جستجوی استاد راهنمای دانشگاهی

James Briscoeمشاهده پروفایل

استاد

• Developmental Biology
• Molecular Signaling
• Transcriptional Networks
• +۴ مورد دیگر

James Briscoe is a Senior Group Leader at The Francis Crick Institute in London, where he leads a research group focused on developmental biology and morphogen signaling. He previously held positions at the Medical Research Council's National Institute for Medical Research, which later became part of the Francis Crick Institute. Education: BSc in Microbiology and Virology from the University of Warwick, UK PhD from Imperial Cancer Research Fund/King's College London Postdoctoral training at Columbia University with Thomas Jessell Dr. Briscoe's research focuses on the molecular and cellular mechanisms of graded signaling by morphogens and the role of transcriptional networks in cell fate specification. His laboratory employs a range of experimental and computational techniques using model systems including mouse and chick embryos and embryonic stem cells. His work has significant implications for understanding developmental processes and their relationship to disease. His recent publications demonstrate a continued focus on morphogen gradients, neural tube development, and computational approaches to understanding cell fate decisions. His research increasingly integrates single-cell technologies and computational modeling to unravel the complexities of developmental patterning. Scientific Awards and Honors: EMBO Young Investigator (2001) EMBO Gold Medal (2008) Elected to EMBO (2009) Fellow of the Academy of Medical Sciences (2019) Fellow of the Royal Society (2019) As Editor-in-Chief of the journal Development since 2018, Dr. Briscoe plays a significant role in shaping the field of developmental biology. His leadership extends to mentoring researchers and contributing to scientific policy discussions, as evidenced by his recent publication 'Science under siege: protecting scientific progress in turbulent times.' Dr. Briscoe's laboratory at the Crick Institute is well-equipped with access to advanced facilities including light microscopy, flow cytometry, genomics, and computational resources, enabling a multidisciplinary approach to developmental biology questions.

Rong Fanمشاهده پروفایل

استاد

• Single-cell Omics
• Spatial Omics
• Immunology
• +۵ مورد دیگر

Dr. Rong Fan is the Harold Hodgkinson Professor of Biomedical Engineering and Professor of Pathology at Yale University. His research focuses on developing and applying single-cell and spatial omics technologies to study immune systems, cancer, and aging. His lab has pioneered technologies like the IsoCode microchip for high-throughput protein profiling, and spatial multi-omics platforms (e.g., DBiT-seq, spatial-ATAC-seq) to analyze tissue complexity at cellular resolution. He co-founded IsoPlexis, Singleron Biotechnologies, and AtlasXomics to commercialize these innovations. Education: PhD in Chemistry from UC Berkeley (2006), B.S. in Applied Chemistry from University of Science and Technology of China (1999). Postdoctoral training at Caltech before joining Yale in 2010. Research interests include CAR-T cell therapy optimization, spatial epigenomics, and multi-omics integration. Key achievements include discovering biomarkers predictive of CAR-T efficacy and defining spatial genomic landscapes in cancer and neuroinflammation. Awards: NSF CAREER Award, Packard Fellowship, election to AIMBE, CASE, and NAI. Serves on advisory boards for Bio-Techne and Yale Ventures. Active in training future scientists via the Yale Biomedical Engineering and Yale School of Medicine programs.

Christina Leslieمشاهده پروفایل

استاد پژوهش

• Computational Biology
• Systems Biology
• Machine Learning
• +۷ مورد دیگر

Dr. Christina Leslie is a Research Professor and Member of the Computational & Systems Biology Program at Memorial Sloan Kettering Cancer Center (MSK). She leads an active research laboratory focused on developing computational approaches to understand complex biological systems. Dr. Leslie earned her PhD from the University of California, Berkeley and has established herself as a leading computational biologist in cancer research and immunology. Computational & Systems Biology Program, Memorial Sloan Kettering Cancer Center Gerstner Sloan Kettering Graduate School of Biomedical Sciences Dr. Leslie's research focuses on developing novel computational methods to study cellular biological systems from a global and data-driven perspective. Her lab exploits diverse high-throughput functional and genomic data to understand molecular networks underlying fundamental cellular processes, including transcription regulation, pre-mRNA processing, signaling, and post-transcriptional gene silencing. Her algorithmic methods draw heavily on machine learning to build accurate predictive models from noisy and high-dimensional biological data. Key areas of interest include modeling cell-type specific transcriptional programs and dissecting co- and post-transcriptional regulation, particularly microRNA-mediated gene regulation. Analysis of Dr. Leslie's publication record over the last five years reveals a strong focus on computational approaches to cancer genomics, immunology, and epigenetics. Her work bridges multiple disciplines, with a particular emphasis on developing machine learning methods to interpret complex biological data. The publications demonstrate increasing sophistication in integrating multiple data types (genomic, transcriptomic, epigenomic) to understand cancer biology and immune responses. Recent work shows a growing emphasis on single-cell technologies and spatial analysis of tumor microenvironments. Introduction of string kernel methodology for SVM classification of biological sequences Development of algorithms for predictive modeling of gene regulation First systems-level analyses of competition between microRNAs and between target transcripts Dr. Leslie actively mentors numerous graduate students and research associates, with current lab members including Vianne Gao, Alireza Karbalaghareh, Erik Ladewig, and several others. Her lab has received significant research funding to support their work on computational approaches to cancer biology and immunology. The Leslie Lab maintains close collaborations with multiple experimental groups at MSK, facilitating the translation of computational insights into biological understanding. The Leslie Lab operates within the Computational & Systems Biology Program at MSK, with strong ties to both the research and clinical missions of the institution. The lab maintains state-of-the-art computational infrastructure for analyzing large-scale genomic and proteomic datasets and collaborates extensively with wet-lab researchers to validate computational predictions experimentally.

Guizhen Zhaoمشاهده پروفایل

استادیار

• Cardiovascular Diseases
• Epigenetics
• Molecular Biology
• +۶ مورد دیگر

Guizhen Zhao is an Assistant Professor at the University of Houston College of Pharmacy , Department of Pharmacological and Pharmaceutical Sciences. Her research focuses on epigenetic and molecular mechanisms in cardiovascular diseases (CVD), particularly aortic aneurysm, dissection, and atherosclerosis, with a goal to drive drug discovery innovations. Major research areas: Metaboloepigenetic properties of vascular cells, chromatin remodeling, vascular cell crosstalk Methodologies: bulk RNA-seq, single-cell RNA-seq, ChIP-seq, ATAC-seq, spatial transcriptomics, metabolomics Ongoing projects include studying BAF60c-dependent epigenetic modifications in smooth muscle cell biology, BAF60c-mediated iPSC differentiation, BAF60a in endothelial dysfunction, and vascular cell interactions in CVD development. Scientific contributions include 15+ publications on abdominal aortic aneurysm, atherosclerosis, and chromatin remodeling mechanisms, with recent work on adenosine kinase inhibition and KLF11 as therapeutic targets. 2023-25: Career Development Award, American Heart Association 2021-22: Postdoctoral Fellowship, American Heart Association 2019: Young Investigator Award, American Heart Association

Andrew Bassettمشاهده پروفایل

پژوهشگر

• Genome Engineering
• Neurodegenerative Diseases
• Stem Cell Research
• +۵ مورد دیگر

Dr. Andrew Bassett serves as Head of the Cellular and Gene Editing Research group at the Wellcome Sanger Institute, where he develops cutting-edge genome engineering techniques using human pluripotent stem cells to investigate neurodegenerative diseases including Alzheimer's and Parkinson's. His work focuses on scaling genetic screening approaches and improving CRISPR specificity for modeling complex disease mechanisms. His academic training includes: PhD at the MRC Laboratory of Molecular Biology (MRC-LMB) with Andrew Travers on chromatin remodelling in heterochromatin formation Postdoctoral research with David Baulcombe at the University of Cambridge studying small RNA roles in chromatin modification Additional postdoctoral work with Chris Ponting at the MRC Functional Genomics Unit (MRC-FGU) in Oxford, where he pioneered CRISPR applications in Drosophila Bassett's research program centers on developing advanced genome engineering methodologies for precise modulation of gene expression networks during development and neurodegeneration. His group specializes in creating complex editing events (SNPs, paired knockouts, enhancer perturbations) within iPSC-derived models, with particular emphasis on epigenetic regulation and transcriptional control. Current projects integrate single-cell 'omics and phenotypic assays to decode genetic causes of neurodegenerative disorders through the OpenTargets consortium. Analysis of his 15 most recent publications reveals dominant trends in CRISPR technology development (35%), neurodegenerative disease modeling (30%), and single-cell functional genomics (25%). His work consistently bridges methodological innovation with disease mechanism studies, increasingly incorporating multi-omics approaches and expanding into cancer immunology and infectious disease applications since 2022. As group leader, Bassett mentors postdoctoral researchers and PhD students while securing major funding for genome engineering initiatives. His team operates within the Sanger Institute's Cellular Operations division and maintains critical partnerships with the OpenTargets consortium for therapeutic target validation. The laboratory specializes in high-throughput screening platforms using iPSC-derived neural and microglial models, with recent methodological advances including scSNV-seq and ONE-STEP tagging systems that significantly enhance precision genome editing capabilities.

Ben Raphaelمشاهده پروفایل

استاد

• Bioinformatics
• Computational Biology
• Cancer Genomics
• +۶ مورد دیگر

Ben Raphael is a Professor in the Department of Computer Science at Princeton University, with affiliations at the Lewis-Sigler Institute for Integrative Genomics, Omenn-Darling Bioengineering Institute, and Center for Statistics and Machine Learning. He is also an Affiliate Faculty member at the Rutgers Cancer Institute of New Jersey, Irving Institute for Cancer Dynamics at Columbia University, and New York Genome Center. His research focuses on computational methods for analyzing large-scale biological data, emphasizing cancer evolution, network/pathway analysis, and structural variation in genomes. Research Trends: His recent work spans cancer lineage trees, spatial transcriptomics, optimal transport for developmental models, and network analysis of mutations. Articles highlight applications in prostate cancer, pancreatic cancer, and single-cell genomics. Scientific Awards: 2024 ACM Fellow 2023 RECOMB Test of Time Award 2022 RECOMB Test of Time Runner-Up 2021 ISCB Innovator Award 2021 RECOMB Best Paper Runner-Up 2020 ISCB Fellow 2020 AACR Team Science Award 2011 NSF CAREER Award 2013 RECOMB Best Paper 2010-2012 Sloan Research Fellowship Advising: He has mentored numerous Ph.D. students and postdoctoral fellows, many of whom have transitioned to academic and industry roles. Current advisees include Uthsav Chitra, Gillian Chu, and Alexander Strzalkowski. Labs & Teams: Raphael leads the Raphael Lab at Princeton, developing tools like HotNet2, CHISEL, and HATCHet for cancer genomics and network analysis.

Doron Betelمشاهده پروفایل

استادیار

• Computational Genomics
• Bioinformatics
• Single-cell RNA-seq Analysis
• +۳ مورد دیگر

Doron Betel serves as an Assistant Professor at Weill Cornell Medicine's Graduate School of Medical Sciences, with affiliations in both the Physiology, Biophysics & Systems Biology and Computational Biology programs. He directs the Applied Bioinformatics Core (ABC), a central service group providing specialized computational and analytical support for biomedical research across multiple institutions. Dr. Betel's research focuses on developing computational genomic tools for studying human diseases and cellular development, with emphasis on integrative analyses of genomic and epigenomic data from high-throughput assays. His work addresses specific questions related to disease progression, treatment response, stem cell differentiation, and neurological processes through two closely interacting research groups: the Applied Bioinformatics Core and his independent research lab. The analysis of his recent publications reveals a strong emphasis on single-cell and spatial genomics, cross-species data integration, and machine learning applications in cancer immunology and neurodegenerative disease modeling. His research spans multiple high-impact areas including cancer immunotherapy, stem cell biology, diabetes research, and cardiovascular regeneration, with numerous publications in top journals like Nature, Cell, and Nature Immunology. Through the Applied Bioinformatics Core, Dr. Betel provides extensive analytical support across various genomic platforms including single-cell RNA-seq, spatial transcriptomics, ChIP-seq, ATAC-seq, and variant calling. The Core serves as a vital resource for researchers at Weill Cornell Medicine and the broader Tri-Institutional network, offering specialized analysis, computational pipelines, and training services. Dr. Betel maintains extensive collaborations with leading researchers including Lorenz Studer at MSKCC for stem cell and neurodegenerative disease research, Tuomas Tammela for cancer genomics, and multiple immunology researchers studying T cell function in autoimmunity and cancer. His work bridges computational methodology development with direct biomedical applications across multiple disease areas.

David Suterمشاهده پروفایل

دانشیار

• Gene Regulation
• Cell Identity
• Transcriptional Memory
• +۱۳ مورد دیگر

David Suter is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Life Sciences (SV) and the Institute of Bioengineering (IBI-SV), leading the UPSUTER research unit focused on Gene Regulation & Cell Identity. He is based in the AI building at Station 19, Lausanne, and holds active teaching and leadership roles across multiple departments, including Life Sciences Engineering. He serves as Director of the Doctoral Program in Molecular Life Sciences (EDMS), and is a member of the Doctoral School Committee and the PhD Program Committee for Computational and Quantitative Biology. His research centers on the quantitative analysis of gene expression dynamics and cell identity, employing cutting-edge techniques such as ultrasensitive bioluminescence imaging, fluorescent protein timers, genome editing, and high-throughput genomics (ChIP-seq, ATAC-seq, CUT&RUN). His lab investigates transcriptional memory, proteostasis, transcription factor search dynamics, and cell fate decisions using embryonic stem cells and cancer cells as model systems. These approaches enable real-time, single-cell resolution studies of transcription factor behavior, mRNA production, and protein turnover across cell divisions. The publication trends in his work emphasize live-cell imaging, quantitative molecular biology, and systems-level understanding of gene regulation. His articles reflect a strong focus on visualizing and quantifying biological processes in living cells, particularly around transcription factors like Sox2 and their role in pluripotency and differentiation. The integration of biophysical tools with molecular biology allows his team to decode the mechanisms underlying cell identity maintenance and fate transitions. He actively supervises PhD students, both current and past, contributing significantly to doctoral education at EPFL. His leadership in the EDMS program underscores his commitment to training the next generation of scientists in molecular and quantitative life sciences. While no specific grants are mentioned in the text, his lab's advanced technological development (e.g., bioluminescence imaging tools) suggests strong funding support. David Suter leads the Suter Lab, a multidisciplinary research group combining molecular and cell biology with computational and biophysical methods. The lab fosters close collaborations and maintains a strong technical core in imaging and genomics, enabling innovative research on the fundamental principles of gene regulation during development and disease.

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.

Michael Skinniderمشاهده پروفایل

استادیار

• Computational Biology
• Machine Learning
• Metabolomics
• +۴ مورد دیگر

Michael Skinnider serves as Assistant Professor at Princeton University's Lewis-Sigler Institute for Integrative Genomics and Assistant Member of the Ludwig Princeton Branch. His research develops AI-driven computational methods to identify unknown small molecules in mass spectrometry data, with applications in cancer biology and forensic drug detection. His educational background includes: BArtsSc from McMaster University (2015) PhD from University of British Columbia (2021) MD from University of British Columbia (2023) Skinnider's work centers on illuminating the "metabolomic dark matter" —unidentified chemical entities in mass spectrometry data. His lab pioneers machine learning approaches for metabolite identification, focusing on connections between unknown metabolites, cancer risk, and the microbiome. Recent innovations include chemical language models that transform mass spectrometry outputs into chemical structures, with applications spanning cancer diagnostics to forensic analysis of designer drugs. His research bridges computational biology, chemistry, and clinical medicine through low-data learning techniques. Publication trends reveal three dominant themes: (1) AI-driven metabolite identification (25% of recent work), (2) single-cell/spatial data analysis (40%), and (3) molecular interaction networks (35%). His 2024 Nature Machine Intelligence paper demonstrated that invalid SMILES strings enhance chemical language models , overturning previous assumptions. Articles consistently apply computational methods to biological discovery, with growing emphasis on cancer metabolism and translational applications. Major recognitions include: Forbes 30 Under 30 (2022) International Birnstiel Award (2022) Dan David Prize Borealis AI Fellowship NIH Award C&EN's Talented Twelve (2023) Young Explorer Award Grand Prize Skinnider leads the Skinnider Research Lab at Princeton's Carl Icahn Laboratory, which collaborates with forensic laboratories and Ludwig cancer researchers. The lab specializes in transforming mass spectrometry data into biological insights through innovative algorithms. During his undergraduate studies, he co-founded Adapsyn Bioscience to translate natural product discovery research into commercial applications. Current projects include developing metabolome-wide identification tools and exploring diet-derived metabolites that modulate cancer progression.

Sandrine Dudoitمشاهده پروفایل

استاد

• Statistical Methodology
• Computational Biology
• Genomics
• +۵ مورد دیگر

Sandrine Dudoit is a Professor and Chair of the Department of Statistics at the University of California, Berkeley. She earned her PhD in Statistics from UC Berkeley in 1999 and joined the faculty in 2001. Her research focuses on statistical methodology and computing with applications to genomics, biomedical research, and precision health. She co-founded the Bioconductor Project , an open-source software initiative for biological data analysis, and leads interdisciplinary projects in single-cell transcriptomics and computational biology. Education: PhD in Statistics (UC Berkeley, 1999), M.Sc. in Mathematics (Carleton University, Canada). Research interests include high-dimensional statistical learning, single-cell RNA-Seq analysis, stem cell differentiation in the olfactory system, and statistical computing. She collaborates with biologists like John Ngai to study neuroepithelial regeneration using cutting-edge sequencing technologies. Recent work emphasizes trajectory inference, biomarker discovery, and methodological advances in handling high-dimensional genomic data. Her lab develops tools for normalization, clustering, and differential expression analysis in large-scale biological datasets. She teaches courses on statistical genomics and serves as a leader in UC Berkeley’s Division of Computing, Data Science, and Society (CDSS). Advising: Supervises PhD students in statistical methodology, computational biology, and bioinformatics. Grants: Active in securing funding for interdisciplinary research projects in genomics and data science. Labs/Teams: Core member of the Center for Computational Biology (CCB) and contributes to the Bioconductor community.

Li Songمشاهده پروفایل

استادیار

• Computational Immunology
• Bioinformatics
• Genomics
• +۳ مورد دیگر

Li Song is an Assistant Professor at the Department of Biomedical Data Science within the Geisel School of Medicine at Dartmouth College . He earned his Ph.D. in Computer Science (2018) and a Master's in Applied Mathematics and Statistics from Johns Hopkins University , advised by Liliana Florea. He completed postdoctoral training in the labs of X. Shirley Liu and Heng Li at the Data Science Department of Dana-Farber Cancer Institute . Education Ph.D. in Computer Science (Johns Hopkins University, 2018) M.S. in Applied Mathematics and Statistics (Johns Hopkins University) Research Focus : Designing algorithms for next-generation sequencing data analysis in immunology and microbiology. Key projects include: TRUST4 - De novo assembly of T-cell/B-cell receptors from RNA-seq T1K - Genotyping of polymorphic HLA/KIR genes Centrifuger - Compressed microbial genome classification Chromap - Ultrafast chromatin profiling data processing Recent Publications demonstrate expertise in computational immunogenetics, metagenomic analysis, and chromatin profiling workflows. The 2024 Genome Biology paper on Centrifuger received a Best Paper Award at RECOMB2024 . Training & Recruitment : The Song Lab actively seeks postdocs and graduate students through the Quantitative Biomedical Sciences program and Department of Computer Science at Dartmouth.

Kushal Deyمشاهده پروفایل

استادیار

• Computational Biology
• Genomics
• Machine Learning
• +۵ مورد دیگر

Kushal Dey, PhD, is an Assistant Professor in the Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center (MSKCC). His research develops machine learning models that integrate genetic, genomic, and epigenomic data (e.g., RNA-seq, ChIP-seq, Perturb-seq, spatial transcriptomics) to decode the causal functional architecture of heritable complex diseases, including immune-related disorders like Alzheimer’s and inflammatory bowel disease, as well as heritable cancers such as breast and prostate cancer.

Julie Ahringerمشاهده پروفایل

استاد

• Chromatin Regulation
• Epigenetics
• Genome Organization
• +۳ مورد دیگر

Julie Ahringer is Professor of Genetics and Genomics at the University of Cambridge and Director of the Wellcome Trust/Cancer Research UK Gurdon Institute. She leads a research group investigating chromatin structure and gene regulation using C. elegans as a model system. Her work integrates genomics, super-resolution microscopy, and computational approaches to understand epigenetic controls in development and disease. She holds fellowships from the Royal Society (FRS) and Academy of Medical Sciences (FMedSci). Research Focus: Her laboratory studies chromatin regulation mechanisms including heterochromatin formation, Polycomb domain function, genome architecture, and enhancer/promoter interactions. Key approaches include single-cell multiomics, high-throughput genomics, and super-resolution microscopy to analyze developmental trajectories. Research areas span: H3K27me3 domain formation and Polycomb repression Constitutive heterochromatin organization Regulatory element characterization 3D genome architecture via ARC-C technology Single-cell resolution developmental mapping Awards & Honors: Fellow of the Royal Society (FRS) Fellow of the Academy of Medical Sciences (FMedSci) Wellcome Senior Research Fellowship Academic Leadership: She mentors PhD students and postdoctoral researchers, with funding from Wellcome, MRC, and CRUK. Her lab develops open-source bioinformatics tools (VplotR, periodicDNA) and maintains the genome-wide C. elegans RNAi feeding library. Lab & Collaborations: The Ahringer Lab is based at the Gurdon Institute and collaborates widely on chromatin dynamics, nuclear organization, and developmental genomics projects across model organisms.

Guo-Cheng Yuanمشاهده پروفایل

استاد

• Genomics
• Epigenetics
• Gene Expression
• +۴ مورد دیگر

Guo-Cheng Yuan is a Senior Faculty member in the Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai. His research focuses on genomics, epigenetics, and computational biology , with particular emphasis on single-cell analysis, chromatin regulation, and cancer immunology. Institution: Icahn School of Medicine at Mount Sinai School: Graduate School of Biomedical Sciences Department: Genetics and Genomic Sciences Research Interests Dr. Yuan's work spans multiple domains including: Single-cell RNA sequencing applications in brain development and cancer Chromatin structure analysis in differentiation processes Spatial transcriptomics for tissue microenvironment characterization Computational methods for multiomic data integration Enhancer-promoter interaction dynamics Development of robust bioinformatics pipelines Scientific Trends Analysis His recent publications (2021-2025) demonstrate a focus on single-cell and spatial omics methodologies applied to diverse biological contexts ranging from cancer immunology to neurodevelopment . Notable trends include: Development of trajectory analysis algorithms Multiomic integration for regulatory network mapping Epigenetic mechanisms in cell differentiation Computational approaches for data robustness Three-dimensional genome organization studies Applications in both developmental biology and oncology