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(Siddharth Patwardhan)
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urban-bus-scaling-law
November 25, 2025 – December 8, 2025
Code for the analysis of frequency–demand scaling laws in urban bus systems across 20 global cities.
View ProjectMultiplex-Epidemic-Spreading
September 15, 2023 – November 20, 2023
GitHub repository for the paper Epidemic spreading in group-structured populations
View ProjectOptimal_Subway_Networks
August 18, 2023 – August 18, 2023
Optimal_Subway_Networks — GitHub repository
View ProjectDivide_and_Conquer
October 11, 2022 – March 22, 2023
Divide_and_Conquer — GitHub repository
View Projectproj_naive-embedded-reconstruction
September 2, 2022 – October 18, 2023
Research project exploring multiplex reconstruction techniques informed by graph embeddings.
View Projectparametric-dyadic-multiplex-reconstruction
April 21, 2022 – April 18, 2023
Research project exploring multiplex reconstruction techniques informed by Naive Bayesian classifiers.
View ProjectSurvival_Evasive
November 14, 2019 – November 7, 2020
Codes for the numerical simulations in the paper, 'Survival probability of a lazy prey on lattices and complex networks'
View ProjectRandomWalks
July 27, 2019 – July 27, 2019
MATLAB implementation of various random walks including biased walks, lazy random walk, Levy flights, intermittent and some persistent (non-Markovian) walks on networks.
View ProjectRandom-Walks
April 28, 2019 – April 28, 2019
Codes for Random Walks on complex networks (Matlab)
View ProjectMasters-Thesis
April 4, 2019 – October 4, 2020
For my Masters' thesis, I am working on the application of machine learning models to predict the percolation threshold of large networks. The percolation threshold is an essential indicator of the resilience of a network to random attacks. However, computing the percolation threshold is computationally expensive, and the existing methods for estimating the percolation threshold are prone to significant errors. I plan to use machine learning algorithms to estimate the percolation threshold of a given network as a function of various statistical and structural properties that affect the percolation threshold (like mean, variance, range, clustering coefficient, planarity, etc.). I have created a dataset of over 2000 real and synthetic networks; the data-set consists of statistical and structural properties that can affect the percolation threshold, as features and the percolation threshold of networks, as the output attribute. I aim to apply a shallow neural network regression model to t
View ProjectCultural Fit Analysis
The candidate's project portfolio demonstrates a strong inclination towards research and academic applications of data science, particularly in complex systems and network theory. This aligns well with roles requiring deep analytical thinking and problem-solving. The diversity of projects, from urban planning to epidemic modeling, suggests adaptability and a broad intellectual curiosity. However, the absence of team-based or industry-specific projects makes it difficult to fully assess cultural fit in a corporate environment.
Soft Skills & Operational Fit
The provided data does not contain sufficient information to assess soft skills or operational fit. The candidate's project descriptions indicate a strong research-oriented approach and independent work.