Ph.D. with over 10 years of experience in Bioinformatics, AI, and Data Science at leading institutions (University of Zurich, ETH Zurich, A*STAR Singapore) and industry (Boehringer Ingelheim).
Proven track record of highly cited publications (h-index 25, ~3000 citations) with a translational focus on cancer, obesity, and diabetes biomarker and drug discovery.
Computational & Theoretical Expertise (Click for Details)
Biomarker & Drug Discovery
Applying machine learning to multi-omics datasets (TCGA, RNA-seq, scRNA-seq, spatial transcriptomics, GWAS) to identify biomarkers and drug targets.
NGS & Bioinformatics Pipelines
Expertise in NGS pipelines, scRNA-seq, Variant Calling, ChIP-seq, and pathway enrichment analysis.
Machine Learning & AI
Experienced in applying Deep Learning, Supervised, Unsupervised ML, Bayesian Inference, and Survival Analysis to complex biomedical questions.
Python & R Programming
Proficient in Python and R for data analysis, model development, and visualization (NumPy, SciPy, Pandas, Keras, PyTorch, JAX, Bioconductor, Tidyverse).
Web Apps & Data Visualization
Developing custom web platforms (Flask, Dash, Wordpress) for interactive visualization and on-the-fly statistical analysis of large datasets.
Cloud & DevOps
Expert in Cloud/DevOps tools (AWS, Google Cloud, Docker, Git) for managing large-scale data and deploying reproducible analysis environments.
I am open to job and consulting opportunities. Feel free to reach out!
ugdastider@gmail.com
Zurich, Switzerland
LinkedIn Profile
Biomarker & Drug Discovery
Biomarker Identification: Using statistical and AI/ML algorithms on large-scale datasets (TCGA, CPTAC, GWAS) to find novel therapeutic targets and predictive biomarkers for cancer, obesity, and diabetes.
Drug Repurposing: In silico screening and analysis to identify existing drugs that could be repurposed for new indications.
Translational Research: Collaborating with clinicians and pharmaceutical partners to validate and move computational findings toward clinical application.
NGS & Bioinformatics Pipelines
Pipeline Development: Building robust, scalable, and reproducible NGS pipelines using Snakemake and Nextflow.
Single-Cell Analysis: Advanced processing and analysis of scRNA-seq and scATAC-seq data using Seurat, Scanpy, and Monocle.
Genomics/Transcriptomics: Expert in RNA-seq, spatial transcriptomics, ChIP-seq, and ATAC-seq processing, alignment, quantification, and differential expression analysis.
Variant Analysis: Variant calling (GATK, Mutect), annotation (VEP), and interpretation.
Machine Learning & AI
Deep Learning: Implementing models using Keras/TensorFlow and PyTorch, including Transformer architectures, CNNs, and Autoencoders for genomic sequence and image data.
Supervised Learning: Classification and regression using Random Forests, Gradient Boosting (XGBoost, LightGBM), and SVM.
Unsupervised Learning: Clustering, dimensionality reduction (PCA, UMAP, tSNE) for data exploration and subtype identification.
Statistical Modeling: Linear and Non-linear models, Bayesian Inference, Time-series analysis, and Survival Analysis (Cox models).
Python & R Programming
Python Stack: Expert in the scientific computing ecosystem (NumPy, SciPy, Pandas), data science toolkits (scikit-learn), and deep learning libraries (Keras, PyTorch, JAX).
R Environment: Proficient in the Tidyverse for data manipulation and visualization (ggplot2), and the Bioconductor ecosystem for genomic data analysis.
Clean Code: Focus on writing efficient, modular, and maintainable code adhering to best practices and version control (Git).
Web Apps & Data Visualization
Interactive Dashboards: Creating dynamic, user-friendly data exploration tools using Dash (Plotly), Shiny (R), and basic JavaScript/HTML/CSS.
Web Backend: Developing lightweight backend APIs using Flask and deploying applications on Wordpress or custom servers.
Visualization: Generating high-quality static and interactive plots for publication using ggplot2, Matplotlib, Seaborn, and Plotly.
Cloud & DevOps
Cloud Computing: Utilizing services on Amazon AWS (EC2, S3, RDS) and Google Cloud for scalable data storage and high-performance computing.
Containerization: Managing and distributing reproducible analysis environments using Docker and Singularity.
Version Control: Expert use of Git and GitHub/GitLab for collaborative code development and version control.
High-Performance Computing (HPC): Experience with job scheduling systems (SLURM, LSF) for running large-scale bioinformatics workflows.