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The Data Mine - Physics
In this cohort of The Data Mine, students will experience an active research environment targeted at the analysis of large data sets from particle physics experiments. Students will be introduced to and employ a variety of tools and programming languages. Prior research topics have included investigations of elusive phenomena such as quantum entanglement and the Higgs Boson using Machine Learning techniques, Quantum Computing, time-dependent AstroPhysics using LSST telescope data, or the search for dark matter particles using the XENON experiment in Italy, just to name a few.
If your application is accepted into this learning community, you will be placed onto one of the below teams:
Prof. Andreas Jung: High-energy physics, CMS experiment on the Large Hadron Collider in Switzerland, including investigations of the newly-discovered Higgs boson (http://www.physics.purdue.edu/jung/)
Prof. Andreas Jung: Quantom Computing Applications (http://www.physics.purdue.edu/jung/)
Prof. Danny Milisavljevic: Supernovae Explosions, Astronomy (http://www.physics.purdue.edu/milisavljevic/)
Additional information can be found at datamine.purdue.edu/physics.
Eligibility
Open to any undergraduate student enrolled in the College of Science or College of Engineering with an interest in the Data Sciences
Residential Component
- Students will indicate interest in learning communities (LCs) and apply during their housing application process. Students will be informed of the status of their LC application in mid-May.
- Roommate selection will occur after LC decisions have been made. If admitted to an LC, students will need to match with other student(s) admitted to the same LC. If not selected for an LC, the student's original housing preferences will be considered during general housing placement and the student will be able to roommate match with other general housing students.
- The location of learning community housing will be determined based on the incoming class size and the needs of the learning community.
- For specific question regarding learning communities, email learningcommunities@purdue.edu.
- For housing questions, visit (https://www.housing.purdue.edu/) or email housing@purdue.edu.
Duration
Full Academic Year
Associated Courses and Information
Fall
- PHYS 32300 (3 credits) Research with Big Data I
- TDM 10100 (1 credit) The Data Mine Seminar I
Spring
- PHYS 32400 (3 credits) Research in Big Data II
- TDM 10200 (1 credit) The Data Mine Seminar II
Additional information can be found at datamine.purdue.edu/physics.
Every fee eligible student involved with a learning community (LC) at Purdue will be assessed the $200 LC fee to their university account. These funds support activities and travel, signature LC events, housing assignments and/or course registration and instructor stipends.
Events and Activities Included:
- Weekly dinners with Data Mine LC participants
- Faculty and TA office hours in Hillenbrand
- Seminars by visiting speakers, including practicing data scientists
- Social gatherings with Data Mine LC members
- Meals with campus and community leaders
- Game / recreation nights
- Career and graduate school panels
- Hackathons / data competitions
- Professional development activities
- Tour of Purdue's computational facilities
Information above is subject to change. If you are placed in the LC, the associated courses will be on your schedule prior to you registering for the rest of your courses.
