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Click on individual projects to view more information including project descriptions from corporate partners

  • Lecture time: Monday: 13:30:00 - 14:20:00 ET
  • Lab time: Friday: 13:30:00 - 15:20:00 ET
  • Domain: Athletics
  • Keywords: Pricing, Secondary Market Data, Stadium Mapping, Ticket Sales, Ticket Usage
  • Tools: Dash, Plotly, Python
  • Citizenship: Open to all students
Summary

Use ticket sales data and game day scan data from static files and auto generated .igy files. The map will provide an on-demand color coded visual of ticket type combinations and purchasing patterns. Scan data visual mapping and look at secondary market ticket pricing.

  • Lecture time: Tuesday: 11:30:00 - 12:20:00 ET
  • Lab time: Thursday: 11:30:00 - 13:20:00 ET
  • Domain: Athletics
  • Keywords: Optimized Training, Profiling Athletes
  • Tools: Data Management, Python, R, Tableau
  • Citizenship: Open to all students
Summary

Infer outcomes and recommendations for optimal training. Create an understanding of outcomes based on individual profiles and trends from training data. Identifying metrics most impactful on performance and reporting through Tableau that can be utilized in the Apollo AMS system.

  • Lecture time: Tuesday: 13:30:00 - 14:20:00 ET
  • Lab time: Thursday: 13:30:00 - 15:20:00 ET
  • Domain: Research and Development
  • Keywords: Orbital Modeling, Satellite, Space
  • Tools: Data Modeling, GitHub, Machine Learning, Python, R
  • Citizenship: Open to all students
Summary

This project seeks to understand (via modeling) the thresholds of over population in space to assess a sustainable carrying capacity (considerate of risk/impact) that is driven by multiple threat scenarios.

  • Lecture time: Thursday: 13:30:00 - 14:20:00 ET
  • Lab time: Tuesday: 13:30:00 - 15:20:00 ET
  • Domain: Aerospace
  • Keywords: Aerospace, Simulation modeling
  • Tools: Data Modeling, Linear Optimization, Machine Learning, Programming, Statistical Modeling, Stimulation
  • Citizenship: U.S. Citizens Required
Summary

Ground, flight, and simulation predictions disagree: how do we understand and predict the differences? The team will utilize Talon-P, a derived rocket-place data source, and statistical modeling to coorelate between flight, ground, and simulation data durin.

  • Lecture time: Tuesday: 15:30:00 - 16:20:00 ET
  • Lab time: Thursday: 15:30:00 - 17:20:00 ET
  • Domain: Energy
  • Keywords: AI, Anomaly Identification, Electrification, Energy Management, Energy & Renewables, Forecasting, Machine Learning
  • Tools: Azure, Python, R
  • Citizenship: U.S. citizens and permanent residents preferred
Summary

The Reliability Imperative is the term MISO uses to describe the shared responsibility that MISO, its members, and states have to keep electricity moving. This involves analyzing large amounts of data to identify trends and patterns that impact the electric grid's reliability.

  • Lecture time: Thursday: 11:30:00 - 12:20:00 ET
  • Lab time: Tuesday: 11:30:00 - 13:20:00 ET
  • Domain: Insurance
  • Keywords: Machine Learning, Python
  • Tools: Pattern Identification
  • Citizenship: Open to all students
Summary

When healthcare providers change reimbursement contracts, billed procedure codes may change in an attempt to maximize the provider’s revenue. Early identification through claims data analytics will help keep healthcare costs affordable for our members.

  • Lecture time: Tuesday: 11:30:00 - 12:20:00 ET
  • Lab time: Thursday: 11:30:00 - 13:20:00 ET
  • Domain: Insurance
  • Keywords: Data Manipulation
  • Tools: Machine Learning, Python, Snowflake, SQL
  • Citizenship: Open to all students
Summary

Some high-cost healthcare services require pre-authorization to ensure members are receiving appropriate, medically necessary care. Pre-authorization data patterns can provide early insight into healthcare patterns before paid claims data is available.

  • Lecture time: Tuesday: 11:30:00 - 12:20:00 ET
  • Lab time: Thursday: 11:30:00 - 13:20:00 ET
  • Domain: Technology
  • Keywords: AI, High Performance Compute, infrastructure, Software Developement
  • Tools: Ansible, NodeJS, Python, ROCm, Ubuntu
  • Citizenship: Open to all students
Summary

Concrete Engine builds compute and storage into shipping containers and connects them directly to green and under-utilized energy sources. Using software-defined infrastructure, we empower AI solutions for the 21st century.

  • Lecture time: Tuesday: 09:30:00 - 10:20:00 ET
  • Lab time: Thursday: 09:30:00 - 11:20:00 ET
  • Domain: Insurance
  • Keywords: abnormality, Security, Unsupervised, Visualizations
  • Tools: Machine Learning, Pandas, Python, SQL
  • Citizenship: Open to all students
Summary

In the normal course of business, the security programs amount a staggering sum of data. Sifting through this data to discover irregular issues for devices, users, and logs is difficult and unwieldy. Author a framework capable of making these discoveries and detections.

  • Lecture time: Tuesday: 12:30:00 - 13:20:00 ET
  • Lab time: Thursday: 11:30:00 - 13:20:00 ET
  • Domain: Insurance
  • Keywords: Data Engineering, Fraud, Imbalanced Classification, Parameter Selection, Security
  • Tools: Jupyter, Machine Learning, Python, SQL
  • Citizenship: Open to all students
Summary

Using security-oriented telemetry, find actionable instances of fraud in near real-time. Determine the probability that a transaction constitutes fraud and begin workflow.