Experiential Learning
Hands-on, data-driven, student-centered.
Below you will find the projects I mentored—whether inside my courses, through the Krenicki Center for Business Analytics & Machine Learning at Purdue University, or at academic conferences—together with any awards the work has received.
2025
2025 Fall Undergraduate Research Expo
I mentored 33 student projects that were presented as Posters. Below, you will find the projects that were awarded in this conference, followed by a complete list of all the projects. You may access the abstracts for each project here.
Presentations Awarded With Distinction
Early Detection of Diabetes Risk Among Indian Women
Susan Chen; Allison Margaret Neff; Brayden Ryan Zink; Srikar Kolla
Predicting Fourth Down Risk
Kabeer Singh Khubbar; Samuel Anthony Fiore; Aditya Venugopal Nair
How The City Decides When You Can Breathe
Max Elliott Matteucci; Andrew Logan Oberholzer; Andrew Lawrence House; Ella Sage Dawes
Workforce Retention: Leveraging Synthetic Data to Predict Employee Attrition
Bocheng Wang; Ru Yi Cai; Yihao Zhou
Predicting 30-day Readmission Risk in Diabetic Patients
Cole Anthony Werner; William Gray Gattoni; Owen Alexander Hershberger; Grant Thomas Romero
What Makes a Song a Hit? Predicting Spotify Popularity through Music Analytics
Megan Zhu; Alexandra Li Alderink; Rachel Kalyn Spear; Angela Peng GaoAll Projects Presented at the 2025 Fall Undergraduate Research Expo
| # | Project Title | Authors | Type |
|---|---|---|---|
| 1 | Data Analytics for Transparent Health Insurance Pricing | Cooper William Springs; Ethan Mathew Lebon; Matthew Wyatt Baratian | Poster |
| 2 | Forecasting Short-Term EV Charging Demand Spikes for Smarter Grid Management in the U.S. | Zachary David Sullivan; Bernardo Bradley Chalaca Moreira; Thomas Guy Engle; Lucas Erwin Gerbsch | Poster |
| 3 | What Makes a Song a Hit? Predicting Spotify Popularity through Music Analytics | Megan Zhu; Alexandra Li Alderink; Rachel Kalyn Spear; Angela Peng Gao; Tiffany Sui | Poster |
| 4 | Creating a Box-Office Hit: Determining Key Financial Indicators for Films Through Predictive Modeling | Sara Claire D’Urso; Ashley Mei Hung; Anishka Pateriya; Akhand Bindra | Poster |
| 5 | The Price is Right (Skewed): Using Features to Predict Wine Prices | Nathaniel John Hiatt; Ethan Dewen Louie | Poster |
| 6 | Fraud Prediction on Synthetic Pop-up Retail Data: a Transaction-Level Risk Assessment | Adam Benjamin Houser; Sasank Nunna; Kumar Shreyansh; Shreyaa Karan | Poster |
| 7 | Predicting Term Deposit Subscription: Insights from Bank Marketing Campaign Data | Gavin Craig Kulak; Sulav Shrestha; Viswanath Jay Nair; Henry Patrick Merchant | Poster |
| 8 | What Predicts College GPA? A Data-Driven Look at Study Habits and Test Scores | Eliza Jane Lutgen; Gonzalo Antonio Silva; Uddipta Sarkar; Kyle Anthony Fernandez; Angel Wang | Poster |
| 9 | Forecasting Credit Card Losses for Capital One Using Multi- Bank Data | Grant A Moreland; Kiefer Alexander Bell; Colin Patrick Budreau; Justin Thomas Brady | Poster |
| 10 | From Sound to Streams: Predicting Music Success Through Analytics | Brendan William Polese; Rachel Elizabeth Carlson; Abigail Claire Smith; Mert Ryan Kiroglu | Poster |
| 11 | Predicting Retail Sales Trends Using a Synthetic Dataset | Neha Mary Regi; Zijing Zhang; Shih-En Wang; Christopher Cruz | Poster |
| 12 | Signals of Stardom: Explainable Prediction of NBA All-Star Selections | Tyson Mark Tucci; Syuan-Rang Sean Wu; Po-Wei Lee; Francesco Joseph Facente | Poster |
| 13 | Workforce Retention: Leveraging Synthetic Data to Predict Employee Attrition | Bocheng Wang; Eli Nash Chandler; Ru Yi Cai; Yihao Zhou | Poster |
| 14 | Forecasting U.S. Flight Delays with Pre-Flight Predictors | Paul Ryan Warfel; Theodore Walter Moritz; Maxwell Christopher Klug; Reece Robb | Poster |
| 15 | Benchwarmer to Breakout: Predicting the Next NBA Breakout Star Player | Eric Yixiang Zhang; Paola Godina Salas; Harshini Madhusudhanan; Jadon Conor Salazar | Poster |
| 16 | U.S. Stock Market Movements through News Headlines: A Natural Language Processing Approach | Caleb Zachariah Brunton; Jose Jorge Bueso; Jose Manuel Estrada Garcia; Alfonsina Michelle Rodriguez | Poster |
| 17 | Early Detection of Diabetes Risk Among Indian Women | Susan Chen; Allison Margaret Neff; Brayden Ryan Zink; Srikar Kolla | Poster |
| 18 | Predicting Undergraduate Dropout Risk for Early, Actionable Intervention | Leonard Shien Chiu; Michael Ryan Gjorseski; Joshua Ginste; Artemii Chirkov | Poster |
| 19 | What Makes a House Valuable? Predicting Home Prices to Improve Transparency in the U.S. Market | Sebastian Felipe Gil Eskildsen; James William Pope; Roberto Andres Chinchilla Varela; Juan Antonio Sevilla | Poster |
| 20 | Predicting Consumer Credit Default Risk | Aidan Joshua Hershberger; Yutian Ye; Jizheng Li; Cole R Bailey | Poster |
| 21 | Predicting Best Actors and Directors for Up-and-Coming Netflix Movies | Kinaya Arielle Hines; Robert Pedro Chambers; Somin Yang; Emi Victoria Robinson | Poster |
| 22 | How The City Decides When You Can Breathe | Max Elliott Matteucci; Andrew Logan Oberholzer; Andrew Lawrence House; Ella Sage Dawes | Poster |
| 23 | U.S. Hub Airports: Predicting Airline Arrival Delays | Newla Moo; Alacya Madison Lynch; Kristen Taylor O’Leary; Cydney Allyn Culver | Poster |
| 24 | How Many Hours Will You Sleep? Utilizing Synthetic Data to Predict Quantity of Sleep | Kundana Nittala; Esther L Larson; Emilio Andres Pino; Shree Krishna Tulasi Bavana | Poster |
| 25 | Predicting Country GDP Growth: A data driven approach to economic modelling and decision making | Ace Setiawan; Gavin Patrick Connolly; Gabriel John Carlson; Sean Patrick Patterson | Poster |
| 26 | Predicting Sports Injury Risk: A Data-Driven Approach to Athlete Health Management | Yusuf Anis Sherali; Shashank Venkata Seerum; Sanjay Anthony Jaikaran; Jeenay Vipul Dedhia | Poster |
| 27 | Predicting 30-day Readmission Risk in Diabetic Patients | Cole Anthony Werner; William Gray Gattoni; Owen Alexander Hershberger; Grant Thomas Romero | Poster |
| 28 | Beyond the Box Score – Predicting Playoff Success in the NBA | Michael Downing Yancy; Diego Alberto Cavero; Nicholas Richard Boyd; Elliott Jameson Soderberg | Poster |
| 29 | Predicting Fourth Down Risk | Kabeer Singh Khubbar; Samuel Anthony Fiore; Bruno Andres Knize Simon; Aditya Venugopal Nair | Poster |
| 30 | Formula 1 Race Performance: An Analytical Dive into Pit Stop Strategy | Rishita Korapati; Mia Bell Foulk; Chih-Yu Lee; Varsha Devisetty | Poster |
| 31 | Understanding Product Returns in E-Commerce: A Predictive Analysis Using Synthetic Data | Vitoria Machado Machado Didone; Jack Dorsey Feehan; Ashton Anthony Price; Owen Thomas Edwards | Poster |
| 32 | Predicting Long-Term Brand Loyalty from Influencer Exposure: A Multi-Model Segment-Aware Approach | Ashish Krishna Mallur; Annika Anders Nelson; Brandon J Moss | Poster |
| 33 | Predicting Student Academic Stress for Early Intervention | Jari L Warner; Arush Sowreddy Medam; Ansh Pahwa; Destinee Walker | Poster |
2025 Summer Undergraduate Research Symposium
Second Place Research Talk Award 2025 – Daniels School of Business
2025 Spring Undergraduate Research Conference
I mentored 28 student projects that were presented either as Posters or Research Talks. Below, you will find the projects that were awarded in this conference, followed by a complete list of all the projects. You may access the abstracts for each project here.
Second Place Research Talk Award 2025 – Daniels School of Business
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First Place Poster Award 2025 - Daniels School of Business
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Predicting Fan Engagement: A Similarity Test Enhancing Future Bracketology by Lynden Tate Oliver
All Projects Presented in the Conference
| # | Project Title | Authors | Type |
|---|---|---|---|
| 1 | Smart Cookies: A Predictive Approach to Girl Scout Cookie Sales | Gabriel Morales Nunez†; Rishita Korapati† | Research Talk |
| 2 | Predicting Fan Engagement: A Similarity Test Enhancing Future Bracketology | Lynden Tate Oliver†; Aubrey Zak‡; James Graeme Tolland‡; Charles Andrew Dempewolf‡; Maanav Narasimha Kyabarsi‡ | Poster |
| 3 | Predicting the Future of Electric Vehicles: Adoption Trends, Reliability, and Market Growth in Washington State | Maoxiong Chen†; Abhinav Bondlela†; Dane Alexander Kniola†; Theodore Dean Schmidt† | Poster |
| 4 | Bracket Bias: Predicting NCAA March Madness Outcomes and the Influence of Fan Loyalty | Kyle Steven Emgenbroich†; Kaitlin Rose Otto†; Ishita Tripathy†; Vaibhavi Chamiraju† | Poster |
| 5 | Forecasting Nike’s 2025 Earnings: A Data‑Driven Approach | Aditya Ghorpade†; Shreem Bhavesh Amin†; Ronak Bhagia†; Gabriel E. Calleja Sanchez†; Jayanth Madhava Kurup† | Poster |
| 6 | Predicting GDP Growth: Using Data‑Driven Insights to Uncover Economic Trends | Anisa Krvavac†; Adam Taesoo Bae†; Ian Spencer Lutz†; Jacob Matthew Embleton†; Alex Lukowiecki Caridi† | Poster |
| 7 | Forecasting Monthly Bag Distribution: Improving Delivery Efficiency Through Data | Josiah John Linnemann† | Poster |
| 8 | Analyzing Fan Predictions of Basketball Bracket Winners: Driven by Fan Brackets and Affinity of Teams | Adlyn Aliette Hernandez† | Poster |
| 9 | Optimizing Enrollment Processes: Leveraging Automation for Operational Efficiency and Multilingual Support | Parth Kapila† | Poster |
| 10 | Predicting Peoples’ Perfect Bracket: A Data‑Driven Approach to March Madness Forecasting | Lauren Mackenzie Knowlton† | Poster |
| 11 | Predicting Bracket Outcomes: Using Predictive Analytics to Understand How Customer School Affinity Affects Decision‑Making Bias | William V. Mehra†; Thomas Daniel Holland†; Nathan Andrew Summers†; Ethan Arthur Haeberle†; Mahek Gupta† | Poster |
| 12 | Predicting March Madness Bracket: The Influence of School Affinity | Viet Vu Hoang Ngo†; Rachel Kalyn Spear‡; Anishka Pateriya‡; Marcus Victor Page‡; Sana M. Khambati‡ | Poster |
| 13 | Predicting Consumer Purchasing Trends: A Comparative Analysis of Walmart and Target Customers in West Lafayette | Alexander Chase Alfele†; Lauryn J. Crumbley†; Annika Anders Nelson†; Brendan Lawrence Ludwig† | Poster |
| 14 | Predicting NCAA Bracket Champions with Data‑Driven Insights | Maura K. Flood†; Mackenzie Elizabeth Arnish†; Nicholas Patrick Zebell†; Haley Grace Henson†; Olivia Francis Hojnicki† | Poster |
| 15 | Beyond the Bracket: Data‑Driven Predictions of March Madness Selection Patterns | Alyssa A. Forester†; Sophia Ling‡; Isabella Chiara Lagioia‡; Zainab Waheed‡; Aadi Agrawal‡ | Poster |
| 16 | Predicting NCAA March Madness Outcomes: Assessing School Affinity and Bracket Forecasting Accuracy | Aryaa Madan†; Rayan Siddiqi†; Pahal Vishalkumar Kapatel†; Jacob Michael Zawacki† | Poster |
| 17 | Predicting March Madness Bracket Accuracy: The Role of School Affinity in Final Round Forecasting | Avi Manik†; Ark Kedia†; Somansh Hamen Shah†; Anurag Koripalli‡; Brady Ivan Yoder‡ | Poster |
| 18 | Predicting Electric Vehicle Sales: A Model‑Based Approach to Forecast Purchase Trends | Ricardo Andres Pena Rojas†; Brandon James Rodarmel†; Colin Lucas Wellington†; Alec Michael Walter‡ | Poster |
| 19 | Clicks to Conversions: Leveraging Social‑Media Engagement for Teenage Purchasing Behavior | Pranshu Aryal†; Ria Trikha Singh†; Tishia Talia Darmawan† | Poster |
| 20 | Predicting March Madness Fan Brackets: Leveraging Data for Sponsorship Insights | Michael Lance Whitfield†; Filippa Maria Rodriguez Pinzon†; Chalen Alexander Jack†; Varsha Raj†; Albert Joseph Burton‡ | Poster |
| 21 | Predictive Modeling of Liver Cancer Risk | Ethan Thomas Garcia†; Jakub John Jasinski†; Cooper J. Keillor†; Shenghua Wu†; Meenakshi Radhakrishnan† | Poster |
| 22 | Predicting March Madness Finalists: A Data‑Driven Approach to Understanding School Affinity | Drew Christophe Lawler†; Omar Haytham Al Husseini†; Kelly Ann Igo†; Benjamin James Wright‡ | Poster |
| 23 | Predicting Customer Ratings: A Data‑Driven Approach to Optimizing EV Charging Stations | Yun Jing Lin†; Cooper Jacob Wylie†; Theodore Christian Lewis†; Harleen Kaur Sohal†; Rick Lee† | Poster |
| 24 | Predicting NCAA Bracket Participation: Analyzing Fan Engagement Through Data | Chi Lin†; Brianna Annie Yu†; Shuhan Yang†; Kexin Han†; Joan Zhi Wei Lu† | Poster |
| 25 | Unveiling Fan Decision‑Making: Predicting Sports Championship Outcomes Through User Prediction Strategies | Samyukta Rajaraman†; Kriti Bagchi†; Brandon Michael Dries†; Alexander Logan Kapala†; Christina Wan† | Poster |
| 26 | Customer Return Likelihood: Based on Demographics, Service Usage, and Customer Support Interactions | Charlotte Ann Warren†; Lahari Krishna Bikkavilli†; Aksheet Sameer Paralkar†; Madeeha Sadiq† | Poster |
| 27 | Predicting Cryptocurrency Price Movements: A Data‑Driven Analysis of Trading Volume Trends | Tyler Nathan Wichman†; Hunter McCormick Danton†; Robert Paul Bogdajewicz†; Sruthi Bhamidipati†; Shreya Maganti† | Poster |
| 28 | Maximizing Revenue: Analyzing Hotel Rates and Booking Patterns | Xinyue Zhao†; Jackson McKinney†; Brody Lee Stevens†; Shih‑En Wang‡ | Poster |
† Presenting Undergraduate Author, ‡ Contributing Undergraduate Author, * Undergraduate Acknowledgment.
2025 INFORMS Analytics+ Conference
I mentored 2 student projects through the Krenicki Center for Business Analytics & Machine Learning at Purdue University the that were presented as Posters in the 2025 INFORMS Analytics+ Conference. Both teams combine undergraduate and graduate students from Purdue University. To check these projects details, check the next section.
Smart Cookies: A Predictive Approach to Girl Scout Cookie Sales
The Enrollment Autobahn: Streamlining NGO’s enrollment process via no-code automation
Krenicki Center for Business Analytics & Machine Learning at Purdue University
Girl Scouts Project
This initiative enhances cookie sales forecasting for the Girl Scouts of Central Indiana by advancing their predictive methods beyond the current naive approach, which relies solely on prior-year data and explains about 70% of sales variability. By utilizing advanced time series forecasting techniques applied to comprehensive data from 1,000 troops collected over five years, I mentored the student to develop a more complex predictive model and incorporate critical factors such as troop participation rates, regional market dynamics, and seasonal fluctuations. This refined approach yields superior predictive accuracy exceeding 80%, significantly improving inventory management by reducing surplus and optimizing sales opportunities. The resulting analytical framework not only improves immediate decision-making and fundraising effectiveness but also provides a foundation for future organizational advancements in forecasting and resource allocation.
Indy Reads Project
In this project, I mentored students that collaborated with Indy Reads to enhance the organization’s enrollment process by identifying inefficiencies and integrating current data sources. The project aimed to reduce manual data entry and create a more streamlined experience for applicants, staff, and volunteers. By reviewing the existing process—spanning Form Assembly, Salesforce, SharePoint, Calendly, and InTERS—the team ensured seamless data flow across all systems. Implementing simple automations, the students improved operational efficiency, boosted responsiveness for applicants, and significantly lowered staff workload.
2022
Verbo ao Voto
Product of UFPE’s Computational & Experimental Political Science Lab (@cpcex_lab), portraying the 2022 Brazilian presidential race. Built in collaboration with Mônica Rocabado and Antonio Pires.
reeLegis
STM‑based analysis of 30k legislative proposals (2019‑2022). Developed in collaboration with Renata Cavalcanti and Bhreno Vieira.
2013
Retórica Parlamentar
Web app produced during the first Hackathon Câmara dos Deputados (2013). I engineered the web crawler and topic‑model pipeline that analysed 14k speeches from over 400 deputies (2011‑2013), surfacing 70 rhetorical themes. A team of undergraduate students at Federal University of Pernambuco worked with me to relaunch the project in 2022.