I'm a first year Computer Science Ph.D. student at Stanford University advised by Christopher Ré. My research interests generally include computer systems and machine learning. In May 2017, I graduated from Cornell University with a double major in Electrical & Computer Engineering and Computer Science. At Cornell, I had the opportunity to do research in computer architecture with Christopher Batten.


  • High-Accuracy Low-Precision Training (HALP). In collaboration with Chris De Sa and others, I worked on HALP, a gradient descent variant which is able to theoretically converge to highly accurate solutions while using low-precision fixed-point arithmetic. We empirically verified HALP on linear regression and logistic regression problems, as well as LSTMs and CNNs. Code coming soon. [blog] [pdf] [slides]

Past Projects

  • Proxy Kernel for RISC-V Processor. In Christopher Batten's research group, I extended a RISC-V pipelined processor to support system calls via a proxy kernel. The work was done in PyMTL (Python-based hardware modeling framework) and C.

  • Neural Network Accelerator. As a final project for ECE 5745 Complex Digital ASIC Design, I built an accelerator to classify handwritten digits. The design was pushed through the ASIC flow using Synopsys and evaluated on power, performance, and area.


  • C. De Sa, M. Leszczynski, J. Zhang, A. Marzoev, C. R. Aberger, K. Olukotun, and C. Ré. High-Accuracy Low-Precision Training. arXiv Preprint. Mar. 2018.

  • H. Freeman, M. Leszczynski, and G. Ratnaparkhi. iOS Controlled, Low Cost, Low Power Massage Vest Driven by PIC32. Circuit Cellar. Jan. 2018.

  • M. Leszczynski and J. Moreira. Machine Solver for Physics Word Problems. NIPS Intuitive Physics Workshop. Dec. 2016.

Teaching Experience

  • Cornell University
    • ECE 4750: Computer Architecture, Undergraduate Teaching Assistant (Fall 2016)
    • CS 1110: Introduction to Python, Consultant (Fall 2014, Spring 2015, Fall 2015, Spring 2016)