Alice Liu

Class of 2019


Alice is a rising junior hailing from Long Island. Her main passions are computer science and visual arts. Her role models include Ada Lovelace and Katie Bouman. She is an enthusiastic participant of her school's Math Team and Computer Science Club, as well as a prospective officer for the Science Olympiad Team. She is also an illustrator for her school's newspaper. Alice hopes to continue pursuing computer science and enter the field of cyber security in the future. During the summer, Alice is excited to dabble in the Wolfram language and machine learning.

Project: Packet Journey: A Visualization


In computer networks, packets make multiple hops to different locations in order to reach their destination. This project finds the route traveled by a packet as it makes its way to a given domain. The traceroute command was used in order to gather this data on various websites from across the globe. This data was then cleaned and formatted for use in visualizing a map of the results. Furthermore, a graph was created based off of these results with the nodes set to the unique locations visited by the packets and the edges weighted by the average ping time.

Summary of Results

This project gave us a visualization of the routes of packets to many different websites centered at our current location. Furthermore, we were able to successfully convert these routes into a graph, which we could use a shortest path algorithm on in order to determine the optimal route a packet can travel through in order to arrive at its destination. Finding the shortest path returns a list of cities the packet would go through. There are many limitations with our results that prevent them from being fully accurate. For example, the coordinates associated with an IP address does not always give us an accurate approximation of the host location. Furthermore, on some hops, the nodes did not provide us with an IP address, and consequently, they could not be used in our dataset. Additionally, in order to find the most efficient route, our algorithm relies on the current data we have already gathered, which raises the need for potentially crowdsourcing more data from the public.

Future Work

Potential future results include the implementation of this project in different geolocations in order to gather information and see how packets travel from different parts of the world. This could be accomplished through crowdsourcing data from people of different parts of the world. We could also compare the optimal theoretical routes to the ones that exist in real life and examine the feasibility of implementing theoretical routes in order to design more efficient network systems.