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Elevance Health - Detection of Digital Fraud
Closed for Registration INDY
  • Lecture time: To-be-determined: 00:00:00 - 00:00:00 ET
  • Lab time: To-be-determined: 00:00:00 - 00:00:00 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.

  • Lecture time: To-be-determined: 00:00:00 - 00:00:00 ET
  • Lab time: To-be-determined: 00:00:00 - 00:00:00 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: To-be-determined: 00:00:00 - 00:00:00 ET
  • Lab time: To-be-determined: 00:00:00 - 00:00:00 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: To-be-determined: 00:00:00 - 00:00:00 ET
  • Lab time: To-be-determined: 00:00:00 - 00:00:00 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: 15:30:00 - 16:20:00 ET
  • Lab time: Thursday: 15:30:00 - 17:20:00 ET
  • Domain: Biomedical Research
  • Keywords: biology, Biomedical Science, Clustering, SPAC, Spatial Analysis, Transformation, Visualizations
  • Tools: GitHub, Python, R
  • Citizenship: Open to all students
Summary

To accelerate the pace of delivering tools to analyze spatial single-cell datasets by leveraging the contributions of students and to contribute to the advancement of scientific knowledge and understanding in the field of single-cell biology and spatial analysis.

  • Lecture time: Tuesday: 16:30:00 - 17:20:00 ET
  • Lab time: Thursday: 15:30:00 - 17:20:00 ET
  • Domain: Manufacturing
  • Keywords: Automation, Chemicals, Corrosion, Data Analytics, Materials, Pharmaceutical
  • Tools: Data Analytics, Power BI, Python, UX, Web-scraping
  • Citizenship: Open to all students
Summary

This project will work with Evonik Engineers to create a dynamic program that will generate Materials of Construction Matrices to determine compatibility using corrosion rates, temperatures, and concentrations.

  • Lecture time: Tuesday: 15:30:00 - 16:20:00 ET
  • Lab time: Thursday: 15:30:00 - 17:20:00 ET
  • Domain: Pharmaceutical
  • Keywords: AI, Cheminformatics, Computational Chemistry, Drug Research and Development, Ligand Receptor Docking, Machine Learning, Pharmaceutical
  • Tools: AWS, Docker, Python, Schrödinger
  • Citizenship: U.S. citizens and permanent residents preferred
Summary

The project focuses on developing drug candidates using cutting edge AI tools for computational chemistry and drug development. The project provides one data stream necessary in creating the multimodal AI platform that Aromarc is building.

  • Lecture time: Thursday: 13:30:00 - 14:20:00 ET
  • Lab time: Tuesday: 13:30:00 - 15:20:00 ET
  • Domain: Agriculture
  • Keywords: Agriculture, Market Development
  • Tools: Data Exploration, Feature Engineering, Machine Learning, Python, Time Series Data Analysis
  • Citizenship: Open to all students
Summary

In the ever-evolving world of agri-business, understanding market trends and predicting future prices is crucial for strategic decision-making. The project aims to combine various market indicators to identify trends and forecast prices in key market areas.

BASF - Market Models
Open for Registration NDMN/IDM
  • Lecture time: Friday: 09:30:00 - 10:20:00 ET
  • Lab time: Monday: 09:30:00 - 11:20:00 ET
  • Domain: Agriculture
  • Keywords: Agriculture, Market Development, Market Trends
  • Tools: Data Exploration, Feature Engineering, Machine Learning, Python, Time Series Data Analysis
  • Citizenship: Open to all students
Summary

In order to better understand how to deliver solutions for our customers we wish to better understand the market dynamics a grower faces. Specifically, within key market segments. Tools for evaluation will be at the student's discretion.

  • Lecture time: Monday: 09:30:00 - 10:20:00 ET
  • Lab time: Friday: 09:30:00 - 11:20:00 ET
  • Domain: Agriculture
  • Keywords: Agriculture, Climate Change, Crop Yield, Public data access, Weather Data
  • Tools: Linux, Public Data Access, Python, R, Spatio-temporal analysis, Statistics
  • Citizenship: Open to all students
Summary

Improve our understanding of potential climate change effects on business operations and agriculture in general.