Machine Learning, C++, optimization

Devam Shah

Chicago 630-***-**** *******@********.*** linkedin.com/in/devams

Education

University of Illinois Urbana Champaign Aug. 2022 - May 2026 BS in Mathematics and Computer Science GPA 3.84/4.0 Experience

Argonne May 2024 – May 2025

Mathematics and Computational Sciences (MCS) Researcher Lemont, IL

• Reduced the error rate for spectral analysis by using the Nystr om method, solving Fredholm equations, and Slepian sequences to find solutions for the 3-dimensional Delta Pair Differential Functions

• Cut run time in half by using multithreading and parallel programming tools Google February 2024 – May 2024

ExploreCSR Intern Chicago, IL

• Analyzed error rates in Google’s quantum computer systems using Cirq

• Applied Variational Quantum Eigensolvers to solve the max-cut problem

• Used Machine Learning algorithms to approximate solutions to NP-Hard problems for Gurobi FermiLab August 2023 - February 2024

Research Intern Batavia, IL

• Achieved a 13% increase in qubit swapping efficiency as well as reduced error during logical qubit communication by simulating a set of surface codes

• Developed Quantum Error Correction codes using C++ for simultaneous qubit swapping in a square lattice to analyze communication methods between logical qubits CERN Jan 2023 - Jan 2024

HEP Physics Intern Geneva Switzerland

• Analyzed Higgs boson behavior by studying Monte Carlo simulations and signal kinematics using CMS run 2 to perform data acquisition and analysis

• Optimized and improved the testing of semiconductor pixel tracking detectors for the high-luminosity LHC upgrade by using C++ to automate data processing, analysis, and visualization tasks Projects

Tong-Zhang-ML Lab Jan 2025-Present

Senior Thesis: Adaptive Acceleration for optimizing AdaGrad for stochiastic learning UIUC UIUC SIGRobotics Oct. 2024 – Present

• Developing and implementing a ROS2 Humble-based auto-navigation system for TurtleBot3 to fetch coffee in the UIUC ACM room, leveraging Hugging Face models for perception and decision-making. Investor Sentiment Analysis Aug. 2024 – Jan. 2025

• Developed a tool for evaluating industry trends and projecting market movements based on investor sentiment. Awards

IMC Trading Competition Aug. 2023 – Present

• Held 3rd place by earning the most profit in the competition by devising and executing optimized short positions on the market as we received information about new stocks. Courses

Courses: Machine Learning, Artificial Intelligence, Graph Threory, Algorithms, Systems Programming, Data Structures, Numerical Methods, Non-Linear Programming, Optimization for Stochiastic Learning Skills & Languages

Skills: NumPy, NeXpy, ROOT, Pandas, PyTorch, Tensor Flow Languages: Python, Julia, C++, Java, Verilog, Assembly, Qiskit

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