M.Tech (Communication & Signal Processing), IIITDM Jabalpur 🇮🇳
I work in computational genomics, focusing on the application of machine learning and AI to biological sequence data and large-scale genomic datasets.
My background in signal processing and data science allows me to approach genomic sequences as structured signals, enabling data-driven modeling and representation learning.
Currently, my research centers on disease-associated non-coding RNAs (lncRNAs), where I study how sequence patterns, expression profiles, and genomic variation can be integrated to improve predictive modeling and biological insight.
- Machine learning for genomic and transcriptomic data
- Computational analysis of non-coding RNAs (lncRNAs)
- Feature engineering and representation learning for biological sequences
- Variant-aware and data-driven genomics workflows
Programming & Scientific Computing
Python · MATLAB · NumPy · Pandas
Machine Learning & AI
Scikit-learn · PyTorch · Classical ML · Deep Learning
Data Analysis & Visualization
Plotly · Matplotlib · Seaborn · Statistical analysis
Systems & Workflow
Linux · Git · Bash · Reproducible data pipelines
Application, Data & Deployment (Research Support)
Streamlit · Flask · MySQL · Basic AWS (EC2/S3)
I am particularly interested in pursuing research that lies at the intersection of genomics, machine learning, and data-driven biology, and I am open to academic collaborations and PhD opportunities in these areas.
📧 ashwanisiwach132003@gmail.com
🔗 LinkedIn
💻 GitHub

