Info Challenge Projects
Dataset Levels
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.
Past Projects
Projects & Outcomes March 2023
Note: Cybersecurity and Design projects do not have a level assigned.
Congratulations to our 2023 Grand Prize winners! Dharini Chandrashekar, UMD and Amanpreet Kaur, UMD. Challenge: CoPAR Website Concept Design – UMD iSchool (Design). See below for more challenge winners.
The University of Maryland Global Classrooms program allows UMD students to gain international experience in a virtual learning environment. After the COVID-19 pandemic, Global Classrooms courses and participants have increased significantly, and the Office of International Affairs seeks to understand student diversity trends for past semesters. Students will use their creative insights on how variables such as gender, race, ethnicity, age, and residency from past student data can be used to compare diversity trends by semester, academic year, and student status.
Project Participants:
Alanna Hart, Anita Conteh
Mentor: Margaret Kahl
Link to project
Brita Laveck, Ellen Kim, Tartela Tabassum
Mentor: Adam Lee
Link to project
Jaehyun Lee, Daniel Yu, Eojin Kim
Link to project
Majdoleen Mohammedaman, Reem Saleh
Mentor: Prasad Senesi
Link to project
Grant Glasgow, Ben Keenan
Mentor: Mark Magsino
Link to project
Harshitha Ramachandra, Shashank Ramprasad
Mentor: Tatyana Yevgrafova
Link to project
Osinakachi Amaefule, Cecilia Chavez, Jaclyn Tran
Mentor: Jeffrey Zuback
Link to project
Christine Wang, Anthony Avelar
Mentor: Jeffrey Zuback
Link to project
Ramith Wijesinghe, Matthew Chin, Kidus Solomon, Courtney Brandon
Mentor: Maria Bardossy
Link to project
Sehba Wani, Josue Tlapechco, Vienna Nguyen
Link to project
When crisis responders react to ongoing emergencies, they need to know what new critical developments have occurred in the past several hours. Oftentimes, responders are able to find this information through social media, however, finding the most important information first is crucial. By analyzing different social media platforms and news sources, construct daily summaries of Hurricane Florence and Hurricane Dorian using a digital version of FEMA’s Incident Status Summary form and utilize this data to assess which platforms provide the most useful information for emergency response personnel.
Project Participants:
Ivan Thomas
Mentor: Dan Roche
Link to project
Bennett Huffman, Maurice Salayon
Mentor: Dan Roche
Link to project
Hanna Zakharenko, G Goodwin
Link to project
Sadaf Davre, Tiara Eltrevoog, Sarah Zachariah
Link to project
In 2018, Washington State enacted stronger distracted driving laws, and in response, a survey was developed to evaluate residents’ understanding of the new Driving Under the Influence of Electronics Act, which was conducted in one-month time periods over the past four years. We are curious to know how public perceptions of this new act have changed over time, particularly among demographic subgroups. This analysis will be used by the Public Health Seattle-King County (PHSKC) to target specific subgroups for media outreach, materials, and infographics, which students can also contribute to in addition to their analysis.
Project Participants:
Aditya Kiran Aswin Kumar, Rajeevan Madabushi, Sravya Lenka, Srikanth Parvathala
Mentor: Brian Wilkinson
Link to project
Manas M Bhat, Shantanu Parab, Vineet Singh
Mentor: Jeffrey Zuback
Link to project
Gunakshi Sharma, Dhiraj Lahoti, Shweta Salelkar, Sakshi BR Patil
Link to project
Thomas Cho, Jenna Han, Joshua Kang, Daniel Shin
Mentor: Duy Duong-Tran
Link to project
Malcolm Rivers, Ryan Grafman, Christopher Hom
Mentor: Nate Chambers
Link to project
Angelina Cheng, Caroline Irwin, Harrison Morrow
Mentor: Duy Duong-Tran
Link to project
Aedan Ounsamone, Lindsey Beiche
Mentor: Nate Chambers
Link to project
Justin Werner, Alexander Grande
Mentor: Duy Duong-Tran
Link to project
Aiden Patel, Chris Kim, Myles Evans, Ola Kwasniewski
Mentor: Justin Blanco
Link to project
Ashley Yi, Nicholas Munoz
Mentor: Nate Chambers
Link to project
Since the beginning of the COVID-19 pandemic, Washington State has experienced an unprecedented year-over-year increase in traffic fatalities. The Washington Traffic Safety Commission (WTSC) manages traffic safety behavior programs and media outreach for various communities. However, we are curious to know if the people living in these communities are the same people involved in the crashes where occur there. By analyzing datasets of crash data, students can answer questions such as: what proportion of drivers are involved in crashes in communities where they live? Are there specific zip codes where higher-risk drivers tend to be more common than others?
Best Team Presentation
Micah Tracy, US Naval Academy
Rhys Winter, US Naval Academy
Kaosi Unini, US Naval Academy
Ryan Zhang, US Naval Academy
Team IC23044
Challenge: Washington Fatal Crash Files – WTSC (Data Analysis, Level 4)
Mentor: Fiona Knoll
Outstanding Data Analytics Project (Tie)
Gabriel Duran, UMD
Keshav Gupta, UMD
Soham Pawaskar, UMD
Team: IC23048
Challenge: Washington Fatal Crash Files – WTSC (Data Analysis, Level 4)
Mentor: Vikrant Aute
Outstanding Data Analytics Project (Tie)
Anna Lavrentieva, UMD
Iris Yu, UMD
Ethan Taggart, Montgomery College
Christopher Way, UMBC
Team: IC23058
Challenge: Washington Fatal Crash Files – WTSC (Data Analysis, Level 4)
Mentor: Chauncey Robbs
Project Participants:
Jarrar Haider, Akhil Reddy, Eshan Agarwal, Chaitanya Pohnerkar
Mentor: James Li
Link to project
Xirui Han, Johannah Ryan, Tam Thu Doan, Kevin Weiner
Mentor: Kunpeng Zhang
Link to project
Jimmy Nguyen, Anthony Capasso, John Dutan
Mentor: James Li
Link to project
Zizheng Zhang, Xiaoze Liu, Wangxi Pan
Mentor: Vikrant Aute
Link to project
Matthew Purvis, Aidan Aguilar, Olivia Love, Danny Taylor
Mentor: Justin Blanco
Link to project
Caden Chau, Christopher Jones
Mentor: Dan Roche
Link to project
Rhys Winter, Micah Tracy, Ryan Zhang, Kaosi Unini
Mentor: Fiona Knoll
Link to project
Keshav Gupta, Soham Pawaskar, Gabriel Duran
Mentor: Vikrant Aute
Link to project
Kent O’Sullivan, Nicole Schneider
Link to project
Iris Yu, Anna Lavrentieva, Ethan Taggart, Christopher Way
Mentor: Chauncey Robbs
Link to project
The Council on the Preservation of Anthropological Records (CoPAR) was an organization founded in the 1990s to promote archiving and discovery of archival materials for cultural materials around the world. Since then, its website has remained the main resource for information about where to find anthropology archives, and how to encourage archiving practices among anthropologists. The current format of the CoPAR website is outdated, counterintuitive, and difficult to search and navigate, and resources are not presented in a visually compelling format. Students are tasked with brainstorming ideas and redesigning the website, improving user-friendliness, and developing visualizations that will facilitate greater access.
Grand Prize
Dharini Chandrashekar, UMD
Amanpreet Kaur, UMD
Team IC23063
Challenge: CoPAR Website Concept Design – UMD iSchool (Design)
Project Participants:
Dharini Chandrashekar, Amanpreet Kaur Sareen
Link to project
Candace Sun
Mentor: Brittany Johnson
Link to project
Hannah Milillo
Mentor: Jacob Davidson
Link to project
The Pygmalion tool can be used to create interactive visualizations of the meanings associated with a word, or vice versa of different words associated with similar concepts, to provide researchers, teachers, and learners with an instrument to visually represent the heterogeneous and complex information presented by dictionaries in a more intuitive way. The tool offers both interactive linear visualizations and network visualizations of a word, however, the interactivity and readability of these maps could be improved. Students will brainstorm ideas and design concepts to increase the interactivity of these visualizations and make them more user-friendly, as well as brainstorm ideas for the use of this tool in educational environments.
Outstanding Design Project
Fatema Motiwala, UMD
Pooja Gajera, UMD
Pratya Nellore, UMD
Shaunak Bhanarkar, UMD
Team: IC23025
Challenge: Pygmalion Tool Maps Design – University of Neuchâtel (Design)
Mentor: Dan Forest
Project Participants:
Fatema Motiwala, Pooja Gajera, Pratya Nellore, Shaunak Bhanarkar
Mentor: Dan Forest
Link to project
Vimbainashe Mabvaru, Runyararo Mucheche
Mentor: Dan Forest
Link to project
Controls to mitigate against cybersecurity attacks are an important component of an entity’s overall IT control environment. As such, it is increasingly important for external auditors to be aware of the risks that clients face because of poor or inadequate cybersecurity processes and controls. For this challenge, students will play the role of an external auditor for a public company, tasked with evaluating and testing internal controls. More specifically, students will evaluate and test passwords–a crucial component of access controls. Students will evaluate, perform, and document a series of steps as part of reviewing password management as a key Information Technology General Control (ITGC) and have the option to develop a system to automate the process.
Outstanding Cybersecurity Project
Ivan Bajceta, US Naval Academy
Michael Huizenga, US Naval Academy
Strahinja Janjusevic, US Naval Academy
Eric Liu, US Naval Academy
Team: IC23038
Challenge: Cybersecurity Controls: Audit, Automate, and Advise – EY (Cybersecurity)
Mentor: Fiona Knoll
Projects & Outcomes March 2022
Note: Cybersecurity and Design projects do not have a level assigned.
Each project’s final recommendations can be found through the provided GitHub link.
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 Analytics
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: INFO College 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.
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 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
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: Design/Data Analytics
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.
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
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
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
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