About
I am a Machine Learning, Deep Learning and Computer Vision Engineer with a background in Computer Science and Software Development. I have 2+ years of professional experience in developing software and applying AI methods to real-world challenges across different domains, including computer vision and network security. I am passionate about building computer vision systems that can perform visual tasks efficiently. I enjoy keeping up with the recent advancements in the field and continue to grow my expertise in it. If you are looking for a Deep Learning or Machine Learning Engineer, please check out my CV, LinkedIn, GitHub or email me.
Skills
- Programming: Python (numpy, pandas, sklearn etc.), C, Software Architechture and Testing
- DL: DL Architectures, Computer Vision, OpenCV, Keras, PyTorch, Tensorflow
- ML: ML Methods and Theory; Developing, Deploying and Debugging ML Pipelines
- Back-end: Django, Flask, REST API, Database Design, SQL, NoSQL, MongoDB, PostgresSQL
- Front-end: React, JavaScript, HTML
- DevOps: Docker, AWS, Google Cloud
Pet Projects
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Product Detection and Classification using Deep Learning
During my deep learning internship at Focal Systems I worked on the tasks of detection and classification of products on grocery store shelves. One of my tasks at the company was to prepare this demo for a conference:
Tech stack: Python, Keras, Docker.
Joint effort with Focal Systems's team.
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Can Neural Networks learn L1-Norm Operation?
As a part of a coding challenge I was asked to create and train a Neural Network to take a random array of real valued numbers that is variable in length, and let it learn the L1-Norm of the array.
Tech stack: IPython, Tensorflow.
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Machine Learning Project: "Censored Bayesian Polynomial Regression"
A term project for the course CS-E4820 - Machine Learning: Advanced Probabilistic Methods at Aalto University, taught in spring 2017. The goal of the project was to construct a Bayesian regression model for incomplete censored data. We considered polynomial regression and used the EM algorithm to obtain a Maximum a Posteriori (MAP) estimate for the parameters of the model. We used Bayesian Information Criterion (BIC) to select the degree of the polynomial.
Tech stack: Matlab.
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Data Mining Project: Netstat
A term project for the course CS-E4600 - Algorithmic Methods of Data Mining at Aalto University taught in fall 2016. The goal of the project was to compute statistics - mean, median, diameter and effective diameter - for large networks. We implemented the exact algorithm (only feasible on small networks) and approximate algorithms: sample random pairs, sample random sources, Approximate Neighborhood Function (ANF). To speed up the running time we have parallelized the algorithms.
Tech stack: Python, SciPy, Cython.
Joint effort with Max Reuter.
[git repo]
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Google Summer of Code 2016 "Carsus - TARDIS support package for creating atomic datasets"
Carsus is a python package for storing and manipulating atomic data, such as atomic masses, ionization energies, levels and transitions. This data is used by TARDIS - an open-source scientific code for rapid spectral modelling of supernovae. Carsus downloads and parses data from a number of sources, stores it in an SQLite database and outputs it in the HDF5 format.
Tech stack: Python, Python scientific stack, pandas, SQLAlchemy and SQLite, pyparsing, pytest.
[GSoC link] [git repo] [quickstart] [TARDIS]
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Web Software Development Project: Haze
A term project for the course CS-C3170 - Web Software Development at Aalto University, taught in spring 2017. In this project we needed to implement an online game store for JavaScript games. The service has two types of users: players and developers. Developers can add their games to the service and set a price for it. Players can buy games on the platform and then play purchased games online. We implemented the project using the Django framework and deployed it to Heroku.
Tech stack: Python, Django, JavaScript, jQuery, Heroku.
Joint effort with Max Reuter and Ekaterina Dorrer.
[deployed app] [git repo]