Aumkesh Chaudhary

Researcher in Machine Learning and Applied AI

∞ About Me

Academic Journey

BS in Computer Science and Data Analytics at the Indian Institute of Technology, Patna. Investigating complex systems through a lens of both structure and curiosity.

Research Passion

Exploring the depths of Machine Learning, Mathematical Modeling, and Scientific Simulations. Bridging the gap between theoretical knowledge and real-world applications.

Philosophy

Driven by curiosity and guided by logic, I believe in learning through building, experimenting, and asking the right questions. Every problem is a puzzle waiting to be solved.

Education

Indian Institute of Technology, Patna

Bachelor of Science (Honours) in Computer Science and Data Analytics

CPI: 9.12

Work Experience

Research Intern
Ahmedabad University (On-site)
Project: Understanding Decision Making and Coordination in Animal Groups
Supervisor: Dr. Jitesh Jhawar — Funded by Max Planck Society, Germany
Reports: Technical Report I | Technical Report II
May 2025 – June 2025
  • Studied collective behavior in animal groups via simulations and mathematical modeling.
  • Configured a multi-user Linux workstation with fstab-based persistent disk mounting, user environment management, and centralized Anaconda deployment for shared computational workflows.
  • Processed and annotated video data collected daily over 4 months, labeling frames with multiple bird species for behavior and identity tagging.
  • Adapted and compared multiple YOLO architectures for multi-species bird detection and species-specific behavior analysis.
  • Utilized Idtracker.ai to track ants and spiders, rendering trajectory data to visualize movement paths and analyze spatiotemporal group dynamics.
Project Intern
IIT Mandi iHub and HCi Foundation (Remote)
Apr 2025 – July 2025
  • Developed backend infrastructure for an educational platform using Django, including role-based access control, REST APIs, and OAuth 2.0 authentication, supporting 1,000+ users across student and instructor workflows.
  • Designed and implemented interactive dashboards for students and instructors, enabling activity tracking, course progress visualization, role-based permissions, and secure content access with separate workflows for each user type.
  • Collaborated with cross-functional teams to architect scalable backend solutions and enforce security best practices across the platform.

Projects

Progressive Token Drop: Training-Efficient Vision Transformers | Report
Investigated Progressive Token Drop (PTD), a training-time strategy that progressively removes low-saliency tokens based on attention weights, reducing training compute while preserving model architecture and inference performance.
  • Trained hybrid CNN-ViT model from scratch on CIFAR-100 and applied PTD curriculum with Mixup/CutMix, achieving 1.3× (25.3% faster) training speedup with 2.53% accuracy improvement (74.12% vs 71.59% baseline), demonstrating efficiency gains with superior generalization for compute-constrained training.
  • PTD significantly mitigated overfitting compared to the baseline, sustaining validation accuracy under strong augmentations rather than memorizing the dataset.
  • Fine-tuned the model on CIFAR-10 and expanded the classifier to 110 classes, demonstrating scalable architecture adaptation and robust cross-dataset generalization.
Advanced Text to Speech Optimization
  • Fine-tuned Microsoft’s SpeechT5 transformer model for technical speech synthesis, achieving 25% improvement in Mean Opinion Score through custom phonetic preprocessing pipeline for technical terms, abbreviations, and acronyms.
  • Optimized the baseline model to generate a native Italian voice by enhancing pronunciation, prosody, and stress patterns in line with the phonological rules of the Italian language, significantly improving speech quality and naturalness compared to other existing models.
  • Implemented 8-bit dynamic quantization to linear layers using PyTorch’s native API, reducing memory usage by 30% while maintaining inference accuracy.
Solar Panel Detection System
  • Developed an object detection model using YOLOv8n to identify and locate solar panels in low resolution satellite imagery.
  • Achieved 94.27% precision, 91.77% recall, and 96.8% mean Average Precision (mAP50, significantly improving solar infrastructure mapping capabilities.
  • Applied Non-Maximum Suppression with IoU threshold of 0.3 for optimal detection accuracy.
CareerNavigator
Developed CareerNavigator, a Machine Learning model that evaluates candidates' employability by analyzing key attributes and predicting suitability for a job role.
  • Cleaned, pre-processed, and performed feature engineering on the dataset containing 70k+ datapoints.
  • Designed, trained, and evaluated multiple algorithms, utilizing performance metrics such as accuracy, confusion matrix, and F1-score to optimize model effectiveness.
  • Selected kernel support vector machine as the best-fit model with the highest accuracy of 80% and F1 score of 0.82.
  • Presented findings to a panel of faculty and industry experts, receiving recognition for its innovation and effectiveness.

Extracurricular Activities

🎹 Piano

8+ years of experience in classical and contemporary styles. Actively compose original pieces that explore and innovate across diverse genres and elements of music.

🎸 Guitar

2+ years of experience in Indian and western contemporary styles. Exploring fingerstyle techniques and songwriting.

Technical Competence

Languages
Python
Java
R
C
MATLAB
Web Development
Django
Flask
Node.js
React
HTML/CSS/JS
Bootstrap
Databases & APIs
MySQL
SQLite
MongoDB
REST APIs
Operating Systems
Linux (Ubuntu)
Windows
macOS
The most beautiful thing we can experience is the mysterious. It is the source of all true art and science.

— Albert Einstein