Highly accomplished Computer/Electrical Engineer with over 10 years experience delivering advanced connectivity, high-precision positioning, RF systems, embedded software, and hardware design solutions. Demonstrated track record in autonomous vehicle (AV) technology, cybersecurity, network security, and IoT integration, backed by a 4.0 M.S.E. GPA and dual B.S.E. degrees.
Possess in-depth expertise in 5G/mmWave, RTK/NRTK GNSS, MEC, Edge AI, SDR, and DevSecOps. Adept at leading complex R&D projects, optimizing embedded systems, and managing multiple large-scale deployments in fast-paced, deadline-driven environments. Excellent communication, analytical, and leadership abilities, reinforced by hands-on experience in machine learning, blockchain, and cloud technologies. Passionate about driving innovation, scalable solutions, and secure vehicular communications that power the next generation of mobility.
Docker, Kubernetes, AWS, Git, TensorFlow, PyTorch, Qt, QNX, Altium Designer, Cadence Allegro, MATLAB, GNU Radio / SDR (Software-Defined Radio), Wireshark, OpenGL, Eagle CAD, Jenkins, SolidWorks, Simulink
FPGA (Spartan 3E), ARM Cortex-M3, SoC Integration, IoT Devices, Raspberry Pi, Arduino, PCB Design (2-8 layers, blind/buried vias), Power Management, Signal Integrity Analysis, Thermal Management, SDR Devices, MPC57xx Calypso/Bolero
Real-Time Operating Systems (RTOS), Embedded Linux, IoT Protocols (MQTT, CoAP), Cross-Platform Development, Low-Level Coding, SoC Testing, Firmware Development, Board Support Level (BSL) Integration, CAN Bus, Device Drivers
Python, C, C++, VHDL, Rust, JavaScript, TypeScript, SQL, Assembly, MATLAB, PHP, Swift
5G/mmWave, SDN (Software-Defined Networking), Network Virtualization, Network Security Protocols (TLS, SSL), TCP/IP, IPv4, IPv6, Cellular/Wi-Fi/Bluetooth/RF Connectivity, VPN, Advanced Routing Techniques, MEC (Mobile Edge Computing), NTRIP/RTCM, Network Automation
Machine Learning, Artificial Intelligence, DevSecOps, High-Level Software Development, ASIC Design, VLSI CAD, Relative GNSS (Network-RTK, PPP-RTK, DGNSS, Sapcorda), Signal Processing, EMC Testing, Cyber-Physical Systems, Blockchain, Agile/Scrum Methodologies
Rackham Graduate School, University of Michigan
September 2016 – December 2017 | GPA: 4.0
University of Michigan – Dearborn, MI
September 2012 – April 2016 | GPA: 3.94
Senior Computer/Electrical Engineer
Dearborn, MI & Palo Alto, CA
May 2014 – June 2023
Network Management Intern
Troy, MI
April 2013 – September 2013
Computer Engineering Intern
Novi, MI
March 2012 – June 2012
Description: Developed an innovative MATLAB‑based system employing the Viola‑Jones algorithm, KLT tracking, and Eulerian Magnification to detect and amplify subtle heart rate signals in real time.
Technologies: MATLAB, Computer Vision, Deep Learning, Real‑Time Processing, Viola‑Jones Algorithm, KLT Tracker.
Achievements: Achieved high-fidelity BPM extraction under challenging low-light and noisy conditions by integrating advanced signal processing techniques.
Description: Demonstrated critical cybersecurity vulnerabilities by exploiting rolling code encryption through innovative HackRF SDR and GNU Radio replay attack techniques.
Technologies: HackRF SDR, RTL‑SDR, GNU Radio (GRC), DVB‑T USB Dongle, Signal Filtering, Cryptanalysis.
Achievements: Successfully unlocked a 2011 Sequoia and a 2016 Fusion, highlighting the pressing need for enhanced vehicular security measures.
Description: Engineered a mathematically unbreakable Vernam Cipher system using an ARM M3 Teensy and Spartan 3E FPGA for hardware-accelerated encryption, featuring a custom messaging protocol for secure data transmission.
Technologies: ARM M3 Teensy, Spartan 3E FPGA, VHDL, Cryptography, Secure Protocol Design.
Achievements: Delivered a robust encryption solution that ensures secure communication across diverse hardware architectures.
Description: Designed, fabricated, and rigorously tested an 8‑bit ALU using advanced VLSI CAD and a 50‑micron photolithography process in the Lurie Nanofabrication Lab, successfully validating eight distinct ALU functions on dual 8‑bit operands.
Technologies: VLSI CAD, ASIC Design, Photolithography, Spartan 3E FPGA, Digital Design.
Achievements: Demonstrated superior expertise in digital circuit design and ASIC development, ensuring efficient hardware implementation.
Description: Developed a robust HMM‑based speech recognition system in MATLAB, utilizing the Baum‑Welch algorithm and advanced feature extraction techniques to achieve high classification accuracy in low-resource environments.
Technologies: MATLAB, Hidden Markov Models, Natural Language Processing (NLP), Feature Engineering.
Achievements: Reinforced machine learning capabilities and signal processing expertise to deliver a highly accurate speech recognition solution.
Description: Constructed a cutting-edge digital synthesizer/MIDI controller utilizing laser diodes and the DMX512 protocol to deliver professional DJ lighting effects with real-time audio-visual integration.
Technologies: Laser Diodes, DMX512, MIDI Protocol, Digital Signal Processing, Real‑Time Control.
Achievements: Showcased multifaceted expertise in electronics and creative system design while ensuring seamless integration of audio and visual controls.
Description: Programmed an 8‑bit graphics card and developed an interactive elevator simulation on a Spartan 3E FPGA using VHDL; incorporated VGA output and hardware switches for real-time animation and control.
Technologies: VHDL, Spartan 3E FPGA, VGA Interface, Real‑Time Processing.
Achievements: Demonstrated advanced hardware/software integration and real-time system design, extending expertise in FPGA development.
Description: Enhanced the Xv6 Unix kernel by integrating a sophisticated Multilevel Feedback Queue (MLFQ) scheduler to optimize system performance, concurrency, and resource management.
Technologies: C, Xv6 Kernel, QEMU, MLFQ Scheduling.
Achievements: Demonstrated in-depth kernel-level programming skills and substantial improvements to OS scheduling efficiency.
Date Issued: January 7, 2025
Summary: Managing vehicle application usage of computing resources is provided. A request for computing resources of a communications network is received from a vehicle application installed to a vehicle. An access point name (APN) is assigned to the vehicle application based on an application identifier corresponding to the vehicle application by accessing stored APN information including a mapping of application identifiers to corresponding APNs. The computing resources are accessed by the vehicle application connecting to the communications network using the APN.
Date Issued: December 31, 2024
Summary: A system includes a plurality of vehicles and at least one first processor in a first vehicle and at least one second processor in each other of the plurality of vehicles. The first vehicle wirelessly receives remote driving commands, from a remote computing system, instructing control of the first vehicle and executes the remote driving commands to control the first vehicle in accordance with the remote driving commands. The first vehicle wirelessly broadcasts the remote driving commands, including a location of the first vehicle where a given of the driving commands was executed. The second vehicle wirelessly receives the broadcast remote driving commands, stores the received remote driving commands in sequence, and executes the given of the driving commands when a location of the second vehicle corresponds to the location of the first vehicle where the given of the driving commands was executed.
Date: December 12, 2024
Summary: Systems and methods for implementing edge computing assisted vehicle alerts. A multi-access edge computing (MEC) server may be configured to obtain a plurality of messages from one or more vehicles, determine formation of a traffic queue based on the plurality of messages, determine a plurality of zone-based speed limits, determine when a vehicle approaching the traffic queue may be traveling faster than desired based on its location relative to an applicable zone-based speed limit, and generate one or more alerts.
Date: October 3, 2024
Summary: An ego user equipment (UE) receives absolute position and relative distance information for m other UEs at a timestamp and a fingerprint location based on signal strength information between the UE and n different femtocells. The ego UE determines, using round trip time between the ego UE and the other UEs, first distances of the ego UE from each of the other UEs. The ego UE determines, using the absolute position of the ego UE via fingerprinting information and absolute position of other UEs received through device-to-device (D2D) communication, second distances of the ego UE to each of the other UEs. The ego UE utilizes the fingerprint location as a current location of the UE responsive to a predefined threshold subset of discrepancies between the first distances and the second distances being within a predefined distance tolerance threshold.
Date Issued: July 16, 2024
Summary: An autonomous vehicle may determine that it has an amount of power remaining projected to be needed to reach a recharging point by autonomously traveling with predefined systems disabled. The vehicle may disable the predefined systems and travel towards the recharging point. Subsequent to the disabling, the autonomous vehicle may determine that it no longer has the amount of power remaining projected to be needed to reach the recharging point. The vehicle may then communicate with a server and request assistance. The vehicle may then travel to an instructed rendezvous point with a second autonomous vehicle, the rendezvous received from the server. The two vehicles may then communicate to allow the first vehicle to leverage a capability of the second autonomous vehicle, responsive to the first and second autonomous vehicles being within communication range, allowing the first vehicle to reach the recharging point via the leveraging.
Date Issued: May 21, 2024
Summary: Monitoring of a vehicle is provided. A plurality of video feeds captured from cameras of the vehicle are received, over a network from a vehicle, each of the plurality of video feeds including a plurality frames, each of the frames of each of the video feeds being assigned a sequence number that increases for each successive frame. The sequence numbers are analyzed to identify missing frames, delayed frames, or stale frames. The plurality of video feeds is displayed, to one or more monitors, the sequence numbers corresponding to the displayed frames, and for each of the plurality of video feeds, indications of whether any missed frames, delayed frames, or stale frames were identified.
Date Issued: January 23, 2024
Summary: The disclosure generally pertains to systems and methods for providing targeted content to users. In an example method, audio data can be received from a device. Sensor data associated with the device may also be received, and the sensor data may include location data. Upon receiving the audio data, an intent associated with the audio data can be determined. At least one of a product, service, or entity may be determined based on the intent. Content may then be determined based on the sensor data and at least one of the product, service, or entity. The content may be associated with a vehicle. An indication of the content can then be sent to the device.
Date: November 16, 2023
Summary: Managing vehicle application usage of computing resources is provided. A request for computing resources of a communications network is received from a vehicle application installed to a vehicle. An access point name (APN) is assigned to the vehicle application based on an application identifier corresponding to the vehicle application by accessing stored APN information including a mapping of application identifiers to corresponding APNs. The computing resources are accessed by the vehicle application connecting to the communications network using the APN.
Date Issued: November 7, 2023
Summary: Drone-based vehicle connectivity systems and methods are disclosed herein. A method can include determining a loss of network connectivity by any of a vehicle and/or a drone associated with the vehicle, receiving an emergency message from the vehicle, launching the drone to navigate to a location where network connectivity exists, and transmitting the emergency message to a service provider when a connection to a network is established.
Date Issued: September 5, 2023
Summary: A vehicle receives a first portion of content via ATSC broadcast, generates a random nonce, responsive to receiving the content, and sends the nonce and a request for content verification to a remote server. The vehicle receives a message from the remote server indicating whether the first portion of content is likely valid, the message including a second portion of content and a hash value when the content is likely valid. The vehicle then calculates a second hash value, using the random nonce and the first portion of content. The vehicle compares the second hash value to the first hash value, and responsive to the second hash value matching the first hash value, combines the second portion of content and the first portion of content to create combined content. The vehicle then uses a security strategy to convert the combined content into utilizable content, and utilizes the content.
Date: July 27, 2023
Summary: Location-aware content recommendations.
Date: June 22, 2023
Summary: Broadcasts remote-driving commands.
Date: May 18, 2023
Summary: Detects dropped/stale frames.
Date: April 13, 2023
Summary: AVs share capabilities for recharging.
Date Issued: January 10, 2023
Summary: A system includes a stationary infrastructure element including a camera mounted to the infrastructure element and an infrastructure server. The infrastructure server includes a processor and a memory, the memory storing instructions executable by the processor to receive a request from a movable vehicle, the request identifying a data anomaly including at least one of (1) a sensor of the vehicle collecting data below a confidence threshold or (2) a geographic location outside a geographic database of the vehicle, to actuate the camera to collect image data of one of the vehicle or the geographic location, to identify geo-coordinates of the vehicle or the geographic location based on identified pixels in the image data including the vehicle or the geographic location, and to provide the geo-coordinates to the vehicle to address the data anomaly.
Date: November 24, 2022
Summary: Launches drones for emergency communications.
Date: October 6, 2022
Summary: Random-nonce-based broadcast verification.
Date: July 19, 2022
Summary: A point-to-multipoint retransmit of the parking availability data is performed with a predefined update period. A quantity of unicast requests for parking availability is received via one or more cellular towers. If the quantity of unicast requests received within the predefined update period exceeds a ratio of point-to-multipoint bandwidth to unicast messaging bandwidth of the one or more cellular towers, a transition is made to use of the point-to-multipoint messaging for sending the parking availability data via the one or more cellular towers; and otherwise, a transition is made to use of the unicast messaging for sending the parking availability data via the one or more cellular towers.
Date: June 9, 2022
Summary: A system determines at least one driving style, reflecting driving behavior for a driver of a vehicle and based on inputs from a plurality of vehicle systems. The system receives a plurality of wireless signals from elements within a wireless communication range, each signal indicating the behavior of one or more drivers of other vehicles within a predefined distance of the vehicle. Further, the system determines, based on the wireless signals, whether one or more drivers are exhibiting behavior comparable to the driver of the vehicle based on predefined comparison thresholds and, responsive to determining that one or more drivers are exhibiting comparable behavior, provides guidance as to how to maneuver the vehicle to reach the vehicles driven by the one or more drivers exhibiting comparable behavior.
Date Issued: December 28, 2021
Summary: A computing device includes a camera configured to capture images of an area of a road, the area defining a geofence; and a processor, configured to responsive to detecting a traffic density within the geofence exceeding a predefined threshold, wirelessly broadcast a directional message within the geofence to request vehicles located within the geofence to temporarily disable individual messaging services having low priorities identified in the directional message, analyze vehicle traffic using images captured by the camera to detect a predefined traffic situation, responsive to detecting the predefined traffic situation initiated by one of the vehicles, generate a safety message reflecting the traffic situation, and broadcast the safety message to vehicles within the geofence.
Date: December 23, 2021
Summary: Infrastructure cameras assist AV geopositioning.
Date: September 2, 2021
Summary: Instructs low-priority services to pause.
Date Issued: January 21, 2020
Summary: Systems and methods for managing communications equipment of a vehicle. The vehicle includes a first modem and a second modem coupled to the first modem. Responsive to the second modem wireless receiving first data for the first modem when the vehicle is in an inactive state, the first modem is in an off state, and the second modem is in a low power state, the vehicle is configured to wake the first modem from the off state, and process the first data via the first modem.
Date Issued: May 28, 2019
Summary: Method and apparatus are disclosed for communicatively coupling mobile devices to wireless local area networks of vehicles. An example vehicle includes a communication module for a wireless local area network including an internal antenna and an external antenna. The example vehicle also includes an antenna adjuster to communicatively couple, in response to determining a mobile device is inside a vehicle cabin, the internal antenna to the mobile device and to communicatively couple, in response to determining the mobile device is outside of the vehicle cabin, the external antenna to the mobile device.
Date: January 3, 2019
Summary: Wakes primary modem from low-power state.
Date: July 26, 2018
Summary: Optimizes connectivity via internal/external antennas.
Date Issued: March 15, 2004
Summary: A rodent trap contains a hollow container with an open inlet and a cut in the bottom from the side of the rear wall, whereon a cross-beam, which is rotary around the horizontal axis, is placed, the one end of which lies on the bottom from the side of the inlet. At the end of the cross-beam from the side of the entry a weight is placed and the weight centre of the cross-beam is shifted from rotation axis thereof to the rear wall, which is made in such a manner as to allow turning outside and has a bait inside.
President, Secretary, Advisor (2014 – 2016). Led workshops, mentorships, service projects.
Vice President, Pledge Educator (2012 – 2016). Oversaw operations, events, leadership training.
Vice President, Treasurer, Trustee (2012 – 2016). Managed finances and events for engineering community.
Languages: English (Fluent), Ukrainian (Fluent), Russian (Fluent), German (5 years of study)
Interests: AI, Blockchain, Cyber-Physical Systems, GNSS Augmentation, High-Frequency RF Systems,
Advanced Robotics, Edge Compute, Smart City Infrastructure, Hiking, Mtn Biking