جستجوی استاد راهنمای دانشگاهی

• Chemometrics
• Metabolomics
• Food Science
• +۳ مورد دیگر

Klaas Smilde serves as an Adjunct Professor in the Department of Food Science (Design and Consumer Behavior section) and holds a postdoctoral position in the Department of Plant and Environmental Sciences (Section for Environmental Chemistry and Physics) at the University of Copenhagen. His dual appointments bridge food science and environmental research, with institutional email addresses at both University of Copenhagen (smilde@plen.ku.dk) and University of Amsterdam (A.K.Smilde@uva.nl). His research centers on advanced chemometric methodologies applied to complex biological systems, particularly metabolomics and lipoprotein analysis. Key interests include multiway data modeling, sparse PCA interpretation, longitudinal study design, and integration of mechanistic models with high-dimensional datasets. This work spans food science applications, environmental chemistry, and human health investigations. Recent publications (2020-2025) reveal consistent methodological innovation in data analysis for metabolomics and environmental screening. Dominant themes include development of robust protocols for lipoprotein profiling using NMR and ultracentrifugation, application of multiway models to postprandial dynamics, and novel approaches to non-target screening in environmental samples. His work demonstrates strong interdisciplinary collaboration across food science, plant sciences, and medical research domains.

• Epidemiology
• Respiratory Health
• Allergic Disease
• +۷ مورد دیگر

Dr. Angela Pinot de Moira is a Research Fellow in Epidemiology at Imperial College London's School of Public Health and Faculty of Medicine, and a Guest Researcher at the Department of Public Health, Section of Epidemiology at the University of Copenhagen. She holds an NHLI Fellowship at Imperial and previously held a Lundbeck Foundation fellowship at the University of Copenhagen. With a background in immunology, her research bridges epidemiology, public health, and biomedical sciences through extensive collaboration across European birth cohort studies. Dr. Pinot de Moira's research focuses on understanding how early-life social and environmental conditions impact respiratory health and allergic disease. Her work examines the role of the microbiome and immune response in health outcomes, with particular interest in cross-birth cohort analysis and making data FAIR (findable, accessible, interoperable and reusable). She has developed expertise in federated data analysis through her involvement with DataSHIELD, where she serves on the steering committee and co-leads the Communications and Outreach theme. Her research spans epidemiological methods, environmental health, social determinants of health, and respiratory medicine, with a strong emphasis on collaborative, multi-center studies across European birth cohorts. Her recent publications reveal a strong trend toward large-scale collaborative research using federated analysis methods to examine respiratory health outcomes across multiple birth cohorts. Her work increasingly focuses on social inequalities in health, environmental exposures (particularly green space), and the development of methodologies for secure multi-site data analysis. She has made significant contributions to understanding asthma epidemiology, allergic diseases, and the impacts of early-life exposures on long-term health outcomes across European populations. NHLI Fellowship at Imperial College London Lundbeck Foundation fellowship at University of Copenhagen HDRUK funding as part of the Social and Environmental Determinants of Health Driver Programme Membership on DataSHIELD steering committee Dr. Pinot de Moira has secured significant research funding through fellowships and collaborative grants. She is actively involved in the EU Child Cohort Network and the UNICORN (Unified Cohorts Research Network) project at Imperial College. Her work emphasizes cross-institutional and cross-national collaboration, particularly through federated data analysis approaches that maintain data privacy while enabling large-scale research. She has developed expertise in both traditional epidemiological methods and advanced computational approaches for data analysis across multiple sites. Dr. Pinot de Moira is a key contributor to the DataSHIELD infrastructure, which enables remote and non-disclosive analysis of sensitive research data. She co-leads the DataSHIELD Communications and Outreach theme and has been instrumental in developing and applying federated analysis methods across multiple European birth cohort studies. Her work is central to the EU Child Cohort Network, which has established a FAIR (findable, accessible, interoperable and reusable) network of European and Australian birth cohort data.

• Causal Machine Learning
• Mechanistic Machine Learning
• Dynamical Systems
• +۶ مورد دیگر

Niki Kilbertus is a Professor in the Department of Informatics at the Technical University of Munich and a group leader at Helmholtz AI (Helmholtz Munich). They are also affiliated with MCML, the Konrad Zuse School relAI, and the Munich Unit of ELLIS. Since 2024, they have been a member of the Junge Akademie and received the Leopoldina Prize for Young Scientists. In 2025, they were awarded an ERC Starting Grant and achieved tenure at TUM. Professor Kilbertus's research focuses on causal machine learning, mechanistic ML, dynamical systems, and AI for science. Their work spans theoretical foundations of causal inference and practical applications across scientific domains. They have made significant contributions to causal effect estimation, causal discovery in stochastic processes, learning differential equations, and fair machine learning. Their research often bridges computer science with physics, biology, and climate science, demonstrating the interdisciplinary nature of their work. Professor Kilbertus has published extensively in top machine learning venues including NeurIPS, ICML, and ICLR, with numerous publications in 2024-2025. Their recent work shows a strong trend toward causal discovery in continuous-time systems, intervention modeling, and physics-informed machine learning applications. Scientific Awards: Leopoldina Prize for Young Scientists (2024) ERC Starting Grant (2025) Professor Kilbertus actively supervises multiple PhD students and collaborates with researchers across institutions including Max Planck Institutes and Helmholtz centers. They serve as an Action Editor for TMLR and regularly review for major ML conferences. The research group is well-funded through the ERC grant and institutional support from TUM and Helmholtz AI, enabling active recruitment of new PhD students and postdocs. Based at Technical University of Munich and Helmholtz AI, Professor Kilbertus's team works at the intersection of theoretical machine learning and scientific applications, with particular strengths in causal reasoning for complex dynamical systems.

• Bone Marrow Transplantation
• Hematopoietic Stem Cell Transplantation
• Oncology
• +۵ مورد دیگر

Mary M. Horowitz is a Professor in the Department of Medicine, Division of Hematology and Oncology at the Medical College of Wisconsin. She serves as Scientific Director Emeritus of the Center for International Blood and Marrow Transplant Research, Deputy Cancer Center Director, and Director of the Data and Coordinating Center for the Blood and Marrow Transplant Clinical Trials Network. Dr. Horowitz is also a member of both the Cancer Center and the Mellowes Center for Genomic Sciences and Precision Medicine at MCW. Dr. Horowitz's research focuses on bone marrow transplantation, hematopoietic stem cell transplantation, and related clinical trials. Her specific areas of interest include: Adult Allografts and Bone Marrow Transplantation Clinical Trials methodology and design Data Collection and Databases for transplant outcomes Donor Selection criteria and optimization Hematopoietic Stem Cell Transplantation techniques Management of Neoplasms and Second Primary cancers Risk Factor analysis for transplant recipients Transplantation, Autologous approaches Her recent publications (2024-2025) demonstrate a continued focus on advancing the field of hematopoietic cell transplantation, with particular emphasis on improving outcomes for elderly patients, developing risk assessment models, and optimizing graft-versus-host disease prophylaxis. Dr. Horowitz has been instrumental in several major clinical trials through the Blood and Marrow Transplant Clinical Trials Network, including studies on alternative donor transplantation, haploidentical bone marrow transplantation for sickle cell disease, and the use of gilteritinib for FLT3-ITD mutated acute myeloid leukemia. Dr. Horowitz has received significant recognition through her leadership roles in major national and international transplant research organizations. Her role as Scientific Director Emeritus of the Center for International Blood and Marrow Transplant Research reflects her substantial contributions to the field. She has also played key roles in numerous multicenter clinical trials that have shaped current practices in bone marrow transplantation. As Deputy Cancer Center Director and Director of the Data and Coordinating Center for the Blood and Marrow Transplant Clinical Trials Network, Dr. Horowitz oversees significant research infrastructure and funding. Her work bridges clinical practice and research, with a strong emphasis on translating evidence into practice to improve patient outcomes in transplantation medicine. Dr. Horowitz leads research initiatives focused on data collection, analysis, and dissemination related to bone marrow and stem cell transplantation. While a specific lab name isn't mentioned in the provided information, her leadership of the Data and Coordinating Center suggests a strong team focused on clinical trial coordination, data management, and statistical analysis for transplantation research.

• Trauma and Critical Care
• Post-Traumatic Stress Disorder
• Mental Health Outcomes after Injury
• +۵ مورد دیگر

Dr. Terri A. deRoon Cassini is a Professor in the Department of Surgery with a focus on Trauma and Critical Care at the Medical College of Wisconsin. She serves as the Center Director for CTSI Research Informatics REDCap and maintains active affiliations with the Center for Microbiome Research, Comprehensive Injury Center, and Neuroscience Research Center. Dr. deRoon Cassini's research program centers on understanding the psychological and neurological consequences of traumatic injury, with particular emphasis on post-traumatic stress disorder (PTSD), mental health outcomes, and the role of the endocannabinoid system in stress response. Her work examines how social determinants of health—including race, neighborhood disadvantage, and social support—influence trauma recovery trajectories. She has made significant contributions to understanding how racial discrimination increases vulnerability to PTSD and worsens physical and emotional quality of life after traumatic injury, particularly for Black Americans. Her publication record demonstrates expertise in both clinical assessment and biological mechanisms of trauma response, with over 160 publications spanning neuroimaging, biomarker research, health disparities, and clinical intervention studies. Her recent work has increasingly focused on the intersection of social determinants of health and neurobiological responses to trauma, employing sophisticated analytical approaches to understand complex biopsychosocial pathways. Dr. deRoon Cassini is an active collaborator in major research consortia including the ENIGMA-PGC PTSD working group and the Psychiatric Genomics Consortium, contributing to large-scale efforts to understand the neurobiology of trauma response across diverse populations. Her research has important implications for developing more effective screening tools, prevention strategies, and treatment approaches for trauma survivors.

• Clinical Trials
• Machine Learning
• Survival Analysis
• +۷ مورد دیگر

Michael Martens, PhD is an Assistant Professor in the Division of Biostatistics within the Data Science Institute at the Medical College of Wisconsin, where he also serves as Assistant Director of the Biostatistics Shared Resource for the Cancer Center. Additionally, he holds an adjunct Assistant Professor position at the University of Wisconsin-Milwaukee. Dr. Martens serves as Statistical Director for the Blood and Marrow Transplant Clinical Trials Network Data and Coordinating Center and for the Infection & Immune Reconstitution Working Committee of the Center for International Blood and Marrow Transplant Research. Dr. Martens' research focuses on developing and applying advanced statistical methodologies to clinical trials in hematopoietic cell transplantation and oncology. His work spans survival analysis, adaptive trial design, machine learning applications, Bayesian statistics, and statistical safety monitoring. He has made significant contributions to the analysis of time-to-event data, incorporation of historical data into clinical trials, and statistical methods for immune reconstitution studies. His publication record demonstrates consistent productivity with 43 peer-reviewed articles through late 2025, including publications in high-impact journals such as NEJM, JAMA Oncology, Blood, and Journal of Clinical Oncology. His recent work (2023-2025) shows a strong focus on statistical methodology development applied to critical questions in transplantation research, with emphasis on graft-versus-host disease prophylaxis, vaccine responses in immunocompromised patients, and immune recovery after cellular therapies. Selected Scientific Awards: Outstanding Graduate School Educator, Medical College of Wisconsin (2020-2023, three consecutive years) Distinguished Student Paper Award, Eastern Northern American Region of the International Biometric Society (2017) Biostatistics Fellowship, Medical College of Wisconsin (2012) Dr. Martens has successfully advised multiple PhD and MS students to completion and currently mentors several doctoral candidates. His substantial research funding includes major NIH grants as co-investigator and principal investigator, supporting both methodological development and clinical applications. His role as Statistical Director for national networks provides unique opportunities for students to work with large-scale clinical trial data and contribute to advancing statistical methodology in transplantation research.

Bobbie-Jo Webb-Robertsonمشاهده پروفایل

استاد بالینی

• Statistics
• Machine Learning
• Bioinformatics
• +۵ مورد دیگر

Bobbie-Jo Webb-Robertson serves as Division Director and Chief Scientist of computational biology in the Biological Sciences Division at Pacific Northwest National Laboratory (PNNL). She holds multiple academic appointments including Clinical Volunteer Professor at the University of Colorado Anschutz Medical Campus, Courtesy Assistant Professor at the University of Florida, and Clinical Associate Professor at Oregon Health and Sciences University. Dr. Webb-Robertson earned her PhD in Engineering Systems from Rensselaer Polytechnic Institute in 2002, an ME in Statistics from the same institution in 2000, and a BA in Mathematics from Oregon State University in 1997. With over 20 years of experience in statistics and data science, she has established herself as a leader in computational biology with an h-index of 30 and more than 100 publications. Her research focuses on developing and applying advanced statistical and machine learning methods to address challenges associated with large and complex omics data. She specializes in mass spectrometry-based omics data analysis, statistical data integration, predictive modeling, and biomarker discovery. Her work has significant applications in biomedical research, particularly in understanding type 1 diabetes through multi-omics approaches. Dr. Webb-Robertson's recent publications demonstrate a strong trajectory in type 1 diabetes biomarker discovery, multi-omics data integration, and the application of machine learning to biological problems. Her work spans proteomics, metabolomics, and transcriptomics with a focus on developing computational tools that can integrate multiple data types for improved biological insights. Chauncey and Doris Starr Graduate Fellowship, 1997 GE Future Faculty Scholarship, 1999-2000 National Science Foundation Program in Mathematics and Molecular Biology Fellowship, 1999-2001 HPC Analytics challenge in biology first place team member, 2007 Spirit of nPOD award from the Network for Pancreatic Organ Donors with Diabetes Dr. Webb-Robertson serves in various editorial and advisory roles including Executive Editor for the Journal of Proteomics and Genomics Research, Review Editor for Frontiers in Artificial Intelligence, Scientific Advisory Board Member for the Juvenile Diabetes Research Fund IBM Machine Learning Project, and member of the American Statistical Association. She actively contributes to the scientific community through these service roles while maintaining a productive research program focused on computational methods for biological data analysis. Her laboratory develops computational tools for multi-omics data analysis with particular emphasis on applications in type 1 diabetes research. She leads a team that integrates expertise in statistics, machine learning, and biological domain knowledge to tackle complex biomedical problems, with a focus on biomarker discovery and early disease prediction.

Ryan Renslowمشاهده پروفایل

دانشیار

• Metabolomics
• Chemical Biology
• Exposure Science
• +۴ مورد دیگر

Ryan Renslow serves as a Chemical Engineer at Pacific Northwest National Laboratory (PNNL) and holds a Research Associate Professor position at Washington State University's Gene and Linda Voiland School of Chemical Engineering and Bioengineering. His interdisciplinary work bridges computational modeling, advanced imaging, and experimental biology to address complex challenges in metabolomics and microbial systems. Education BS in Chemical Engineering, Washington State University MS in Chemical Engineering, Washington State University PhD in Chemical Engineering, Washington State University Linus Pauling Distinguished Postdoctoral Fellowship, Pacific Northwest National Laboratory Renslow's research centers on identifying novel metabolites in complex biological samples, deciphering microbial community structure-function relationships, and understanding emergent properties in multispecies systems. He employs computational mathematics, machine learning, and high-resolution imaging techniques including mass spectrometry and nuclear magnetic resonance. His work spans diverse applications from human health diagnostics and bioenergy production to ecological monitoring and national defense solutions, with particular emphasis on biofilm dynamics and metabolite characterization in challenging environments. Analysis of his recent publications reveals a strong trend toward integrating ion mobility spectrometry with computational modeling for metabolite identification, developing in silico libraries for small molecule annotation, and applying machine vision to biological systems. His work consistently demonstrates cross-cutting applications across energy, environmental science, and biomedical research through sophisticated data analysis frameworks. Scientific Awards: No awards listed in available documentation. Renslow's collaborative research program involves extensive partnerships across PNNL's Environmental Molecular Sciences Laboratory and Washington State University. His work receives institutional support through DOE-funded initiatives focused on chemical biology and exposure science, with emphasis on developing advanced analytical capabilities for complex sample analysis. While specific grant details aren't provided, his publication record indicates consistent funding for interdisciplinary projects combining experimental and computational approaches. At PNNL, Renslow contributes to the Chemical Biology and Exposure Science group within the Biological Sciences division. He leverages specialized facilities including high-field mass spectrometers, nuclear magnetic resonance microimaging systems, and biofilm reactors to investigate microbial community dynamics. His research team integrates expertise from chemical engineering, microbiology, and computational science to develop novel approaches for characterizing complex biological systems at multiple scales.

Ollivier Hyrienمشاهده پروفایل

استاد

• Statistical methods for bioassays
• Machine learning
• Dynamic modeling
• +۶ مورد دیگر

Ollivier Hyrien is a Professor in the Biostatistics, Bioinformatics and Epidemiology Program within the Vaccine and Infectious Disease Division, and also a Professor in the Herbold Computational Biology Program within the Public Health Sciences Division at the Fred Hutchinson Cancer Center. He is additionally a Member of the Translational Data Science Integrated Research Center (TDS IRC) at Fred Hutch. Dr. Hyrien is a biostatistician who applies advanced technologies such as machine learning and dynamic modeling to address problems in laboratory experiments and clinical studies. His research focuses on statistical methods for flow cytometry, single-cell sequencing and other bioassays. He holds a PhD in Statistics from the University of Rennes 1, Rennes, France (2001). His research interests include: Statistical methods for bioassays (e.g., flow cytometry, sequencing) Machine learning (e.g., nonparametric clustering and classification) Dynamic modeling (e.g., B-cell repertoire) Dr. Hyrien leads the Hyrien Lab, which specializes in developing and applying statistical methods, computational tools, and mathematical models to study the immune system and other complex biological systems, such as the microbiome, the hematopoietic system, and the central nervous system, using high-dimensional, high-throughput data. A core focus of his current research is supporting the development of vaccines and passive immunization strategies against diseases caused by pathogens such as HIV, malaria, SARS-CoV-2, herpes simplex virus, as well as treatments against cancer. One exciting area of his research is the study of the evolution of the B-cell repertoire, involving the use of machine learning and statistical tools as well as stochastic processes to quantify and predict how somatic hypermutation accumulates over time in immunoglobulin genes. His recent publications demonstrate a strong focus on HIV vaccine development, statistical methods in immunology, and mathematical modeling of immune responses. His work spans across multiple disciplines including biostatistics, immunology, virology, and computational biology, with significant contributions to understanding antibody development and vaccine efficacy. Dr. Hyrien is highly collaborative, working closely with numerous research groups across various institutions to advance the understanding of complex biological systems and their interactions with human health and treatments.

Jing Maمشاهده پروفایل

دانشیار

• Network analysis
• High-dimensional data analysis
• Microbiome data analysis
• +۳ مورد دیگر

Jing Ma is an Associate Professor in the Biostatistics Program within the Public Health Sciences Division at Fred Hutchinson Cancer Center. She also holds affiliate appointments as an Assistant Professor in the Department of Biostatistics at the University of Washington and the Department of Statistics at Texas A&M University. As a member of the Translational Data Science Integrated Research Center at Fred Hutch, Dr. Ma leads research at the intersection of statistics, machine learning, and biological applications. Dr. Ma received her PhD in Statistics from the University of Michigan in 2015 and her BS in Mathematics from Fudan University in 2010. Her academic journey has positioned her as an expert in developing novel statistical methodologies for complex biological data. Dr. Ma's research focuses on network analysis, high-dimensional data analysis, and microbiome data analysis. Her lab specializes in statistical and computational methods for genomic data, particularly microbiome data. The Ma Lab employs various statistical learning methods including dimensionality reduction, graphical models, and high-dimensional inference to address analytical challenges in interpreting omics data. Key research areas include: Statistical methods for microbiome data analysis Statistical data integration Network-based pathway enrichment analysis High-dimensional graphical models Her current projects include "Statistical Methods for Network-based Integrative Analysis of Microbiome Data" funded by an R01 award from the National Institute of General Medical Sciences, and a pilot project on "Systems biology analysis of the immunomodulatory influence of circulating gut microbe-derived metabolites after transplantation" funded by the Translational Data Science Integrated Research Center. Dr. Ma's publications reveal a strong focus on applying statistical methodology to solve problems in cancer biology, aging research, and microbiome studies. Her work frequently involves collaborations with researchers across multiple institutions and leverages large datasets like those from the Dog Aging Project. She has made significant contributions to methods for differential network analysis, microbial community structure analysis, and integrative analysis of multi-omics data. Among her notable scientific achievements are: R01 award from the National Institute of General Medical Sciences Translational Data Science Pilot Award Microbiome Research Pilot Award Jayne Koskinas Ted Giovanis Foundation Award Dr. Ma actively mentors students and postdocs, with several former trainees now holding positions at academic institutions and tech companies. She also serves as Chair of the ASA Section on Statistics in Genomics and Genetics, where she organizes the Distinguished Student Paper Award Competition. Her work bridges statistical methodology development with impactful biological applications, particularly in understanding the role of microbiomes in human health and disease.

• Chronic disease epidemiology
• Cancer prediction modeling
• Medical big data research
• +۴ مورد دیگر

Lin Zhang MD, PhD, FRSPH serves as a PhD Supervisor at Monash University's School of Public Health and Preventive Medicine and Director of the Ethic Committee of Monash University Suzhou Campus. She maintains active roles as Vice President of the Shanghai Alumni Association of the University of Melbourne, Harvard University Entrepreneur Association consultant, and Deputy Secretary-General of the Senior Health Professional Committee of the China Geriatric Health Association. PhD in Cancer Epidemiology from University of Melbourne (2019) Master of Medicine and MPH from Peking Union Medical College/Tsinghua University (2018) Master of Public Administration from Chinese Academy of Governance (2018) Master of Medicine from Peking Union Medical College Hospital (2009) Medical Degree from Anhui Medical University (2005) Dr. Zhang's research focuses on chronic disease epidemiology with emphasis on cancer prediction models, medical big data analytics, and AI applications in healthcare. Her work spans real-world evidence generation, health economics, and patient-reported outcomes research, addressing critical gaps in diabetes, cardiovascular disease, and cancer management. She employs advanced methodologies including RCTs, meta-analyses, and observational studies to develop prevention algorithms and improve clinical decision-making. Her recent publications demonstrate growing interdisciplinary integration, with increasing emphasis on machine learning applications in medical research, particularly in cancer prediction and chronic disease management. The articles show strong focus on practical applications of big data analytics to address real-world healthcare challenges across multiple disease domains. Victorian International Education Award by Victorian State Government of Australia Global Outstanding Graduate by Ministry of Education of China (2020) Winner of Award for Research and Science in Australia China Alumni Association (2022) 2017 Award for Excellence from University of Melbourne (research team) Dr. Zhang actively supervises PhD students with backgrounds in Public Health, Biostatistics, Epidemiology, Bioinformatics, and Artificial Intelligence. She has secured significant research funding for projects involving pre-marketing clinical trials, post-marketing real-world research, and pharmacoeconomics studies. Her editorial responsibilities include serving as Editor-in-Chief for Frontiers in Molecular Biosciences, International Journal of Genomics, and Frontiers in Immunology. She leads research initiatives focused on medical big data analysis, AI applications in smart health, and real-world evidence generation, collaborating with international organizations including the Global Burden of Disease Collaborator Network. Her work bridges clinical practice and data science to develop evidence-based interventions for chronic disease prevention and management.

Carmen Hernández Brenesمشاهده پروفایل

استاد

• Food Metabolomics
• Food Processing Technologies
• Nutraceuticals
• +۵ مورد دیگر

Carmen Hernández Brenes is a Full Professor and Researcher in the Department of Bioengineering at Tecnologico de Monterrey's Campus Monterrey. She directs the Institute for Obesity Research and leads significant work in Biochemistry Technology and Food Processing. Her research group has published in prestigious peer-reviewed journals and holds more than 18 granted international patents. Her educational background includes: Ph.D. in Food Science from University of Arkansas (USA) M.Sc. in Food Science and Technology from Texas A&M University (USA) B.Sc. in Food Engineering from Tecnologico de Monterrey (Mexico) Hernández-Brenes specializes in food metabolomics and health, focusing on emerging extraction/processing technologies to improve nutritional stability, safety, and flavor of processed foods. Her research group employs chemical analytical techniques, sensory evaluation, biometrics and informatics tools to enhance foods and bioproducts. A parallel research line develops natural alternatives to synthetic food additives, particularly avocado lipids for applications across food, pharmaceutical and cosmetic industries. Her recent publications (2023-2025) demonstrate a strong focus on food authentication methods, particularly for beer and avocado oil, alongside research on functional foods, gut microbiota interactions, and innovative processing techniques like high hydrostatic pressure. These works span food chemistry, analytical methods, nutritional biochemistry, and sensory evaluation. Her scientific distinctions include: Level 2 Scientist from Mexican National Research System (SNI) Certified Food Scientist (CFS) from Institute of Food Technologists Lead Instructor for FDA-FSMA Preventive Controls courses Professional Member Distinction from IFT (2003) Mexican Researcher Certification Level 3 As an educator, she teaches undergraduate courses in Sensory Evaluation, Nutrigenomics and International Certification in Food Safety, while directing graduate student research. Her research group actively pursues projects in food processing technologies, with particular emphasis on sustainable applications aligned with UN Sustainable Development Goals including Zero Hunger, Good Health and Well-being, and Responsible Consumption and Production.