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Hey 👋, I'm Aniketh

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Hi there, I'm Aniketh Sukhtankar, a Software Engineer working at Google in San Francisco 🌍. I am a Computer Science Masters student 🚀 from University of Florida. I am currently working on privacy infrastructure for Google's business logs, everything from search to youtube to maps 👨🏽‍💼. I am Passionate about my work and always eager to connect with other people.

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Talking about Personal Stuff:

  • 👨🏽‍💻 I am a Backend Software Engineer with Google working on something fun;
  • 🌱 I am currently learning Design Patterns taught by this guy -> Design Patterns;
  • 🤔 My interests are with Algorithms/Data Structures, Software Architecture, Clean Code, Quantitative Analysis etc..;
  • 💼 I have a Master's degree in Computer Science;
  • 💬 Ask me about anything, I am happy to help;
  • 📫 Please email via [email protected] to reach me.
  • 📝 See my Curriculum Vitae to get more info.

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⭐️ From Aniketh Sukhtankar

Aniketh Sukhtankar's Projects

awesome-java icon awesome-java

A curated list of awesome frameworks, libraries and software for the Java programming language.

bitcoin-mining-elixir icon bitcoin-mining-elixir

The goal of this project was to use Elixir and the actor model to build a good solution to the bitcoin mining problem that runs well on multi-core machines. Our code was simultaneously run on 8 machines with one functioning as server and the other 7 as workers, and we were able to mine a coin with 7 leading zeroes. Technologies used: Erlang, Elixir

clrs icon clrs

Some exercises and problems in Introduction to Algorithms 3rd edition.

competitive-programming icon competitive-programming

Solution of problems on diffrent competitive programming websites, code templates, Data Structures and Algorithms, hackthons and much more. I push at least one commit daily to this repository.

data-mining-classification icon data-mining-classification

The goal of the project is to increase familiarity with the classification packages, available in R to do data mining analysis on real-world problems. Several different classification methods were used on the given Life Expectancy dataset. The dataset was obtained from the Wikipedia website. The continent column was added as per the requirements to be used as class label. kNN, Support Vector Machine, C4.5 and RIPPER were the classification methods used on the data set.

data-mining-clustering icon data-mining-clustering

The goal of the project is to increase familiarity with the clustering packages, available in R to do data mining analysis on real-world problems. Several different clustering methods were used on the given datasets. The dataset was as provided. The original cluster column was used as initial label for comparison. kMeans, Hierarchical, DBScan and SNNClust were the clustering methods used on the smaller data set and kMeans was chosen for large data set.

dbcoin-react-frontend icon dbcoin-react-frontend

Responsive dynamic website to analyze & derive data statistics from a database containing information from the SEC filings of the companies and their stock prices for the years 2012-2016 using React JS, Spring framework & advanced Oracle PL/SQL dynamic queries, object oriented features etc.

dbcoin-spring-backend icon dbcoin-spring-backend

Responsive dynamic website to analyze & derive data statistics from a database containing information from the SEC filings of the companies and their stock prices for the years 2012-2016 using React JS, Spring framework & advanced Oracle PL/SQL dynamic queries, object oriented features etc.

exchat icon exchat

A Slack-like app by Elixir, Phoenix & React(redux)

face-recognition icon face-recognition

Python implementation of Face Detection and Recognition system using Eigen Faces, Fisher Faces and Local Binary Pattern Histograms. Performed comparative analysis with the Linear Discriminant Analysis technique.

freecodecamp icon freecodecamp

The https://freeCodeCamp.com open source codebase and curriculum. Learn to code and help nonprofits.

gossip-push-sum-protocol icon gossip-push-sum-protocol

The goal of this project was to determine the convergence of Gossip type algorithms through a simulator based on actors written in Elixir. Full, Line, 2D and Imperfect 2D topologies were implemented for both Push Sum and Gossip algorithms. We could demonstrate convergence of these algorithms with nodes upto 80000 without hitting system limits. Technologies used: Erlang, Elixir, Gossip Protocol

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