Tiyyagura Naveen Reddy
Professional Summary
Computer Vision Engineer with 0 years of experience in developing and evaluating deep learning models for image restoration and applied ML. Experienced in end-to-end PyTorch model development (CNN-based encoder-decoder StudentNet), knowledge distillation from Restormer, and edge-focused model optimization using pruning and quantization. Built and validated image-restoration pipelines on 3,214 paired GoPro images achieving 0.87 SSIM and significant training-loss reduction. Complementary ML experience includes model selection and evaluation with Scikit-learn, Random Forest and XGBoost, and imbalance handling using SMOTE and ADASYN on environmental and IoT datasets. Strong data-processing skills with Pandas, NumPy, SQL and R, plus visualization with Matplotlib, Seaborn and Plotly. Demonstrated ownership of data pipelines, reproducible evaluation, and delivering compact models suitable for constrained inference environments.
Technical Skills
Work Experience
- Engineered a lightweight CNN encoder-decoder StudentNet in PyTorch for image restoration on 3,214 GoPro blurred–sharp pairs to enable compact edge inference.
- Integrated knowledge distillation by transferring Restormer teacher outputs into StudentNet to preserve perceptual detail while reducing model complexity.
- Combined four loss components in the training pipeline and reduced training loss from 0.0567 to 0.0237 (58% reduction) to stabilize restoration quality.
- Trained the motion-deblurring pipeline for 100 epochs and achieved 0.87 SSIM on the validation set, preserving structural similarity in restored images.
- Optimized inference for constrained devices by applying pruning and quantization workflows to StudentNet, improving CPU inference latency for edge scenarios.
- Validated restoration outputs with automated evaluation scripts using OpenCV and NumPy to compute SSIM and support qualitative inspection.
- Plotted loss and SSIM trends with Matplotlib and Seaborn to guide hyperparameter tuning and convergence decisions during training.
- Analyzed water-quality datasets covering 21 chemical and biological parameters using Pandas and NumPy to identify missing values and inconsistent records.
- Conducted feature engineering and exploratory visualizations in R; applied StandardScaler and executed an 80/20 stratified split with 10-fold cross-validation.
- Trained and tuned Random Forest models in Scikit-learn, achieving 95% accuracy on holdout folds through hyperparameter search and feature selection.
- Mitigated class imbalance with SMOTE and ADASYN to improve minority-class recall during validation and reduce bias in predictions.
- Benchmarked XGBoost and SVM using stratified validation to compare tree-based and kernel-based performance for target water-quality labels.
- Evaluated K-Nearest Neighbors and Decision Tree baselines; automated report generation in Excel using VBA macros to deliver reproducible summaries to faculty.
Projects
- Resolved a severe 1:7,739 class imbalance in the BoT-IoT dataset by designing hybrid oversampling strategies combining SMOTE and ADASYN to synthesize minority examples.
- Constructed preprocessing, feature selection, encoding pipelines and trained models with Scikit-learn and XGBoost to compare algorithmic robustness across imbalanced splits.
- Built interactive Plotly dashboards to inspect class distributions and feature importance; achieved a 99.95% F1-score on the final stratified evaluation.
Education
Certifications
Achievements
- 3rd place — IEEE Code Wars Coding Challenge — 3 Oct 2024: Secured 3rd position in the IEEE Code Wars coding challenge at GITAM Bengaluru.
- Intel Unnati Industrial Training 2025: Completed a 54-day industrial training and delivered the Image Sharpening Using Knowledge Distillation project.
- B.Tech First Class with Distinction: Graduated with 8.21/10 CGPA in B.Tech Computer Science Engineering (Data Science).
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