physics, biological fluid dynamics, and applied mathematics. Dominant themes include nematic film instabilities, stochastic navigation in crowded environments, solid-state battery interfaces, and soft biological structures. Work consistently bridges theoretical modeling with experimental validation, drawing biological inspiration for engineering solutions while addressing challenges from cellular to planetary scales. The Mahadevan group at Harvard SEAS operates as a collaborative hub for mathematicians, physicists, biologists, and engineers. It maintains extensive cross-departmental collaborations within Harvard and international partnerships, emphasizing fundamental questions with tangible applications in biotechnology, materials science, and robotics. The group's culture prioritizes curiosity-driven exploration of everyday phenomena to reveal profound scientific principles.

Saso IvanovskiView profile

Professor

• Periodontal regeneration
• Dental implants
• Bone tissue engineering
• +7 more

Professor Saso Ivanovski is a distinguished academic at the University of Queensland, serving as Head of School of Dentistry within the Faculty of Health, Medicine and Behavioural Sciences. He also directs the Centre for Orofacial Regeneration, Reconstruction and Rehabilitation (COR3) and is affiliated with the Centre for Extracellular Vesicle Nanomedicine. His leadership positions reflect his significant contributions to dental research and education. Professor Ivanovski earned his academic credentials at the University of Queensland, completing a Bachelor of Dental Science, a Masters (Coursework) of Dental Science, and a Doctor of Philosophy. His educational foundation at UQ has supported his extensive research career spanning over two decades. Ivanovski's research focuses on cutting-edge approaches to periodontal regeneration, dental implantology, and tissue engineering. His work integrates nanotechnology, 3D printing, and stem cell biology to develop innovative solutions for oral health challenges. He investigates the molecular mechanisms of tissue healing, biofilm dynamics, and the application of extracellular vesicles in regenerative dentistry. His interdisciplinary approach bridges basic science with clinical applications, aiming to translate laboratory discoveries into practical dental treatments. The 15 most recent publications demonstrate a strong trend toward personalized dental medicine, with emphasis on bioengineered scaffolds, nanotechnology applications, and immunomodulation strategies. His research spans fundamental science exploring cellular and molecular mechanisms to clinical trials evaluating novel therapeutic approaches for periodontitis and bone regeneration. A significant portion of his work focuses on 3D printing technologies for creating patient-specific dental implants and scaffolds. Professor Ivanovski actively supervises numerous PhD and Master's students across diverse research topics including biomaterials development, stem cell applications, and novel diagnostic approaches for periodontal disease. He has secured substantial research funding from various sources including the International Team for Implantology Foundation, Osteology Foundation, Australian Dental Research Fund, NHMRC, and industry partners such as Colgate-Palmolive. His laboratory work centers around the Centre for Orofacial Regeneration, Reconstruction and Rehabilitation (COR3), where interdisciplinary teams investigate innovative approaches to dental tissue regeneration. The research environment integrates expertise in biomaterials science, nanotechnology, molecular biology, and clinical dentistry to address complex challenges in oral health care.

Jan EngvallView profile

Visiting Professor

• Cardiovascular Imaging
• Cardiac MRI
• Patient-Based Diagnostics
• +3 more

Jan Engvall is a Visiting Professor at Linköping University, working within the Department of Health, Medicine and Care and the Department of Diagnostics and Specialist Medicine. He divides his time between research in patient-based diagnostics and clinical work in cardiac imaging. His educational background includes a Medical degree from the University of Gothenburg (1979), a Dissertation from Linköping University (1993), and a Postdoctoral fellowship at Stanford University (2001). He achieved Associate Professor status in 2002 and was appointed Adjunct Professor in 2011. Dr. Engvall's research focuses on cardiovascular imaging, particularly using MRI techniques to develop more accurate and less stressful examination methods for patients. His work is closely tied to the Swedish Cardiopulmonary BioImage Study (SCAPIS), a large population-based study examining cardiovascular and pulmonary health in 30,000 individuals aged 50-65. He also contributes to the CARDIPP study, which investigates cardiovascular risk factors in patients with type 2 diabetes, and the Cardiovascular Imaging and Modeling (CIM) research cluster, which develops new image-based methods for assessing heart function and hemodynamics. Analysis of his recent publications reveals a strong focus on advanced cardiovascular imaging techniques, particularly 4D Flow MRI applications. His research spans multiple areas including valvular heart disease, atherosclerosis detection, cardiac remodeling, and the relationship between metabolic factors and cardiovascular health. Much of his work leverages data from the SCAPIS cohort, examining subclinical manifestations of cardiovascular disease in the general population. Dr. Engvall serves on the local steering group for SCAPIS in Linköping and acts as a technical assessor for Swedac regarding the quality standard ISO 17025. His educational contributions include teaching functional anatomy of thoracic organs in the medical program and pressure and flow courses in the Master of Science in Medical Technology program. He collaborates extensively with colleagues including Professor Tino Ebbers, Professor Carl-Johan Carlhäll, and Adjunct University Lecturer David Kylhammar within the Cardiovascular Sciences Unit at Linköping University.

Eric V. SludView profile

Professor

• Mathematical Statistics
• Probability
• Census Statistics
• +11 more

Eric V. Slud is a Professor in the Statistics Program within the Department of Mathematics at the University of Maryland, College Park. He has taught numerous graduate-level statistics courses including Mathematical Statistics, Categorical Data Analysis, and High-Dimensional Statistics since at least 2017. Dr. Slud's research spans several key areas of mathematical statistics and probability. His work focuses on: Census statistics, particularly demographic modeling of nonresponse to national surveys Small Area Estimation (SAE) with applications to the SAIPE program Survival data analysis including semiparametric inference and clinical trial design Meta-analysis in biostatistics Pharmaceutical statistical methods Large-scale data problems with emphasis on cross-classified data His publications demonstrate continued contributions across these domains, with particular emphasis on small area estimation methodologies, survival analysis techniques, and applications in survey statistics. Dr. Slud has developed innovative approaches to weighting adjustment, nonresponse correction, and estimation with complex survey data. Dr. Slud has advised several PhD students including Yang Cheng (2004), Sophie Tsou (2005), and others working on topics ranging from Factor Analysis to Survival Data. He has collaborated extensively with researchers at the Census Bureau and FDA. Dr. Slud maintains active research programs through multiple Research Interaction Teams (RITs) at the University of Maryland, focusing on advanced statistical methodologies and their applications to real-world problems in census methodology, healthcare, and other domains requiring sophisticated statistical analysis.

• Efficient Algorithms
• Computational Complexity
• Network Foundations
• +5 more

Dr. George B. Mertzios is an Associate Professor at the Department of Computer Science, Durham University , UK. His career spans roles including Senior Lecturer (2015-2017) and Lecturer (2011-2015) at Durham, with additional positions as an Invited Assistant Professor at LaBRI, University of Bordeaux/CNRS, France (2012), and postdoctoral research fellowships at the University of Haifa and Technion, Israel (2010-2011). He earned his PhD in Computer Science from RWTH Aachen University (2009) and a Diplom in Mathematics from Technische Universität München (2005). Education PhD in Computer Science, RWTH Aachen University, 2009 Diplom in Mathematics (minor in Computer Science), Technische Universität München, 2005 Research Interests focus on efficient algorithms and computational complexity in temporal graphs , with significant contributions to dynamic network optimization , evolutionary graph theory , and parameterized complexity . His work bridges combinatorial optimization , algorithmic game theory , and intersection graph models . Scientific Activities include organizing Co-Chair of the PC for SAND 2026 Co-Organizer of Dagstuhl Seminar 26251 (2026) Organizer of the Algorithmic Aspects of Temporal Graphs workshops (2018-2025) His 15 most recent articles (2025-2021) address problems in temporal graph realization , dynamic network optimization , Hamiltonian cycles , and epidemic control on temporal networks , published in top venues like Theoretical Computer Science , Journal of Computer and System Sciences , and Algorithmica . Scientific Awards include a Gold Medal in the 1998 Balkan Mathematical Olympiad, Distinguished Diploma in the 1998 Bulgarian National Mathematical Competition, and Best Paper Awards at ALGOWIN 2025 and ICALP 2010 Track C. Research Supervision includes advising PhD students: David Fairbairn (in progress), David Kutner (2025), Nina Klobas (2024), Charles Murray (2021), Sepehr Meshkinfamfard (2016), Ioannis Lignos (2016) Postdoctoral researchers: Christoforos Raptopoulos (2020), Viktor Zamaraev (2017-2019), André Nichterlein (2016-2017), Archontia Giannopoulou (2014-2015), Konrad Dabrowski (2012-2013) Grants led include EPSRC grants EP/P020372/1 (2017-2020) on algorithmic aspects of temporal graphs and EP/K022660/1 (2013-2015) on intersection graph models. He also contributed to the EU IP MULTIPLEX (2012-2016).

• Wellbeing
• Eating Disorders
• Personality Disorders
• +5 more

Mirjam Radstaak is an Assistant Professor in Psychology, Health & Technology with a focus on mental health, well-being, and clinical psychology. Her research spans eating disorders, personality disorders, posttraumatic stress disorder, and psychometric network analysis. She has published extensively in peer-reviewed journals, emphasizing therapeutic interventions and longitudinal studies. Key Research Areas: Well-being, Eating Disorders, Personality Disorders, PTSD, Psychometrics, Emotion Regulation Her recent work explores psychometric network models, intervention mapping for creative therapies, and compassion-based exercises for affect regulation. She received the Journal of Eating Disorders Paper of the Year award (2018). Current affiliations suggest roles in clinical and academic research, though institutional details remain unspecified.

Zaigham SaghirView profile

Associate Clinical Professor

• Respiratory Medicine
• Lung Cancer Screening
• Pulmonary Nodule Analysis
• +3 more

Zaigham Saghir serves as a Clinical Associate Professor in the Department of Clinical Medicine at the University of Copenhagen's Faculty of Health and Medical Sciences. His clinical role as a Consultant at Copenhagen University Hospital (Region H) is based at Gentofte Hospitalsvej 1 in Hellerup, with academic affiliation to the Blegdamsvej campus in Copenhagen. His dual appointment bridges clinical respiratory medicine with academic research, focusing on translational applications in thoracic oncology. Dr. Saghir's research centers on lung cancer screening optimization , pulmonary nodule management , and AI integration in thoracic imaging . His work explores the intersection of deep learning algorithms with clinical decision-making, particularly in risk stratification for pulmonary nodules and post-treatment surveillance. Recent publications demonstrate expertise in validating AI models against European screening data and developing safety mechanisms for out-of-distribution detection in medical imaging systems. His research program addresses critical gaps in lung cancer early detection through both technological innovation and population health approaches. Analysis of his 28 research outputs (2023-2025) reveals strong emphasis on translational respiratory oncology with three dominant threads: (1) AI-driven pulmonary nodule assessment (40% of recent work), (2) lung cancer screening program design (30%), and (3) post-treatment surveillance protocols (30%). His publications consistently bridge computational methods with clinical implementation challenges, particularly within the Danish healthcare context. Notable collaborations include the NELSON-POP consortium and SUPE_R Study Group, indicating leadership in European multicenter trials. His research is supported through clinical-academic channels at Copenhagen University Hospital and the University of Copenhagen, with evidence of participation in major European trials. The collaborative network spans radiology, respiratory medicine, oncology, and medical informatics departments across multiple Danish hospitals.