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| \title{ | |
| Saad Mufti | |
| } | |
| Alpharetta, GA 30005 | (508)-361-8811 | smufti3@gatech.edu | U.S. Citizen | |
| linkedin.com/in/saad-mufti-662b2918b | github.com/Saad-Mufti | stackoverflow.com/users/13351293 | |
| \section*{Objective} | |
| Computer fanatic with an appetite for hard problems. Highly adaptive, stress tolerant. Diverse background in embedded/web/cloud software development, ML applications. Interest in silicon engineering, RTL design, reinforcement learning. | |
| \section*{Education} | |
| Georgia Institute of Technology | Atlanta, GA | |
| Bachelor of Science in Computer Engineering, GPA 4.0 | |
| Worcester Polytechnic Institute | Worcester, MA | |
| Transfer with 90 Credit Hours, GPA 4.0 / 4.0 | |
| Aug 2022 - Present | |
| Expected Graduation (After MS): May 2025 | |
| Aug 2021 - Jun 2022 | |
| \section*{Skills} | |
| Programming: Python, C/C++, SystemVerilog/Verilog/VHDL, Tcl, JavaScript/Node.js, MATLAB, Java, SQL, Swift, Kotlin | |
| Platforms: RISC-V, Intel x86, AWS (EC2, Load Balancing), GCP (Cloud Run, Firebase, Cloud Functions, App Engine) | |
| Hardware: Nvidia Jetson, Raspberry Pi, ARM mbed microcontroller, FPGAs, oscilloscope, logic analyzer, TI MSP430, Arduino | |
| Software: Vivado, Altera Quartus, PyTorch/Tensorflow, Synopsys VCS/DVE, Docker, Cadence Virtuoso, Android Studio, Git, Flask | |
| \section*{Experience} | |
| Georgia Tech | Atlanta, GA | |
| RISC-V Processor Design + Tapeout | |
| Aug 2023 - May 2024 | |
| - Design and verification of an SoC including RISC-V processor, UART, SPI, CORDIC modules, using $65 \mathrm{~nm}$ TSMC PDK. | |
| - Theory, design, verification, test of fabricated synchronous CMOS digital circuit. Using synthesis, autoplace and route (SAPR) as industry standard tools. | |
| Tektronix Inc. | Beaverton, OR | |
| Jun - Aug 2023 | |
| Applications Engineering Intern | |
| - Researched and validated a framework (using mmWave FMCW + CNNs) for federated learning on beam prediction using low power devices (Nvidia Jetson) for test + measurement. | |
| - Optimized training routine (in PyTorch) to accommodate resource constrained devices, enabling inspection of model performance relating to different layer types, using GPU acceleration (CUDA + TensorRT). | |
| - Identified possible solutions to improve model accuracy and performance, increasing model metrics by $5-10 \%$ with $20 \%$ smaller memory footprint. | |
| - Helped pitch solution for object tracking using Bispectral NNs (Sanborn, 2023), flexible replacement over conventional FFT. | |
| Yousefi Lab @ WPI | Worcester, MA | |
| Reinforcement Learning and Data Pipeline Researcher | |
| Jun-Aug 2022 | |
| - Researched and validated a reinforcement learning model that fit design requirements, assisted in its development using TensorFlow, producing a proof-of-concept. | |
| - Orchestrated development of a data pipeline using GCP tools (Pub/Sub, Dataflow, BigQuery, Vertex Al) to ingest, preprocess, and store neural data for training and running inference on a developed RL model, demonstrating scalability. | |
| Shoptaki Inc. I New York City, NY (Remote) | |
| Aug 2021 - Aug 2022 | |
| Fullstack Engineer (Began as SWE Intern) | |
| - Led full-stack (frontend + backend) development of a demo website for newcomers in data science, using ReactJS, Flask, Express, and Arango DB. | |
| - Implemented CD pipelines in various development workflows using GitHub Actions and GCP Cloud Run/Build, reducing errors in manual deployment to $<5 \%$ of deployments. | |
| - Assisted cloud migration of various AWS services to GCP with minimal impact on service or user experience. | |
| \section*{Relevant Coursework} | |
| Data Structures + Analysis of Algorithms: Implementing and evaluating time complexity of arrays, Binary Search Trees (BSTs), Linked Lists, stacks, graph algorithms, searching/sorting, Dynamic programming, NP-Completeness, Linear Programming, Cryptography. Machine Learning (WPI, Graduate Level): Markov Chains, Maximum Likelihood Estimation, Graphical Models, Gaussian Processes, Neural Networks, Reinforcement Learning, and building a deep mathematical foundation to understand them. |