جست‌وجوی استاد و پژوهشگر

Wenjie Luoمشاهده پروفایل

پژوهشگر

• Neuroscience
• Alzheimer's Disease
• Microglial Research
• +۵ مورد دیگر

Wenjie Luo is a researcher at the Brain and Mind Research Institute, Weill Cornell Medical College, focusing on neurodegenerative diseases and neuroimmunology. His role involves investigating molecular pathways linking tauopathy, microglial function, and genetic risk factors for Alzheimer's disease. Education: B.S. and M.S. from Peking University (China), Ph.D. from Albert Einstein College of Medicine (2001) His research explores the interplay between neuroinflammatory mechanisms, tau pathology, and genetic variants in Alzheimer's disease progression. Key areas include the cGAS-STING pathway, TREM2 agonism, and microglial interactions with oligodendrocytes. Recent publications highlight his work on tau propagation models, microglial signaling pathways (NF-κB, AKT), and sex-based differences in neurodegeneration. He collaborates on grants from the National Institute on Aging and BrightFocus Foundation, focusing on tau metabolism, TREM2 ligands, and APOE mutations in neurodegenerative diseases.

Christopher A. Cassaمشاهده پروفایل

استادیار

• Statistical Genetics
• Genomic Medicine
• Machine Learning in Biomedical Research
• +۳ مورد دیگر

Christopher A. Cassa is an Assistant Professor of Medicine at Harvard Medical School and a Geneticist at Brigham and Women's Hospital's Division of Genetics. He is also a Lecturer at Massachusetts Institute of Technology and an Associated Scientist at the Broad Institute of Harvard and MIT. His research focuses on the statistical genetics of Mendelian disorders, particularly methods for assessing genomic variant pathogenicity and predicting clinical impacts using population-scale data and machine learning approaches. He actively contributes to clinical variant interpretation through the Brigham Genomic Medicine Program. At MIT, he teaches courses on databases and data analytics (SCM.264) and data science/machine learning (SCM.256), with MITx versions available on edX. His program affiliations include the Medical and Population Genetics Program at the Broad Institute, Harvard Medical School Bioinformatics and Integrative Genomics PhD Program, and the Boston Evolutionary Genomics Supergroup.

Jian Kangمشاهده پروفایل

استاد

• Biostatistics
• Bayesian Methods
• Gaussian Processes
• +۱۱ مورد دیگر

Jian Kang is a Professor and Associate Chair for Research at the University of Michigan School of Public Health , specializing in Biostatistics . His work focuses on developing advanced statistical methods for large-scale biomedical data , with applications to precision medicine , neuroimaging , and genomics . Education: PhD in Biostatistics, University of Michigan (2011) MS in Mathematics (Statistics), Tsinghua University (2007) BS in Statistics, Beijing Normal University (2005) Research Interests include Bayesian nonparametric methods , deep learning for medical imaging , ultra-high-dimensional variable selection , and graphical models for network inference . His 2025-2023 publications demonstrate expertise in Bayesian hierarchical modeling , spatial statistics , and machine learning for healthcare . Scientific Awards : Michigan SPH Excellence in Research Award (2025) ICSA President's Citation Award (2024) Statistics in Biopharmaceutical Research Best Paper (2023) Best Paper in Biometrics by IBS Member (2022) Fellow, American Statistical Association (2021) Grants include NSF-IIS (2021-2025) for BCI statistical learning , NIGMS (2020-2022) for metabolomics biomarker selection , NIDA (2020-2025) for imaging data analysis , and NIMH (2014-2025) for multidimensional neuroimaging methods . Labs and Teams develop Bayesian computational tools for neuroimaging and spatial transcriptomics , collaborating with institutions like Emory University and University of North Carolina.

Alysson Neves Bessaniمشاهده پروفایل

دانشیار

• Distributed Systems
• Dependability
• Fault Tolerance
• +۵ مورد دیگر

Alysson Neves Bessani is an Associate Professor at the Informatics Department of Faculdade de Ciências da Universidade de Lisboa, Portugal, and a member of the LaSIGE research group. His work focuses on distributed systems, Byzantine fault tolerance, and cybersecurity, with significant contributions to blockchain consensus and intrusion-tolerant architectures. Academic Rank: Associate Professor University: Universidade de Lisboa School: Faculdade de Ciências Department: Informatics Department Research Groups: LaSIGE, Navigators Research Interests span distributed systems design, Byzantine fault tolerance, adaptive consensus protocols, and secure multi-cloud storage. His work bridges theoretical foundations with practical implementations like the BFT-SMaRt library and the Vawlt startup. Scientific Awards include multiple Test-of-Time Awards (DSN'24, DSN'21), IBM Faculty Award (2017), and Best Student Paper at Middleware'19. He has advised numerous PhD and Master’s students, contributing to advancements in fault-tolerant systems. Publications (15 most recent) reveal trends in Byzantine consensus optimization, blockchain integration, and AI-driven threat detection. His interdisciplinary work combines distributed computing with genomics and IoT security, reflecting a broad impact across computer science.

• Sleep
• Circadian rhythms
• Bioinformatics
• +۴ مورد دیگر

Ron C Anafi serves as Associate Professor of Medicine (Sleep Medicine) at the Hospital of the University of Pennsylvania and holds key affiliations as Faculty Scholar at the Penn Institute for Bioinformatics, Co-Investigator at the Center for Subcellular Genomics, and member of both the Chronobiology and Sleep Institute and ITMAT. His work bridges computational biology with clinical sleep medicine within the Perelman School of Medicine. His educational trajectory includes a B.S.E. in Biomedical Engineering and Philosophy from Tulane University (1995), followed by a Ph.D. in Applied Mechanics (2002) and M.D. (2004) from the University of Minnesota. This dual training underpins his unique approach to circadian research. Dr. Anafi's laboratory pioneers computational methods to decode sleep and circadian influences on human physiology, focusing on neurodegeneration and cancer. By applying machine learning and systems biology to large-scale omics data, his team reconstructs molecular rhythms from cross-sectional human tissue databases—overcoming the critical barrier of limited longitudinal sampling in patients. His research demonstrates how circadian disruptions in ribosomal function link to Alzheimer's pathology and how tumor clock strength predicts breast cancer outcomes. Analysis of his 15 most recent publications (2022-2025) reveals intensifying focus on cell-type-specific circadian disruptions in neurodegenerative diseases, with significant expansion into cancer chronobiology and contactless sleep monitoring technologies. His methodological innovations like CYCLOPS enable rhythmic signal extraction across diverse tissues—from brain to skin—while increasingly integrating multi-omics layers to identify therapeutic targets. As mentor in the Genomics and Computational Biology and Neuroscience graduate groups, he guides students through rotation projects at his Translational Research Laboratories facility (Suite 2100, 125 South 31st Street). His research is supported by NIH grants investigating circadian mechanisms in Alzheimer's disease and cancer progression, with recent work exploring sex-specific metabolic impacts of circadian genes and non-invasive sleep monitoring.

Lotte Asveldمشاهده پروفایل

دانشیار

• Responsible Innovation
• Synthetic Biology
• Ethics of Technology
• +۵ مورد دیگر

Lotte Asveld is an Associate Professor at Delft University of Technology, specializing in the societal aspects of biotechnological innovations. Her work focuses on responsible innovation in biotechnology and synthetic biology, addressing moral, technical, and institutional uncertainties, integrating societal values into innovation trajectories, and analyzing actor responsibilities. Her research spans the bioeconomy, social sustainability, and risk governance. Key themes in her recent publications include: Responsible innovation frameworks and ethical education Inclusive design in biobased value chains Social Life Cycle Assessment (SLCA) for justice in bioeconomy Safe-by-Design (SbD) principles and risk management Digital Sequence Information (DSI) equity and bioethics

• Medical History
• Vaccinology
• Public Health
• +۳ مورد دیگر

Andrea Grignolio Corsini serves as Associate Professor in the Department of Medical History at Vita-Salute San Raffaele University's School of Medicine, Milan. His academic profile integrates historical scholarship with contemporary public health challenges, particularly focused on vaccine policies and societal perceptions. His research spans critical domains including: Historical evolution of vaccination policies in Italy Social determinants of vaccine hesitancy Risk communication during public health crises Science-policy interfaces in mandatory vaccination debates Historical analysis of medical innovation adoption Cross-cultural perspectives on vaccine trust Analysis of his 59 publications (2019-2024) reveals consistent thematic focus on the interplay between historical context and modern vaccine acceptance, with particular emphasis on Italian societal responses during the COVID-19 pandemic. His work bridges medical humanities and epidemiology, examining how historical precedents shape current public health strategies and risk perception frameworks. No scientific awards are documented in available materials. Information regarding student advising, grant funding, or laboratory infrastructure remains unspecified in institutional records.

• Fuzzy Logic
• Soft-Computing
• Health Monitoring
• +۹ مورد دیگر

Nishchal K. Verma is a Professor at the Department of Electrical Engineering, Indian Institute of Technology Kanpur. He holds a PhD from IIT Delhi (2007), an M.Tech from IIT Roorkee (2003), and a B.Tech from DEI Agra (1996). His postdoctoral research includes work at the University of Tennessee (2009) and Louisiana Tech University (2008). Specialization: Fuzzy Logic, Health Monitoring, Intelligent Informatics Current Research Interests: Intelligent Data Mining, Computer Vision, Smart Grids, Biomedical Applications His research focuses on Fuzzy Systems , Machine Learning , and Health Monitoring with applications to power systems, biomedical data, and wireless sensor networks. He has developed technologies like the Transducers and Instrumentation Virtual Laboratory and Brain Computer Interface Laboratory , emphasizing predictive modeling and fault diagnosis. Key sponsored projects include DST-funded Fuzzy Rule-Based Image Prediction and DRDO-supported Visual Surveillance Systems . His work spans 15+ years of interdisciplinary publications in journals and conferences. Scientific Awards : Devendra Shukla Young Faculty Research Fellowship (2013-16) He has served as Associate Editor for journals and Chairman of IEEE chapters, with leadership roles in academic administration at IIT Kanpur.

Bo Huمشاهده پروفایل

استاد

• Biostatistics
• Bioinformatics
• Neurosurgery
• +۷ مورد دیگر

Bo Hu is Professor of Biostatistics & Bioinformatics and Professor of Neurosurgery at Duke University, where he leads methodological and collaborative research at the intersection of biostatistics, bioinformatics, and clinical neurosciences. His dual appointments situate him within the Division of Biostatistics in the Department of Biostatistics & Bioinformatics and within the neurosurgical faculty. Education: Ph.D. in Biostatistics, University of Wisconsin–Madison, 2006 Research Interests: Professor Hu’s methodological work centers on advanced biostatistical and machine-learning techniques for high-dimensional biomedical data, including generative AI, synthetic data generation, and predictive analytics in medicine. Clinically, he collaborates on precision-medicine trials in oncology, neurodegeneration (Alzheimer’s disease), metabolic disease (type 2 diabetes and bariatric surgery), and treatment-resistant depression. His neuroimaging genetics portfolio explores structural brain endophenotypes in bipolar disorder and epilepsy using single-cell transcriptomic integration. Complementing his medical research, he maintains a vigorous program in remote-sensing informatics, developing deep-learning solutions for object detection, domain adaptation, and energy-infrastructure mapping from overhead imagery. Recent Grant Portfolio: Empagliflozin to Improve Right Ventricular Function in Pulmonary Arterial Hypertension – Cleveland Clinic Lerner College of Medicine (2025-2030) Gender and Asthma – Mayo Clinic Hospital-Arizona (2025-2027) Engaging Patients in Prenatal Genetic Testing Decisions – Cleveland Clinic Lerner College of Medicine (2025-2027) Laboratory & Collaborative Networks: Professor Hu leads interdisciplinary teams that bridge Duke’s Department of Biostatistics & Bioinformatics with clinical departments (Neurosurgery, Psychiatry, Medicine) and external partners such as Cleveland Clinic, Mayo Clinic, and multiple NIH consortia. These collaborations support large-scale clinical trials, multi-omics neuroimaging studies, and AI-driven remote-sensing analytics.

• Theoretical Computer Science
• Sublinear Algorithms
• Complexity Theory
• +۴ مورد دیگر

Elena Grigorescu is an Adjunct Associate Professor in the Department of Computer Science at Purdue University, where she has been a faculty member since Fall 2012. Her research program spans theoretical computer science with a focus on foundational algorithmic challenges in large-scale data processing and computational limits, maintaining strong connections to cryptography, communications, and optimization applications. Her educational background includes a PhD from the Massachusetts Institute of Technology (MIT), establishing her expertise in rigorous theoretical frameworks. Professor Grigorescu's research emphasizes designing algorithms that operate in sublinear time or space for massive datasets, analyzing complexity of error-correcting codes and lattices, and exploring information-theoretical computation limits. Current investigations integrate differential privacy with learning-augmented techniques to solve online optimization problems, network design challenges, and data stream processing bottlenecks. Her work bridges abstract theory with practical implementations in cryptographic systems and quantum computing paradigms, demonstrating consistent innovation in algorithmic foundations. Analysis of her recent publications (2022-2025) reveals a dominant focus on sublinear-time algorithms, particularly at the intersection with differential privacy and machine learning augmentation. Key contributions include novel spanner constructions for network design, privacy-preserving clustering frameworks, and breakthroughs in trace reconstruction and coding theory. A pronounced trend shows increasing integration of learning-based predictions to enhance classical online algorithms for packing/covering problems while maintaining theoretical guarantees, alongside sustained contributions to error-correcting code analysis and graph-theoretic foundations. No specific scientific awards or major fellowships were documented in the provided materials, though her publication record in premier venues like STOC, FOCS, and APPROX/RANDOM indicates significant peer recognition. Professor Grigorescu actively mentors graduate students in theoretical computer science research, guiding investigations in sublinear algorithms, complexity theory, and coding theory. Her collaborative projects involve interdisciplinary teams across institutions, focusing on cryptographic applications and quantum information theory, though specific grant details were not included in the source texts. Ongoing work suggests expansion into quantum algorithm design and privacy-preserving machine learning frameworks. While dedicated laboratory facilities were not specified, her research operates within Purdue's theoretical computer science group, leveraging university-wide computational resources and fostering collaborations through conference participation and workshop organization.

Albert Erivesمشاهده پروفایل

دانشیار

• Regulatory Genomics
• Evolutionary Genomics
• Evolution of Metazoa
• +۶ مورد دیگر

Albert Erives is an Associate Professor in the Department of Biology at the University of Iowa , specializing in regulatory genomics, evolutionary genomics, and the evolution of metazoans. His research focuses on decoding gene regulatory logic in DNA sequences, particularly during animal embryogenesis, using comparative genomics to identify regulatory modules driving phylogenetic transitions. PhD from University of California, Berkeley His work spans eukaryotic gene regulation, integrating biochemistry, molecular genetics, and computational biology to uncover principles of organismal evolution. Recent studies include viral nucleosome structure, Notch signaling pathway evolution, and enhancer organization in model organisms like Drosophila and Ciona . Notable scientific awards include the NSF CAREER Award (2012, 2010) . He actively seeks graduate students and postdoctoral researchers for his laboratory, which utilizes core facilities such as the Carver Center for Genomics.

Ian Simpsonمشاهده پروفایل

استاد

• Biomedical Informatics
• Machine Learning
• Genomics
• +۳ مورد دیگر

Professor Ian Simpson is a faculty member at the University of Edinburgh's School of Informatics , holding the Chair in Biomedical Informatics and serving as Director of the UKRI Artificial Intelligence CDT in Biomedical Innovation . He teaches courses including Foundations in Biomedical Artificial Intelligence Research and supervises PhD students across multiple programs. Current Supervision: Aidan Marnane, Antoine Lain, Barry Ryan, Tariro Chatiza, Sebestyén Kamp, Maria Dolak, Hanane Issa, Juliana Rodriguez Cubillos Past Supervision: Xin He (2017), Maciej Pajak (2018), Emilia Wysocka (2019), Samuel Heron (2019), Alba Crespi (2022), Magdalena Navarro (2022), Michael Yates (2023), Jamie Campbell (2023), Nicholas Moir (2024) Research Focus: Developing computational and machine learning methodologies for biological systems analysis, particularly brain development and neurological diseases. Key interests include high-throughput brain data analysis, graph-based biological modeling, molecular phylogenetics, and brain disease molecular modeling. Publication Trends: Recent work emphasizes multi-omic data integration through graph neural networks (MOGDx), batch correction challenges in breast cancer datasets, and longitudinal stratification approaches for Parkinson's disease. His articles demonstrate applications of AI in heterogeneous disease diagnosis while preserving biological validity. Scientific Recognition: Fellow of the Alan Turing Institute (2021-) Member of RSE Young Academy of Scotland BBSRC Pool of Experts member (2021-) STEM Ambassador Laboratory Affiliations: Leads the Biomedical Informatics group within the Institute for Adaptive & Neural Computation, with affiliations to Edinburgh Neuroscience, Patrick Wild Centre, and the Simons Initiative for the Developing Brain.

Arthur B Prindleمشاهده پروفایل

دانشیار

• Synthetic Biology
• Microbial Communities
• Biofilms
• +۴ مورد دیگر

Arthur B Prindle is an Associate Professor in Biochemistry and Molecular Genetics and Microbiology-Immunology at Northwestern University's Feinberg School of Medicine, with a secondary appointment in McCormick School of Engineering. His research integrates synthetic biology and quantitative approaches to study collective behaviors in microbial communities. Education: PhD: University of California, San Diego (2014) Postdoctoral Fellow: University of California, San Diego, Molecular Biology (2016) Prindle's lab investigates molecular mechanisms of cell communication in biofilms using synthetic biology, microfluidics, and quantitative microscopy. Research focuses on engineering microbial communities for biomedical applications including inflammatory bowel disease detection, respiratory health improvement, and cancer biomarker discovery. The group develops "smart" biofilms capable of environmental detoxification and disease sensing through electrochemical signaling pathways. Recent publications (2021-2025) demonstrate interdisciplinary work spanning microbiome engineering, bacterial pathogenesis, and diagnostic tool development. Key themes include microbiome manipulation for therapeutic applications, multi-omics approaches to disease biomarkers, and fundamental studies of bacterial communication systems. Scientific Awards: NSF CAREER Award U.S. Army Research Office Early Career Award Pew Biomedical Scholar (2019) Packard Fellowship (2018, $875,000) Prindle leads an active research group supported by NSF, Army Research Office, and Packard Foundation grants. His lab recruits postdoctoral scholars, graduate students, and undergraduates for projects combining computational modeling with experimental synthetic biology. Current initiatives include developing engineered probiotics for human disease surveillance and exploring bacterial electrochemical signaling networks. The Prindle Lab operates within Northwestern's Simpson Querrey Institute for Epigenetics and collaborates with the Chemistry of Life Processes Institute and Robert H. Lurie Comprehensive Cancer Center. Located in the Simpson Querrey Research Building, the team utilizes custom microfluidic devices and advanced imaging to characterize metabolic and electrochemical dynamics in microbial communities.

Ralf Herwigمشاهده پروفایل

پژوهشگر

• Computational Biology
• Bioinformatics
• Systems Biology
• +۵ مورد دیگر

Dr. Ralf Herwig is a computational biologist at the Max Planck Institute for Molecular Genetics in Berlin, Germany. His work focuses on statistical methods for integrative analysis of gene expression, proteomics, and metabolomics data to model biological processes in human diseases like cancer and diabetes. He develops tools such as ConsensusPathDB for molecular interaction networks and IsoTools for long-read RNA-seq analysis. Education: Diploma in mathematics (Free University of Berlin), PhD in mathematics/statistics (FU Berlin) on clustering algorithms and information-theoretic methods. Research Interests include computational network biology, multi-omics data integration, machine learning for cancer survival predictions, and alternative splicing analysis. His group pioneered network propagation frameworks to explain drug toxicity and black-box ML models. Key Publications cover deep learning in drug combinations, long-read sequencing for cancer isoforms, and systems biology approaches to metabolic disorders. Tools developed by Herwig's lab are widely cited (~2,500 citations for ConsensusPathDB). Labs & Collaborations: Leads the Herwig Lab, collaborating on projects involving cancer, diabetes, and cardiotoxicity. The lab maintains critical computational resources for the biomedical community.

Mariaelena Pierobonمشاهده پروفایل

دانشیار

• Biomedical Research
• Medical Proteomics
• Precision Medicine
• +۳ مورد دیگر

Mariaelena Pierobon is an Associate Professor at George Mason University's School of Systems Biology, affiliated with the Institute for Advanced Biomedical Research and the Center for Applied Proteomics and Molecular Medicine (CAPMM). Her research focuses on precision medicine for cancer patients, integrating translational studies and clinical trials with emphasis on metastatic disease and tumor microenvironment analysis. MD, University of Padova (Italy), School of Medicine MPH, George Mason University Her work leverages in vitro models and patient-derived specimens with state-of-the-art technologies to explore cancer dynamics and develop tailored therapies. Recent projects include proteogenomic profiling of breast and ovarian tumors, KRAS mutation targeting, and spatial heterogeneity mapping in gynecologic cancers. Scientific trends in her publications include precision oncology, signaling pathway inhibition in KRAS-mutant cancers, tumor microenvironment analysis, and multi-omic diagnostics for therapeutic response prediction. Fulbright Scholar (2019) Pierobon collaborates with clinical teams using reverse phase protein arrays (RPPA) and laser capture microdissection to dissect molecular mechanisms in pancreatic, breast, and ovarian cancers. Her lab contributes to databases like the Side-Out Foundation Metastatic Breast Cancer portal for open-access multi-omic research.