Eeshwar Krishnan
Computer Science and Robotics Student
I am a Robotics Engineer
Pioneering the Future at University of Michigan
Jump to ExperienceAbout Me
As a senior at the University of Michigan, I'm on a mission to revolutionize the intersection of AI and Robotics. My dual degree in Computer Science and Robotics Engineering fuels my passion for creating cutting-edge solutions to real-world challenges. With a background in FIRST Robotics and a track record of innovation, I'm dedicated to pushing the boundaries of what's possible in technology.
Skills
Experience Timeline
Redux Robotics
Founder & CEO
May 2023 - Present | Exton, PA
- Pioneered development of next-gen robotics sensors with CAN network integration
- Architected comprehensive firmware and API ecosystem for seamless user interaction
- Innovated QC processes through automation, drastically improving product reliability
- Optimized logistics pipeline, slashing average shipping time by 60%
KBR Consulting
AI Research Intern
May 2024 - August 2024 | Exton, PA
- Developed a cutting-edge local large language model for advanced records management
- Engineered an AI-powered solution resulting in significant time savings across departments
- Collaborated directly with department heads to optimize AI integration
- Presented groundbreaking research findings to KBR leadership, influencing company-wide AI strategy
ANSYS Government Initiatives
Software Engineering Innovator
May 2023 - Aug 2023 | Exton, PA
- Engineered high-performance benchmarking infrastructure for mission-critical codebases
- Implemented advanced multithreading architecture, significantly boosting system efficiency
UM::Autonomy
Lead Embedded Systems Architect
Sep 2022 - Present | Ann Arbor, MI
- Spearheaded development of robust electrical systems for autonomous boats
- Designed cutting-edge HALs and firmware for next-gen control systems
- Conducted rigorous real-world testing, ensuring peak performance in extreme conditions
Research Publication
A Novel Framework to Predict Protein Folding Using a Weighted Approach
This research introduces an innovative framework for predicting protein folding, combining various machine learning approaches through a principled weighted voting method. The study demonstrates a significant improvement in the accuracy of protein fold type predictions compared to individual methods.
Key Contributions:
- Developed a weighted voting method to combine results from different machine learning approaches
- Improved accuracy of protein fold measurements over individual methods
- Contributed to the field of bioinformatics and structural biology
Academic Pursuit
University of Michigan
B.S. in Computer Science
B.S. in Robotics Engineering
Expected Graduation: May 2026 | Ann Arbor, MI