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Meet Your Mentor!
We sat down with our 2025 Anvil REU Mentors to discuss their role at RCAC, what their REU students will be working on, and much, much more. Keep reading below to learn more about your mentor.
Bithi De
Please introduce yourself
My name is Bithi De. I am a Senior Computational Climate Scientist at RCAC.
What do you do?
Senior Computational Climate Scientist
Why would I come to you for help?
Guidance on Linux/HPC and understanding the big picture of your project goal
What's one professional skill you're currently working on?
Learning how AI-driven climate modeling
What's your go-to productivity trick?
Maintain a personal note and set up priority
What behavior or personality trait do you most attribute your success to, and why?
Open to learning and persistence to finish
What led you to this career?
Openness to merge my research and technical background to do something new
What are you currently excited about in your job?
I'm learning new skills on HPC which seems amazing!
What's the biggest misconception people have about your position?
How long have you been on the Anvil team/at RCAC?
Little over 6 months
Education, publications, engagement (groups part of), etc...
PhD, Purdue University
Why did you decide to become a mentor for the REU program?
It's always exciting to share knowledge and helping the next generations of bright minds
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What do you hope the REU gets out of the program?
Experience on solving a new scientific question, hands on technical skills and understanding on how to do independent research
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How can this help them in their college career?
Applying skills and concepts in solving problems is essential for future career path
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How can this help them in their professional career?
Experience and confidence
What value do you get from being a mentor for the REU program?
Looking forward to training the next generations of researchers and learning with them
What will your REU students be working on specifically?
You will be working with Pelican platform on Anvil supercomputer to make a pipeline for a streamline use of data repository, regardless of their origin. This would be an important addition for dealing with big data, helping storage issues, as well as huge improvements in training AI/ML models. You will contribute technical knowledge, such as coding, in building the data federation with a final goal to make a user-friendly, high-performance data access interface that enables a faster and reliable research pipeline across multiple scientific disciplines.