Veneta Haralampieva

Veneta Haralampieva

London Area, United Kingdom
528 followers 494 connections

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About

An engineer with a passion for applying Machine Learning in solving challenging and…

Experience

  • Amazon Graphic

    Amazon

    London, England, United Kingdom

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    London, United Kingdom

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    London, United Kingdom

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    London, United Kingdom

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    London, United Kingdom

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    Manchester, United Kingdom

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    Manchester, United Kingdom

Education

  • Imperial College London Graphic

    Imperial College London

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    Activities and Societies: DocSoc

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    Activities and Societies: Dance society, Tennis society

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Licenses & Certifications

Volunteer Experience

  • benefacto Graphic

    Volunteer

    benefacto

    - Present 10 years 8 months

    Environment

    My work was in the Forest Farm Peace Garden based in Hainault which benefits both the people and the environment.
    The have committed to organic gardening and other ecologically sound practices, as well as contributing to local and sustainable production, benefiting the physical and mental health of the community, and promoting a diverse, tolerant and equitable society.
    I worked with a number of people in the communal growing space. I was asked to help with tending the herb garden and…

    My work was in the Forest Farm Peace Garden based in Hainault which benefits both the people and the environment.
    The have committed to organic gardening and other ecologically sound practices, as well as contributing to local and sustainable production, benefiting the physical and mental health of the community, and promoting a diverse, tolerant and equitable society.
    I worked with a number of people in the communal growing space. I was asked to help with tending the herb garden and harvesting fruit as well as general help in the area

  • The University of Manchester Graphic

    Peer-Assisted Study Sessions Leader

    The University of Manchester

    - 10 months

    Education

    PASS (Peer-Assisted Study Sessions) is a student mentoring scheme in which second and third year undergraduate students run study sessions for first year students. In the Computer Science department that focuses mainly on providing help with programming skills and concepts.
    As a PASS leader my main responsibilities include:
    1. Organising useful sessions for the first years which complement well the teaching material and labs
    2. Providing support and advice to first year…

    PASS (Peer-Assisted Study Sessions) is a student mentoring scheme in which second and third year undergraduate students run study sessions for first year students. In the Computer Science department that focuses mainly on providing help with programming skills and concepts.
    As a PASS leader my main responsibilities include:
    1. Organising useful sessions for the first years which complement well the teaching material and labs
    2. Providing support and advice to first year students with problems that might occur during their team project
    3. Creating a nurturing and friendly environment where first year students will feel comfortable asking questions and participating in discussions

  • The Prince's Trust Graphic

    Fundraiser

    The Prince's Trust

    - Present 10 years 5 months

    During my time in Accenture me and fellow placement students were promoted to participate in The Prince's Trust fundraising day. All of the undergraduate students currently on industrial experience were split across into several teams of ten people. Each team was required to come up with fundraising ideas, plan for day and raising money on the actual day. The team I was part of managed to raise £692.27 on our Just Giving Page.

Courses

  • 3rd Year Project

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  • AI and Games

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  • Advanced Computer Graphics

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  • Agile Software Engineering

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  • Algorithms and Imperative Programming

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  • An Introduction to Current Topics in Biology

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  • Compilers

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  • Computer Graphics and Image Processing

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  • Computer Vision

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  • Cryptography and Network Security

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  • Deep Learning

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  • Distributed Computing

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  • First Year Team Project

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  • Fundamentals of Artificial Intelligence

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  • Fundamentals of Computation

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  • Fundamentals of Computer Architecture

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  • Fundamentals of Computer Engineering

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  • Fundamentals of Databases

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  • Fundamentals of Distributed Systems

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  • Independent Study Option: Privacy Preserving Machine Learning

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  • Introduction to Machine Learning

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  • Logic and Modelling

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  • Machine Learning and Optimisation

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  • Machine Learning for Imaging

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  • Mathematical Techniques for Computer Science

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  • Mathematics for Machine Learning

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  • Micro controllers

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  • Natural Language Processing

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  • Object Oriented Programming with Java 1

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  • Object Oriented Programming with Java 2

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  • Operating Systems

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  • Privacy Engineering

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  • Reinforcement Learning

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  • Software Engineering

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  • System Architecture

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  • User Experience

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Projects

  • First Year Team Project - Confess Thy Sins

    First year team project - 4 people join efforts into creating a website form scratch.Time - 3 months

    See project

  • Evaluation of Mutual Information versus Gini Index for Stable Feature Selection

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    The selection of highly discriminatory features has been crucial in aiding further advancements in domains such as biomedical sciences, high-energy physics and e-commerce. Therefore evaluation of the robustness of feature selection methods to small perturbations in the data, known as feature selection stability, is of great importance to people in these respective fields. However, little research has been focused on investigating the stability of feature selection algorithms independently from…

    The selection of highly discriminatory features has been crucial in aiding further advancements in domains such as biomedical sciences, high-energy physics and e-commerce. Therefore evaluation of the robustness of feature selection methods to small perturbations in the data, known as feature selection stability, is of great importance to people in these respective fields. However, little research has been focused on investigating the stability of feature selection algorithms independently from any learning models. This project address the problem by providing an overview of several established stability measures and reintroduces Pearson’s correlation coefficient as another. The coefficient has then been employed in the empirical evaluation of four commonly used feature selection criteria, Mutual information maximisation, Mutual information feature selection, Gini index and ReliefF. A high overall stability of Mutual information maximisation and Gini index can be observed for small data samples, with a slightly lower stability being seen for ReliefF. All criteria exhibit low stability when applied to high-dimensional datasets, consisting of small number of samples, with Mutual information feature selection performing poorly across all datasets.

    See project

  • Second Year Team Project

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    Build applications for Transport for Greater Manchester. Requirements included scheduling of buses and drivers – solution based on Knapsack problem implemented in Java; travel planner feature including fastest route generation.
    The project involved four people in total and spanned across four months.

Languages

  • English

    Native or bilingual proficiency

  • German

    Professional working proficiency

  • Bulgarian

    Native or bilingual proficiency

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