Vlad Slyusar

Intro Contact

Welcome















































Introduction

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.


Vlad Slyusar

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.

Technical Skills

Software & Tools

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

Hardware

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

Embedded Systems

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

Programming Languages

Python, C, C++, VHDL, Rust, JavaScript, TypeScript, SQL, Assembly, MATLAB, PHP, Swift

Networking

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

Other Skills

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

Education

Master of Science in Computer Engineering

Rackham Graduate School, University of Michigan
September 2016 – December 2017 | GPA: 4.0

Bachelor of Science in Electrical Engineering

Bachelor of Science in Computer Engineering

Minor in Computer Science

University of Michigan – Dearborn, MI
September 2012 – April 2016 | GPA: 3.94

Relevant Courses

    Advanced Embedded Systems, Digital Signal Processing, Wireless Communication, Analog & Digital Communication Systems, Assembly Programming, Automatic Control Systems, Computer Hardware Organization & Design, Embedded System Design, Electromagnetic Compatibility, Microelectronics, Circuit Design, Control Systems, Data Structures & Algorithms, Database Management Systems, Electrical Materials & Devices, Introduction to VLSI, Operating Systems, Power Electronics, System Design & Microcontrollers,

Awards

  • James B. Angell Scholar: Earned exclusively A grades over 8 terms (covering two Engineering Degrees + a CIS minor).
  • Chancellor’s Scholarship Recipient: Awarded full tuition for exceptional academic performance (High School GPA > 4.0).

Work Experience

Ford Motor Company

Senior Computer/Electrical Engineer
Dearborn, MI & Palo Alto, CA
May 2014 – June 2023

Advanced Connectivity Research

  • Spearheaded multiple high-impact Proof-of-Concept (POC) initiatives for Cellular, WiFi, Bluetooth, RF connectivity, and IoT integration, elevating Ford’s autonomous vehicle (AV) communication infrastructure and ensuring robust, secure data transmission.
  • Developed proprietary methods for adaptive network access by dynamically selecting the optimal APN for remote computing demands, ensuring proper resource allocation [US12192868B2].
  • Conducted comprehensive testing on 5G/mmWave performance—including 28GHz frequencies and 5G NSA mode—to optimize advanced networking and routing protocols, thereby significantly enhancing network reliability and speed.
  • Managed and operated a dedicated private 5G mmWave cell network integrated with cutting-edge Mobile Edge Computing (MEC) and Edge AI, enabling seamless teleoperation and real-time control of autonomous vehicles [US12181869B2].
  • Implemented MEC-based alert processing algorithms to rapidly generate traffic alerts and mitigate radio congestion by pausing low-priority messaging [US11210943B2].
  • Collaborated with top cell tower equipment vendors to refine 4G/5G handover processes by meticulously optimizing RF thresholds and hysteresis parameters using QXDM diagnostic tools.
  • Developed and deployed a proprietary VPN server to secure modem-to-modem communication, facilitating controlled and secure connectivity for autonomous vehicles over public cellular networks.

High Precision Positioning Research

  • Conducted rigorous testing of multi-constellation, multi-frequency GNSS solutions to substantially enhance AV positioning and integrate infrastructure-assisted sensing for precise geolocation [US11551456B2].
  • Researched and implemented advanced Single State Real-Time Kinematic (SSR) and Observed State RTK (OSR) correction protocols over NTRIP/RTCM, significantly improving real-time positioning precision.
  • Proposed, kicked-off, and led a University Alliance project focused on partial integer ambiguity resolution for moving-base RTK in collaboration with Ohio State University (OSU) RF Department, advancing GNSS reliability.
  • Developed innovative urban canyon multipath mitigation techniques using non-directional, non-geodetic receivers with multi-frequency supercorrelation, enhancing positional reliability in complex urban settings.
  • Executed sophisticated GPS simulation, jamming, spoofing, and detection strategies using dual USRP X310 SDRs and proprietary third-party solutions to fortify AV positioning against malicious interference.
  • Integrated SLAM (Simultaneous Localization and Mapping) and sensor fusion techniques to further enhance the precision and robustness of autonomous navigation systems.

AV Fleet - Network Connectivity (Ford + ArgoAI)

  • Personally designed and managed the complete end-to-end network infrastructure pipeline for Ford's first autonomous vehicle fleet, integrating over 100 advanced Telematics Control Units (TCU—phone/modem) and 100 Enhanced Central Gateways (ECG—security gateways).
  • Discovered and implemented SW/HW/BSP-level hacks for CAN + Ethernet to integrate both modules (TCU+ECG designed in-house for future F150) as the primary pipeline for ArgoAI's AV hardware (Ford Fusion).
  • Personally conducted full-cycle hardware modifications, including precision soldering of 0201 components and rigorous component-level rework for CAN, Ethernet, and power subsystems on Ford TCU and ECG.
  • Delivered scalable, high-speed connectivity solutions that bolstered network performance and security, setting the foundation for Ford’s next-generation autonomous operations achieving >99% network reliability across deployments in five major U.S. cities

RF Systems & Advanced Antenna Research

  • Directed multi-institutional research collaborations in advanced antenna design, RF channel modeling, and printed antenna development with Ohio State University (OSU) and Michigan State University (MSU), yielding innovative connectivity solutions.
  • Integrated experimental flat antenna technology with future TCUs on live vehicles for high-impact demonstrations at the IDT Expo, showcasing enhanced Cellular and WiFi performance.
  • Implemented MIMO (Multiple Input Multiple Output) and beamforming techniques to substantially improve antenna performance, signal quality, and overall network capacity.

Embedded Software Development

  • Engineered robust cross-platform Qt applications for embedded devices running on QNX and Embedded Linux, delivering intuitive user interfaces and reliable functionality.
  • Developed low-level firmware for VMCU hardware—including MPC57xx Calypso/Bolero microcontrollers—using C and Assembly to ensure seamless hardware-software integration.
  • Created comprehensive testing software for SoC and peripheral boards, interfacing with protocols such as GPIO, I2C, UART, JTAG, SPI, CAN, and Ethernet to rigorously validate system performance.
  • Integrated Board Support Level (BSL) software for multi-vendor SoCs (NXP, Renesas, Qualcomm), streamlining development cycles and enhancing cross-platform compatibility.
  • Implemented advanced CI/CD pipelines and automated testing frameworks to enhance software reliability, reduce development time, and accelerate deployment cycles.
  • Implemented dynamic in-vehicle communications management systems that wake primary modems from low-power states and seamlessly couple mobile devices to the vehicle’s wireless network [US10542493B2].

Hardware Design & Development

  • Designed and fabricated next-generation VMCU and Ethernet boards for Ford Sync SoC platforms using Altium Designer, collaborating with Flextronics Ltd to optimize manufacturing and assembly processes.
  • Engineered custom breakout boards for ultra-high-density connectors by managing complete block diagrams, schematic layouts, and component lifecycles using Altium Vault and Eagle CAD.
  • Executed complex PCB designs (2-8 layers with blind/buried vias) and implemented advanced soldering techniques—including 0201 SMD soldering and precise BGA rework/reflow—to ensure high-quality hardware performance.
  • Successfully completed Masters-level EMC courses and WCCA training from AEi Systems, performing rigorous EMC lab testing to guarantee compliance with industry standards.
  • Conducted meticulous signal integrity analyses and implemented state-of-the-art thermal management strategies to maximize hardware reliability and longevity.

High-Level Software Engineering

  • Developed and maintained sophisticated text parser systems for detailed log file analysis with advanced graphical user interfaces (GUIs) in C++, significantly enhancing data analytics and real-time visualization.
  • Created cross-platform GPU/CPU benchmark applications leveraging OpenGL to deliver critical performance metrics and optimize embedded system functionality.
  • Engineered and rigorously tested Telematics Control Unit (TCU) software with AT command integration using C/C++, thereby improving vehicle connectivity and diagnostic capabilities.
  • Designed and implemented advanced data analytics tools and visualization frameworks to support high-level debugging, performance monitoring, and strategic decision-making.
  • Engineered advanced video streaming anomaly detection systems that analyze sequential frame data to identify missing or delayed frames and provide real-time diagnostic feedback [US11991346B2].
  • Implemented targeted content delivery mechanisms leveraging sensor and geolocation data to deliver personalized vehicle infotainment and context-aware information [US11880867B2].

RF Diagnostics

  • Utilized advanced Software-Defined Radio (SDR) systems and WiFi Pineapple diagnostic tools to isolate, analyze, and resolve RF interference issues in trailer backup camera systems, employing Wireshark and PCAP analysis to optimize network performance.

Ideation & Innovation

  • Pioneered diagnostic strategies enabling autonomous vehicles to detect power shortages and coordinate ride service assistance through rendezvous protocols [US12037021B2].
  • Developed drone-based connectivity solutions that deploy airborne relays to re-establish network access during emergencies [US11812355B2].
  • Pioneered secure broadcast delivery and verification techniques using randomized nonce generation and hash validation to ensure content integrity [US11750393B2].
  • Developed multicast-assisted parking lot management systems that dynamically switch between point-to-multipoint and unicast messaging to optimize data delivery and manage network load [US11395107B1].
  • Pioneered breakthrough initiatives including Secure Broadcast Delivery and Verification, Drone-based Vehicle Connectivity Systems, and Edge Computing Aided Radio Congestion Mitigation, driving next-generation connectivity and cybersecurity advancements.




Valeo Inc.

Network Management Intern
Troy, MI
April 2013 – September 2013



  • Managed comprehensive server backup protocols and swiftly resolved complex technical issues on both Windows and Linux platforms, ensuring robust data integrity and operational continuity.
  • Delivered exceptional 2nd and 3rd level technical support for escalated issues beyond the standard IT call center scope, significantly improving overall IT support efficiency.
  • Administered Active Directory and WSUS, and executed critical company-wide software updates—including a successful migration from Windows XP to Windows 7 - thereby enhancing network security and performance.
  • Developed detailed procedural documentation and conducted training for new employees, streamlining IT operations and optimizing onboarding processes.




Vector CANtech

Computer Engineering Intern
Novi, MI
March 2012 – June 2012



  • Conducted in-depth analysis of embedded devices and interface boards in strict adherence to customer specifications, ensuring precise product alignment and regulatory compliance.
  • Collaborated effectively with cross-functional teams to define detailed hardware requirements for OEMs and Tier 1 suppliers, facilitating seamless project execution.
  • Organized and rigorously tested embedded hardware and debugging equipment, significantly enhancing inventory management through custom scripting and comprehensive documentation.

Projects

Human Heart Rate Extraction from Video

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.

Vehicle Keyfob Security Bypass

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.

One-Time-Pad Secure Communication

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.

Custom 8-Bit ALU ASIC Design

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.

Hidden Markov Models for Speech Recognition

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.

Laser Harp DMX Light Controller

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.

VHDL-Based Graphics Card & Elevator Controller

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.

Custom Xv6 Unix Kernel Scheduler

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.

Publications & Patents

United States Patents

[US-12192868-B2] - Adaptively selecting network APN for vehicle application remote computing demand

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.


[US-12181869-B2] - Method and apparatus for remote driving a plurality of vehicles in a platoon

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.


[US-20240412630-A1] - EDGE COMPUTING ASSISTED VEHICLE ALERTING SYSTEM AND METHODS

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.


[US-20240329184-A1] - High-Accuracy Device Positioning

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.


[US-12037021-B2] - Electric autonomous vehicle ride service assistance

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.


[US-11991346-B2] - Video streaming anomaly detection

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.


[US-11880867-B2] - Systems and methods for providing targeted content to users

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.


[US-20230370822-A1] - ADAPTIVELY SELECTING NETWORK APN FOR VEHICLE APPLICATION REMOTE COMPUTING DEMAND

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.


[US-11812355-B2] - Drone-based vehicle connectivity systems and methods

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.


[US-11750393-B2] - Secure broadcast delivery and verification

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.


[US-20230237529-A1] - Systems for Providing Targeted Content

Date: July 27, 2023

Summary: Location-aware content recommendations.


[US-20230195105-A1] - Remote Driving a Plurality of Vehicles

Date: June 22, 2023

Summary: Broadcasts remote-driving commands.


[US-20230156179-A1] - Video Streaming Anomaly Detection

Date: May 18, 2023

Summary: Detects dropped/stale frames.


[US-20230110300-A1] - Electric Autonomous Vehicle Ride

Date: April 13, 2023

Summary: AVs share capabilities for recharging.


[US-11551456-B2] - Enhanced Infrastructure

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.


[US-20220377519-A1] - Drone-based Vehicle Connectivity

Date: November 24, 2022

Summary: Launches drones for emergency communications.


[US-20220321352-A1] - Secure Broadcast Delivery

Date: October 6, 2022

Summary: Random-nonce-based broadcast verification.


[US-11395107-B1] - Multicast assisted parking lot management

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.


[US-20220176986-A1] - ADAPTIVE GUIDANCE FOR GROUP VEHICULAR TRAVEL

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.


[US-11210943-B2] - Edge computing aided radio congestion mitigation

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.


[US-20210397853-A1] - Enhanced Infrastructure

Date: December 23, 2021

Summary: Infrastructure cameras assist AV geopositioning.


[US-20210272454-A1] - Edge Computing Aided Radio Congestion

Date: September 2, 2021

Summary: Instructs low-priority services to pause.


[US-10542493-B2] - Vehicle communications management

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.


[US-10305527-B2] - Communicatively coupling mobile devices to wireless local area networks of vehicles

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.


[US-20190007900-A1] - Vehicle Communications Management

Date: January 3, 2019

Summary: Wakes primary modem from low-power state.


[US-20180213414-A1] - Communicatively Coupling Mobile Devices

Date: July 26, 2018

Summary: Optimizes connectivity via internal/external antennas.







International Patents

[UA-64952] - Trap for Rodents (Ukraine)

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.

Leadership

Tau Beta Pi – Engineering Honor Society

President, Secretary, Advisor (2014 – 2016). Led workshops, mentorships, service projects.

Theta Tau – Professional Engineering Fraternity

Vice President, Pledge Educator (2012 – 2016). Oversaw operations, events, leadership training.

DCE – Dearborn Campus Engineers

Vice President, Treasurer, Trustee (2012 – 2016). Managed finances and events for engineering community.

Volunteering

  • MATHCOUNTS Foundation: Organized math competitions, mentored students, handled scoring.

  • Michigan International Speedway: Race weekend volunteering, annual.

  • Northville Civic Concern: Sorting/organizing donations for local families in need.

  • CYM USA: Helped organize and run annual events for Ukrainian American Youth Association.




Additional Information

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

Contact

Reach out to collaborate on cutting-edge tech: [email protected]

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