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]
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.
- 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)