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Na YouView profile

Senior Lecturer

• Biostatistics
• High-Dimensional Data Analysis
• Nonparametric Statistics
• +8 more

Dr. Na You is a Senior Lecturer in Statistics at the University of Essex's Department of Mathematical Sciences, part of the School of Mathematics, Statistics and Actuarial Science (SMSAS). She joined Essex in May 2023, having previously served as a Professor (2018–2023) and Associate Professor (2011–2018) at Sun Yat-sen University in Guangzhou. Her academic journey includes a PhD in Statistics from Peking University (2005) and a five-year postdoc at the University of California, Riverside (USA). Dr. You's research focuses on Biostatistics , particularly in developing methodologies for high-dimensional data analysis and their medical applications. Key interests include multiple testing, variable selection, mixture models, nonparametric statistics, and genomic biomarker identification. Recent work emphasizes nonparametric testing, regression with non-Euclidean predictors, and image data analysis for treatment evaluation. Her publications span cancer research , genomic data analysis, survival analysis, and biomarker development. Notable contributions include studies on breast cancer prognosis, lung adenocarcinoma recurrence, and microbiome correlations with autism. Recent work (2024–2023) explores sequential estimation and survival curve analysis, while earlier studies (2017–2016) addressed TCGA breast cancer data platforms and SNV detection in genomics. No scientific awards are explicitly listed, though her extensive publication record reflects sustained academic impact. Her teaching and supervision roles at Essex and Sun Yat-sen University underscore her commitment to mentoring in statistical research. Her work is embedded within interdisciplinary teams addressing complex medical and biological challenges.

• Machine Learning
• Medical Image Analysis
• Computer Vision
• +2 more

Claudia Mazo is Assistant Professor at Dublin City University's School of Computing, specializing in AI for medical imaging. Her research develops machine learning methods for brain tumor detection, stroke diagnosis, and surgical planning. Holder of a Marie Curie Fellowship and management committee member for a European brain cancer network. Publications focus on explainable AI in neuroimaging, transfer learning for pathology, and multimodal medical fusion. Dual Ph.D. from Universidad de León and Universidad del Valle.

• Neuroscience
• Biomedical Engineering
• Neurodegenerative Diseases
• +7 more

Professor Habib Benali is a faculty member in the Department of Electrical and Computer Engineering at Concordia University. His research focuses on computational neuroscience, neurodegenerative diseases, and translational biomedical engineering. Key areas include traumatic brain injury (TBI) outcomes, Alzheimer's disease modeling, and sleep physiology. He leads projects involving large-scale clinical cohorts (e.g., CENTER-TBI, TRACK-TBI) and develops novel imaging techniques for brain network analysis. His work bridges engineering, mathematics, and clinical neuroscience, with contributions to biomarker discovery and neurotechnology. Research interests prominently feature: Computational models of brain lactate metabolism Mathematical modeling of tau protein propagation Machine learning applications in neuroimaging Sleep-dependent memory consolidation mechanisms Biomarker development for TBI and neurodegenerative disorders Publications emphasize interdisciplinary approaches, combining experimental data with advanced statistical methods (e.g., Bayesian workflow) and computational simulations. Recent work explores closed-loop neurostimulation protocols and phase aberration correction in ultrasound imaging. The BHealthyAgeLab under his direction investigates aging-related neurological challenges through integrated systems approaches.

Jeffrey A. ThompsonView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Healthcare Informatics
• +4 more

Jeffrey A. Thompson is an Associate Professor in the Department of Biostatistics & Data Science at the University of Kansas Medical Center (KUMC) and concurrently serves as the joint Chief Research Informatics Officer for KUMC and the University of Kansas Health System. He holds a PhD in Quantitative Biomedical Science from Dartmouth College and a BS in Computer Science from the University of Southern Maine. His research focuses on leveraging artificial intelligence, machine learning, and informatics to improve healthcare outcomes through innovative tools like the Curated Cancer Clinical Outcomes Database (C3OD), which accelerates clinical trial patient recruitment. Thompson has pioneered digital research infrastructure modernization at KUMC, including HIPAA-compliant cloud-based platforms and centralized data systems. He is an award-winning educator, having received the KU Edwards Campus Faculty Teaching Excellence Award (2021) and the Department of Urology Teacher Appreciation Award (2022). Thompson leads key roles such as co-lead of the Frontiers Clinical and Translational Science Institute’s Informatics Core and Associate Director of the Quantitative Omics Core at the Kansas Institute of Precision Medicine. His work spans genomic data analysis, clinical trial optimization, and advancing data science education through summer workshops. Publications highlight his contributions to predictive modeling in oncology, EHR analytics, and bioinformatics tools. Thompson’s work emphasizes interdisciplinary collaboration, evidenced by his leadership in integrating research systems across electronic health records, biospecimen management, and clinical imaging. His efforts in rural cancer outcome disparities and personalized screening frameworks further demonstrate his commitment to equitable healthcare solutions.

• Machine Learning
• Public Health
• Environmental Epidemiology
• +7 more

Daniele Pala is an Adjunct Professor at the University of Pavia's Faculty of Medicine and Surgery, where he teaches 'Principles of Informatics.' He concurrently serves as a Postdoctoral Researcher at the University of Pennsylvania's Perelman School of Medicine, focusing on machine learning approaches for Alzheimer's disease prediction. His academic journey includes a PhD in Bioengineering and Bioinformatics from the University of Pavia (2017-2020), with a dissertation on spatial enablement tools for urban public health. Pala's research spans environmental epidemiology, geospatial analytics, agent-based modeling, and machine learning applications in healthcare, supported by EU projects like BRAINTEASER and PERISCOPE. His work integrates multi-source data (e.g., GWAS, imaging, environmental sensors) to address public health challenges like MS progression and urban health disparities. Education: PhD in Bioengineering and Bioinformatics, University of Pavia (2020) Master's in Bioengineering, University of Pavia (2017) Bachelor's in Bioengineering, University of Pavia (2015) Research focuses on leveraging machine learning and geospatial techniques to study disease-environment interactions. Key areas include: AI-driven Alzheimer's progression modeling Environmental exposure analysis for MS and asthma Urban health metrics via GIS and agent-based simulations Covid-19 data visualization tools (PERISCOPE Atlas) His publications emphasize cross-disciplinary approaches, with recent work on phenotypic age modeling, ALS progression prediction, and the impact of containment policies on pandemic dynamics. Pala has advised four undergraduate theses and contributed to conference organization (e.g., BIBM 2022 session chair). His lab affiliations include the Shen Lab (UPenn) and the Biomedical Informatics Lab (Pavia).

Christos PapadimitriouView profile

Professor

• Computational Biology
• Artificial Intelligence (AI)
• Machine Learning (ML)
• +8 more

Christos Papadimitriou is the Donovan Family Professor of Computer Science at Columbia University, where he has worked since 2017. Prior to this, he served as the C. Lester Hogan Professor at UC Berkeley for 22 years and held academic positions at Harvard, MIT, Stanford, and the National Technical University of Athens. Education: BS in Electrical Engineering from Athens Polytechnic (1972), PhD from Princeton University (1976). Research Focus: Algorithms, computational complexity, and interdisciplinary applications in biology, economics, and neuroscience. His work applies the algorithmic lens to diverse fields, including evolution, game theory (he co-founded algorithmic game theory), robotics, and brain modeling. Since 2013, he has focused on computational models of the brain and language acquisition. Recent publications center on medical applications of computational methods, such as genetic testing , NGS panels , and cancer biomarker detection , reflecting his interdisciplinary approach. Scientific Honors: Member of National Academy of Sciences and Engineering. Fellow of American Association for Arts and Sciences. Knuth Prize (2002), Gödel Prize (2012), EATCS Award (2015), IEEE John von Neumann Medal (2016), Technion Harvey Prize (2019), IEEE John von Neumann Theory Prize (2024). As a dedicated educator, he authored seminal textbooks like Computational Complexity and Algorithms , alongside novels such as Logicomix and Independence . His email is christos@columbia.edu .

Jorge Rodriguez-FernandezView profile

Assistant Professor

• Clinical Informatics
• Machine Learning
• Predictive Modeling
• +2 more

Dr. Jorge Rodriguez-Fernandez is an Assistant Professor of Neurology and Associate Chief Medical Information Officer at the University of Texas Medical Branch. His research integrates clinical informatics, deep learning, and predictive modeling to address healthcare challenges including documentation burden and COVID-19 outcomes. Primary research areas focus on developing AI-driven tools for mortality prediction from medical imaging, detecting racial disparities in diagnostic algorithms, and optimizing clinical workflows through process mining. His work emphasizes value-based care applications and algorithmic fairness in diverse patient populations. Honors include the UIC Gutenberg Award (2022) for technological innovation and multiple recognitions for excellence in clinical teaching. He maintains active collaborations in computational health research and contributes to institutional informatics governance.

Alessandro BellofioreView profile

Associate Professor

• Biomedical Engineering
• Hemodynamics
• Cardiovascular Mechanics
• +4 more

Alessandro Bellofiore, PhD, is an Associate Professor in the Department of Biomedical Engineering at San José State University (SJSU), where he has served since 2014. He holds a PhD and M.S. in Chemical Engineering from Universitá Federico II di Napoli, Italy. His research integrates experimental fluid mechanics, cardiovascular engineering, and biomedical device design, with a focus on mechanical heart valve optimization, pulmonary hypertension mechanisms, and point-of-care diagnostics for chronic kidney disease. Education: M.S. in Chemical Engineering, Universitá Federico II di Napoli, Italy Ph.D. in Chemical Engineering, Universitá Federico II di Napoli, Italy Professional Appointments: Associate Professor, Biomedical Engineering, SJSU (2020–current) Assistant Professor, Biomedical Engineering, SJSU (2014–2020) Postdoctoral Researcher, National University of Ireland, Galway (2008–2011) Research Interests: His work emphasizes fluid dynamics of biomedical devices, hemodynamics in cardiovascular systems, and wearable medical technologies. Key projects include: Developing anticoagulant-free mechanical heart valves via high-resolution PIV analysis. Investigating right ventricular-vascular coupling in pulmonary hypertension using MRI and animal models. Designing smartphone-integrated microfluidic platforms for chronic kidney disease (CKD) monitoring using machine learning. Awards: Instructor Award, University of Wisconsin-Madison Students (2013) Combustion Institute Travel Award (2006) Advising & Grants: Principal Investigator: NSF MRI grant for high-speed PIV instrumentation. Co-Investigator: Wavelet Health device performance testing grant. Advises over 20 undergraduate and graduate students in experimental and computational biomedical engineering. Labs & Teams: Leads the CardioLab at SJSU, focusing on cardiovascular fluid mechanics and translational biomedical technologies. Collaborates with institutions like UW-Madison and the Golden Jubilee National Hospital (UK).

• Computational Medicine
• Predictive Analytics
• Cardiac Arrhythmias
• +2 more

Raimond Winslow is Professor of Bioengineering and Director of Life Science and Medical Research at Northeastern University's Roux Institute. His research develops computational models for predicting cardiac arrhythmias and critical health events using AI/ML approaches. Research highlights: Predictive analytics for cardiac surgery outcomes AI models for hypoxemic event detection in ICU Computational frameworks for cardiac arrhythmia mechanisms HEART Project developing real-time clinical AI systems Winslow's laboratory integrates mathematical modeling, machine learning, and dynamical systems theory to create patient-specific diagnostic tools. The HEART Impact Engine project translates computational models into clinical decision support systems for cardiothoracic care. He contributes to NPJ Digital Medicine and serves on multiple editorial boards, advancing trustworthy AI deployment in healthcare settings.

• Pain Mechanisms
• Neuroimmunology
• Neuroregeneration
• +2 more

Clifford Woolf, MB, BCh, PhD, is Director of the F.M. Kirby Neurobiology Center at Boston Children's Hospital and Professor in Neurobiology at Harvard Medical School. His research focuses on neuronal plasticity mechanisms underlying pain, regeneration, neurodegenerative disorders, and developmental neurobiology using multidisciplinary approaches including genetics, electrophysiology, and stem cell technology. Research interests include neuro-immune interactions, transcriptional control of neuronal function, and targeted therapies for chronic pain and neural injuries. His lab uses bioinformatics and human stem cell models to identify novel analgesic targets. Awards include the Javits Neuroscience Investigator Award, Bristol-Myers Squibb Pain Research Award, and election to the American Academy of Arts and Sciences (2020). Recent work explores tetrahydrobiopterin pathways in pain, genetic pain phenotyping, and optogenetic modulation of inflammation.

Hanno SteenView profile

Associate Professor

• Proteomics
• Mass Spectrometry
• Biomarker Discovery
• +2 more

Hanno Steen is Associate Professor of Pathology at Harvard Medical School and Principal Investigator at Boston Children's Hospital. He obtained his Ph.D. in Biochemistry and Molecular Biology from University of Southern Denmark and completed postdoctoral fellowships at Harvard Medical School. His laboratory develops novel mass spectrometry methods for protein modification analysis with emphasis on tyrosine phosphorylation in carcinogenesis. Research focuses on: Proteomic analysis of complex protein mixtures Biomarker discovery in pediatric diseases Post-translational modification characterization Diagnostic and prognostic marker screening Publications demonstrate expertise in quantitative proteomics and multi-omics approaches, with recent emphasis on COVID-19 immunopathology, neurodegenerative proteomics, and computational method development. Article trends highlight translational applications in virology, neonatology, and precision medicine. Professional service includes editorial roles and leadership in proteomics consortia. The Steen Laboratory collaborates extensively with clinical researchers to advance diagnostic applications of proteomic technologies.

Emanuele Di AngelantonioView profile

Professor

• Clinical Epidemiology
• Big Data Analysis
• Chronic Disease Studies
• +4 more

Emanuele Di Angelantonio is a Professor of Clinical Epidemiology at the University of Cambridge , leading the Health Data Science Centre and the Di Angelantonio & Ieva Group. His work focuses on big data analytics applied to chronic diseases, cardiovascular risk prediction, and integration of omics/genetic data with electronic health records (EHRs). He has held roles at NHS Blood and Transplant, WHO, and the European Society of Cardiology. Education: M.D. in Medicine (Italy/France) Master's in Medical Statistics (London School of Hygiene & Tropical Medicine) PhD in Epidemiology (University of Cambridge) Research Interests: Development of risk prediction models for non-communicable diseases, molecular epidemiology, personalized medicine via omics/EHR integration, and causal inference in healthcare. His group bridges genotype-to-phenotype gaps using advanced analytics for biomarker discovery and therapeutic targeting. Key Publications Trends: Focus on cardiovascular risk prediction (e.g., SCORE2 algorithms), alcohol/alcoholism impacts, obesity epidemiology, and epigenetic biomarkers (DNA methylation). Recent work explores AI-driven radiomics and precision medicine applications in oncology and cardiology. Awards: Fellow of Royal College of Physicians (2018) Viviane Conraads Achievement Award (2019) National Institute for Health Senior Investigator (2022) Academy of Medical Sciences Fellowship (2023) Advising & Labs: Mentors 10+ PhD students/postdocs in Cambridge's Health Data Science Centre. Active in multidisciplinary teams developing novel statistical methods (e.g., functional Cox models) and translational tools like the MOTox chemotherapy toxicity score.

• Machine Learning
• Medical Imaging
• Computational Pathology
• +3 more

Nicola Altini is an Assistant Professor at Politecnico di Bari, focusing on advanced computational methods in biomedical engineering and precision medicine. His work bridges machine learning, radiomics, and pathomics for clinical applications. Academic Rank: Assistant Professor (RTD-a) Key Research Areas: Medical Imaging Multi-omics Integration Explainable AI Recent publications highlight his expertise in multimodal segmentation models, cancer prognosis, and nephropathology. His work often combines deep learning with clinical validation across pancreatic cancer, kidney diseases, and lymphoma survival prediction. Emerging trends in his research include radiogenomics for mutational prediction, adaptive therapy modeling, and climate-responsive renewable energy monitoring systems.

• Medical AI
• Machine Learning
• Clinical Decision Support
• +2 more

Sunil Kalmady Vasu serves as Adjunct Professor in Computing Science and Senior Machine Learning Specialist in the Department of Medicine. His interdisciplinary research bridges artificial intelligence with medical applications, particularly in cardiovascular diseases and psychiatric disorders. Research domains include: Predictive modeling using electronic health records Electrocardiogram signal analysis Neuroimaging pattern recognition Cheminformatics applications His work has resulted in publications in high-impact journals including Nature Digital Medicine, focusing on diagnostic and prognostic tool development.

• General Surgery
• Hepatobiliary Surgery
• Surgical Oncology
• +3 more

Guido Torzilli is a Full Professor of General Surgery and Director of the Department of General Surgery at Humanitas Research Hospital. He also leads the Division of Hepatobiliary and General Surgery. His academic career includes roles as Assistant Professor at the University of Tokyo (1998–99), Researcher and Associate Professor at the University of Milan's Faculty of Medicine and Surgery (2002–2015). He holds an MD (summa cum laude, 1988), a postgraduate degree in General Surgery (summa cum laude, 1993), and a PhD from the University of Tokyo (1999). His research focuses on hepatobiliary malignancies, liver surgery innovations, and minimally invasive techniques. Key interests include anatomical liver resections, radiomics-driven decision-making, and optimizing outcomes for hepatocellular carcinoma and cholangiocarcinoma patients. His work integrates surgical expertise with advanced imaging and machine learning to predict treatment efficacy and surgical risks. Recent publications highlight advancements in gallbladder cancer management, robotic versus laparoscopic approaches, and predictive models for post-hepatectomy complications. His studies emphasize global collaboration, with international multicenter trials addressing surgical margins, tumor biology, and personalized treatment strategies. As a leader in surgical oncology, his contributions span clinical practice, education, and translational research, positioning Humanitas as a hub for hepatobiliary excellence. His expertise bridges surgical innovation with evidence-based medicine, impacting global standards in liver and gastrointestinal cancers.