Taufia H.

I help researchers, biotech companies, and scientific organizations build AI-powered research workflows, analyze complex biological datasets, develop scientific software and transform experimental data into actionable insights. I am a PhD-trained Computational Biologist and Data Scientist with over 14 years of experience in biological research and scientific data analysis. I am also a cofounder of DataLens.Tools, where we design AI-powered knowledge systems, research automation tools, scientific data analysis platforms, retrieval-augmented applications and custom AI workflows for research and industry. I specialize in computational biology, bioinformatics, machine learning, scientific Python development and AI-powered research workflows. I help researchers, biotech teams and scientific organizations transform complex biological data into actionable insights through data analysis, computational modeling and custom software solutions. Alongside my research and computational work, I have experience teaching and mentoring students and researchers in biological data analysis, Python, statistics, machine learning and computational research methods. I particularly enjoy helping researchers understand not only how to use computational tools, but also why a particular analytical or machine-learning approach is appropriate for their scientific question. My expertise spans computational biology, bioinformatics, machine learning, scientific image analysis, cheminformatics, protein modeling and data visualization. I also have extensive experience working with fluorescence microscopy, calcium imaging, electrophysiology datasets and high-dimensional biological data. Key Areas of Expertise • Computational Biology & Bioinformatics • Machine Learning & AI for Biological Data • AI Systems & Knowledge Engineering - Agentic AI Workflows - Retrieval-Augmented Generation (RAG) - AI Memory & Knowledge Systems - Scientific AI Applications - Research Automation - AI Workflow Architecture • Scientific Python Development (Pandas, NumPy, Scikit-learn, PyTorch) • Data Analysis, Statistics & Visualization • Image Processing & Quantitative Microscopy • Cheminformatics & Protein Modeling • Computational Neuroscience & Electrophysiology • Research Automation & Scientific Software Development Selected Research & Technical Projects • Developed reproducible Python pipelines for fluorescence imaging and quantitative biological data analysis. • Designed machine-learning workflows for biological and physiological datasets. • Conducted bioinformatics analyses involving genomic and VCF-based datasets. • Developed AI-assisted approaches for fluorescence microscopy and scientific image analysis. • Applied statistical modeling to investigate calcium dynamics and muscle physiology in Drosophila flight systems. • Built scientific software for data visualization, analysis, and workflow automation. • Developed and taught computational workflows that help researchers understand and independently analyze biological datasets. • Co-founded DataLens.Tools, where we develop AI-powered tools and workflows for scientific research and data analysis. I have authored peer-reviewed scientific publications and presented research at international conferences. I enjoy collaborating on interdisciplinary projects at the intersection of biology, AI, data science and scientific computing. I am particularly interested in projects where researchers need more than a black-box analysis, whether that means developing a reproducible computational workflow, validating a machine-learning approach, building a scientific AI system or helping a research team understand and adopt computational methods. If you are working with complex biological data or exploring how AI, machine learning or scientific computing could support your research, I would be happy to connect.