جست‌وجوی استاد و پژوهشگر

Hutan Ashrafianمشاهده پروفایل

استاد

• Artificial Intelligence in Medicine
• Metabolic Surgery
• Health Policy
• +۴ مورد دیگر

Professor Hutan Ashrafian is a clinician-scientist and surgeon at the University of Leeds Business School, holding dual roles as Professor of Research Impact and Senior Research Fellow at Imperial College London. His expertise spans AI in healthcare, metabolic surgery, and ancient history. He pioneered STARD-AI and QUADAS-AI guidelines for AI diagnostics, collaborated on AI-driven breast cancer screening with Google and NHS, and led global health policy initiatives. Ashrafian’s work bridges medicine, history, and philosophy, including his contributions to understanding historical figures’ medical conditions, such as King Tutankhamun’s epilepsy. He has authored over 550 publications, 12 books, and holds a PhD in computational physiology and an MBA with distinction. Awards include the Hunterian Prize and Wellcome Trust Fellowship. His research also explores AI ethics, quantum physics paradoxes, and art-based medical diagnostics. Education: PhD in Computational Physiology (Imperial College London) MBA (Warwick Business School) MD (University College London) Bachelor of Science in Immunology (University College London) Research Interests: Ashrafian’s work spans: AI in Medicine: Diagnostic algorithms, guideline development (STARD-AI/QUADAS-AI), and ethical frameworks. Metabolic Surgery: Bariatric interventions, gut microbiome, and obesity treatments. Ancient History: Medical analyses of historical figures (e.g., Tutankhamun, Julius Caesar) and art-based pathology identification. Philosophy: AI rights (AIONAI law), temporal paradoxes, and the Simulation Argument. Awards: Royal College of Surgeons Arris and Gale Lectureship Hunterian Prize Wellcome Trust Research Fellowship Advising & Innovation: Supervised >50 PhD students, co-founded Oxford Medical Products (weight loss tech), and serves as CSO at Flagship Pioneering’s Preemptive Health division. He advises on NHS digital transformation and global health policy. Labs/Teams: Leads AI initiatives at Imperial’s Institute of Global Health Innovation and collaborates with Google, NICE, and international institutions on health tech solutions.

Anamaria Crisanمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Data Visualization
• Applied AI/ML
• +۳ مورد دیگر

Anamaria Crisan is an Assistant Professor at the University of Waterloo, affiliated with the Insight Lab. Her research focuses on interdisciplinary work at the intersection of Human-Computer Interaction (HCI), Data Visualization, and Applied AI/ML. She explores human-centered approaches to AI/ML systems, visualization design for decision-making, and healthcare data science applications. Dr. Crisan holds a PhD in Computer Science from the University of British Columbia (2019), an MSc in Bioinformatics (2010), and a BComp in Biomedical Computing from Queen’s University (2008). Her educational background bridges computer science, biology, and healthcare informatics. Her research interests include responsible AI/ML systems, interactive visualization for data-driven decisions, and leveraging visualization in healthcare to improve outcomes. She emphasizes transparency, trustworthiness, and human alignment in AI technologies. Her work spans diverse applications such as genomic epidemiology, dashboard design, and ethical AI evaluation. Notable contributions include studies on human-AI collaboration, visualization linters, and scalable dashboard census methodologies. She has published widely in top-tier venues like IEEE VIS and ACM CHI. Dr. Crisan’s lab (UW Insight Lab) focuses on human-centered approaches to automating data science and improving visualization practices in critical domains like healthcare and public health.

Jimeng Sunمشاهده پروفایل

استاد

• Artificial Intelligence
• Healthcare Analytics
• Drug Discovery
• +۵ مورد دیگر

Dr. Jimeng Sun is a Health Innovation Professor at the Siebel School of Computing and Data Science and Carle Illinois College of Medicine at the University of Illinois Urbana-Champaign. Co-founder of Keiji AI , he leads groundbreaking research at the intersection of artificial intelligence and healthcare, actively deploying clinical AI systems and developing frameworks like PyHealth and Therapeutics Data Commons . His research spans four major areas: Clinical AI Systems : Developing interpretable models (e.g., RETAIN) for patient similarity, temporal event prediction, medication recommendation, and clinical outcome forecasting Drug Discovery : Creating molecular optimization frameworks, drug-target interaction models, and AI-driven platforms Clinical Trials : Pioneering patient-trial matching, outcome prediction, and optimization frameworks using deep learning and graph neural networks Biosignal Analysis : Advancing sleep staging, seizure classification, and automated EEG/Cardiac monitoring systems With over 500 top-tier publications (including in Nature , NEJM AI , and leading AI conferences) and an h-index of 99, his work has been recognized with the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare award. He maintains active collaborations with institutions like Massachusetts General Hospital , Medidata Solutions , and OSF Healthcare . His recent publications reveal a strong focus on: Reinforcement learning applications in medical data analysis Large language model adaptation for clinical tasks Knowledge graph integration with AI systems Synthetic data generation for healthcare Multi-modal learning in clinical contexts Explainable AI for medical applications Dr. Sun's lab ( Sunlab ) emphasizes practical impact over theoretical work, actively collaborating with hospitals and healthtech companies. He welcomes contributions from clinicians, researchers, and industry partners through initiatives like his AI for Health webinar series .

• Bayesian statistics
• Stochastic simulation
• Markov chain Monte Carlo
• +۵ مورد دیگر

Professor Chris Holmes is a Professor of Biostatistics at the University of Oxford, where he moved from Imperial College London in February 2004. He is a Fellow at St Anne's College and works in the Department of Statistics. His research focuses on applications and statistical methods development in genomic sciences and genetic epidemiology, holding a prestigious Programme Leaders Grant in Statistical Genomics from the Medical Research Council. Prior to his position at Oxford, Professor Holmes completed his doctorate in Bayesian statistics at Imperial College London, investigating novel nonlinear pattern recognition methods. This was followed by a post-doctoral position and then a lectureship at Imperial. Before his academic career, he worked in industry for several years in scientific computing, developing techniques for real-time pattern recognition models in defense and SCADA systems. Professor Holmes has a broad interest in the theory, methods and applications of statistics and statistical modeling, with a particular foundation in Bayesian statistics which he views as providing a unified framework for stochastic modeling and information processing. His specific research interests include: Bayesian statistics and stochastic simulation Markov chain Monte Carlo methods Pattern recognition and nonlinear, nonparametric methods Spatial statistics Statistical genetics and genomics Genetic epidemiology His recent publications (2023-2025) demonstrate a strong focus on the intersection of biostatistics, artificial intelligence, and healthcare applications. His work spans multiple domains including AI-driven disease classification in neurology, genomic data analysis for health equity, machine learning tools for healthcare prediction, and addressing bias in medical AI systems. A notable trend across his research is the application of advanced statistical methods to solve pressing problems in genomics, epidemiology, and medical diagnostics, with an increasing emphasis on health equity and the ethical implications of AI in healthcare. Professor Holmes currently supervises PhD students Oscar Clivio, Sahra Ghalebikesabi, and Natalia Garcia Martin. His research is supported by multiple grants, including the MRC Programme Leaders Grant in Statistical Genomics which funds his work in statistical genomics. He is actively involved in three research groups at Oxford that reflect the breadth of his scholarly interests: Computational Statistics and Machine Learning Statistical Genetics and Epidemiology Statistical Theory and Methodology

Aleksandar Jeremicمشاهده پروفایل

دانشیار

• Biomedical Signal Processing
• Statistical Signal Processing
• Biometrics
• +۷ مورد دیگر

Aleksandar Jeremic is an Associate Professor in both the Electrical & Computer Engineering department and the McMaster School of Biomedical Engineering at McMaster University. His research focuses on biomedical signal processing, statistical signal processing, and biometrics, with applications in healthcare and biomedical systems. He holds a Dipl. Ing. from the University of Belgrade, and an M.S. and Ph.D. from the University of Illinois at Chicago. His expertise spans physiological signal analysis (e.g., ECG/EEG), medical imaging, and machine learning for biomedical applications. He has authored over 50 technical articles and two book chapters, and has been recognized with a teaching award from the McMaster Electrical and Computer Engineering Society (2018). He supervises graduate students in both theoretical and applied research areas. Dr. Jeremic teaches advanced courses such as Biomedical Signal Modeling and Processing and Advanced Probability and Random Processes , emphasizing practical applications of signal processing in healthcare. His work includes clinical implementations like neonatal seizure monitoring software and microwave imaging for breast cancer detection.

Allison Buchananمشاهده پروفایل

دانشیار

• Cone Beam Computed Tomography
• Digital Radiography
• Quality Assurance
• +۲ مورد دیگر

Allison Buchanan is an Associate Professor at the Dental College of Georgia , part of Augusta University . She serves as Interim Chair and Associate Chair for Oral Biology, while teaching at both predoctoral and graduate levels. Her work focuses on Cone Beam Computed Tomography (CBCT) and Digital Radiography , with a special emphasis on Quality Assurance and Obstructive Sleep Apnea imaging. Recent publications address radiation safety, disinfection methods for imaging plates, and software quality control in dental radiology. 2024 : Outstanding Faculty Award, Augusta University 2021 : Honorable mention awardee for Oral and Maxillofacial Radiology (OMR) section 2020 : Inducted into PHI KAPPA PHI Honor Society 2018 : Faculty Research and Scholarship Achievement Award Buchanan serves on multiple committees including the Standards Committee on Dental Informatics (2022-present) and the American Dental Association (2021-present), with editorial board memberships since 2018. Her teaching portfolio includes courses like Radiology Clinic II and Diagnostic Sciences Conference .

Wengong Jinمشاهده پروفایل

استادیار

• AI for drug discovery
• Generative AI models
• Geometric deep learning
• +۵ مورد دیگر

Wengong Jin is an Assistant Professor at the Khoury College of Computer Sciences, Northeastern University, and a visiting research scientist at the Eric and Wendy Schmidt Center at the Broad Institute. He holds a PhD from MIT CSAIL, advised by Prof. Regina Barzilay and Prof. Tommi Jaakkola. Research Interests: His work focuses on geometric and generative AI models for drug discovery, biology, and chemical engineering. Key areas include equivariant neural networks (e.g., FAFormer), diffusion models for binding energy prediction, antibody/enzyme design (RefineGNN, SurfPro), and molecular design through graph neural networks (Junction Tree VAE). He also explores domain generalization and systems for autonomous molecular discovery. Publications: His research has been published in top venues like NeurIPS, ICLR, ICML, Nature, Science, and Cell. Recent breakthroughs include discovering novel antibiotics using explainable AI and designing synergistic drug combinations for cancer treatment. Awards: He has received the BroadIgnite Award, Dimitris N. Chorafas Prize, and MIT EECS Outstanding Thesis Award for his contributions to computational biology and AI-driven drug discovery. Teaching: Currently teaches a PhD seminar on AI for Science, focusing on integrating machine learning into scientific discovery processes.

Itsik Pe'erمشاهده پروفایل

استاد

• Human Genetics
• Computational Biology
• Genomics
• +۳ مورد دیگر

Itsik Pe'er is a Full Professor and Vice-Chair in the Department of Computer Science at Columbia University's Fu Foundation School of Engineering & Applied Science, and holds a joint appointment as Professor of Systems Biology at the Vagelos College of Physicians and Surgeons. His research focuses on computational methods in human genetics, including genetic variation analysis, disease association studies, and algorithm development for genomic data. He leads the Itsik Pe'er Lab of Computational Genomics, which develops tools like Xplorigin, Germline, and SEACells to address challenges in genomics and medical research. His work spans machine learning applications in healthcare, microbiome analysis, and cancer genomics. Notable contributions include studies on hypertensive disorders in pregnancy, bias correction in predictive models, and the development of non-Euclidean learning libraries like Manify. Pe'er has advised students including Vladimir Vacic, Anat Kreimer, and Arthi Ramachandran, and collaborates on grants addressing genetic epidemiology and computational biology. His lab's location is in the Computer Science Building at Columbia's Morningside Campus.

• Nuclear Medicine
• GATE Simulation
• Internal Radiation Therapy
• +۲ مورد دیگر

Francesca De Benetti is a Researcher at the Chair of Computer Aided Medical Procedures (Prof. Navab) at the Technical University of Munich (TUM), affiliated with the Interdisciplinary Research Laboratory (IFL) and NARVIS Lab at the Garching Campus. Her research focuses on Nuclear Medicine and Machine Learning for medical image processing, particularly in internal radiation therapy simulations and AI-driven segmentation. Education : M.Sc. in Biomedical Computing (TUM, 2018-2020), B.Sc. in Information Engineering (Università di Padova, 2015-2018) Francesca's recent publications highlight her work in Monte Carlo dosimetry , dynamic PET tracer modeling , and deep learning-based anomaly detection in medical imaging. Her projects emphasize personalized radiation therapy and cross-modality image translation , often involving collaborations with nuclear medicine experts and radiologists. She contributes to teaching at TUM, leading lectures and practical courses on topics including Medical Augmented Reality , Computer Aided Medical Procedures , and Deep Learning for Medical Applications . Francesca is actively involved in labs such as the IFL Lab and NARVIS Lab , focusing on interdisciplinary applications of computer vision and generative AI in medicine.

Kelly Arnoldمشاهده پروفایل

دانشیار

• Biomedical Computation and Modeling
• Drug Delivery and Therapeutics
• Systems Immunology
• +۵ مورد دیگر

Kelly Arnold is an Associate Professor in the Department of Biomedical Engineering at the University of Michigan. Her research integrates systems engineering principles with immunology to investigate variability in immune responses across infection, vaccination, and injury, with a focus on computational modeling and clinical translation. Research Focus Systems-level immune response modeling Vaccination and antibody functionality Vaginal microbiome-host interactions Chronic lung disease progression Computational serology and proteomics Recent Work Her 2025 studies examine SARS-CoV-2 vaccination responses in cancer patients and computational frameworks for vaginal probiotics. Earlier works (2024-2007) span COPD progression, lupus fibrosis, HIV susceptibility, and tissue engineering for fertility preservation. Methodologies include proteomic profiling, network modeling, and microfluidic systems.

• Computational Social Science
• Social Inequality
• Network Science
• +۳ مورد دیگر

Dr. Kenneth Joseph is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo , part of the School of Engineering and Applied Sciences . He serves as Associate Director of the Institute for Artificial Intelligence and Data Science and leads the Computation and Equity Lab (cubelab) , focusing on social inequality through computational measures and models. Education: PhD, MS, and BS in Societal Computing from Carnegie Mellon University (2016, 2012, 2010) Research Interests: Computational Social Science, Network Science, Gender Studies, and AI for Social Good Notable Work: Gender disparities in academia, predictive modeling for foster care and urban policy, and social media rumor analysis Awards: UB Exceptional Scholar—Young Investigator Award (2021) Advising: Mentored students like Yuhao Du, Jason Yan, Arjunil Pathak, and Navid Madani on projects spanning Twitter bios, foster youth services, and algorithmic fairness.

• Internet of Things (IoT)
• Wireless Sensor Networks (WSN)
• Active Learning Classrooms
• +۵ مورد دیگر

Maher Elshakankiri is an Assistant Professor, Teaching Stream at the University of Toronto's Faculty of Information. He holds a Ph.D. in Computer Engineering from Ain Shams University (Egypt), followed by a postdoctoral fellowship at the University of Regina. His research focuses on IoT, Wireless Sensor Networks (WSN), and pedagogical integration of technology and gaming. He has supervised over 100 student projects and authored a book on WSNs. Education: Ph.D. in Computer Engineering, Ain Shams University, Egypt M.Eng., B.Eng. in Engineering, Ain Shams University, Egypt Postdoctoral Fellowship, University of Regina, Canada Research Interests: IoT in healthcare, agriculture, and sports Active learning classrooms and technology in education Wireless communication protocols (V2V, UAV, RFID) Leadership & Grants: Director, Bachelor of Information (BI) Program (2024–present) Coordinator, Information Systems and Design (ISD) Concentration (2023) SSHRC Grant: 'Gaming in teaching towards a more inclusive class' (2022–2023) Professional Activities: Member, SCC IoT & Digital Twins Standards Committee Reviewer for journals including Wireless Networks , Telematics and Informatics , and Computational Intelligence Technical Program Committee member at multiple conferences Teaching: Courses include INF1340 (Programming for Data Science), INF1005/1006 (IoT Workshops), and INF452 (Information Design Coding).

John P.A. Ioannidisمشاهده پروفایل

استاد

• Meta-research
• Evidence-based Medicine
• Research Reproducibility
• +۱۲ مورد دیگر

John P.A. Ioannidis is the C.F. Rehnborg Professor in Disease Prevention and Professor of Medicine, Health Research and Policy, Biomedical Data Science, and Statistics at Stanford University. He is Co-Director of the Meta-Research Innovation Center at Stanford (METRICS) and an Einstein BIH Visiting Fellow at Charité - Universitätsmedizin Berlin. His academic appointments span multiple departments and institutes at Stanford, including the Stanford Prevention Research Center, Biomedical Data Science, and Statistics. He is internationally recognized for his work in meta-research, evidence-based medicine, and research reproducibility. Ioannidis holds an MD and DSc in Biopathology from the National University of Athens, with training in internal medicine and infectious diseases from Harvard and Tufts. He previously chaired the Department of Hygiene and Epidemiology at the University of Ioannina Medical School and held adjunct positions at Harvard, Tufts, and Imperial College. He joined Stanford in 2010, where he launched the PhD program in Epidemiology & Clinical Research, the MS in Community Health & Prevention Research, and METRICS in 2014. His research focuses on improving research methods, appraising biases, enhancing reproducibility, and integrating evidence across scientific disciplines. He is a pioneer in meta-research, with seminal contributions on the reliability of published findings, statistical practices, and research synthesis. His influential 2005 paper, "Why Most Published Research Findings Are False," is the most-accessed article in PLoS history. His recent work examines peer review, data sharing, AI in medicine, and pandemic research impact, consistently advocating for transparency and methodological rigor. His publications span epidemiology, statistics, genomics, clinical trials, and meta-analysis, with a strong emphasis on bias detection, replication, and open science. Trends in his recent articles highlight concerns about research integrity, citation practices, peer review reform, and the scientific response to global health crises. Founders' Medal for Lifetime Contributions to Meta-science (2024) Honorary doctorates from McMaster, Thessaloniki, Edinburgh, Tilburg, Athens, and Rotterdam Elected member, US National Academy of Medicine (2018) Elected member, European Academy of Sciences and Arts (2015) President, Association of American Physicians (2023–2024) President, Society for Research Synthesis Methodology Gordon Award, NIH (2019) Chanchlani Global Health Award (2017) Highly Cited Researcher (Clarivate) in Clinical Medicine, Social Sciences, and Psychiatry Ioannidis has advised numerous students and mentored early-career researchers. He has served as Senior Advisor for Knowledge Integration at the National Cancer Institute (2012–2016) and Editor-in-Chief of the European Journal of Clinical Investigation (2010–2019). He has received over 700 invited lectures and is deeply involved in shaping research policy and scientific infrastructure. He leads METRICS, a hub for meta-research innovation, and is affiliated with multiple Stanford institutes, including Bio-X, the Cardiovascular Institute, and the Stanford Cancer Institute.

Ashli Owen-Smithمشاهده پروفایل

پژوهشگر

• Mental Health
• Behavioral Science
• Trauma
• +۶ مورد دیگر

Ashli Owen-Smith is a behavioral scientist affiliated with the School of Public Health at Georgia State University , where her research focuses on mental health disparities, suicide prevention, and integrative/complementary approaches for complex mental-physical health conditions. She works with underserved populations including refugees/immigrants, incarcerated individuals, and LGBTQ+ communities through community-based participatory research and mixed-methods frameworks. Her current projects are funded by CDC , DBHDD , and DPH . Education: Ph.D. in Behavioral Sciences (Emory, 2009), S.M. in Public Health (Harvard, 2005), B.A. in Psychology (Smith College, 2001) Her research spans mental health , trauma , suicide prevention , and mindfulness-based interventions . Recent work examines telehealth adaptations for chronic pain and mental health, vaccine hesitancy in refugee communities, and social determinants of suicide . She leads studies on gender-affirming care and health disparities in LGBTQ+ populations. Key article trends include epidemiological analysis of suicide risk factors, COVID-19 impacts on mental health, ICD-10 coding for autism, and complementary medicine in trauma recovery. Subfields span telehealth , health equity , mental-physical comorbidity , and community engagement . Students she has mentored include C.A. Scarlett , T. Griner , and M.M. Sesay . Her work integrates public health policy , clinical research , and health systems analysis .

• Microbiology
• Infectious Diseases
• Cell Biology
• +۷ مورد دیگر

Manuel R. Amieva is a Professor at Stanford University School of Medicine , holding joint appointments in Pediatrics - Infectious Diseases and Microbiology & Immunology . He is also a member of the Maternal & Child Health Research Institute (MCHRI) . His clinical practice at Stanford Medicine Children's Health focuses on pediatric infectious diseases. Education: Medical Education: Stanford University School of Medicine (1997) Fellowship: Stanford University Pediatric Infectious Disease Fellowship (2004) Internship & Residency: Stanford Health Care at Lucile Packard Children's Hospital (1998-1999) Dr. Amieva's research investigates host-pathogen interactions at epithelial barriers, with specific expertise in Helicobacter pylori , Listeria monocytogenes , Salmonella enterica , and Staphylococcus aureus . His lab develops innovative organoid culture systems with controlled polarity to study microbial colonization and oncogenic mechanisms. Key discoveries include: H. pylori's manipulation of epithelial junctions via the CagA protein Listeria's exploitation of cell extrusion sites for invasion Staphylococcus toxin interactions with adherens junctions Gastric stem cell activation by pathogens Recent publication trends show continued leadership in infectious disease mechanisms (2020-2025), with a focus on: Pathogen-specific epithelial breach strategies Organoid modeling of viral/bacterial interactions Redox-dependent host factor regulation Single-cell spatial transcriptomic analyses Multi-institutional educational frameworks His scientific collaborations span disciplines including: Gastric cancer genomics initiatives COVID-19 lung infection models Stem cell-microbe interactions Medical education reform projects Dr. Amieva maintains active clinical research while mentoring students in both the Microbiology & Immunology and Pediatrics programs. His lab at Stanford employs advanced 3D confocal microscopy and organ-on-a-chip technologies to visualize epithelial colonization dynamics.