Final Projects

Final project links are posted!

Below are the projects for IC22. Under each project is the list of teams that worked on the project for IC22 and the link to their GitHub site that shows their outcome. 



Organization: Economic Research Service (ERS), US Department of Agriculture (USDA)
Project Name: FoodAPS
Category: Data Analytics

Description: The National Household Food Acquisition and Purchase Survey (FoodAPS) collects nationally-representative, comprehensive data on household food purchases and acquisitions, in what quantity, at what price, and from where, as well as nutrition, economics, environment, and health. USDA ERS has developed interactive charts related to food spending and food access using FoodAPS data. USDA ERS is interested in expanding these visual analyses to address issues related to equity. Specifically, USDA ERS is interested in carrying out an assessment on the extent to which programs and policies perpetuate systemic barriers for people of color and other underserved communities. To evaluate this, USDA ERS is looking for students to analyze the FoodAPS data and identify trends among vulnerable populations with regard to food access, food choice, and food prices.

Team IC22025 | Project Title: Food Access
Joshua Kaplowitz | US Naval Academy
Tongbun Pengkaew | US Naval Academy
Ryan Samotis | US Naval Academy
https://github.com/rysamotis/UMD_Datascience

Team IC22028 | Project Title: Team22028-Food Accessibility
Aboli Dahiwadkar | University of Maryland
Anuja Bendre | University of Maryland
Tasnim Obaied | University of Maryland
https://github.com/Anuja-Bendre/IC22Team2208FoodAPS

Team IC22050 | Project Title: Top Deterrents to Food Access in Highly Underserved U.S. Populations from National Household Food Acquisition and Purchase Survey (FoodAPS) in 2012
Amani Mbonimpa | Montgomery College
Michelle Nguyen | Montgomery College
Mai Tran | Montgomery College
Matthew Chin | Montgomery College
https://github.com/pastel-pickup/ic22050 

Team IC22059 | Project Title: Food Spending and Food Access in different Target Groups
Tanna Nguyen | Montgomery College
Thanh Trinh | Montgomery College
Luis Valderrama | Montgomery College
https://github.com/TVThanh163/IC22059.git


Organization: Bea Hardy
Project Name: Naval Officer Shipping Lists (NOSLs)
Category: Data Analysis

Description: The Chesapeake colonies were a vibrant part of the world economy during the 18th century, trading extensively with the British empire and other foreign ports. Tax officials documented these trades in Naval Officer Shipping Lists (NOSLs) which recorded all ships entering and clearing the Chesapeake ports, along with detailed lists of their cargoes. These records have been transcribed into a relational database, including summaries of imports and exports, types of goods, quantities, destinations, and prices. Students can use this data in a variety of ways and answer several questions regarding the logistics of the economy and trading hundreds of years ago.

Team IC22022 | Project Title: Exploring Shipping in the Eighteenth Century Chesapeake
Jenny Luo | US Naval Academy
Krystal Kim | US Naval Academy
Everett Stenberg | US Naval Academy
Young Kim | US Naval Academy
https://github.com/m223534/ic22_Team22022.git


Organization: UMD iSchool and the Library of Congress
Project Name: Rosa Parks in her Own Words
Category: Data Analytics

Description: Rosa Parks and Dr. Martin Luther King are both known and praised for their roles in the Montgomery Bus Boycott in 1955, which campaigned for equal rights of use of public services and spaces. Over 40,000 African Americans participated in this movement, yet the majority of their contributions are not publicized. The Library of Congress currently houses over 7,000 documents from Parks’ legacy, 1,769 of which include the names and details of many carpool participants from the Montgomery Bus Boycott. The College of Information Studies is interested in using this collection to create a visualization of those involved in the Montgomery Bus Boycott, their contributions, and their experiences.

Team IC22045 | Project Title: Analyzation of Rosa Parks Data
Janushaa Bala Krishnan Muthiah | US Naval Academy
Mingyu Han | US Naval Academy
Courtney Weir | US Naval Academy
https://github.com/Janushaa/UMD-CHALLENGE 


Organization: Joe Bonsignore
Project Name: The Armed Conflict Location and Event
Category:
Data Analytics

Description: The Armed Conflict Location and Event (ACLED) project collects real-time data on the locations, dates, actors, fatalities, and types of all reported political violence and protests across multiple countries. ACLED aims to capture how various attacks and demonstrations of violence occur in different states. Focusing on a subset of this data for the United States, use data analytics skills to understand how the number of events have changed over time, whether there are any significant locations of events, and how the types of events have changed over time.

Team IC22011 | Project Title: Unrest in America
Matthew Lewis | US Naval Academy
Jeff Peters | US Naval Academy
Julian Muniz | US Naval Academy
James Fiscus | US Naval Academy
https://github.com/Jeff-Peters2158/IC22011 

Team IC22035 | Project Title: Police incidents and protests
Prathamesh Malage | University of Maryland
Eduardo Gonzalez | Montgomery College
Tai Ngo | Montgomery College
https://github.com/edux2711/IC2022-team-22035 

Team IC22037 | Project Title: ACLED – BLM Focus
Elizabeth Farmer | US Naval Academy
Samuel Chanow | US Naval Academy
Ashley Chung | US Naval Academy
https://github.com/Sam-Chanow/BlueYp713_UMD_2022 

Award Winner: Outstanding Data Analytics Project
Team IC22043 | Project Title: Trends with Conflicts in the U.S.

Liam Bailey | US Naval Academy
Isaac Cho | US Naval Academy
Paul Hendron | US Naval Academy
Antawn Weg | US Naval Academy
https://github.com/Eridanus22/ACLED 

Team IC22058 | Project Title: Protests in the US
Mark Roh | US Naval Academy
Nathan Utesch | US Naval Academy
Brigitta Szepesi | US Naval Academy
https://github.com/m225580/datachallenge.git


Organization: US Small Business Administration (SBA)
Project Name: Paycheck Protection Program
Category: Data Analytics

Description: Enacted by Congress in 2020 to respond to the economic impact of the COVID-19 pandemic, the Paycheck Protection Program provided nearly $800 billion in loans to small businesses in order to retain payrolls. While the Small Business Administration has released data on more than 11.5 million approved applications from the program, they have also removed applications from the dataset that have been previously present. Using data analysis on two datasets, one which includes loans that remain in the PPP database, and one that includes loans that were removed from the PPP database, develop an understanding of why these loans may have been removed by analyzing defining characteristics in the datasets.

Team IC22001 | Project Title: Analysis of PPP Loans in Georgia
Yu-Cheng Lai | University of Maryland
Farid Freyha | University of Maryland
Nan Wang | University of Maryland
https://github.com/chimken1/hello-world 

Team IC22004 | Project Title: Predicting Removed Loan –  Paycheck Protection Program Application
Chung-Hao Lee | University of Maryland
Chia-Lin Tsai | University of Maryland
Wang-Han Li | University of Maryland
https://github.com/lch99310/UMD_Info_Challenge_2022 

Team IC22007 | Project Title: PPP data analysis
Jieqian Xiao | University of Maryland
Tianli Ding | University of Maryland
Zihan Zhang | University of Maryland
Rui Jin | University of Maryland
https://github.com/amatate/ppp-infochallenge1 

Team IC22009 | Project Title: The Story of Georgia
Dishant Vakte | University of Maryland
Radiah Tahsin Tanisha | University of Maryland
I-Ju Lin | University of Maryland
Naila Sharmin | University of Maryland
https://github.com/Ijulin/The-Story-of-Georgia 

Team IC22015 | Project Title: Paycheck Protection Program
Chido Shamuyarira | University of Maryland
Emma Darkwa | University of Maryland
Shu-Ping Chen | University of Maryland
Wei-Yu Jen | University of Maryland
https://github.com/pippi-chen/Info-Challenges 

Team IC22021 | Project Title: An Exploration of Why Loan Records Are Removed from SBA Database
Amy Chan | University of Maryland
Amola Patel | University of Maryland
Yufei Deng | University of Maryland
Ziyu Liu | University of Maryland
https://github.com/ziyuliuzilla/PPP-Removed-Applications 

Team IC22023 | Project Title: How To Give A Best Shoot of your Paycheck Protection Program
Kexin Xu | University of Maryland
Zhaoying Ren | University of Maryland
https://github.com/Kxinxu/IC22-PPP.git 

Team IC22029 | Project Title: Paycheck Protection Program Data Analysis
Hsin Chen | University of Maryland
Jinping Guo | University of Maryland
Jing Lin | University of Maryland
Po-Han Yen | University of Maryland
https://github.com/skyhuman69/UMD_Info_Challenge_029.git 

Team IC22030 | Project Title: Prediction & Analysis of Removed Loan Applications
Haoning Ke | University of Maryland
Ling Fang | University of Maryland
Upasana Mohapatra | University of Maryland
Yu-Tung Chang | University of Maryland
https://github.com/upasanam20/PPP_Team-IC22030.git 

Team IC22031 | Project Title: Analysis of the paycheck protection program
Abhijit Haridas | University of Maryland
Drishti Jain | University of Maryland
Girish Saraf | University of Maryland
Pratyush Gupta Uddagiri | University of Maryland
https://github.com/girish03/PPP_22031 

Team IC22032 | Project Title: Serving the Underserved: PPP Loans in Georgia, USA
Danny Rivas | University of Maryland
Javan Reuto | University of Maryland
https://github.com/danny-rivas/PPP-Loan-Prediction 

Team IC22046 | Project Title: Paycheck Protection Program
Ramith Wijesinghe | University of Maryland
Priyanka Chib | University of Maryland
Yi-Hua Huang | University of Maryland
https://github.com/Ramareigns/IC22—ic22046.git 

Team IC22047 | Project Title: PPP removed applications, Why?
Rijul Newalkar | University of Maryland
Theophile Sadio Nanzo | University of Maryland
Govinda Sri Charan Duggirala | University of Maryland, Baltimore County
Maryam Alomair | University of Maryland, Baltimore County
https://github.com/tsadio/Team-IC22047.git 

Team IC22052 | Project Title: Paycheck protection program: Analysis
Manikanta Koneru | University of Maryland
Jayasree Karthik Nandula | University of Maryland
https://github.com/mani1821/IC22_Team052 


Organization: UMD Department of Transportation Services
Project Name:
Veo E-Scooter Sidewalk Usage
Category:
Data Analytics/Design


Description
: Veo is a mobility service company that provides e-scooters throughout the University of Maryland’s campus. Asking anyone about the shared e-scooter service on campus would probably elicit a wide range of opinions, but one prevailing opinion — and regulation — is an important one: deterring e-scooter operation on campus sidewalks. DOTS and Veo have provided a dataset of e-scooter route data on campus, as well as GIS map data denoting the location of all sidewalks on UMD College Park campus. The Department of Transportation Services would like to know the most traveled sidewalks that are used by e-scooter users, the hours of the day when the most infractions occur, and any other significant patterns of e-scooter routes that occur on campus sidewalks, including infractions on football or basketball game days.

Team IC22010 | Project Title: E-Scooter Sidewalk Usage at UMD
Fabienne Yang | University of Maryland
Chu-Hsuan Tsao | University of Maryland
Kevin Chou | University of Maryland
https://github.com/tristatsao/INFO-Challenge-22010.git 

Team IC22013 | Project Title: An Analysis to Optimize Strategies for Reducing Veo Scooter Traffic Infractions
Kelly Bye | US Naval Academy
West Gapasangra | US Naval Academy
https://github.com/kellybye/IC22.git 

Team IC22014 | Project Title: The battle for sidewalk space: Understanding pedestrian safety through VeoRide e-scooter trip data
Ruthwik Kuppachi | University of Maryland
Govind Nageswaran | University of Maryland
Iskander Lou | University of Maryland
Alibi Shokputov | University of Maryland
https://github.com/Ken2399/Info_Challenge_22014 

Team IC22016 | Project Title: VEO Ride Safety Measures
Gauri Goel | University of Maryland
Rohin Bhagavatula | University of Maryland
Parth Kodnani | University of Maryland
https://github.com/rohinb99/Team22016 

Team IC22019 | Project Title: Veo E-Scooter Sidewalk Usage
Anshika Patel | University of Maryland, Baltimore County
Sanaa Mironov | University of Maryland, Baltimore County
Jaganathan Velraj | University of Maryland, Baltimore County
Nick Corbin | University of Maryland, Baltimore County
https://github.com/sanaamironov/UMD_Challenge_22 

Award Winner: Outstanding Data Analytics Project
Team IC22062 | Project Title: Creating a safe campus for e-scooter riders and Pedestrian
Richmond Yeboah | Montgomery College
Sumeet Ram | University of Maryland
Uzair Masih Israrahmed | University of Maryland
https://github.com/palebluedot13/IC22062 


Organization: UMD Department of Astronomy
Project Name: All Sky Imagery
Category: Data Analytics

Description: The UMD Department of Astronomy Observatory captures thousands of high-quality, 180 degree images of the sky every night. These images are stored in the Flexible Image Transport System (FITS) file format which can be read in Python as a numpy array as well as transformed into other graphic formats. These images can capture stars, planets, meteors, planes, satellites, bugs sitting on the protective dome, and even rocket launches. The Department of Astronomy is interested in using this dataset to identify and analyze the different types of objects that are captured in this imagery, as well as photometry of particular stars and any possible correlation between exposure lengths and clear or dark skies.

Award Winner: Grand Prize
Team IC22017 | Project Title: AllSky Tool
Aryan Anwar | Montgomery College
Rohit Sharma | Montgomery College
Matthew Nanas | Montgomery College
https://github.com/aryananwar/info22 


Organization: BigBear.ai
Project Name: ISCXIDS2012 Cybersecurity Dataset
Category: Cybersecurity

Description: Malicious hackers and automated malware attacks are one of the biggest threats facing cybersecurity experts today. To help encourage novel solutions to these problems, researchers at the Canadian Institute for Cybersecurity have assembled a number of datasets cybersecurity researchers and practitioners can use to evaluate their malware detection methodologies. The dataset for this challenge represents a subset of their 2012 Intrusion Detection Evaluation Dataset. Here, the UNB researchers employed real cyber attack and malware scenarios, along with background network traffic simulations to create an evaluation dataset. This dataset contains multiple possible challenges, ranging from dataset exploration, to machine learning, to novel visualizations.

Team IC22005 | Project Title: iscxIDS
Jay Siliphet | University of Maryland
Ben Nordmann | University of Maryland, Baltimore County
https://github.com/BenNordmann/InfoSci-Challenge-2022 

Team IC22024 | Project Title: Following the Attack Chain
Kade Heckel | US Naval Academy
Jennifer Jung | US Naval Academy
Christian Rose | US Naval Academy
Brenton Pieper | US Naval Academy
https://github.com/blackgazzelle/UMD_INFOCHAL_IC22024.git 

Team IC22033 | Project Title: BigBear.ai – Malware Attack Classification
Nishant Jadhav | University of Maryland
Rakshit Sinha | University of Maryland
Sanjit Mahajan | University of Maryland
https://github.com/Raksh710/Malware_Attack_Classification 

Award Winner: Outstanding Cybersecurity Project
Team IC22034 | Project Title: ISCXIDS2012 Cybersecurity 
Elvin Vanathayan | University of Maryland, Baltimore County
Khang Nguyen | University of Maryland, Baltimore County
https://github.com/Elvin-98/UMD-IC


Organization: UMD DOTS
Project Name: Traffic Flow at UMD
Category: Design
Description: With the continuing and widespread increase of construction projects on campus, the flow of traffic at the University of Maryland oftentimes experiences significant disruptions. Not only do these construction projects cause impacts to vehicle traffic, but pedestrian, bike, scooter, and skateboard traffic are also disrupted across campus. The College of Information Studies is seeking creative solutions for how all types of traffic flow on campus can have minimal disruptions. This design challenge includes addressing questions such as how traffic flow can be better on campus, how pedestrian safety can be improved, and actions the university can take to mitigate traffic disruptions during future construction projects.

Team IC22036 
Kieran Hatton | University of Maryland
Shardul Aggarwal | University of Maryland
Tusharkanth Karlapudi | University of Maryland

Award Winner: Outstanding Design Project
Team IC22054 | Project Title: Traffic Pattern Design Challenge
Siao-Ting Lin | University of Maryland
Jing Wang | University of Maryland
Jialun Yang | University of Maryland
https://github.com/jwgitnet/IC22054.git 


Organization: UMD iSchool Career Center
Project Name: WireFrames for iSchool Careers Newsletter
Category: Design
Description: iSchool Careers is a service point within the College of Information Studies that provides support for students and alumni from all of our degree programs (InfoSci, InfoDesign, HCIM, MIM, MLIS, and PhD). iSchool Careers offers a weekly newsletter that lists career-related events happening on campus as well as internship and job opportunities that match our students’ skills. The current newsletter is very simple in its content and formatting, and the iSchool Careers team would like to improve the newsletter in both areas. They would like have creative ideas about the content and wireframes of potential formatting changes.

Award Winner: Best Team Presentation
Team IC22020 | Project Title: iSchool Career Newsletter Redesign Project
Kinny Chen | University of Maryland
Zi Lin | University of Maryland
https://github.com/lululin0324/IC2020_iSchool_Newsletter.git 

Team IC22038 | Project Title: UMD iSchool Career Center Project
Scott Mobarry | University of Maryland
Salahdin Waji | University of Maryland
Alexandra Beleho | Montgomery College
https://github.com/smobarry/info_challenge_team_22038



Dataset Levels

Note: Cybersecurity and Design projects do not have a level assigned. 

Level 1: Participants with little to no knowledge in data science. The problem statement is straightforward about what the final product may look like. The dataset contains enough information to answer the questions in the problem statement. Start from the basics and create an interesting story.
  


Level 2: Participants with basic data analysis knowledge. The problem statement is open-ended yet straightforward. The dataset has a standard structure suitable for beginners. Creative and interdisciplinary solutions are welcomed.


Level 3: Participants with some data analysis background. The problem statement is open-ended about what the final product may look like. The dataset may contain many variables of interest. Analyses from different angles by various techniques are encouraged.


Level 4: Participants with advanced data analysis skills. The problem statement is open-ended and requires multitudes of analytical perspectives and visualizations. The dataset has a complex structure, numerous variables of interest, and spatial-temporal dimensions.



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