جست‌وجوی استاد و پژوهشگر

• Synthetic Biology
• Enzyme Engineering
• Genetic Circuits
• +۳ مورد دیگر

Dr. Seung-gu Lee is a Principal Scientist and Director of the Synthetic Biology Institute at the Korea Research Institute of Bioscience and Biotechnology (KRIBB). He holds a Ph.D. in Biotechnology from KAIST and serves as the President of the Korean Society of Enzyme Engineering. His leadership extends to multiple national research initiatives focused on advancing synthetic biology infrastructure and applications. Dr. Lee is a leading expert in genetic circuits and enzyme engineering for synthetic biology. He pioneered the development of GESS (Genic Enzyme Screening System/Circuits) technology, which enables ultra-high-speed screening of trace enzyme activities at the single-cell level. His research focuses on applying intelligent genetic circuits to biosensors, metabolic control, and bioprocess development. He is actively integrating AI-based automated workflows with synthetic biology to revolutionize the speed and scale of biological research. His work spans multiple disciplines including enzyme engineering, genetic circuit design, metabolic engineering, and biofoundry development. Dr. Lee currently leads several major research initiatives including the Korea-North America Biofoundry Collaboration Global Joint Research Center, the development of core technologies for advanced synthetic biology, and the establishment of biofoundry infrastructure. His research team at KRIBB works at the intersection of synthetic biology, enzyme engineering, and AI-based automation to develop next-generation bio-manufacturing platforms. The lab has made significant contributions to genetic circuit design, biosensor development, and plastic biodegradation technologies.

Charu Gupta Kumarمشاهده پروفایل

استاد پژوهشی

• Systems and Computational Biology
• Microbiome/Pathogen Computational Genomics
• Metabolic Network Modeling
• +۳ مورد دیگر

Charu Gupta Kumar is a Research Professor in the Department of Bioengineering at the University of Illinois Urbana-Champaign, affiliated with the Neuroscience program. Her research focuses on systems and computational biology, integrating computational genomics, bioinformatics, and systems biology approaches to study microbiome/pathogen dynamics, neurological diseases, and evolutionary processes. She leads the Systems and Computational Biology Group, emphasizing interdisciplinary methodologies to address complex biological questions. Her academic roles include a zero-time appointment in the Department of Neuroscience (2013–present) and service as an admission reviewer for the Carle Illinois College of Medicine (2020–2022). She advises a diverse cohort of undergraduate and graduate students engaged in bioengineering and computational biology projects. Her work bridges environmental microbiology with human health, exploring astrocyte roles in neurological disorders like glioblastoma and autism, and investigating evolutionary mechanisms of metabolic networks across species. Publications highlight contributions to bovine genomics, microbial community analysis in subsurface environments, and computational tools for genomic data management. Her research trends emphasize translational applications of computational biology to environmental and clinical challenges, with recent focus on microbiome-disease interactions and astrocyte signaling pathways. Advising spans mentoring over 18 students across bioengineering and computer science disciplines, with notable alumni advancing to institutions like MIT and Johns Hopkins. She collaborates with跨学科 teams in bioengineering and neuroscience, contributing to the broader Grainger College of Engineering research ecosystem.

• Plant Developmental Biology
• Epigenetics
• Plant Genetics
• +۵ مورد دیگر

Ueli Grossniklaus is an Ordinary Professor at the University of Zurich within the Faculty of Mathematical and Natural Sciences , affiliated with the Department of Plant and Microbiology . His work focuses on plant developmental biology, particularly epigenetic and genetic mechanisms governing reproduction and adaptation. Key Courses: Epigenetics, Plant Biology Workshop, Group Seminars on Current Research Laboratory Techniques: Advanced methods in plant cell mechanics, transcriptomics, and genome editing Research Interests span plant epigenetics, reproductive biology, and the interplay between environmental stress and genetic regulation. He investigates: Mechanistic control of gametogenesis and fertilization Epigenetic contributions to plant adaptation Evolutionary implications of asexual reproduction Biophysical forces in plant cell growth Publication Trends (2025–2018) reveal expertise in: Arabidopsis and fern model systems Epigenetic regulation (DNA methylation, histone dynamics) Apomixis and hybrid seed failure mechanisms Biomechanics of pollen tubes and carnivorous plants Genome editing tools (CRISPR) and long-read sequencing Scientific Collaborations include interdisciplinary projects on: Microfluidic devices for plant cell analysis Gene drive ecology and ethics 3D imaging of plant reproductive structures Advising and Grants focus on mentoring through research internships in developmental biology, genetics, and systems biology. His lab engages in: Epigenetic response to environmental stress Cell wall mechanics in reproduction Computational modeling of plant growth Laboratory Teams integrate plant biologists, bioengineers, and computational scientists to study: Mechanistic gene regulation Evolutionary developmental biology Microrobotics for cellular force measurement

Gerard D Schellenbergمشاهده پروفایل

استاد

• Alzheimer's Disease Genetics
• Neurodegenerative Disorders
• Genomics
• +۴ مورد دیگر

Gerard D Schellenberg is a Professor of Pathology and Laboratory Medicine at the University of Pennsylvania Perelman School of Medicine, with graduate affiliations in Genomics and Computational Biology and Neuroscience. His research focuses on the genetic architecture of Alzheimer's disease and related neurodegenerative disorders, particularly through large-scale genomic studies and neuropathological correlations. Education: B.S. in Biochemistry (minor: Cell Biology), University of California at Riverside, 1973 Ph.D. in Biochemistry (minor: Cell Biology), University of California at Riverside, 1978 NIH Post Doctoral Fellowship, National Institute of Health, 1980-1982 Senior Research Fellow positions at University of Washington departments (1978-1983) Dr. Schellenberg's research program centers on identifying genetic risk factors for Alzheimer's disease through genome-wide association studies (GWAS), whole-genome sequencing, and multi-omics integration. His work emphasizes population diversity, with significant contributions to understanding genetic risk in African American cohorts and sex-specific effects in neurodegeneration. Key focus areas include tauopathies, TDP-43 pathology, and the role of immune-related genes in disease progression. He has pioneered studies on progranulin mutations and their variable phenotypic expression across neurodegenerative conditions. Recent publications reveal a strong trajectory toward multi-ethnic genetic studies, with emphasis on Alzheimer's disease genetics in underrepresented populations , sex differences in cognitive resilience , and novel risk genes like MGMT and DCDC2. His team integrates neuropathological data with genomic findings to establish causal mechanisms, frequently publishing in high-impact journals like Nature Genetics and Alzheimer's & Dementia . Key Scientific Contributions: Leadership in the Alzheimer's Disease Sequencing Project (ADSP) expanding ethnic diversity in genetics research Development of the Alzheimer's Disease Variant Portal (ADVP) for harmonized genetic data Pioneering work on APOE-ε4 modifying loci in African ancestry populations Identification of sex-specific genetic predictors for memory maintenance Dr. Schellenberg directs genomic research initiatives that bridge basic science with clinical neuropathology. His laboratory maintains extensive collaborations with neuropathology cores for autopsy-confirmed diagnoses and leverages multi-ethnic cohorts to address health disparities in dementia research. Current work focuses on elucidating how genetic variants influence tau and TDP-43 pathology across diverse populations, with implications for precision medicine approaches to neurodegenerative diseases.

Facundo M. Fernandezمشاهده پروفایل

استاد

• Analytical Chemistry
• Bioanalytical Mass Spectrometry
• Ambient Sampling/Ionization
• +۴ مورد دیگر

Facundo M. Fernandez is a Regents' Professor and Vasser-Woolley Chair in Bioanalytical Chemistry at the Georgia Institute of Technology, where he leads the Fernandez Research Group within the School of Chemistry and Biochemistry in the College of Sciences. His research spans multiple cutting-edge areas of analytical chemistry with significant applications in medicine, forensics, and basic science. Dr. Fernandez earned his M.Sc. in Chemistry (1996) and Ph.D. in Analytical Spectrometry/Metallomics (1999) from the Facultad de Ciencias Exactas y Naturales at Buenos Aires University, Argentina. His research program focuses on Bioanalytical Mass Spectrometry with particular emphasis on Ambient Sampling/Ionization & Molecular Imaging, Ion Mobility Spectrometry, Metabolomics, and Pharmaceutical Forensics. His work has pioneered new approaches in ambient ionization techniques that enable direct analysis of complex samples without extensive preparation. His recent publications reveal a strong trend toward spatial metabolomics, particularly in traumatic brain injury and ovarian cancer research, with increasing integration of machine learning approaches for data analysis. His work also extends to pharmaceutical quality control, exercise physiology through the MoTrPAC consortium, and prebiotic chemistry investigations. The interdisciplinary nature of his research is evident in collaborations across Georgia Tech's campus and with external institutions. NSF CAREER Award (2007) 3M Non-tenured Faculty Award (2008) CETL/BP Junior Faculty Teaching Excellence Award (2009) Ron A. Hites Award for Outstanding Research Publication (2010) Sigma Xi (GT Chapter) Best Faculty Paper Award (2010) Vasser-Wooley Faculty Fellow (2012) Dr. Fernandez has secured significant funding for his research, including NSF CAREER support, and leads projects related to metabolomics for ovarian cancer detection, pharmaceutical forensics through the CODFIN network, and participation in the large-scale Molecular Transducers of Physical Activity Consortium (MoTrPAC). His laboratory develops advanced instrumentation for mass spectrometry applications and maintains strong collaborations with the Integrated Cancer Research Center, the College of Computing, and the Center for Chemical Evolution at Georgia Tech.

Karin Verspoorمشاهده پروفایل

استاد

• Artificial Intelligence in Medicine
• Biomedical Natural Language Processing
• Health Informatics
• +۳ مورد دیگر

Professor Karin Verspoor is the Dean of the School of Computing Technologies at RMIT University in Melbourne, Australia. She previously held roles as Director of Health Technologies and Deputy Head of the School of Computing and Information Systems at the University of Melbourne, and as Scientific Director of Health and Life Sciences at NICTA's Victoria Research Laboratory. Her research focuses on applying artificial intelligence methods to biomedical discovery and clinical decision support, particularly through natural language processing of clinical texts and biomedical literature. Affiliations: RMIT University (STEM College), Australian Alliance for Artificial Intelligence in Health (Victorian Node Lead) Industry Experience: Intelligenesis/Webmind Corp., Applied Semantics, Los Alamos National Laboratory, National ICT Australia Research Interests: Artificial Intelligence in Medicine Biomedical Natural Language Processing Health Informatics Computational Biology Cheminformatics Her work emphasizes cross-modal data integration, EHR analytics, and AI-driven clinical tools to address challenges in healthcare outcomes, musculoskeletal disorders, and infectious disease surveillance. Advising & Grants: Supervises research on AI-based decision-making frameworks, EHR data quality, and chemical knowledge extraction. Leads projects funded by initiatives like CANAIRI (Collaboration for Translational AI in Healthcare). Labs & Collaborations: Co-founder of the Australian Alliance for AI in Health, advancing national AI healthcare policy and translational research.

Miler T. Leeمشاهده پروفایل

دانشیار

• Gene Regulation
• Developmental Biology
• Computational Genomics
• +۹ مورد دیگر

Miler T. Lee is an Associate Professor at the University of Pittsburgh , focusing on gene regulation during early embryonic development through high-throughput experimental and computational genomics. He earned his Ph.D. in Genomics and Computational Biology in 2009 from the University of Pennsylvania under Dr. Junhyong Kim, followed by postdoctoral work with Dr. Antonio Giraldez at Yale University. Joining the university in 2016, his research spans maternal-to-zygotic transition (MZT), RNA stability, pluripotency networks, and evolutionary developmental biology, utilizing model organisms like zebrafish, Xenopus, and Hydractinia symbiolongicarpus. Key Research Themes: Maternally inherited RNA dynamics during embryogenesis Mechanisms of RNA degradation and transcriptome remodeling Evolution of pluripotency networks in hybrid species Role of zinc signaling in fertilization barriers Computational tools for RNA regulation and sensing Scientific Awards: Pan-American Society for Evolutionary Developmental Biology Junior Faculty Award (2024) Outstanding New Investigator – International Xenopus Board (2023) Basil O'Connor Scholar – March of Dimes (2017-2019) Recent publications highlight his work on enhancer classification, RNA degradation mechanisms, and cross-species MZT comparisons. His lab develops innovative methods like RESA for regulatory sequence analysis and studies evolutionary divergence in RNA localization patterns. While the articles span computational and experimental approaches, they consistently address RNA's role in cellular identity, developmental timing, and evolutionary adaptation. Applications include understanding pluripotency, designing RNA biosensors, and elucidating fertilization barriers. Prospective Ph.D. students are encouraged to contact him for opportunities in gene regulation, development, evo-devo, and computational genomics.

April Weiمشاهده پروفایل

استادیار

• Computational Genetics
• Population Genetics
• Statistical Genetics
• +۱۰ مورد دیگر

April Wei is an Assistant Professor in the Department of Computational Biology at Cornell University, where she leads the Wei Lab in Computational Genetics. Her research focuses on developing scalable computational methods to analyze massive genomic datasets, addressing fundamental questions in evolutionary processes and human genetics. B.Sc. in Biology, Fudan University (2013) Ph.D. in Computational Biology, University of Michigan (2018) Postdoctoral Research at UC Berkeley and UCLA Joined Cornell in January 2022 The Wei Lab explores diverse areas such as population genetics , computational genetics , and natural selection , with applications in human evolution , gene conversion , and complex traits . Their work leverages graph-based data structures and deep learning to improve genomic analysis scalability. April's research trajectory, as reflected in her publications, emphasizes genomic data compression , selective sweep detection , and ancestral recombination graph (ARG) analysis . Her lab's outputs span algorithm development for biobank-scale data, evolutionary modeling of overlapping genes, and statistical frameworks for gene conversion landscapes. April mentors a team of Ph.D. students and postdoctoral researchers, including current advisees Ziqing Pan, Meera Chotai, Jinmin Li, Aditya Girish, and Lin Yuan, with notable alumni such as Siddharth Avadhanam (2023). The lab is supported by NIH and NSF grants. Lab culture prioritizes collaboration , inclusivity , and scientific curiosity , with social activities like lab barbecues, game nights, and group hikes. April is also a passionate fan of Star Wars and water sports.

Tao Huanمشاهده پروفایل

دانشیار

• Metabolomics
• Exposomics
• Analytical Chemistry
• +۵ مورد دیگر

Tao Huan is an Associate Professor in the Department of Chemistry , University of British Columbia , and holds the Canada Research Chair in Metabolomics and Exposomics . His research focuses on advancing mass spectrometry (MS) for metabolomics , integrating bioinformatics to address challenges in cancer metabolism , disease biomarker discovery , and exposome characterization . Education: Ph.D. in Analytical Chemistry (University of Alberta, 2015), Postdoctoral Research Associate (The Scripps Research Institute, 2015-2018). Dr. Huan’s work emphasizes systems biology , combining metabolomics with genomics and proteomics to decode complex biological mechanisms. He has pioneered methods for chemical isotope labeling and multimodal data integration , enhancing metabolite identification and pathway analysis. His recent publications (2020-2019) highlight innovations in LC-MS/MS workflows , freeze-thaw sample stability , and applications in colorectal cancer and Alzheimer’s disease . Dr. Huan’s lab actively recruits students and postdocs in analytical chemistry, metabolomics, and bioinformatics. Awards: Fred Beamish Award (2025), President’s Award, Metabolomics Society (2025), UBC Killam Faculty Research Award (2024), Michael Smith Health Research BC Scholar Award (2023). He serves as a faculty member in UBC’s Graduate Program in Bioinformatics , Genome Science and Technology , and the Cluster for Microplastics, Health and Environment . Lab alumni include Ph.D. and M.Sc. students now in academia and industry.

Fengzhu Sunمشاهده پروفایل

استاد

• Computational Biology
• Bioinformatics
• Statistical Genetics
• +۶ مورد دیگر

Dr. Fengzhu Sun is a Professor of Quantitative and Computational Biology and Mathematics at the University of Southern California. His research spans computational biology, bioinformatics, statistical genetics, and mathematical modeling, with a focus on metagenomics, protein interaction networks, and genome sequence analysis. Dr. Sun earned his Bachelors in Mathematics from Shandong University, Masters in Probability and Statistics from Peking University, and PhD in Applied Mathematics from USC. He returned to USC in 2000 as an associate professor after serving at Emory University (1995-2000), becoming a full professor in 2006. His research interests encompass protein interaction networks, gene expression, SNPs, linkage disequilibrium, and their applications in predicting protein functions, gene regulation networks, and disease gene identification. He pioneered alignment-free methods for genome and metagenome sequence comparison, with recent work focusing on virus-host interactions in metagenomic data. His publication record shows consistent innovation, with recent work (2023-2025) emphasizing deep learning approaches (DeepMicroClass, DeepDecon, DeepLINK) and novel statistical methods. His research demonstrates strong interdisciplinary integration of computational methods with biological applications. Fellow of American Association for the Advancement of Sciences (AAAS, 2012) Fellow of American Statistical Association (ASA, 2015) Fellow of Institute of Mathematical Statistics (IMS, 2023) Fellow of International Society for Computational Biology (ISCB, 2024) Fellow of Asia-Pacific Artificial Intelligence Association (AAIA, 2025) Member of International Statistical Institute (ISI, 2012) USC Mellon Mentoring award for faculty mentoring (2012) USC Dornsife College senior Raubenheimer Outstanding Faculty Award (2017) Dr. Sun has mentored numerous successful students and postdocs, many now in academic positions or at leading tech and biotech companies. His research group develops computational methods for complex biological data analysis, with current focus on advanced deep learning for metagenomic classification, cancer cell fraction estimation, and virus-host interaction analysis. He has created influential software tools including DeepMicroClass, ImputeCC, DeepDecon, and ViralCC that have become standard resources in computational biology.

Nilo Merino Recaldeمشاهده پروفایل

پژوهشگر

• Animal Behavior
• Ecology
• Machine Learning
• +۳ مورد دیگر

Dr. Nilo Merino Recalde is a Postdoctoral Researcher at the Edward Grey Institute, University of Oxford. His research focuses on animal culture, acoustic monitoring of biodiversity, and applied machine learning. He employs interdisciplinary approaches spanning visual arts, anthropology, and biology to study animal behavior and ecology. His current work emphasizes large-scale acoustic data collection and analysis, with a particular interest in bird song and cultural evolution. Research interests include: Animal culture and its transmission mechanisms Acoustic ecology and biodiversity assessment using sound Machine learning applications in ecological research Data visualization and open science practices His publications highlight advancements in phylogenomic analysis, acoustic datasets for wild bird populations, and software tools like pykanto for bioacoustic research. He actively develops open-source tools to democratize data analysis in ecology. No scientific awards or grants are listed. He currently advises no students but collaborates widely in multidisciplinary teams. His primary affiliation is the Edward Grey Institute's Sheldon Group, where he integrates fieldwork and computational methods to understand animal behavior at ecological and evolutionary scales.

Ben Sheldonمشاهده پروفایل

استاد

• Evolution
• Ecology
• Animal Behavior
• +۳ مورد دیگر

Professor Ben Sheldon FRS holds the Luc Hoffmann Chair of Ornithology and directs the Edward Grey Institute within the University of Oxford's Department of Biology. His research focuses on evolutionary ecology, particularly individual variation, environmental adaptation, and long-term ecological studies like the Wytham Woods great tit project. Current themes include spatial scaling of ecological interactions, life-history evolution, and social dynamics in bird populations. He has secured major grants from ERC, UKRI, NERC, and BBSRC, and hosted postdocs via Marie Curie Fellowships. Research interests integrate field studies, genetics, and quantitative analysis to explore how environmental and social factors shape animal behavior and adaptation. Recent work examines phenological mismatches, climate impacts, and social network effects on reproduction and survival. Awards include Fellowship of the Royal Society. Advising includes 7-8 graduate students and postdocs, with a focus on PhD and postdoc project supervision. Administrative roles include past Head of the Department of Zoology (2016–2021) and Senior User Representative for the Life and Mind Building (2019–2022). The Edward Grey Institute serves as a key research hub for avian ecology and ornithology.

Alejandro Sánchez Graciaمشاهده پروفایل

دانشیار

• Molecular Evolutionary Genetics
• Population Genomics
• Comparative Genomics
• +۳ مورد دیگر

Alejandro Sánchez Gracia is an Associate Professor at the Universitat de Barcelona's Faculty of Biology, affiliated with the Department of Genetics, Microbiology and Statistics. He leads the Molecular Evolutionary Genetics research group and directs the advanced course in 'Phylogenomics and Population Genomics: Inference and Applications.' Education: Llicenciat in Biology (Universitat de Barcelona, 1998), PhD in Biology (Universitat de Barcelona, 2006) Research Focus: Molecular mechanisms of chemosensory gene evolution in arthropods, development of bioinformatics tools for evolutionary and population genomics, and population genomics of adaptation in Drosophila. His work bridges computational methods with evolutionary biology, emphasizing genomic approaches to study adaptation. Key projects include analysis of chemoreceptor gene families across Panarthropoda, genomic studies of Canary Island endemic species, and development of tools like BITACORA for gene family annotation. He has contributed to major genomic resources such as DnaSP 6 and participated in initiatives like the Earth BioGenome Project. Active in collaborative networks like the European Drosophila Population Genomics Consortium and AdaptNET (Adaptive Genomics Network). Grants and Projects: 2021-2024: PID2020-113168GB-I00 (Ministry of Science, Spain) - Poligenic adaptation in Drosophila 2020-2021: Catalan blind scorpion genome project (Institut d'Estudis Catalans) Labs/Teams: Heads the Molecular Evolutionary Genetics group, collaborating on projects involving spider genomics, chemosensory evolution, and population-level adaptation studies.

Kushal Deyمشاهده پروفایل

استادیار

• Statistical Genetics
• Machine Learning in Genomics
• Functional Genomics
• +۵ مورد دیگر

Kushal Dey serves as an Assistant Professor in the Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center (MSKCC), part of the Graduate School of Medical Sciences in partnership with Weill Cornell Medicine. His research integrates statistical and machine learning approaches with genomic data to understand the regulatory architecture of complex diseases. Dr. Dey's research focuses on developing computational methods that integrate human disease genetics with functional genomics data. His work spans immune-related diseases including Alzheimer's and inflammatory bowel disease, as well as heritable cancers like breast and prostate cancer. His lab develops models to prioritize variants, genes, and cell states for disease using genetic, genomic, and perturbation data, with emphasis on causal directed graphs and benchmarking pipelines informed by disease genetics. His recent publications highlight expertise in GWAS, colocalization, spatial transcriptomics, Perturb-seq, and RNA+ATAC multiome analysis. His work frequently appears in top journals like Nature Genetics, with a focus on single-cell multi-omics approaches to understand disease mechanisms at cellular resolution. Scientific Awards: Josie Robertson Investigator (2023–2028) K99/R00 Pathway to Independence Award (NIH/NHGRI) (2022–2026) NIH/NHGRI Early Stage Investigator R01 (2025-2030) NCI P30 CCSG supplement – 'LLMs in cancer research' (2023-2024) Catalog Working Group Co-chair + Disease Focus Group Lead: IGVF consortium (2023-) Dr. Dey mentors several graduate students through the Weill Cornell Graduate School (WGS), including Thahmina Ali, Pretty Garcia, Karthik Guruvayurappan, Louis Liu, Sarthak Tiwari, Berk Turhan, and Harry Zhang. His lab has received multiple grants including the AWS IMAGINE Grant Children's Health Innovation Award 2024-2025 (as Project Co-lead) and PSRP Developmental Funds Awards (2025: Co-lead). The lab actively collaborates with consortia including ENCODE, ADSP, MorPhiC, and IGVF, maintaining strong ties with Columbia University, Stanford University, and Harvard T.H.Chan School of Public Health. The Kushal Dey Lab is part of the vibrant Tri-Institutional Research campus adjacent to Rockefeller University and Weill Cornell Medical College, offering a collaborative environment focused on computational genomics and disease mechanisms.

Jörg Hackermüllerمشاهده پروفایل

استاد

• Bioinformatics
• Omics Data Analysis
• Toxicology
• +۳ مورد دیگر

Prof. Dr. Jörg Hackermüller is a computational biologist with expertise in Omics data integration Toxicology Environmental risk assessment Non-coding RNA biology . He serves as Head of the Department of Computational Biology and Chemistry at the Helmholtz Centre for Environmental Research (UFZ) since 2024 and holds a Professorship at the Faculty of Mathematics and Computer Science at Leipzig University since 2021. His research focuses on Developing AI methods for chemical toxicity prediction Multi-omics integration for mechanistic toxicology Data standardization in environmental monitoring Non-coding RNAs as biomarkers in disease and toxicity and has produced 15+ recent publications spanning tools like multiGSEA and deepFPlearn+ . He collaborates with teams across UFZ Leipzig University Novartis Fraunhofer Institute and leads projects like InCeTo and SafePol , integrating exposome research with systems biology.