Skip to content
View pskalip's full-sized avatar
  • Cornell University
  • New York City

Block or report pskalip

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse

Popular repositories Loading

  1. Compilers Compilers Public

    Lexer and Parser implementation of a sample programming language.

    C++ 1

  2. notgoogle.com notgoogle.com Public

    Forked from karthikrangasai/notgoogle.com

    This is search engine built using Vector Space Model on the Cornell Movie-Dialogs Corpus.

    Python

  3. Reliable-UDP-With-Selective-Repeat Reliable-UDP-With-Selective-Repeat Public

    An implementation of a reliable version of UDP using the selective repeat algorithm.

    Python 2

  4. Measure-and-Contour-of-Iso-Rectangle-Set-Implementation Measure-and-Contour-of-Iso-Rectangle-Set-Implementation Public

    A C++ implementation of the paper "Optimal Divide-and-Conquer to Compute Measure and Contour for a Set of Iso-Rectangles" by Ralf Hartmut Guting as an assignment for the course: "Design and Analysi…

    Jupyter Notebook 1

  5. Line-Fitting-Dynamic-Programming-Segmented-Least-Squares-Implementation Line-Fitting-Dynamic-Programming-Segmented-Least-Squares-Implementation Public

    A C++ implementation supplemented by a visualization of the Line Fitting Dynamic Programming Problem as a part of the course: Design And Analysis of Algorithms.

    Jupyter Notebook 1

  6. applied-econometrics-1 applied-econometrics-1 Public

    Forked from siddarthgopalakrishnan/applied-econometrics-1

    ECON F342 - Applied Econometrics Assignment. Run an econometric regression model and test the model for heteroskedasticity, multicollinearity, normality of error term and omitted variable bias