# Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

## Markdown

This is a page not in th emain menu

## Future Blog Post

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

## Blog Post number 4

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

## Blog Post number 3

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

## Blog Post number 2

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

## Blog Post number 1

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

## Portfolio item number 1

Short description of portfolio item number 1

## Portfolio item number 2

Short description of portfolio item number 2

## Analysis of Probabilistic Temporal Networks

We propose the probabilistic temporal network that we can encode this uncertainty into the network by giving each edge a probability of being ”available” during each time slot. Interesting questions include routing and cascading in such network. Our idea of this probabilistic networks is inspired by the temporal network models, in which edges have some kind of time-relevant labeling. In this paper we focus on the routing problem in a simple probabilistic temporal network where each edge have uniform independent probability of being ”available” in each time slot.

## Rebalancing Citibike Stations

Web & Mobile Tool for Optimally Reallocating Bikes.

## Bike-Share Simulation Project

Simulated the bikes’ distribution in a bike-sharing system over time with a delay model

## Modeling and Analysis of Tagging Networks in Stack Exchange Communities

Large Question-and-Answer (Q&A) platforms support diverse knowledge curation on the Web. While researchers have studied user behaviour on such platforms in a variety of contexts, there is relatively little insight into important by-products of user behaviour that also encode knowledge. Here, we analyse and model the macroscopic structure of tags applied by users to annotate and catalogue questions, using a collection of 168 Stack Exchange websites that span a diversity of sizes and topics. We study the distribution of tag frequencies and also the structure of ‘co-tagging’ networks where nodes are tags and links connect tags that have been applied to the same question. We find striking similarity in tagging structure across Stack Exchange communities, even though each community evolves independently (albeit under similar guidelines). Our findings thus provide evidence that social tagging behaviour is largely driven by the Stack Exchange platform itself and not by the individual Stack Exchange communities. We also develop a simple generative model that creates random bipartite graphs of tags and questions. Our model accounts for the tag frequency distribution but does not explicitly account for co-tagging correlations. Even under these constraints, we demonstrate empirically and theoretically that our model can reproduce a number of the statistical properties that characterize co-tagging networks.

## Gaussian Process Framework for Deep Neural Networks

Recent work (Garriga-Alonso et al., 2018) has shown deep convolutional neural networks(DNNs) can be approximated by a shallow Gaus-sian processes (GP) with much fewer parameters.A lot of features in modern convolutional neuralnetwork (CNN) have not been considered in thiswork. In this paper, to extend the flexibility ofthe transformation from modern neural networkarchitecture to shallow Gaussian process, a frame-work for DNNs is introduced. An average poolingoperation and a concatenation operation are de-rived to support densely-connection structures tofit in Garriga-Alonso et al.’s architecture by trans-forming them into simple matrix multiplication.The newly derived DenseNet-GP significantly re-duce the time for calculating the kernel matrixfor the GP while having comparable accuracy inclassifying images in CIFAR10.

## Low Precision Training in Distributed ML Systems

The behavior of low-precision training in distributed computing environments.

## Analyzing the Spotify Top 200 Through a Point Process Lens

Using the parameters of this model as estimated from the Top 200 data, we apply a clustering algorithm to identify songs with similar features and performance.

## Analysis of Probabilistic Temporal Networks

For course CS 6850 The Structure of Information Networks taught by Prof. Jon Kleinberg

We propose the probabilistic temporal network that we can encode this uncertainty into the network by giving each edge a probability of being ”available” during each time slot. Interesting questions include routing and cascading in such network. Our idea of this probabilistic networks is inspired by the temporal network models, in which edges have some kind of time-relevant labeling. In this paper we focus on the routing problem in a simple probabilistic temporal network where each edge have uniform independent probability of being ”available” in each time slot.

## Rebalancing Citibike Stations

With Prof. David Shmoys

Web & Mobile Tool for Optimally Reallocating Bikes.

## Bike-Share Simulation Project

With Prof. Jamol Pender

Simulated the bikes’ distribution in a bike-sharing system over time with a delay model

## Modeling and Analysis of Tagging Networks in Stack Exchange Communities

With Prof. Austin R. Benson

Large Question-and-Answer (Q&A) platforms support diverse knowledge curation on the Web. While researchers have studied user behaviour on such platforms in a variety of contexts, there is relatively little insight into important by-products of user behaviour that also encode knowledge. Here, we analyse and model the macroscopic structure of tags applied by users to annotate and catalogue questions, using a collection of 168 Stack Exchange websites that span a diversity of sizes and topics. We study the distribution of tag frequencies and also the structure of ‘co-tagging’ networks where nodes are tags and links connect tags that have been applied to the same question. We find striking similarity in tagging structure across Stack Exchange communities, even though each community evolves independently (albeit under similar guidelines). Our findings thus provide evidence that social tagging behaviour is largely driven by the Stack Exchange platform itself and not by the individual Stack Exchange communities. We also develop a simple generative model that creates random bipartite graphs of tags and questions. Our model accounts for the tag frequency distribution but does not explicitly account for co-tagging correlations. Even under these constraints, we demonstrate empirically and theoretically that our model can reproduce a number of the statistical properties that characterize co-tagging networks.

## Gaussian Process Framework for Deep Neural Networks

For course ORIE 6741 Bayesian Machine Learning taught by Prof. Andrew Wilson

Recent work (Garriga-Alonso et al., 2018) has shown deep convolutional neural networks(DNNs) can be approximated by a shallow Gaus-sian processes (GP) with much fewer parameters.A lot of features in modern convolutional neuralnetwork (CNN) have not been considered in thiswork. In this paper, to extend the flexibility ofthe transformation from modern neural networkarchitecture to shallow Gaussian process, a frame-work for DNNs is introduced. An average poolingoperation and a concatenation operation are de-rived to support densely-connection structures tofit in Garriga-Alonso et al.’s architecture by trans-forming them into simple matrix multiplication.The newly derived DenseNet-GP significantly re-duce the time for calculating the kernel matrixfor the GP while having comparable accuracy inclassifying images in CIFAR10.

## Analyzing the Spotify Top 200 Through a Point Process Lens

With Prof. Jamol Pender

Using the parameters of this model as estimated from the Top 200 data, we apply a clustering algorithm to identify songs with similar features and performance.

## Talk 1 on Relevant Topic in Your Field

Published:

This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!

Published:

Published:

## Conference Proceeding talk 3 on Relevant Topic in Your Field

Published:

This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.

## CS 1110: Introduction to Computing Using Python Permalink

Prof. Walker White, Cornell University, 2017

Prof. Arpita Ghosh, Cornell University, 2018

## CS 4780: Machine Learning for Intelligent Systems Permalink

Prof. Kilian Weinberger, Cornell University, 2018

## CS 4820: Introduction to Analysis of Algorithms Permalink

Prof. Robert Kleinberg, Cornell University, 2019