ITU Electronics and Communication Engineering
Electronics & Communication Engineering Student
I work on forecasting, computer vision, and practical tooling around data and models through hands-on projects and early product work.
I like problems where the model is only part of the story: messy data, validation decisions, runtime limits, and the small engineering choices that make an experiment usable.
Featured case study
A two-stage machine learning system for intermittent demand forecasting on daily product-level sales data.
What I Work On
Most of my work starts with an awkward constraint: too many zeros, slow feature code, noisy motion, or a validation setup that needs to be treated carefully.
01
Sparse demand, rolling validation, and recursive predictions without peeking into the future.
02
Checking whether the setup is fair before trusting the score.
03
Replacing slow loops and repeated work with cleaner, faster data transforms.
04
Small backend and data workflows that keep projects usable end to end.
05
Computer vision, speech processing, and mobile ideas tested as working prototypes.
Selected Work
Each case study explains the problem, what I tried, what I measured, and where the constraints shaped the implementation.
A two-stage machine learning system for intermittent demand forecasting on daily product-level sales data.
Products
20k
History
3 yrs
Zero demand
85%
Metrics
WAPE / RMSSE
Optimized a large-scale ML feature engineering and inference pipeline by reducing unnecessary computation, memory bloat, and Python-level bottlenecks.
Runtime
40m -> 4m
Rows
3M
A confidence-aware visual odometry system combining deep learning-based optical flow and classic computer vision.
Branches
2
Selector
Confidence
A full-stack portfolio platform with dynamic projects, notes, media uploads, admin tools, and secure self-hosted deployment. Active feature development continues.
Stack
Next.js + API
Content
Dynamic
Tools
Notes
Signals & Systems formulas
Whisper becomes more useful when transcription is treated as one stage in a larger NLP system.
A confidence selector can turn deep and classical visual odometry branches into a more robust research architecture.