ECE Ph.D. Student
University of Michigan
District Director in District 7
MI-K ‘23 - WMIAC
Tau Beta Pi
Co-Founder and Admin
Darkn.Space
✉ sakhmatd -at- umich.edu
✉ sakhmatd -at- darkn.space (XMPP)
My name is Sergei. I am a doctoral student, pursuing a Ph.D. in Electrical and Computer Engineering (ECE) at the University of Michigan. I am advised by Robert Dick. My research interests lie on the intersection of embedded systems, Privacy-Enhancing Technologies (PETs), and AI/ML research. I am particularly interested in addressing the privacy challenges that would arise from ubiquity of AI-enabled wearable devices.
Previously, I did research 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.
I am a long-time supporter of software freedom and computing minimalism. Aside from my software projects, my contributions to the free software community include work on the D programming language runtime and the FreeBSD operating system.
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.
