جست‌وجوی استاد و پژوهشگر

James McDonaldمشاهده پروفایل

مدرس

• Water Treatment
• Membrane Technology
• Trace Organic Contaminants
• +۶ مورد دیگر

Dr. James McDonald serves as a Lecturer and Research Fellow at the University of New South Wales (UNSW) within the School of Civil and Environmental Engineering's Water Research Centre (WRC). His work at the Vallentine Annexe (H22), Room 102, Kensington Campus focuses on advancing water treatment technologies and understanding contaminant behavior in water systems. His position bridges academic instruction with cutting-edge research in environmental engineering. McDonald's research spans multiple critical areas in water science and engineering, with particular emphasis on trace organic contaminants, membrane technologies, and advanced water treatment processes. His work investigates the fate and removal of pharmaceuticals, personal care products, and industrial chemicals in various water treatment systems. He has made significant contributions to understanding disinfection by-product formation, chiral inversion of pharmaceuticals, and the development of novel membrane technologies for improved water purification. His research combines experimental approaches with computational modeling to address complex water quality challenges. Analysis of McDonald's recent publications reveals a strong focus on emerging contaminants, particularly PFAS compounds and chiral pharmaceuticals, and their behavior in water treatment systems. His work demonstrates expertise in both conventional and advanced water treatment technologies, with increasing emphasis on sustainable approaches including green infrastructure and novel membrane processes. The interdisciplinary nature of his research connects environmental engineering, chemistry, microbiology, and materials science to address contemporary water quality challenges. McDonald actively collaborates with researchers across multiple institutions and has contributed to numerous significant studies on water quality and treatment. His work supports the development of more effective water recycling systems and safer drinking water supplies. While specific grant information isn't detailed in the available text, his extensive publication record suggests sustained research funding in water treatment technologies and contaminant fate studies. As part of UNSW's Water Research Centre, McDonald contributes to one of Australia's leading water research facilities. The WRC provides a collaborative environment for addressing complex water challenges through interdisciplinary research that integrates engineering, chemistry, and environmental science perspectives.

• Pharmacology
• Traditional Chinese Medicine
• Cancer Therapy
• +۷ مورد دیگر

Wing Lam is an Associate Research Scientist in the Department of Pharmacology at the Yale School of Medicine. He holds a BSc in Molecular Biology and a PhD in Biochemical Pharmacology from City University of Hong Kong, followed by postdoctoral training at Yale. His research focuses on developing traditional Chinese medicine (TCM) formulations as adjuvants for cancer therapy, notably YIV-906, which enhances chemotherapy efficacy and mitigates intestinal toxicity. Lam also pioneered the STAR database for herbal drug discovery and the Mechanism-Based Quality Control (MBQC) platform for botanical drug standardization. Education: BSc (Hons) Molecular Biology, City University of Hong Kong, 1995 PhD Biochemical Pharmacology, City University of Hong Kong, 1999 Postdoc, Pharmacology, Yale University, 1999-2002 His research interests span cancer pharmacology, TCM modernization, and mitochondrial toxicity mechanisms. Key projects include YIV-906’s role in enhancing anti-PD1 and CAR T-cell therapies, developing L-nucleoside analogs like troxacitabine, and investigating tylophorine analogs’ antitumor effects. Lam has co-chaired sessions at multiple Consortium for Globalization of Chinese Medicine (CGCM) meetings and contributed to patents on herbal drug formulations and quality control methods. Recent work explores YIV-906’s potential for inflammatory bowel disease (IBD) and phase II clinical trials for colon and liver cancers. Lam’s publications highlight synergistic drug interactions, mitochondrial DNA depletion mechanisms, and TCM’s evidence-based application in chronic diseases. His grants include studies on PHY906 as an adjuvant in rectal cancer therapy and collaborations with Yiviva, Inc. He maintains active roles in editorial boards, including a special issue on herbal drug quality control in Frontiers in Pharmacology . Lam’s lab is embedded within Dr. Yung-Chi Cheng’s group, focusing on translational pharmacology and botanical drug innovation.

Cristian Gómez Canelaمشاهده پروفایل

استاد

• Environmental Chemistry
• Analytical Chemistry
• Mass Spectrometry
• +۵ مورد دیگر

Cristian Gómez Canela is a Full Professor in the Department of Analytical and Applied Chemistry at the School of Engineering, Ramon Llull University (IQS). He serves as Coordinator of the Master's Degree in Analytical Chemistry and is an active member of the Catalan Chemical Society (SCQ), representing SCQ in EuChems-EYCN. His academic journey includes a PhD in Chemistry from the University of Barcelona (2014), followed by postdoctoral research at IDAEA-CSIC and King's College University. Dr. Gómez Canela's research focuses on environmental analytical chemistry, particularly the optimization and validation of analytical methods based on liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) and high-resolution mass spectrometry (HRMS) for determining organic pollutants in environmental samples. His work extends to metabolomics applied to aquatic organisms and the analysis of neurotoxic compounds in water systems. His research fingerprint reveals strong expertise in zebrafish models (100%), neurotransmitter analysis (66%), Daphnia magna studies (64%), and neurotoxicity assessment (21%). His recent publications (2024-2025) demonstrate a clear trend toward environmental neurotoxicology, with emphasis on the effects of pharmaceuticals and industrial pollutants on aquatic organisms. His work integrates advanced analytical techniques with biological endpoints to assess environmental risks, particularly focusing on neurological and cardiovascular impacts. The research spans method development for pollutant detection, environmental monitoring, and mechanistic studies of neurotoxic effects. Dr. Gómez Canela leads multiple significant research projects including CHEMIPARK (2024-2027) on passive sampling methodologies for environmental pollutants, GESPA (2022-2025) as part of the Environmental Process Engineering and Simulation Group, and several projects on neuroactive compounds in water systems. He has an impressive research output with 91 scientific publications from 2011-2025 and an h-index of 27 with over 2,000 citations. As a dedicated educator, he contributes to multiple academic programs including the Master in Analytical Chemistry, Master in Pharmaceutical Chemistry, and undergraduate degrees in Chemistry and Chemical Engineering. His research group GESPA represents a multidisciplinary team combining chemical engineering, biotechnology, and chemical analysis to advance environmental sustainability through theoretical and experimental approaches.

Richard J. Cookمشاهده پروفایل

استاد

• Statistical Methods for Public Health
• Life History Analysis
• Longitudinal Data Analysis
• +۷ مورد دیگر

Richard J. Cook is a University Professor and Mathematics Faculty Research Chair in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds cross-appointments at the School of Public Health and Health Systems at the University of Waterloo and the Faculty of Health Sciences at McMaster University. Previously, he held a Tier I Canada Research Chair in Statistical Methods for Health Research from 2005 to 2019. His educational background includes: BSc in Statistics from McMaster University MMath in Mathematics from University of Waterloo PhD in Statistics from University of Waterloo Professor Cook's research focuses on developing and applying statistical methods for public health research. His primary areas of interest include the analysis of life history data, longitudinal data analysis, methods for incomplete data, clinical trial design, and multivariate analysis. His work provides critical methodological frameworks for understanding disease progression and evaluating interventions in complex health settings. He has made significant contributions to the development of multistate models for disease processes and methods for handling interval-censored data. His extensive publication record demonstrates consistent focus on methodological innovations addressing real-world health research challenges. Recent work emphasizes estimand specification in clinical trials, transportability of research findings, and causal inference methods. His research bridges theoretical statistics with practical applications in autoimmune diseases, transfusion medicine, and public health. Professor Cook has received significant professional recognition: Tier I Canada Research Chair in Statistical Methods for Health Research (2005-2019) Mathematics Faculty Research Chair at University of Waterloo His students have earned prestigious awards including multiple Pierre-Robillard Awards, ISCB Student Conference Awards, and ENAR Distinguished Student Paper Awards, with notable achievements like Dr. Shu (Joy) Jiang being named in the Forbes Top 30 Under 30 North America (2023) for Healthcare. Professor Cook has advised numerous graduate students throughout his career, with many going on to successful academic and industry positions. His research has been supported by various grants, and he collaborates extensively with researchers in rheumatology, transfusion medicine, and public health through affiliations with the Centre for Prognosis Studies in Rheumatic Diseases, the International Psoriasis and Arthritis Research Team, and the McMaster Centre for Transfusion Research. He leads a vibrant research team that includes research associates, post-doctoral fellows, and graduate students working on cutting-edge statistical methodology. His research group maintains strong connections with multiple institutions and research centers focused on health outcomes and disease progression.

• Biostatistics
• Genetics
• Maternal & Child Health
• +۴ مورد دیگر

Heping Zhang is the Susan Dwight Bliss Professor of Biostatistics at the Yale School of Public Health , with secondary appointments in the Child Study Center , Department of Statistics and Data Science , and Department of Obstetrics, Gynecology, and Reproductive Sciences . He directs the Collaborative Center for Statistics in Science (C²S²) and leads the Reproductive Medicine Network data coordinating center. Education: PhD in Statistics, Stanford University (1991) Postdoctoral Fellow, Mathematical Science Research Institute (1991) Research Focus : Zhang specializes in biostatistical methodology for genomic data analysis , clinical trials , and reproductive medicine . His work bridges genetics , mental health , and maternal-child health through innovative statistical approaches. Awards : 2023 Web of Science Highly Cited Researcher 2023 International Chinese Statistical Association Distinguished Achievement Award 2022 Institute of Mathematical Statistics Neyman Award and Lecture 2011 Royan Institute International Research Award 2011 Institute of Mathematical Statistics Medallion Award 2008 Harvard School of Public Health Myrto Lefokopoulou Distinguished Lecturer Professional Roles : He served as President of the International Chinese Statistical Association (2019) and Former Editor of the Journal of the American Statistical Association - Applications and Case Studies . His lab develops open-source software tools like ABESS , STREE , and modSaRa for genomic and clinical data analysis.

Lee Yong Limمشاهده پروفایل

استاد

• Drug Delivery Systems
• Pediatric Medicinal Products
• Taste-Masked Medicines
• +۴ مورد دیگر

Lee Yong Lim is a Professor of Pharmaceutics at The University of Western Australia (UWA), leading the Laboratory for Drug Delivery. She specializes in innovative drug delivery systems, particularly for pediatric populations and marine aquaculture. Her work focuses on overcoming medication challenges for children through taste-masked formulations and stable drug-loaded scaffolds for ear diseases. She has secured over $4.5 million in research grants from diverse funding bodies. Education: BSc (Pharmacy) Hons, National University of Singapore PhD, University of Manchester Research: Her team developed a patented chocolate-based formulation for bitter drugs and conducts pediatric trials. Collaborations include WA hospitals for safe compounded medicines and international projects in nanotechnology. Key research areas include pediatric medicinal products, veterinary drug delivery, and sustainable drug formulations aligned with UN SDGs. Grants & Funding: Major supporters include NHMRC, ARC, Telethon, and industry partnerships. Current active grants focus on pre-procedural chewables for children and perioperative analytics. Labs/Teams: Heads the Laboratory for Drug Delivery and collaborates with the Centre for Optimisation of Medicines and Institute for Paediatric Perioperative Excellence.

George H. Chenمشاهده پروفایل

دانشیار

• Trustworthy Machine Learning
• Time-to-Event Prediction
• Survival Analysis
• +۳ مورد دیگر

George H. Chen is an Associate Professor at Carnegie Mellon University , with dual affiliations in the Heinz College of Information Systems and Public Policy and the Machine Learning Department . His research focuses on trustworthy machine learning methods for temporal reasoning , particularly in health applications such as time-to-event prediction (survival analysis) and electronic health records analysis . He has extensive experience in nonparametric methods requiring minimal data assumptions. Educational Background PhD in Electrical Engineering and Computer Science, MIT (2015) SM in Electrical Engineering and Computer Science, MIT (2012) BS in Electrical Engineering and Computer Sciences & Engineering Mathematics and Statistics, UC Berkeley (2010) His work spans survival analysis , deep learning , and time series modeling , with applications in neurological prognostication , medical adherence , and health equity . He has developed self-contained educational resources including a 2024 monograph on deep survival analysis and tutorials at CHIL and SIGMETRICS. His 2025 course 95-865: Unstructured Data Analytics focuses on practical unstructured data analysis techniques. Notable projects include advising the AgriTech startup CoolCrop , which provides cold storage and market forecasts for Indian farmers serving 9,000+ farmers across 7 states. His Google Scholar publications reveal a strong focus on temporal modeling in healthcare, with recent advancements in neural survival analysis and fairness-aware temporal prediction.

Vikas Singhمشاهده پروفایل

استاد

• Image Analysis
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Vikas Singh is a Professor in the Department of Biostatistics at the University of Wisconsin-Madison, with appointments in Computer Sciences and Statistics. He also serves as a part-time Faculty Researcher at Google DeepMind. His research focuses on image analysis, machine learning, and medical imaging applications, particularly in neuroimaging and Alzheimer's disease studies. Singh holds a Ph.D. in Computer Science from SUNY Buffalo and has taught courses such as BMI/CS 767 (Medical Image Analysis) and CS 766 (Computer Vision). Affiliations: UW Computer Vision Group, Wisconsin Alzheimer's Disease Research Center (W-ADRC), Machine Learning@UW. Research: Develops algorithms for medical image analysis, including tools for neuroimaging and longitudinal biomarker studies. Grants: Collaborates on grants related to Alzheimer's progression modeling and imaging techniques. His work emphasizes interdisciplinary applications, bridging statistics, geometry, and optimization to solve real-world problems in healthcare and engineering.

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.

Sylvia Richardsonمشاهده پروفایل

استاد پژوهشی

• Biostatistics
• Bayesian Statistics
• Spatial Modeling
• +۷ مورد دیگر

Sylvia Richardson is an MRC Investigator at the MRC Biostatistics Unit and holds a Research Professorship at the University of Cambridge, where she served as Director of the Biostatistics Unit from 2012 to 2021. She is affiliated with the Cambridge Mathematics of Information in Healthcare Hub (CMIH) at the Centre for Mathematical Sciences. Her work bridges advanced statistical methodology with critical healthcare applications, particularly in the analysis of complex biomedical data. Richardson's research spans multiple domains of biostatistics with a strong emphasis on Bayesian approaches. Her work has significantly advanced spatial modeling and disease mapping techniques, developed sophisticated methods for handling measurement error in epidemiological studies, and pioneered mixture and clustering models for integrative analysis of heterogeneous data sources. Her research addresses fundamental challenges in analyzing longitudinal health data, multimorbidity patterns, and complex disease trajectories. Her publication record demonstrates consistent methodological innovation applied to pressing healthcare challenges. Recent work focuses on traumatic brain injury outcomes, multimorbidity progression, genomic analysis, and statistical approaches to pandemic data. The articles reveal a strong pattern of methodological development driven by real-world healthcare challenges, with particular attention to longitudinal analysis, Bayesian computation, and integrative modeling approaches that can handle diverse and complex data structures. While specific awards are not detailed in the available information, Richardson's leadership as Director of the MRC Biostatistics Unit for nearly a decade and her continued Research Professorship reflect significant recognition of her contributions to the field. Her work with major international consortia like CENTER-TBI demonstrates her role in large-scale collaborative research efforts addressing critical health challenges. Richardson's research has substantial implications for healthcare policy and practice, particularly in understanding disease progression, developing predictive models for patient outcomes, and creating methodological frameworks that can integrate diverse data sources to generate meaningful clinical insights. Her work continues to influence both statistical methodology and healthcare applications through ongoing research and leadership in the field.

Lars E. Erikssonمشاهده پروفایل

استاد

• Translational Nursing Research
• Biobehavioral Nursing
• Quality of Life
• +۹ مورد دیگر

Professor Lars E. Eriksson is a Registered Nurse with a PhD in Nursing and an MSc in Chemistry, currently serving as a part-time Professor in Nursing at the School of Health and Medical Sciences, City St George's, University of London, and as a full Professor in Nursing at Karolinska Institutet, Stockholm, Sweden, where he leads the Lars E. Eriksson Research Group. His academic journey began with a UC in Nursing in 1985, followed by over a decade of clinical work in hematology, immunology, and HIV care in Stockholm, before transitioning into research and academia. His research is centered on biobehavioral and translational nursing, particularly focusing on health and quality of life among individuals with chronic conditions such as HIV, cancer, and long-term infections. Key areas include disease-related stigma, symptom research, patient-reported outcomes, and biomarker development. He is Principal Investigator for a lung cancer biomarker project funded by the Swedish Research Council and the Swedish PI for the H2020 Magnet4Europe project on mental health in healthcare workplaces. His recent publications show a strong trend in using mixed methods to explore end-of-life communication, death literacy, sexual and reproductive health after cancer, and stigma in chronic illness. He frequently employs patient-reported outcomes, online focus groups, and web-based interventions, often in collaboration with international partners. His work bridges clinical practice, nursing science, and public health. Scientific Awards and Memberships: British HIV Association (BHIVA) National HIV Nurses Association (NHIVNA) Swedish Society of Nursing Swedish Society of Medicine Swedish Society of Medical Microbiology Lars Eriksson has advised numerous PhD and Master’s students, primarily on topics related to quality of life, stigma, and chronic illness. He teaches postgraduate modules including MSc Dissertation, Advanced Practice Dissertation, and Sexual and Reproductive Health. He is affiliated with the Centre for Health Services Research and the Centre for Public Health Research at City St George's.

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).

Sara Häggمشاهده پروفایل

مدرس ارشد

• Epidemiology
• Gerontology
• Medical Genetics and Genomics
• +۳ مورد دیگر

Sara Hägg is a Senior Lecturer at the Karolinska Institutet , affiliated with the Department of Medical Epidemiology and Biostatistics . She is also a Docent in molecular epidemiology. PhD in Computational Biology (Linköping University, 2009) MSc in Molecular Biology (Stockholm University, 2003) BSc in Computer Science (Stockholm University, 2003) Her research focuses on human biological aging , including measurement of aging markers (telomere length, epigenetic clocks, frailty index), causal pathway analysis, and identification of geroprotectors for age-related diseases. She utilizes longitudinal twin studies (SATSA, GENDER, HARMONY), UK Biobank, and Swedish cohorts with methods like Mendelian randomization and genome-wide analyses . Recent articles demonstrate trends in epidemiological aging research , with emphasis on cardiovascular aging , neurological disease interactions , metabolic profiling , and epigenetic clocks . Her work often involves multivariable modeling and cross-cohort validation . Leadership roles include Director of LifeGene Core Facility (2024-) and Founding Board Member of the Nordic Aging Society (2023-). She serves on expert groups for the Swedish Twin Registry and Strategic Research Area in Epidemiology and Biostatistics .

Charles J. Taylorمشاهده پروفایل

استاد

• Analytical Chemistry
• Chemical Sensing
• Environmental Chemistry
• +۳ مورد دیگر

Charles J. Taylor is Professor of Chemistry and Chair of the Chemistry Department at Pomona College, where he has served since 2002. An analytical chemist specializing in instrumental techniques for volatile organic compound (VOC) analysis, his work bridges medical diagnostics, environmental monitoring, and chemical sensing applications. His educational background includes: Ph.D. from University of Minnesota Bachelor of Arts from Macalester College Taylor's research focuses on developing rapid diagnostic methods through VOC analysis, leveraging microhotplate arrays, Raman spectroscopy, and polymer-carbon composites. His work spans biological systems (nematode chemotaxis, wine fermentation flavor compounds) and environmental applications (trace element profiling in coffee beans). Students in his lab gain hands-on experience with advanced analytical instrumentation and multivariate data analysis. Analysis of his publications reveals consistent themes in chemical sensing materials development, with strong emphasis on microsensor arrays, NASA-collaborative electronic nose projects, and applications in medical/environmental diagnostics. His work demonstrates interdisciplinary integration of materials science, analytical chemistry, and data analysis. His scientific achievements have been recognized with: NASA Board Award for Copolymers for Sensors (2013) NASA Board Award for SO 2 Detection (2012) Provisional U.S. Patent #60/861-617 (2007) Multiple NASA Tech Brief Awards (2007) Taylor actively mentors undergraduate researchers, with students co-authoring publications on diverse projects from medical diagnostics to environmental trace analysis. His teaching includes Advanced Analytical Chemistry, Environmental Chemistry, and General Chemistry, emphasizing practical laboratory experience. Research funding has supported instrumentation development and NASA-collaborative sensor projects. His laboratory focuses on chemical sensing materials development, particularly microhotplate-based sensor arrays and VOC analysis systems, with ongoing collaborations with NASA's Jet Propulsion Laboratory for electronic nose applications and environmental monitoring solutions.

• Epidemiology of Birth Defects
• Gene-Environment Interactions in Perinatal Health
• Nutrition and Reproductive Outcomes
• +۴ مورد دیگر

Gary M. Shaw is the Rosemarie Hess Professor and Professor (Research) at Stanford University , with courtesy appointments in the Department of Epidemiology and Population Health and Department of Obstetrics & Gynecology - Maternal Fetal Medicine . He serves as Co-PI of the March of Dimes Prematurity Research Center at Stanford and PI of the California Center for Finding Causes and Preventives of Birth Defects . His research focuses on the Epidemiology of birth defects Gene-environment interactions in perinatal outcomes Nutritional factors in reproductive health . He has developed machine learning approaches for precision parenteral nutrition and predictive models for preterm birth, while investigating persistent metabolomic signatures following hypertensive pregnancy disorders. Shaw's recent work explores Climate change impacts on reproductive health Maternal-fetal immune interactions Epigenetic mechanisms in perinatal disease with applications of multiomics to neonatal intensive care units. As a member of Bio-X and the Maternal & Child Health Research Institute , he contributes to translational research networks while serving as Associate Editor for Birth Defects Research and American Journal of Medical Genetics . He supervises Med Scholar Project student Richard Liang Doctoral co-advisor for Saskia Comess and Richard Liang Master's advisor for Lenae Joe while leading the Division of Neonatology as Associate Chair for Clinical Research (2012-2025). His laboratory work integrates Metabolomic profiling Proteomic analysis Computational modeling Machine learning for biomedical data to advance neonatal care through precision medicine approaches.