Ananth Madan

• Modernized course CI/CD pipeline through database consolidation, API refactoring, and Docker conainerization • Deployed and managed critical course infrastructure with Docker, Azure Entra, Nginx Systemd services, and Flask • Ensured assignment functionality by maintaining glibc hooks, runtime environments, and custom system libraries • Elevated auto-grading for 600+ weekly runs by remedying critical flaws and building a CLI for auto-configuration • Clarifying complex systems topics for 300+ students by redesigning assignments and introducing pre-lab tasks

• Simplified heterogeneous dataflow compilation by writing the WASM backend and runtime for the HPVM compiler • Enhanced HPVM-LLVM’s WASM backend to support GPUs through WebGPU and WGSL codegen via Binaryen • Pioneering single-target compilation for distributed, heterogeneous systems using ZMQ for WASM data transfer • Ensured reliable toolchain deployment and GPU management and utilization through custom Docker solutions

• Accelerated VAIML compiler debug flow by 2x through the implementation of novel BF16 onnx kernels • Automated error detection for the VAIML compiler using model simulation and operator bit-error analysis • Streamlined productivity through updating and automating simulation scripts and build utilities • Improved intern and team performance through the introduction of individual and group communication strategies

• Boosted compile error traceability by patching Pytorch JIT and Flexml compilers to emit accurate debug info • Streamlined operator traceability by creating a Flexml name-transformation standard for Pytorch compilation • Upgraded tracing support to align with industry pivot to TorchFX with a turnaround time of under two weeks

• Developed military-grade airline tracking software with OpenCV and deep learning trackers like GOTURN • Optimized performance for CPU-based thresholding methods through SIMD intrinsics and cache utilization • Enhanced real-time debugging by designing visual tools for live-video tracking and frame-by-frame replay

Developing an application for flood visualization and prediction using augmented reality in Unreal Engine and machine learning in Tensorflow Developed the model pipeline for auto-regressive sea-level rise prediction

Researched and documented Genetec camera systems and network issues Debugged and implemented cloud scripts in client’s application codebase Used machine learning to make codebase merges and changelog tracking faster and more efficient

Researching reinforcement learning (and NLP) strategies and optimization for stock portfolio management

Collected relevant land cover data via the GLOBE App Led a group project to web-scrape and classify data from CEO (one of 14 to present the entire SEES program) Developed a webscaper/API-interceptor to download land cover data from the CEO website Learned and discussed land cover and mosquito data with other student scientists and experts in the field Learned and participate discussions regarding space-faring and general NASA technology

Worked with younger students and helped them learn Math and Reading Helped more advanced students learn more advanced math topics, such as trigonometry and calculus Graded students’ materials and gave feedback on their work

GPA: 3.9/4.0

Freshman GPA: 4.0/4.0