Welcome to my website!
My name is Sergei. I am a doctoral student, pursuing a Ph.D. in Electrical and Computer Engineering (ECE) at the University of Michigan, Ann Arbor. My current research interests lie in computer system optimizations for machine learning and machine vision applications. I am supervised by Dr. Robert Dick.
Previously, I worked in the fields of Computer Architecture and Advanced Sensors at Western Michigan University. I also have extensive experience as a tutor of college-level mathematics, computer science, and physics.
My main hobbies include electronics, microcontrollers, programming, foreign languages/cultures, and music.
I play acoustic and electric guitars, electric bass, autoharp, Arabic oud and ney.
Aside from projects listed below, my contributions to the free software community include work on the D programming language runtime and the FreeBSD operating system.
I am an advisor for the Michigan Kappa (MI K) Chapter of Tau Beta Pi and a member of the West Michigan Alumni Chapter (WMIAC).
I am also one of the founders and maintainers of DarknSpace.
Feel free to contact me if you have any questions or feedback.
A Novel Haptic System with Advanced Force Sensing Capabilities for Soft-Robotic Applications | FLEPS 2023 | S. Akhmatdinov; H. Dogdu; M. Haley; M. Panahi; A. J. Hanson; S. Masihi; A. H. Adineh; V. Palaniappan; D. Maddipatla; M. Z. Atashbar
Tele-operation has seen considerable use in aerospace and medical applications. However, contemporary tele-robotic systems rely exclusively on visual feedback. We propose a haptic feedback system that would allow operators to receive touch-based feedback, increasing their control and dexterity. To address the flexibility and range issues found in modern capacitive touch sensors, we design a custom multi-layered touch sensor with cone and porous structure.
Accurate Performance and Power Prediction for FPGAs Using Machine Learning | FCCM 2022 | Lina Sawalha; Tawfiq Abuaita; Martin Cowley; Sergei Akhmatdinov; Adam Dubs
Calculating power consumption, execution time, and resource utilization for FPGA designs created using High Level Synthesis (HLS) tools requires a complete place & route procedure, which can take weeks or even months for some commercial designs. We propose a fast, accurate, and generalizable machine learning model to predict these design characteristics, bypassing the lengthy setup time required.
Please find my FLOSS projects on Github.
Featured projects:
