Find Academic Supervisors

Andreas WenzelView profile

Professor

• Embedded Systems
• Diagnostic Systems
• Neural Networks
• +6 more

Prof. Dr.-Ing. Andreas Wenzel is a Professor at Schmalkalden University of Applied Sciences in the Faculty of Electrical Engineering, specializing in Embedded Systems. His office is located in Building M, Room 0401, and he can be reached at +49 3683 688 5113. With a distinguished research career spanning over two decades, Prof. Wenzel has established himself as a leading expert in embedded diagnostic systems with applications across multiple domains including biomedical engineering, industrial automation, and assistive technologies. Prof. Wenzel's primary research interests focus on the development and application of embedded diagnostic systems, with particular emphasis on neural networks for pattern recognition, fuzzy logic systems for classification problems, and real-time monitoring solutions. His work bridges theoretical machine learning approaches with practical engineering applications, resulting in innovative solutions for quality control in manufacturing processes, medical diagnostics, and mobility assistance technologies. He has made significant contributions to EEG data analysis for sleep stage and anesthesia depth monitoring, as well as developing smart systems for injection molding quality assessment and advanced mobility solutions for elderly individuals. Analysis of Prof. Wenzel's publication record reveals a consistent research trajectory focused on applying computational intelligence to solve practical engineering problems. His work demonstrates exceptional versatility across domains while maintaining a cohesive research theme centered on embedded diagnostic systems. From 2012-2016, his research output shows increasing focus on real-time embedded solutions with industrial applications, particularly in manufacturing quality control and assistive technologies, while continuing his foundational work in biomedical signal processing. The interdisciplinary nature of his research is evident in collaborations with medical professionals, industrial partners, and robotics specialists. Prof. Wenzel leads the Embedded Diagnostic Systems Research Group at Schmalkalden University of Applied Sciences, which maintains strong industry partnerships, particularly in plastics manufacturing and medical technology sectors. His team develops practical embedded solutions that address real-world challenges in production quality monitoring, medical diagnostics, and mobility assistance. The research group's work is characterized by its practical orientation, with many projects resulting in deployable systems rather than purely theoretical contributions. Prof. Wenzel's strategic focus on applied research ensures that his work has direct industrial relevance and practical impact.

• Lung pathology
• Pulmonary fibrosis
• Lung cancer
• +7 more

Robert Homer, MD, PhD is Professor of Pathology and of Medicine (Pulmonary) at Yale School of Medicine, where he serves as Director of Medical Studies for Anatomic Pathology and Director of Medical Education for Pathology. He previously served as lead thoracic pathologist at Yale from 2004 to 2022 and Director of Anatomic Pathology at VA Connecticut HealthCare system from 1994 to 2022. His institutional affiliations include multiple Yale centers and programs such as the Cancer Signaling Networks, Center for Thoracic Cancers, Interstitial Lung Disease Program, and Pulmonary, Critical Care & Sleep Medicine. Dr. Homer's research focuses on the pathology of neoplastic and inflammatory lung diseases, with particular expertise in pulmonary fibrosis and lung cancer mechanisms. His work bridges basic science and clinical pathology, utilizing both human tissue analysis and murine models of lung disease. He has recently become interested in applying Large Language Models to medical education, reflecting his dual commitment to pathology research and pedagogy. Analysis of Dr. Homer's recent publications (2022-2025) reveals a strong focus on lung cancer genomics, tumor evolution mechanisms, and the molecular basis of pulmonary fibrosis. His work frequently appears in high-impact journals including Nature, Nature Communications, and Cancer Discovery, demonstrating significant contributions to understanding lung disease pathogenesis and developing improved diagnostic approaches. Keynote address for Pulmonary Pathology Society (2009) Dr. Homer maintains an active research program despite being in phased retirement, with numerous publications through 2025. He serves as an editorial board member for the American Journal of Pathology and has presented on innovative approaches to medical education, including 'From Bots to Docs, Harnessing AI in the medical education journey' (2023). His clinical expertise in thoracic pathology continues to inform both his research and teaching activities at Yale.

• Cell Biology
• Nuclear Envelope Biology
• Chromatin Dynamics
• +7 more

Megan C. King is Professor of Cell Biology and of Molecular, Cellular and Developmental Biology at Yale School of Medicine, where she also serves as Co-Leader of the DNA Damage and Genome Integrity Program and Associate Cancer Center Director for Basic Science at Yale Cancer Center. Her primary appointment is in the Department of Cell Biology with a secondary appointment as Associate Professor on Term in Therapeutic Radiology. She is a key member of multiple research programs including Cytoskeletal Dynamics, DNA Damage and Genome Integrity, and the Molecular Cell Biology, Genetics and Development Track. Dr. King's research focuses on fundamental aspects of nuclear structure and function. Her lab investigates how macromolecular complexes embedded in the nuclear envelope physically couple the cytoskeleton to the nucleus (LINC complexes), defining the mechanisms underlying nuclear force response. They also study genome organization through the lens of chromatin dynamics, using innovative live cell assays to examine how nuclear cell biology impacts genome integrity. Her work spans from basic molecular mechanisms to implications for diseases including hereditary breast and ovarian cancer syndrome. Analysis of Dr. King's recent publications (2021-2025) reveals a strong emphasis on nuclear mechanics, chromatin organization, and nuclear pore complex dynamics. Her work integrates approaches from biophysics, cell biology, computational modeling, and genetics, with publications appearing in top journals including Nature Cell Biology, Cell, and Genome Biology. A notable trend is the increasing interdisciplinary nature of her research, combining advanced microscopy techniques with computational approaches to understand nuclear architecture. Allen Distinguished Investigator (2020) from the Allen Institute for pioneering research in nuclear biology NIH New Innovator Award (2011) supporting innovative early-career research Searle Scholar (2011) recognizing exceptional promise in biomedical research Dr. King maintains an active research program with consistent high-impact publications and significant collaborations, particularly with C. Patrick Lusk (co-leader of the LusKing Lab), Ivan Surovtsev, and other Yale researchers. Her lab provides training opportunities for students and postdocs interested in nuclear cell biology, with research spanning from fundamental mechanisms to potential therapeutic applications. The LusKing Lab emphasizes both scientific discovery and creating an inclusive research environment that values diverse perspectives and experiences.

Smita KrishnaswamyView profile

Associate Professor

• Unsupervised Machine Learning
• Manifold Learning
• Deep Learning
• +7 more

Smita Krishnaswamy is an Associate Professor of Genetics and Computer Science at Yale University with joint appointments in both departments. She is affiliated with multiple interdisciplinary programs including the Applied Mathematics Program, Computational Biology and Bioinformatics Program, Yale Center for Biomedical Data Science, Yale Cancer Center, and the Wu Tsai Institute. Her research bridges computational methods development with biomedical applications, focusing on unsupervised machine learning approaches for high-dimensional data analysis. Associate Professor of Genetics, Yale School of Medicine Associate Professor of Computer Science, Yale University Affiliated Faculty, Applied Mathematics Program Affiliated Faculty, Computational Biology and Bioinformatics Member, Yale Center for Biomedical Data Science Member, Yale Cancer Center Member, Wu Tsai Institute Dr. Krishnaswamy's research focuses on developing unsupervised machine learning techniques, particularly manifold learning and deep learning methods, to analyze high-dimensional biomedical data. Her lab creates algorithms for non-linear dimensionality reduction, data geometry learning, denoising, imputation, and inference of multi-granular structures from complex datasets. These methods are applied to diverse data types including single-cell RNA-sequencing, mass cytometry, electronic health records, and connectomic data across multiple biological systems. Her work spans several key application areas including immunology and immunotherapy, cancer research, neuroscience, developmental biology, and health outcomes analysis. The lab employs approaches from geometric deep learning, multiscale graph signal processing, and topological data analysis to extract meaningful biological insights from complex datasets. Recent publications demonstrate the lab's leadership in developing methods for spatial transcriptomics, brain-state trajectory modeling, and organ donation prediction. Excellence in Science Early-Career Investigator Award from FASEB (2022) Yale Cancer Center Class of '61 Cancer Research Award (2025) Dr. Krishnaswamy maintains active collaborations across Yale and secures research funding supporting her work in computational biomedicine. She advises students through multiple programs including Genetics, Computer Science, and the Biological and Biomedical Sciences Graduate Program, fostering interdisciplinary training at the intersection of computation and biomedicine. The Krishnaswamy Lab operates at the forefront of computational biomedicine, developing mathematical approaches that enable new biological discoveries from complex datasets.

• Cancer Genetics
• Colorectal Cancer
• Hereditary Cancer Syndromes
• +3 more

Dr. Rosa Munoz Xicola is an Assistant Professor of Genetics (primary appointment) and Assistant Professor of Digestive Diseases (secondary appointment) at Yale School of Medicine, where she contributes significantly to cancer research through the Yale Cancer Center. She is an active member of both the DNA Damage and Genome Integrity Program and the Genomics, Genetics, & Epigenetics Program. Her research focuses on cancer genetics, particularly hereditary colorectal cancer syndromes including Lynch syndrome and CDH1-related hereditary diffuse gastric cancer. Key areas include molecular mechanisms of DNA repair, mutational signatures in mismatch repair-deficient tumors, and healthcare disparities in cancer, with special emphasis on early-onset colorectal cancer in African American populations. Dr. Xicola's publication record demonstrates consistent contributions to understanding APC mutation-negative colorectal cancers, developing improved diagnostic criteria, and exploring molecular drivers of tumor progression. Her research bridges basic science with clinical applications, particularly in refining genetic testing protocols and diagnostic approaches for hereditary cancer syndromes. Kushlan Junior Faculty Award (2017) from Yale School of Medicine Dr. Xicola serves as a reviewer for multiple prestigious journals including Clinical and Translational Gastroenterology, Carcinogenesis, and Human Molecular Genetics. She is actively involved in professional organizations including the American Association for Cancer Research (AACR) and the American Gastroenterological Association (AGA), where she has held committee positions and contributed to symposium planning.

• Hair follicle development
• Regeneration
• Dermal niche
• +7 more

Peggy Myung, MD, PhD is Vice Chair of Research and Associate Professor of Dermatology and Pathology at Yale School of Medicine. She maintains strong affiliations across multiple Yale entities including the Yale Cancer Center, Yale Stem Cell Center, Cancer Signaling Networks Program, and Pathology Research Program in Translational Biomedicine. Dr. Myung leads the Myung Lab which focuses on hair follicle development and regeneration research, with applications to understanding skin cancer mechanisms. Dr. Myung received her MD and PhD from the University of Pennsylvania in 2003, completed residencies at Hospital of the University of Pennsylvania and University Hospitals Case Medical Center, and a fellowship at Yale University School of Medicine in 2014. She is board certified in both Dermatology (2012) and Dermatopathology (2014, with recertification in 2025). Her research program investigates how heterotypic signals between the skin epidermis and dermis regulate robust and organized hair follicle growth during embryonic development and adult regeneration. The Myung Lab specifically examines: (1) how cellular behaviors such as cell divisions and movement are regulated by key morphogenetic signals during regeneration, (2) how these signals are spatially disseminated to ensure robust but patterned growth, and (3) how these mechanisms contribute to disorganized growth during tumorigenesis. The hair follicle serves as an ideal model for these studies due to its accessibility and well-characterized cyclical regeneration dependent on resident stem cells. Dr. Myung's recent publication record demonstrates consistent productivity in high-impact journals, with a growing emphasis on single-cell genomic techniques and computational approaches to understand cellular interactions at unprecedented resolution. Her work bridges developmental biology, dermatology, and cancer research, with particular focus on Wnt/β-catenin signaling and epithelial-mmesenchymal interactions. Dr. Myung has received notable recognition including: $30K Biotech Accelerator Award (June 2023) Yale Medicine 'Top Doctor' listing (2024) As Vice Chair of Research, Dr. Myung oversees research initiatives within the Department of Dermatology while maintaining an active laboratory program that combines multi-photon imaging, quantitative RNA fluorescent in situ hybridization, single-cell RNA/ATAC sequencing, and in vivo mouse genetic models. Her long-term goal is to develop novel treatments for hair loss and to prevent and treat non-melanoma skin cancer by applying knowledge of hair morphogenetic signals to suppress aberrant growth in carcinogenesis.

• Bioinformatics
• Genomics
• Genetics
• +7 more

Dr. Nallakkandi Rajeevan is a Senior Research Scientist in the Department of Biomedical Informatics & Data Science at Yale School of Medicine, where he also serves as Associate Director for Bioinformatics at the Yale Center for Medical Informatics. He holds additional affiliations with the Genomics, Genetics, and Epigenetics Program at Yale Cancer Center and the Yale-BI Biomedical Data Science Fellowship. Dr. Rajeevan received his B.E., M.S., and Ph.D. from Indian Institute of Science, Bangalore, India, with educational background in Electrical Engineering and Electronics and Communications. He completed a post-doctoral fellowship in Nuclear Medicine from University of Massachusetts Medical Center in 1993. His research focuses on applying theoretical computer science, algorithm development, statistical estimation theory, and biostatistics to problems in genomics, bioinformatics, nuclear medicine, and functional brain imaging. Dr. Rajeevan has established a robust research program analyzing veteran health data, particularly related to infectious diseases and post-acute outcomes. His work bridges computational methods with clinical applications to generate actionable insights for healthcare delivery and policy. Dr. Rajeevan's recent publications (2023-2025) demonstrate a strong focus on methodologically rigorous studies of COVID-19 outcomes using target trial emulation approaches. His research spans vaccine effectiveness, post-COVID conditions, healthcare utilization patterns, and comparative mortality analysis across respiratory viruses, primarily using Veterans Health Administration data. Senior Member, Institute of Electrical and Electronics Engineers (IEEE), 2021 Lifetime Service Award, Indo-American Society of Nuclear Medicine, 2010 Dr. Rajeevan maintains an active research program with numerous collaborations, particularly with researchers like Mihaela Aslan, Lei Yan, and Hongyu Zhao. His interdisciplinary approach combines computational expertise with clinical insights to address complex questions in public health and healthcare delivery, with particular emphasis on generating real-world evidence to inform clinical decision-making.

Juri RappsilberView profile

Professor

• Proteomics
• Mass Spectrometry
• Protein Structure
• +4 more

Prof. Juri Rappsilber is a Professor at the Technical University of Berlin's Institute of Biotechnology and Group Leader of Research Unit E in the UniSysCat Cluster of Excellence. His affiliations include Visiting Professorship at Kyoto University (2017, JSPS Fellow) and Senior Research Fellowships at the Wellcome Trust (2009-2019). He leads the Rappsilber Laboratory, focused on developing mass spectrometry technologies to study protein structure and interactions. Research interests center on proteomics , crosslinking mass spectrometry , and protein-network analysis . Key areas include: Functional protein networks through co-regulation studies Chromatin dynamics and mitotic chromosome composition In-cell structural proteomics integrating deep learning Development of bioorthogonal catalysis for medical applications His publication record demonstrates consistent output in high-impact journals, with recent work emphasizing: Structural mechanisms of protein complexes (γ-tubulin, condensin) Crosslinking MS methodology innovation and error control Integration of AI with experimental structural biology Catalytic processes in bioinorganic systems Awards and honors: EMBO Membership (2021) BSPR Lectureship (2014) Einstein Professorship (2011) Wellcome Trust Senior Research Fellow (2009-2019) Marie Curie Excellence Fellow (2005-2010) He actively advises doctoral students, including DAAD Hilde-Domin Program recipients, and secures substantial funding through ERC Synergy Grants, Wellcome Trust awards, and national initiatives. The lab focuses on developing anti-cancer therapeutics and sustainable chemistry solutions while pursuing Green Lab Certification.

Susanne GerberView profile

Professor

• Bioinformatics
• Clinical Genomics
• Multi-omics Integration
• +4 more

Prof. Dr. Susanne Gerber is a tenured-track Professor (W2) for Clinical Genomics and Bioinformatics at Johannes Gutenberg University Mainz, where she serves as Deputy Director of the Institute of Human Genetics within the University Medical Center. Her academic journey includes a PhD in Theoretical Biophysics from Humboldt University Berlin (2011), MSc in Bioinformatics from Free University Berlin (2007), and BSc from Free University Berlin/MPI for Molecular Genetics (2004). Before her current role, she was Assistant Professor at JGU Mainz (2016-2020) and postdoc at Università della Svizzera italiana (2011-2015). Her research integrates multi-omics approaches (transcriptomics, proteomics, epigenomics) with machine learning to investigate neurodegeneration, resilience mechanisms, and healthy aging. The lab specializes in: Development of novel bioinformatics algorithms for high-dimensional data analysis Third-generation nanopore sequencing applications SNP-SNP association studies for 3D chromatin modeling Cross-omics biomarker discovery for brain disorders Recent publications demonstrate strong focus on meta-analyses of neurodegenerative diseases, computational method development, and glial-neurovascular interactions. Her group maintains active research in miRNA patterns in Alzheimer's/Parkinson's disease and their microbiome interactions. Prof. Gerber leads the Computational Systems Genomics Group with 11+ members, conducting research at the Biomedical Research Center. Her work bridges computational science with clinical applications in human genetics.

Hans-Peter LenhofView profile

Professor

• Bioinformatics
• Cancer Biology
• Personalized Cancer Therapy
• +4 more

Prof. Dr. Hans-Peter Lenhof holds the Chair for Bioinformatics at Saarland University since 2000, where he leads research in computational biology and disease mechanism analysis. His career includes: Postdoctoral research at Max Planck Institute for Informatics (1993-1999) Research Group Leader at MPI (1999-2000) Professor at Saarland University (2000-Present) His group develops innovative bioinformatics methods with three primary focus areas: Personalized Cancer Therapy : Creating AI/ML approaches to optimize drug selection from over 200 cancer therapeutics, addressing tumor heterogeneity through predictive modeling of treatment efficacy. Diagnosis & Prognosis : Pioneering blood-based diagnostic methods using autoantibody and miRNA profiles that demonstrate high clinical sensitivity for multiple cancers and neurological disorders through collaborative studies. Pathogenic Mechanism Analysis : Employing graph-based network models to visualize and analyze deregulated biological processes, particularly signaling cascades in cancer progression. The lab maintains active collaborations with Andreas Keller's Clinical Bioinformatics group and Eckart Meese's Human Genetics team. Computational tools developed include BALLView for molecular visualization, GeneTrail for high-throughput data analysis, and specialized frameworks for therapy optimization (MERIDA) and miRNA networks (miRTargetLink).

Sven RahmannView profile

Professor

• Bioinformatics Algorithms
• Sequence Analysis
• k-mer Counting
• +4 more

Prof. Dr. Sven Rahmann chairs the Algorithmic Bioinformatics group at Saarland University, jointly affiliated with the Mathematics and Informatics Faculty (MI) and the Center for Bioinformatics (ZBI). His research develops computational methods for biological data analysis, specializing in: Efficient k-mer counting algorithms (hackgap) Chromatin state discovery using hidden Markov models (EpiSegMix) Probabilistic data structures for genomics Alignment-free sequence analysis He teaches core courses in Saarland's bioinformatics programs and advises graduate students on genome analysis projects. The lab maintains active collaborations through conference participation and workshops.

• Algorithms
• Bioinformatics
• Comparative Genomics
• +5 more

Séverine Bérard is a Lecturer in the Computer Science Department at the Faculty of Sciences, University of Montpellier, where she holds a Habilitation à Diriger des Recherches (HDR) since 2016. Her institutional roles include: Co-director of the Bioinformatics Master's program Elected member of the University Board of Directors (since 2021) Elected member of the Faculty of Sciences Council (since 2017) Appointed member of the University Training Committee (since 2016) Her research at ISE-M's Phylogeny and Molecular Evolution Team bridges computer science and bioinformatics, with core focuses on: Development of text algorithms and graph-based methods for genomic analysis Comparative genomics and ancestral genome reconstruction Quality assessment of genome assemblies and meta-assemblies Evolutionary modeling of chromosome rearrangements Her recent publications (2016-2021) demonstrate strong emphasis on algorithmic innovations for evolutionary genomics, including genome scaffolding techniques, DCJ rearrangement models, and ancestral genome reconstruction methods applied to organisms ranging from mosquitoes to bacteriophages. In education, she teaches across all academic levels (L1-M2) in both practical computer science (networks, programming) and theoretical disciplines (discrete mathematics, text algorithms), while leading curriculum development as co-director of the Bioinformatics Master's program.

Angelo VallerianiView profile

Research Professor

• Stochastic Processes
• RNA Biology
• Population Genetics
• +8 more

Dr. Angelo Valleriani serves as Group Leader for Stochastic Processes in Complex and Biological Systems at the Max Planck Institute of Colloids and Interfaces in Potsdam, Germany, and coordinates the International Max Planck Research School (IMPRS) on Multiscale Bio-Systems. His research bridges theoretical physics and biological applications with a focus on quantitative modeling of complex biological phenomena. Dr. Valleriani earned his PhD in High Energy Physics from SISSA in Trieste, Italy (1996), following a Laurea Degree in Theoretical Physics from the University of Bologna (1992) with full marks and honors. His academic journey includes Visiting Scientist positions at Max Planck Institutes in Golm and Dresden before becoming a Group Leader in November 2000. His research interests encompass: Stochastic modeling of biological processes RNA biology and translational control mechanisms mRNA and tRNA turnover dynamics Population genetics and evolutionary biology Biostatistical data analysis Dr. Valleriani's recent publications (2022-2024) reveal a strong emphasis on computational approaches to biological problems, particularly in ribosome dynamics, protein synthesis regulation, and cellular remodeling processes. His work demonstrates sophisticated integration of mathematical modeling with experimental biology. He maintains active collaborations with researchers from multiple institutions including the University of Potsdam (Silke Leimkühler, Carsten Beta, Stefanie Barbirz), University of Cambridge (Davide Chiarugi), Weizmann Institute (Ziv Reich, Ruti Kapon), DRFZ Berlin (Ria Baumgrass), and others. The research group actively recruits MSc students from physics, mathematics, engineering, and bioinformatics backgrounds for challenging thesis projects in computational biology and biophysics.

Huaming ChenView profile

Senior Lecturer

• Software Engineering
• Artificial Intelligence
• Software Security
• +3 more

Dr. Huaming Chen is a Senior Lecturer in the School of Electrical and Computer Engineering at The University of Sydney, Australia. His work focuses on trustworthy machine learning systems, software engineering, and software security. With numerous publications in top-tier conferences and journals, Dr. Chen has established himself as a significant contributor to the fields of AI security and software engineering. Dr. Chen's primary research interests lie at the intersection of software engineering and artificial intelligence, with a strong emphasis on trustworthy AI systems. His work spans several key areas including: Software Security for AI-enabled systems Trustworthy and Responsible AI development Computational biology applications Industrial 4.0 implementations Federated learning and privacy-preserving techniques Large language model verification and uncertainty analysis His research addresses critical challenges in ensuring AI systems are secure, reliable, and ethically sound. Dr. Chen's recent publications demonstrate a strong trend toward addressing security and trustworthiness challenges in AI systems. His work spans multiple domains including software security (particularly for AI systems), trustworthy AI development, and applications in computational biology. A significant portion of his recent work focuses on large language models, examining their vulnerabilities, verification methods, and uncertainty analysis. He also maintains active research in federated learning, adversarial machine learning, and software security techniques. Dr. Chen has received several notable awards and recognitions: 2020 IEEE CIS Student Grant for IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE (WCCI) 2017 Student and Early Career Travel Fellowship for The 16th International Conference on Bioinformatics (InCoB 2017) 2017 Student Travel Award for 2017 IEEE World Congress on Services Dr. Chen actively supervises multiple research students working on cutting-edge projects related to trustworthy AI and software security. His current students are exploring topics ranging from blockchain-based governance frameworks to open-source AI security and digital twin platforms. He also serves in numerous committee roles at top conferences including area chair for ACM MM, and PC member for ACM CCS, IJCAI, KDD, and many others. His service as a Guest Editor for journals like Computers & Security and as a Grant Reviewer for UKRI demonstrates his standing in the research community. Dr. Chen organizes workshops focused on Trustworthy and Responsible AI, reflecting his commitment to advancing the field. His research group appears to focus on practical applications of AI security techniques, with projects spanning multiple domains including healthcare, finance, and industrial systems. He maintains active collaborations with researchers across multiple institutions, as evidenced by his co-authorship on diverse publications.

• Perinatal Epidemiology
• Fertility Research
• Assisted Reproductive Technologies
• +3 more

Siri Eldevik Håberg serves as Centre Director of the Centre for Fertility and Health (CEFH), a prestigious Centre of Excellence (SFF) funded by the Norwegian Research Council and hosted by the University of Oslo's Faculty of Medicine. Her primary institutional affiliation is with the Norwegian Institute of Public Health (FHI), where she leads cutting-edge research at the intersection of epidemiology, reproductive health, and public policy. Her research expertise spans perinatal epidemiology, with a focus on fertility problems, fetal loss, and the safety of assisted reproductive technologies (ART). She specializes in integrating national registry data, cohort studies, and biological analyses to investigate genetic and epigenetic associations with pregnancy outcomes. Current projects include large-scale studies on vaccination safety during pregnancy, environmental exposures, and transgenerational health effects. Her methodological strengths include registry-based population studies and advanced genetic epidemiology. Håberg's publication record demonstrates consistent leadership in high-impact reproductive epidemiology, with recent work examining ART outcomes, epigenetic mechanisms, and pandemic-related health impacts. Her research trends show increasing emphasis on interdisciplinary collaborations, particularly through the ERC Synergy Project on fertility in young adults and the Pregnancy and Childhood Epigenetics Consortium. Centre of Excellence (SFF) grant from Norwegian Research Council Principal Investigator for ERC Synergy Project on fertility Leadership in Nordic registry collaborations for Covid-19 research As Centre Director, she oversees substantial research funding and international collaborations, including the Norwegian Mother, Father and Child Cohort Study (MoBa). Her team comprises epidemiologists, geneticists, and clinicians working across multiple Nordic institutions. Current initiatives include clinical sampling of young adults to address declining fertility rates in Norway, reflecting her commitment to translating research into public health action.