Ashwani Siwach, M.Tech

Machine Learning for Computational Genomics | Data-driven Biology | Data Science | Bioinformatics

I work in the field of computational genomics, exploring machine learning and AI approaches to analyze biological sequences and large-scale genomic data. My interests include developing intelligent data-driven methods, high-performance scientific computing, and applying modern algorithms to extract meaningful patterns from complex biological datasets.

I am currently pursuing my M.Tech in Communication & Signal Processing at IIITDM Jabalpur, where I did a course on Genomic and Proteomic Signal Processing and my thesis involves Machine learning and Bioinformatics for predicting disease-associated non-coding RNAs in chronic diseases and exploring AI-driven computational methods for genomics.

I stay updated with emerging trends in AI and Genomics and try to integrate the techniques that excite me into my research. I'm also continuously learning more biology to build a stronger foundation alongside my computational work.

I have strong programming experience with Python, Machine Learning, PyTorch, data processing pipelines, signal processing and Bioinformatics (as per requirements), and I enjoy building reproducible workflows and scalable research solutions in computational biology.

Computational Genomics Machine Learning & DL Bioinformatics Data Science Signal Processing

Publications

Preprint
Ashwani Siwach, Sanjeev Narayan Sharma, and Sunil Datt Sharma. "Multi-Modal Machine Learning for Population- and Subject-Specific lncRNA-Type 2 Diabetes Association Analysis." arXiv:2605.20747, 2026.
Under Review
Ashwani Siwach, Sanjeev Narayan Sharma, and Sunil Datt Sharma. Journal manuscript based on M.Tech thesis research. An IEEE Journal (under review).
Poster
Ashwani Siwach, Sanjeev Narayan Sharma, and Sunil Datt Sharma. "Feature Engineering for AI-Based Genomic Data Analysis." International Conference Inbix2025, ICMR-NIRTH, Jabalpur (M.P.), Nov. 2025.

Publications

Research outputs in computational genomics, machine learning for biology, and non-coding RNA analysis.

Multi-Modal Machine Learning for Population- and Subject-Specific lncRNA-Type 2 Diabetes Association Analysis

Ashwani Siwach, Sanjeev Narayan Sharma, and Sunil Datt Sharma

arXiv preprint, arXiv:2605.20747, 2026

Preprint

Multi-Modal Machine Learning for lncRNA-Disease Association Analysis (journal version)

Ashwani Siwach, Sanjeev Narayan Sharma, and Sunil Datt Sharma

An IEEE Journal (under review).

Under Review

Feature Engineering for AI-Based Genomic Data Analysis

Ashwani Siwach, Sanjeev Narayan Sharma, and Sunil Datt Sharma

Poster Presentation — International Conference Inbix2025, ICMR-NIRTH, Jabalpur (M.P.), Nov. 2025

Poster

Projects

Computational Identification of non-coding RNAs association with chronic diseases

Currently working on developing an AI/ML-based predictive framework to identify potential disease-associated non-coding RNAs (primarily lncRNAs) associated with chronic diseases, with current focus on Type-2 Diabetes (T2D). The project covers the complete end-to-end workflow, including data collection through bioinformatics pipelines, dataset preprocessing, hypothesis formulation, feature extraction, modeling, and interpretation of results.

I am exploring innovative approaches at multiple stages of the pipeline—such as applying advanced feature engineering strategies using sequence, structure, and expression-based representations, and experimenting with hybrid Machine Learning and Deep Learning architectures to improve biomarker discovery and predictive stability. Additionally, I am working on identifying SNPs across multiple datasets to integrate genomic variation into the model. The outcome of this project aims to support precision medicine by facilitating patient-specific diagnosis and enhancing understanding of lncRNA involvement in chronic metabolic disorders.

Key Contributions & Skills Learned:

Bioinformatics workflow: sequence alignment and variant calling using HISAT2, SAMtools, BWA, GATK, VCFtools
VCF preprocessing and variant interpretation, metadata handling, etc.
Feature engineering techniques for genomic data
Modeling using Random Forest, XGBoost, and Deep Learning etc.
Tackling heavy class imbalance, hyperparameter optimization, evaluation
Forming hypothesis and novel solution direction for biomarker prediction

Tools & Libraries: Python, Scikit-learn, Pandas, NumPy, PyTorch, Biopython, PC-PseDNC-General, Plotly, etc.

Mentoring Undergraduate Students in Computational Genomics & Machine Learning

Mentoring undergraduate students working on projects related to Parkinson's Disease, microexon discovery, and splice site identification. Providing guidance through the full workflow from problem formulation to ML result interpretation. Supported them in exploring GEO datasets, performing preprocessing and normalization, and applying machine learning methods for biological insight discovery.

Responsibilities & Skills Applied:

GEO dataset processing, sample grouping, metadata-based filtering
Methylation data handling using methylprep and downstream statistical analysis
EDA, visualization, ML model building with reproducible workflow design
End-to-end guidance: data → preprocessing → ML → final results & documentation

Deep Learning for Genomic Sequence Modeling

Exploring BiLSTM, CNN, and Transformer-based architectures (including LLM-style models for genomics like DNABert, Nucleotide Transformer, BigRNA, etc.) to learn biological sequence patterns beyond manually extracted features. Working on embedding strategies, attention-based learning, and representation learning for improved biological relevance.

Frameworks: PyTorch, HuggingFace Transformers.

Contact

Email: ashwanisiwach132003@gmail.com

LinkedIn: linkedin.com/in/ashwani-siwach-b721151b9

GitHub: github.com/ashwanisiwach

Twitter: x.com/AshwaniSiwach13

CV / Resume: Download CV