K S KnightKotOR

Hello there 👋

I focus on the intersection of applied ML and ML Engineering — from experiments to deployment.

Currently: polishing projects, diving deep into MLOps, and open to Data Scientist / ML Engineer positions.

Languages and Tools

ML / DL:

Data:

Engineering:

📂 My projects 📂:

Music Genre Classifier:
Group project — ML lead contributor
- Designed and trained a 2 MB CNN, achieving F1 macro = 0.82 without data leakage; accuracy is comparable to a 380 MB fine-tuned transformers
- Adapted the model for inference in a container using Docker and RabbitMQ (The container was configured by the other member)
- Team practices: PR reviews, conventional commits, feature-branch flow, CI checks on all merges
Support ticket prioritization:
ML project — classification on imbalanced data with business constraints
- Built a priority classifier (Low / Medium / High) on a 50K synthetic dataset with severe class imbalance
- Eliminated critical errors: zero cases of High→Low misclassification
- Used TreeSHAP to verify model relies on real business factors (downtime, affected users)
Ultrasonic welding optimization:
R&D thesis project for the "Polymer Composite Materials" lab, SPbPU
Bachelor's thesis — defended with excellence 25 June 2025 🔒 private — refactoring in progress 🔒
- Implemented a surrogate assisted bayesian optimization system based on CatBoost + Optuna
- Achieved 1.4–3× reduction in the number of experiments needed to find the global optimum compared to classical Bayesian optimization
- Validated on benchmark functions (Branin, Rosenbrock, harmonic) with systematic methodology
- Compared:
  - CV configurations (4-,6-,8-fold and LOO)
  - ML models for a surrogate (CatBoost, XGBoost, RandomForest, MLP with Nadaraya-Watson kernel)
  - Optimization methods (CMA-ES, TPE, GP, QMC)
Topological Data Analysis research:
Research group project: assisted with TDA pipeline; responsible for the classifier
- Built a TDA pipeline extracting robust geometric features from 3D point clouds using gudhi and giotto-tda
- Improved F1-macro from 0.73 to 0.81 (+10%) over the baseline CNN, demonstrating effectiveness of topological features

Hello there 👋

I focus on the intersection of applied ML and ML Engineering — from experiments to deployment.

Currently: polishing projects, diving deep into MLOps, and open to Data Scientist / ML Engineer positions.

Languages and Tools

ML / DL:

Data:

Engineering:

📂 My projects 📂:

Music Genre Classifier:
Group project — ML lead contributor
- Designed and trained a 2 MB CNN, achieving F1 macro = 0.82 without data leakage; accuracy is comparable to a 380 MB fine-tuned transformers
- Adapted the model for inference in a container using Docker and RabbitMQ (The container was configured by the other member)
- Team practices: PR reviews, conventional commits, feature-branch flow, CI checks on all merges
Support ticket prioritization:
ML project — classification on imbalanced data with business constraints
- Built a priority classifier (Low / Medium / High) on a 50K synthetic dataset with severe class imbalance
- Eliminated critical errors: zero cases of High→Low misclassification
- Used TreeSHAP to verify model relies on real business factors (downtime, affected users)
Ultrasonic welding optimization:
R&D thesis project for the "Polymer Composite Materials" lab, SPbPU
Bachelor's thesis — defended with excellence 25 June 2025 🔒 private — refactoring in progress 🔒
- Implemented a surrogate assisted bayesian optimization system based on CatBoost + Optuna
- Achieved 1.4–3× reduction in the number of experiments needed to find the global optimum compared to classical Bayesian optimization
- Validated on benchmark functions (Branin, Rosenbrock, harmonic) with systematic methodology
- Compared:
  - CV configurations (4-,6-,8-fold and LOO)
  - ML models for a surrogate (CatBoost, XGBoost, RandomForest, MLP with Nadaraya-Watson kernel)
  - Optimization methods (CMA-ES, TPE, GP, QMC)
Topological Data Analysis research:
Research group project: assisted with TDA pipeline; responsible for the classifier
- Built a TDA pipeline extracting robust geometric features from 3D point clouds using gudhi and giotto-tda
- Improved F1-macro from 0.73 to 0.81 (+10%) over the baseline CNN, demonstrating effectiveness of topological features

Hello there 👋

I focus on the intersection of applied ML and ML Engineering — from experiments to deployment.

Currently: polishing projects, diving deep into MLOps, and open to Data Scientist / ML Engineer positions.

Languages and Tools

ML / DL:

Data:

Engineering:

📂 My projects 📂:

Music Genre Classifier:
Group project — ML lead contributor
- Designed and trained a 2 MB CNN, achieving F1 macro = 0.82 without data leakage; accuracy is comparable to a 380 MB fine-tuned transformers
- Adapted the model for inference in a container using Docker and RabbitMQ (The container was configured by the other member)
- Team practices: PR reviews, conventional commits, feature-branch flow, CI checks on all merges
Support ticket prioritization:
ML project — classification on imbalanced data with business constraints
- Built a priority classifier (Low / Medium / High) on a 50K synthetic dataset with severe class imbalance
- Eliminated critical errors: zero cases of High→Low misclassification
- Used TreeSHAP to verify model relies on real business factors (downtime, affected users)
Ultrasonic welding optimization:
R&D thesis project for the "Polymer Composite Materials" lab, SPbPU
Bachelor's thesis — defended with excellence 25 June 2025 🔒 private — refactoring in progress 🔒
- Implemented a surrogate assisted bayesian optimization system based on CatBoost + Optuna
- Achieved 1.4–3× reduction in the number of experiments needed to find the global optimum compared to classical Bayesian optimization
- Validated on benchmark functions (Branin, Rosenbrock, harmonic) with systematic methodology
- Compared:
  - CV configurations (4-,6-,8-fold and LOO)
  - ML models for a surrogate (CatBoost, XGBoost, RandomForest, MLP with Nadaraya-Watson kernel)
  - Optimization methods (CMA-ES, TPE, GP, QMC)
Topological Data Analysis research:
Research group project: assisted with TDA pipeline; responsible for the classifier
- Built a TDA pipeline extracting robust geometric features from 3D point clouds using gudhi and giotto-tda
- Improved F1-macro from 0.73 to 0.81 (+10%) over the baseline CNN, demonstrating effectiveness of topological features

Hello there 👋

I focus on the intersection of applied ML and ML Engineering — from experiments to deployment.

Currently: polishing projects, diving deep into MLOps, and open to Data Scientist / ML Engineer positions.

Languages and Tools

ML / DL:

Data:

Engineering:

📂 My projects 📂:

Music Genre Classifier:
Group project — ML lead contributor
- Designed and trained a 2 MB CNN, achieving F1 macro = 0.82 without data leakage; accuracy is comparable to a 380 MB fine-tuned transformers
- Adapted the model for inference in a container using Docker and RabbitMQ (The container was configured by the other member)
- Team practices: PR reviews, conventional commits, feature-branch flow, CI checks on all merges
Support ticket prioritization:
ML project — classification on imbalanced data with business constraints
- Built a priority classifier (Low / Medium / High) on a 50K synthetic dataset with severe class imbalance
- Eliminated critical errors: zero cases of High→Low misclassification
- Used TreeSHAP to verify model relies on real business factors (downtime, affected users)
Ultrasonic welding optimization:
R&D thesis project for the "Polymer Composite Materials" lab, SPbPU
Bachelor's thesis — defended with excellence 25 June 2025 🔒 private — refactoring in progress 🔒
- Implemented a surrogate assisted bayesian optimization system based on CatBoost + Optuna
- Achieved 1.4–3× reduction in the number of experiments needed to find the global optimum compared to classical Bayesian optimization
- Validated on benchmark functions (Branin, Rosenbrock, harmonic) with systematic methodology
- Compared:
  - CV configurations (4-,6-,8-fold and LOO)
  - ML models for a surrogate (CatBoost, XGBoost, RandomForest, MLP with Nadaraya-Watson kernel)
  - Optimization methods (CMA-ES, TPE, GP, QMC)
Topological Data Analysis research:
Research group project: assisted with TDA pipeline; responsible for the classifier
- Built a TDA pipeline extracting robust geometric features from 3D point clouds using gudhi and giotto-tda
- Improved F1-macro from 0.73 to 0.81 (+10%) over the baseline CNN, demonstrating effectiveness of topological features

Hello there 👋

I focus on the intersection of applied ML and ML Engineering — from experiments to deployment.

Currently: polishing projects, diving deep into MLOps, and open to Data Scientist / ML Engineer positions.

Languages and Tools

ML / DL:

Data:

Engineering:

📂 My projects 📂:

Music Genre Classifier:
Group project — ML lead contributor
- Designed and trained a 2 MB CNN, achieving F1 macro = 0.82 without data leakage; accuracy is comparable to a 380 MB fine-tuned transformers
- Adapted the model for inference in a container using Docker and RabbitMQ (The container was configured by the other member)
- Team practices: PR reviews, conventional commits, feature-branch flow, CI checks on all merges
Support ticket prioritization:
ML project — classification on imbalanced data with business constraints
- Built a priority classifier (Low / Medium / High) on a 50K synthetic dataset with severe class imbalance
- Eliminated critical errors: zero cases of High→Low misclassification
- Used TreeSHAP to verify model relies on real business factors (downtime, affected users)
Ultrasonic welding optimization:
R&D thesis project for the "Polymer Composite Materials" lab, SPbPU
Bachelor's thesis — defended with excellence 25 June 2025 🔒 private — refactoring in progress 🔒
- Implemented a surrogate assisted bayesian optimization system based on CatBoost + Optuna
- Achieved 1.4–3× reduction in the number of experiments needed to find the global optimum compared to classical Bayesian optimization
- Validated on benchmark functions (Branin, Rosenbrock, harmonic) with systematic methodology
- Compared:
  - CV configurations (4-,6-,8-fold and LOO)
  - ML models for a surrogate (CatBoost, XGBoost, RandomForest, MLP with Nadaraya-Watson kernel)
  - Optimization methods (CMA-ES, TPE, GP, QMC)
Topological Data Analysis research:
Research group project: assisted with TDA pipeline; responsible for the classifier
- Built a TDA pipeline extracting robust geometric features from 3D point clouds using gudhi and giotto-tda
- Improved F1-macro from 0.73 to 0.81 (+10%) over the baseline CNN, demonstrating effectiveness of topological features

Hello there 👋

I focus on the intersection of applied ML and ML Engineering — from experiments to deployment.

Currently: polishing projects, diving deep into MLOps, and open to Data Scientist / ML Engineer positions.

Languages and Tools

ML / DL:

Data:

Engineering:

📂 My projects 📂:

Music Genre Classifier:
Group project — ML lead contributor
- Designed and trained a 2 MB CNN, achieving F1 macro = 0.82 without data leakage; accuracy is comparable to a 380 MB fine-tuned transformers
- Adapted the model for inference in a container using Docker and RabbitMQ (The container was configured by the other member)
- Team practices: PR reviews, conventional commits, feature-branch flow, CI checks on all merges
Support ticket prioritization:
ML project — classification on imbalanced data with business constraints
- Built a priority classifier (Low / Medium / High) on a 50K synthetic dataset with severe class imbalance
- Eliminated critical errors: zero cases of High→Low misclassification
- Used TreeSHAP to verify model relies on real business factors (downtime, affected users)
Ultrasonic welding optimization:
R&D thesis project for the "Polymer Composite Materials" lab, SPbPU
Bachelor's thesis — defended with excellence 25 June 2025 🔒 private — refactoring in progress 🔒
- Implemented a surrogate assisted bayesian optimization system based on CatBoost + Optuna
- Achieved 1.4–3× reduction in the number of experiments needed to find the global optimum compared to classical Bayesian optimization
- Validated on benchmark functions (Branin, Rosenbrock, harmonic) with systematic methodology
- Compared:
  - CV configurations (4-,6-,8-fold and LOO)
  - ML models for a surrogate (CatBoost, XGBoost, RandomForest, MLP with Nadaraya-Watson kernel)
  - Optimization methods (CMA-ES, TPE, GP, QMC)
Topological Data Analysis research:
Research group project: assisted with TDA pipeline; responsible for the classifier
- Built a TDA pipeline extracting robust geometric features from 3D point clouds using gudhi and giotto-tda
- Improved F1-macro from 0.73 to 0.81 (+10%) over the baseline CNN, demonstrating effectiveness of topological features

Hello there 👋

I focus on the intersection of applied ML and ML Engineering — from experiments to deployment.

Currently: polishing projects, diving deep into MLOps, and open to Data Scientist / ML Engineer positions.

Languages and Tools

ML / DL:

Data:

Engineering:

📂 My projects 📂:

Music Genre Classifier:
Group project — ML lead contributor
- Designed and trained a 2 MB CNN, achieving F1 macro = 0.82 without data leakage; accuracy is comparable to a 380 MB fine-tuned transformers
- Adapted the model for inference in a container using Docker and RabbitMQ (The container was configured by the other member)
- Team practices: PR reviews, conventional commits, feature-branch flow, CI checks on all merges
Support ticket prioritization:
ML project — classification on imbalanced data with business constraints
- Built a priority classifier (Low / Medium / High) on a 50K synthetic dataset with severe class imbalance
- Eliminated critical errors: zero cases of High→Low misclassification
- Used TreeSHAP to verify model relies on real business factors (downtime, affected users)
Ultrasonic welding optimization:
R&D thesis project for the "Polymer Composite Materials" lab, SPbPU
Bachelor's thesis — defended with excellence 25 June 2025 🔒 private — refactoring in progress 🔒
- Implemented a surrogate assisted bayesian optimization system based on CatBoost + Optuna
- Achieved 1.4–3× reduction in the number of experiments needed to find the global optimum compared to classical Bayesian optimization
- Validated on benchmark functions (Branin, Rosenbrock, harmonic) with systematic methodology
- Compared:
  - CV configurations (4-,6-,8-fold and LOO)
  - ML models for a surrogate (CatBoost, XGBoost, RandomForest, MLP with Nadaraya-Watson kernel)
  - Optimization methods (CMA-ES, TPE, GP, QMC)
Topological Data Analysis research:
Research group project: assisted with TDA pipeline; responsible for the classifier
- Built a TDA pipeline extracting robust geometric features from 3D point clouds using gudhi and giotto-tda
- Improved F1-macro from 0.73 to 0.81 (+10%) over the baseline CNN, demonstrating effectiveness of topological features

Hello there 👋

I focus on the intersection of applied ML and ML Engineering — from experiments to deployment.

Currently: polishing projects, diving deep into MLOps, and open to Data Scientist / ML Engineer positions.

Languages and Tools

ML / DL:

Data:

Engineering:

📂 My projects 📂:

Music Genre Classifier:
Group project — ML lead contributor
- Designed and trained a 2 MB CNN, achieving F1 macro = 0.82 without data leakage; accuracy is comparable to a 380 MB fine-tuned transformers
- Adapted the model for inference in a container using Docker and RabbitMQ (The container was configured by the other member)
- Team practices: PR reviews, conventional commits, feature-branch flow, CI checks on all merges
Support ticket prioritization:
ML project — classification on imbalanced data with business constraints
- Built a priority classifier (Low / Medium / High) on a 50K synthetic dataset with severe class imbalance
- Eliminated critical errors: zero cases of High→Low misclassification
- Used TreeSHAP to verify model relies on real business factors (downtime, affected users)
Ultrasonic welding optimization:
R&D thesis project for the "Polymer Composite Materials" lab, SPbPU
Bachelor's thesis — defended with excellence 25 June 2025 🔒 private — refactoring in progress 🔒
- Implemented a surrogate assisted bayesian optimization system based on CatBoost + Optuna
- Achieved 1.4–3× reduction in the number of experiments needed to find the global optimum compared to classical Bayesian optimization
- Validated on benchmark functions (Branin, Rosenbrock, harmonic) with systematic methodology
- Compared:
  - CV configurations (4-,6-,8-fold and LOO)
  - ML models for a surrogate (CatBoost, XGBoost, RandomForest, MLP with Nadaraya-Watson kernel)
  - Optimization methods (CMA-ES, TPE, GP, QMC)
Topological Data Analysis research:
Research group project: assisted with TDA pipeline; responsible for the classifier
- Built a TDA pipeline extracting robust geometric features from 3D point clouds using gudhi and giotto-tda
- Improved F1-macro from 0.73 to 0.81 (+10%) over the baseline CNN, demonstrating effectiveness of topological features

K S KnightKotOR

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K S KnightKotOR

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K S KnightKotOR

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K S KnightKotOR

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K S KnightKotOR

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K S KnightKotOR

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K S KnightKotOR

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K S KnightKotOR

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📂 My projects 📂:

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