جست‌وجوی استاد و پژوهشگر

• Ophthalmology
• Corneal Endothelial Health
• Cataract Surgery Outcomes
• +۳ مورد دیگر

Clinical Associate Professor Keith Ong is affiliated with the University of Sydney’s Northern Clinical School, specifically within the Department of Clinical Ophthalmology & Eye Health. His clinical specialty is Ophthalmology, with a focus on corneal endothelial health, glaucoma surgery outcomes, and cataract surgical techniques. His research addresses critical areas such as postoperative ocular toxicity, intraocular lens calculations, and infection control in eye surgeries. Dr. Ong’s publications (2011–2023) consistently explore topics like corneal endothelial changes post-trabeculectomy, refractive error management post-cataract surgery, and antibiotic prophylaxis strategies. Recent work emphasizes optimizing surgical techniques to reduce complications, such as BAK-induced corneal toxicity and IOL power calculation accuracy in LASIK patients. His work demonstrates a focus on improving surgical safety and precision, with studies evaluating alternative antibiotic delivery methods and laser therapy impacts. No awards or grants are explicitly noted in the provided text, but his contributions to clinical practice and surgical protocols are evident through his publication record.

Stefan Lutherمشاهده پروفایل

استاد

• Biomedical Engineering
• Cardiac Physiology
• Nonlinear Dynamics
• +۳ مورد دیگر

Prof. Dr. Stefan Luther is a Max Planck Research Group leader (W2, tenured since 2013) at the Max Planck Institute for Dynamics and Self-Organization, Göttingen, and an Honorarprofessor at the Faculty of Physics, University of Göttingen. He holds adjunct roles as Adjunct Associate Professor at Cornell University (2009–2012) and Northeastern University (2016–2018), and serves as DZHK-Professor at the Institute of Pharmacology and Toxicology, University Medical Center Göttingen. His research focuses on nonlinear spatiotemporal dynamics in excitable biological media, particularly cardiac arrhythmias. He pioneered 4D imaging of heart function and developed algorithms for optogenetic and electrical control of arrhythmias. Translational efforts span basic research to preclinical and clinical studies. Education includes a Diplom in Physics (1997) and PhD (2000) from Georg-August-University, Göttingen. Postdoctoral training followed at the University of Twente (2001–2004) and Cornell University’s LASSP (2004–2006). His lab, the Biomedical Physics group, explores electromechanical coupling in cardiac systems and develops novel therapeutic approaches. Collaborations include work on computational modeling, uncertainty quantification in dynamical systems, and fluid dynamics of multiphase flows.

David Alldredمشاهده پروفایل

استاد

• Medicines Optimisation
• Patient Safety
• Deprescribing
• +۵ مورد دیگر

Professor David Alldred is a Professor of Medicines Use and Safety at the University of Leeds' School of Healthcare, within the Faculty of Medicine and Health. His expertise spans medicines optimisation, patient safety, deprescribing, and care home medication management. He leads key research initiatives such as the NIHR Yorkshire and Humber Patient Safety Research Collaboration's 'Decluttering Safely for Safety' theme and co-leads the School of Healthcare's Quality and Safety research theme. Education: PhD (clinical research on care home medication review), MSc in Clinical Pharmacy, PGCert in Learning and Teaching, and BPharm (Hons). Professional memberships include Fellowships of the Royal Pharmaceutical Society and the Higher Education Academy. Research focuses on improving medication use for older adults in care homes and underserved populations, employing systematic reviews, qualitative studies, and RCTs. Notable projects include the CHUMS study (Department of Health-funded medication error analysis in care homes) and leadership roles in NIHR Programme Grants like CHIPPS and CHARMER. He collaborates with NHS England to implement medicines optimisation programs and co-designed multilingual medication review resources to reduce health inequalities. Awards include the Pharmacy Practice UK Research Award (2015) and NIHR Senior Investigator status (2025). His work emphasizes translating evidence into clinical practice, with over 150 publications and supervising NIHR-funded doctoral students in critical care, diabetes, cystic fibrosis, and geriatrics. Led the Care Homes' Use of Medicines Study (CHUMS), contributed to NICE guidelines for care home medicines management, and developed interventions like the Medicines at Transitions Intervention (MaTI) for heart failure patients. His research themes address polypharmacy reduction, medication safety across care transitions, and pharmacist-led deprescribing strategies.

Naisyin Wangمشاهده پروفایل

استاد

• Longitudinal Data Analysis
• Functional Data Analysis
• Biostatistics
• +۵ مورد دیگر

Naisyin Wang is a Professor of Statistics at the University of Michigan, where she has been since 2009. Previously, she served as a faculty member in Statistics and Toxicology at Texas A&M University from 1992. She holds a Ph.D. in Statistics from Cornell University (1992), an M.A. in Statistics from Ohio State University (1987), and a B.S. in Mathematics from National Tsing-Hua University, Taiwan (1986). Her research focuses on longitudinal and functional data analysis, measurement error models, semiparametric methods, and applications in biological and medical fields, particularly genomics and metabolomics. Key contributions include methodologies for handling missing data, mixed effects models, and clustering techniques. Education: Ph.D. in Statistics, Cornell University (1992) M.A. in Statistics, Ohio State University (1987) B.S. in Mathematics, National Tsing-Hua University (1986) Dr. Wang’s honors include the College of Science Distinguished Alumni Award (2012), the Distinguished Achievement Award in Research (2003), and fellowships from the AAAS, ASA, and IMS. She has held leadership roles, including Co-editor of Statistica Sinica (2011–2014) and President of the International Chinese Statistical Association (2010). Her teaching includes courses such as Applied Statistics (STATS 500), Linear Models (STATS 600), and Special Topics in Applied Statistics (STATS 700). She has advised numerous students and contributed to research on cancer genomics, dietary interventions, and statistical methodology.

• Statistics
• Optimization
• Machine Learning
• +۳ مورد دیگر

Andrew Li is an Associate Professor of Operations Research at Carnegie Mellon University's Tepper School of Business since 2024, previously serving as Assistant Professor since 2018. His research bridges statistics, optimization, and machine learning with applications to healthcare operations and retail management. Current teaching: Optimization, Business Analytics Capstone, and Topics in Optimization and Statistics PhD from MIT's Operations Research Center (2018), BS in Operations Research/Applied Mathematics from Columbia University (2012) Research Focus: Dr. Li develops data-driven decision frameworks for complex systems. Key areas include: Experience-based learning models with fairness constraints (organ allocation) Anomaly detection in low-rank matrices (retail inventory accuracy) Nanoparticle-based diagnostic systems for CAD and Alzheimer's Nonstationary demand forecasting in supply chains Publication Trends: Recent work combines bandit algorithms with healthcare applications (split liver transplants, CAD detection) and retail operations (inventory accuracy). Theoretical contributions include regret-optimal policies and entrywise anomaly detection guarantees. Scientific Honors: INFORMS Nicholson Award (2018) INFORMS Pierskalla Award (2021) NSF CAREER Award (2023) Professional Leadership: Active in INFORMS and CMU committees including MBA Analytics Curriculum, Thompson Award, and ENAiBLE AI-driven retail collaborative co-founder since 2021.

Gabriela V. Cohen Freueمشاهده پروفایل

دانشیار

• Statistical Methodology
• High-Dimensional Data Analysis
• Robust Statistics
• +۶ مورد دیگر

Gabriela V. Cohen Freue is an Associate Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver Campus, and holds a Canada Research Chair (CRC Tier 2). She leads an interdisciplinary research program focusing on developing robust statistical methodologies for analyzing high-dimensional data in genomics and proteomics, with applications in medical sciences. Her work addresses challenges such as outliers, collinearity, and measurement errors, with applications in biomarker discovery for diseases like multiple sclerosis, cardiovascular disorders, and asthma. Her academic journey includes collaborations across disciplines, including with the BC Cancer Agency, PROOF Centre of Excellence, and iCAPTURE. She has pioneered methods like the Penalized Elastic Net S-Estimator (PENSE) and contributed to proteomic data analysis tools such as the Protein Group Code Algorithm (PGCA). She also co-developed the MDQC quality control method for microarrays. Research interests include robust regression, biomarker development, and statistical methods for big data. Her team includes postdocs, PhD, and MSc students, with a focus on training in both statistical rigor and interdisciplinary collaboration. Notable grants include a CANSSI Collaborative Research Team Project (CRT) award for robust causal inference and prediction modeling. Teaching responsibilities span statistical consulting, high-dimensional biological data analysis, and generalized linear models. She emphasizes active learning and real-world problem-solving in her courses. Her lab’s work is supported by grants from the Data Science Institute (DSI) and collaborations with institutions like the PROOF Centre. Alumni of her group hold positions in academia (e.g., George Mason University) and industry (e.g., Merck, BC Cancer Research Centre).

John Nassourمشاهده پروفایل

پژوهشگر

• Computational Cognitive Systems
• Robotics
• Wearable Robotics
• +۵ مورد دیگر

John Nassour is a Researcher at the Technical University of Munich's School of Computation, Information and Technology, affiliated with the Chair of Cognitive Systems. He holds engineering degrees from Tishreen University (electronics), a Master's in intelligent systems from University of Cergy-Pontoise/École Nationale Supérieure de l'Électronique, and a joint PhD from University of Versailles/TUM. His interdisciplinary research focuses on computational cognitive systems applied to robotics, including wearable devices, humanoid robots, soft robotics, and robot learning for locomotion/manipulation. Before joining TUM in 2020, he was a lecturer/researcher at Chemnitz University of Technology. He teaches courses in cognitive systems, neuro-inspired engineering, and soft robotics.

Serge Fehrمشاهده پروفایل

استاد

• Cryptology
• Post-Quantum Cryptography
• Information-Theoretic Cryptography
• +۵ مورد دیگر

Serge Fehr is a Senior Researcher in the Cryptology Group at CWI (Centrum Wiskunde & Informatica) in Amsterdam and a part-time Professor at the Mathematical Institute of Leiden University. His research focuses on foundational aspects of cryptology, including post-quantum cryptography, information-theoretic security, zero-knowledge proofs, and secure multiparty computation. He participates in AMSec (Amsterdam Cyber Security Center) and leads work packages in the NWO-funded HAPKIDO consortium. Education: M.Sc. in Mathematics from ETH Zürich (1998) Ph.D. in Cryptography from ETH Zürich and University of Aarhus (2003) Postdoc at Macquarie University (2003-2004) Research Interests: Post-quantum cryptographic primitives (e.g., digital signatures, lattice-based schemes) Quantum-resistant protocols (e.g., non-resignable signatures, Fiat-Shamir transforms) Foundational security proofs in the quantum random oracle model (QROM) Secure multiparty computation and privacy-preserving healthcare systems Key Activities: Editorial Board Member: Journal of Cryptology , IEEE Transactions on Information Theory Program Committee Co-Chair: EUROCRYPT 2025 Steering Committee Member: Beyond IID Information Theory, QCrypt Co-organizer: Symposium Series on Post-Quantum Cryptography Grants/Awards: NWO Cybersecurity consortium grant (HAPKIDO - 2021) NWO Veni Grant (2005) NWO Open & Free Competition Grants (2008, 2013) Students/Advising: Supervised or served on committees for over 15 Ph.D. students, including work on post-quantum signatures, MPC applications, and lattice-based cryptography. Labs/Teams: Active in CWI’s Cryptology Group and Leiden’s Mathematical Institute, collaborating with industry partners (e.g., KPN, Microsoft) in cybersecurity initiatives.

Michael Wiklundمشاهده پروفایل

استاد

• Human Factors Engineering
• Medical Device Design
• Usability Testing
• +۳ مورد دیگر

Michael Wiklund is a current Professor of the Practice in the Department of Mechanical Engineering at Tufts University School of Engineering . He has been in this role since 2014, previously serving as a Lecturer from 1987 to 2013. His work focuses on Human Factors Engineering , Medical Device Design , and Usability Testing in healthcare contexts. His research spans decades, emphasizing ergonomics , software user interfaces , and safety in medical systems . He has authored key publications including books and articles on usability engineering , root cause analysis , and design principles for medical technology. Wiklund teaches courses such as Medical Technology Development and Human Factors in Medical Technology , with a focus on Biomedical Engineering and Mechanical Engineering . His recent publications highlight trends in medical device innovation , user-centered design , and health IT integration. He also serves as General Manager - Human Factors Research & Design at UL since 2012.

Luca Carloniمشاهده پروفایل

استاد

• Computer Science
• Embedded Systems
• Hardware Design
• +۶ مورد دیگر

Luca Carloni is a Professor of Computer Science and Department Chair at Columbia University's Columbia Engineering. He leads the System-Level Design Group, focusing on heterogeneous system-on-chip (SoC) architectures, networks-on-chip (NoC), and embedded systems. Carloni holds a Laurea Summa Cum Laude in Electronics Engineering from the University of Bologna and a PhD in Electrical Engineering and Computer Sciences from UC Berkeley. His work emphasizes specialized hardware design, energy-efficient computing, and FPGA-based prototyping. Research interests include system-level design methodologies for SoCs, embedded accelerators, and quantum computing hardware. He has pioneered frameworks like Embedded Scalable Platforms (ESP) and tools like MosaicSim for rapid SoC prototyping. Carloni has received numerous awards, including the NSF CAREER Award (2006), IEEE Fellow (2017), and multiple best paper awards at DATE and CloudCom conferences. He has served on editorial boards of IEEE Transactions on CAD and ACM Transactions on Embedded Computing , and chaired key conferences like EMSOFT and ESWeek. His research addresses challenges in heterogeneous architectures, power management, and the intersection of machine learning with embedded systems. Current projects explore quantum control systems, brain-computer interfaces, and energy-efficient datacenter computing.

Julian Bergerمشاهده پروفایل

پژوهشگر ارشد

• Human-AI Interaction
• Collective Intelligence
• Machine Learning
• +۳ مورد دیگر

Julian Berger is a postdoctoral researcher at the Max Planck Institute for Human Development in the Center for Adaptive Rationality , where he explores how to enhance decision-making through hybrid human-AI systems. He is also a fellow of the Joachim Herz Foundation and has received funding from the Foundation of German Business and the Danish Data Science Academy. Education: M.A. Psychology in Business and Economics, Universidade Catolica Portuguesa (2021) B.A. Politics, Administration and International Relations, Zeppelin Universität (2018) His research spans human-AI collaboration , collective intelligence , and interpretable machine learning . A recurring theme in his work is developing methods to combine human expertise with AI capabilities for accuracy in domains like medical diagnostics , credit scoring , and football analytics . He has authored publications in high-impact venues such as PNAS , Nature Human Behavior , and Science and Medicine in Football . Scientific awards and funding include: Fellowship for interdisciplinary economics, Joachim Herz Foundation (2024) PhD funding from the Foundation of German Business (Stiftung der deutschen Wirtschaft) Research grant from the Danish Data Science Academy His recent article trends emphasize ensembling techniques that leverage complementary human and AI errors, algorithmic fairness, and practical heuristics like Hybrid Confirmation Trees. These works demonstrate significant improvements in diagnostic accuracy and decision cost-efficiency. Beyond academia, Berger works as a consultant and ML engineer with Simply Rational , focusing on interpretable models for financial and sports analytics. His work bridges theoretical research with real-world applications, prioritizing fairness, transparency, and human accountability in AI systems.

Jürgen Bernardمشاهده پروفایل

استادیار

• Visual Analytics
• Information Visualization
• Interactive Machine Learning
• +۱۲ مورد دیگر

Jürgen Bernard is an Assistant Professor of Computer Science at the University of Zurich , leading the Interactive Visual Data Analysis (IVDA) Group . He is associated with the Digital Society Initiative (DSI) and holds a PhD in Computer Science from Technische Universität Darmstadt (2015) with a focus on time-oriented data analysis. His academic journey includes postdoctoral research at TU Darmstadt and the University of British Columbia. Education : Diploma in Computer Science (2009, TU Darmstadt) PhD in Computer Science (2015, TU Darmstadt) Research Interests : Dr. Bernard specializes in interactive visual data analysis , explainable machine learning , and human-centered AI . His work explores time series analysis , multivariate data exploration , and user-driven preference elicitation . He develops visual analytics systems for domains like healthcare , digital humanities , and industrial applications , with a particular focus on responsible AI and transparency in algorithmic systems . Research Trends : His publications emphasize interactive machine learning workflows , visual analytics for healthcare , and time-stamped event sequence analysis . Recent work includes LLM validation frameworks (Human-Data-Model Interaction Canvas) and personalized ranking systems funded by the Swiss National Science Foundation. He integrates temporal data with multivariate analysis across applications from medical manufacturing to chronic disease management . Scientific Recognition : EuroGraphics Young Researcher Award (2022) EuroVis Young Researcher Award (2021) Best Paper Awards at IEEE VIS (2021), EuroVA (2021, 2025) Dirk Bartz Prize (2017), Hugo-Geiger Preis (2016) Teaching & Grants : He teaches Interactive Visual Data Analysis (6 ECTS), Digital Health Seminars , and People-Oriented Computing . Currently leads a SNF Grant on Personalized Visual Analytics for multi-criteria decision support (2024-2028) with ETH Zurich's Prof. M. El-Assady.

Helmut H. Streyمشاهده پروفایل

دانشیار

• Biomedical Engineering
• Biophysics
• Microfluidics
• +۶ مورد دیگر

Helmut H. Strey is an Associate Professor in the Department of Biomedical Engineering at Stony Brook University. His research focuses on micro- and nanotechnologies for quantitative biology , including single-cell analysis, cancer metabolism modeling, and functional MRI data analysis. He holds academic appointments since 2008 and has pioneered technologies like tumor-on-a-chip and optical decoders for translation stages. Education: PhD in Biophysics (Technical University München, 1993), postdoctoral training at NIH (1994-1998). Awards include the NSF CAREER Award (2000-2005), Dillon Medal (2003), and Weston Visiting Professorship (2020). Research interests span cell-to-cell variability , Warburg effect in cancer , and Bayesian analysis of time-series data . His lab develops tools for 3D tumor microenvironments, MRI-compatible drug delivery systems, and biomimetic neural circuit models. Teaching includes advanced numerical methods in biomedical engineering, quantitative biology, and biomolecular analysis. Active in open hardware projects, including microfluidics controllers and IoT devices for health monitoring.

• Numerical Analysis
• Computational Fluid Dynamics
• Applied Mathematics
• +۳ مورد دیگر

Jennifer Ryan is a Professor of Numerical Analysis and Division Head of Numerical Analysis, Optimization, and Systems Theory at the Department of Mathematics, KTH Royal Institute of Technology. Her research focuses on designing and developing numerical schemes to extract accuracy from simulations, particularly through superconvergence properties and computational efficiency improvements. She applies these techniques to applications such as imaging, fluid visualization, and plasma dynamics. Education: PhD in Applied Mathematics, Brown University; MS in Mathematics, Courant Institute; BA in Applied Mathematics, Rutgers University. Professional Activities: Member of editorial boards for BIT Numerical Mathematics, ESAIM:M2AN, and Communications on Applied Mathematics and Computation; Steering committee member of AWM's Women in Numerical Analysis and Scientific Computing (WINASc). Her publications emphasize discontinuous Galerkin methods, SIAC filtering, and applications in fluid dynamics. She has served on multiple grant review panels and received awards for diversity and inclusion initiatives. Grants: Principal Investigator for projects funded by the Swedish Research Council, NSF, and US Air Force Office of Scientific Research. Awards: Fellow of UK Higher Education Academy, DAAD Fellowship, and Householder Fellowship.

Ravi Aronمشاهده پروفایل

استاد

• Healthcare IT
• Emergent Technologies
• Healthcare Startups Valuation
• +۶ مورد دیگر

Ravi Aron is a Professor of Healthcare Strategy & Technology at the C. T. Bauer College of Business, University of Houston, and Research Director of the Healthcare Business Institute. He holds a joint appointment in the Department of Health Systems & Population Health Sciences at the Tilman J. Fertitta Family College of Medicine. He earned his Ph.D. in Management Information Systems from New York University's Stern School of Business. His research focuses on healthcare IT, emergent technologies in healthcare operations, valuation of healthcare startups, and AI applications in healthcare. He has published widely in top journals like Management Science and Information Systems Research, and his work bridges information systems, operations management, and technology strategy. Dr. Aron has extensive teaching experience at The Wharton School, Johns Hopkins Carey Business School, and NYU Stern, winning multiple teaching awards. He advises Fortune 500 firms, startups, and policymakers on technology strategy, digital transformation, and risk assessment. His executive education programs address AI, machine learning, and digital business models for global executives. Key awards include the Dean's Faculty Excellence Award (2016), multiple teaching accolades from Wharton and Johns Hopkins, and the Herman E. Kross Best Dissertation Award (1999). His current projects explore healthcare supply chains, predictive models using machine learning, and valuing technology-enabled startups. He regularly participates in global forums like the World Economic Forum, advising on healthcare innovation and technology policy.