Kostas Papadopoulos
240 Sidney St, Boston | 617-922-4803 | konpap94@bu.edu
I'm a python programmer with graduate-level experience in solving analytic problems. I've implemented algorithms for searching big data, analyzing streams, mining networks, and understanding text. I enjoy learning new skills and communicating ideas with data. In addition to being an engineer, I'm a passionate musician. My credits as a developer include a visualization package for StratoDem and a cloud scanning tool with IBM Research.
Education
Boston University
Computer Science & Neuroscience double major, BASep 2012 — Jun 2017
Initially, as a Neuroscience student, I became interested in the applications of data and technology. This led me to extend my studies to Computer Science with a coursework focused on data-science.
Experience
StratoDem Analytics
Data Scientist InternJun 2016 — Aug 2016
I was a summer intern at StratoDem, a real-estate consulting startup that uses demographic data to build predictive solutions for investors and businesses.
I worked in a team of analysts, utilizing data-mining tools to build predictive models for real estate and government demographic data.
During my stay, I developed Stratplotlib, a python tool that provided the team with an easy way to create maps and visualize their geospatial data.
An example use-case is provided below.
BIDMC, Department of Neuroscience
Research AssistantJan 2015 — Jan 2016
This was my first ecounter with programming in the workplace. As a research assistant, I was responsible to manually analyze videos for neuroscience research. I saw room for improvement and wrote python automations that yielded signficant increases in analysis speed.
- Analyzed and processed eye-tracking video data, identifying features that
characterize the spatial attention of research subjects. - Took initiative to write Python scripts to partially automate data processing
which made the analysis of the data 50% faster. - Created visualizations that yielded insights on the behavioral patterns of individuals.
Skills
Software and Programming Languages
Python (pandas, numpy, scikit-learn, scipy, networkX, matplotlib,
seaborn, selenium etc.), SQL, HTML/CSS
Analytics
Natural Language Processing, stream sampling, search with hashing, principal component analysis and dimensionality reduction
Misc.
Agile driven development, AWS, webscraping, LaTeX, Linux, Git, Jupyter Notebook
Projects
Duplicate document identification in big data
python, minHash, reservoir sampling2017
Implemented an efficient way to find duplicate book documents in big volumes of data with the use of locality sensitive hashing and sampling.
Amazon natural language processing
python, nltk, pandas, tf-idf, sentiment analysis2016
Built a model that predicts a user’s rating of a product by analyzing his review and comparing it to ratings of similar reviews. This was done by building a TF-IDF vector for each user’s text and finding its euclidean distance from different clusters.
Classifying the safety state of containers
agile, python, docker, elasticSearch2016
Worked in a team of engineers to develop a tool that finds suspicious code in docker images. Intended for use in data centers, the tool works by mining security data from containers and then uses similarity matching to find malicious code snippets in the file binaries. (Mentored by IBM)
Youtube video prediction
python, networkX, AWS, multiprocessing2016
Built a model that predicts the relationship between two youtube videos (i.e. if they are linked) by analyzing shortest-path metrics in a constructed YouTube graph. Computations were done multi-processed on a remote AWS instance.
Tripadvisor web-scraping
webscraping, python, selenium, beautifulsoup2016
Web-scraped all of Boston's hotel reviews in order to build a model of factors that influenced people's ratings.
Yelp text sentiment analysis
python, sentiment analysis, text-processing2016
Implemented a way of predicting user ratings by analysing the number of words with sentiment value in their reviews.
Visualizing StackOverflow
python, networkX, Gephi, webscraping2016
Visualized the relationship of programming tools by constructing a network of “tags” associated with user questions on stackoverflow.com in which a connection defines how often a tag appears with another.
This project was done in order to simplify and understand where technologies fit in the grand scheme of things.
I thought that StackOverflow.com would provide an organic result due to its importance in the tech community.
Making a connection out of 1 post would look something like the above.
With a few million posts, I was able to get a much more descriptive visualization (below).
