Alexander Cieslewicz
Gdańsk, Poland ·
alexander.cieslewicz@gmail.com
Hi, I'm Alexander, a developer who enjoys working close to the metal, especially with Rust. I’ve worked on everything from autonomous drones to neural network side-channel analysis and optimizing heterogeneous SoCs. In my free time, I contribute to open-source projects like touchHLE and explore how software interacts with hardware.
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Skills
Languages, Operating Systems & Tools
Rust
Python
C
Git
Docker
Linux
Machine Learning & AI
PyTorch
TensorRT
Neural Networks
Signal Processing
Hardware & Embedded
Microcontrollers
NVIDIA Xavier
Hardware Integration
Embedded Systems
Drone Systems
Experience
Software Developer & Data Analyst
Apr. 2023 - Present
Freelance
· Morrison, CO
Apr. 2023 - Present
- Built a Retrieval Augmented Generation (RAG) system for medical document search, integrating a React Native front-end with a FastAPI and Haystack backend to enable contextual, on-device access to hospital data
- Led data analysis using Python for a novel equine lung health assessment technique resulting in a peer-reviewed publication in Physiological Measurement
- Developed innovative AI-driven signal processing algorithms for equine gait acoustics, enabling real-time quantification of horse health and performance metrics
- Developed tooling using Vue.js and Python for recording and processing equine acoustics, reducing analysis time by 90%
- Co-engineered pulmonary function measurement instrumentation, interfacing off-the-shelf components to create a system enabling users to gain insights in respiratory health research
Fellow
Summer 2021 & 2022
Air Force Research Laboratory
· Colorado Springs, CO
Summer 2021 & 2022
- Engineered an autonomous drone, including sourcing components and integrating an onboard Nvidia Xavier NX, for adversarial drone tracking capabilities in diverse environments
- Enhanced the drone's performance by developing AI-based scheduling algorithms, optimizing tracking accuracy, power consumption, and latency for adaptability across various operational scenarios
- Developed a user-friendly drone control framework in Python abstracting mavsdk calls, accelerating development cycles and enabling collaborative research among team members with varying expertise levels
- Validated system performance through comprehensive testing in Gazebo simulations and real-world scenarios, ensuring robust functionality and reliability of the autonomous tracking algorithms
Teaching Assistant
Aug. 2019 - Dec. 2020
Colorado School of Mines
· Golden, CO
Aug. 2019 - Dec. 2020
- Guided students in assembling and troubleshooting custom embedded system development boards, enhancing hands-on skills with PIC microcontrollers and embedded programming
- Assisted students with understanding OS concepts and implementing them in programming assignments, improving their grasp of process management, memory allocation, and file systems
- Developed Python scripts to automate project testing and grading, streamlining assessment and enabling faster feedback
IT Intern
June 2017 - Aug. 2020
Wanco Inc.
· Arvada, CO
June 2017 - Aug. 2020
- Reduced user account management time by 80% by developing C# automation applications, streamlining IT operations
- Enhanced infrastructure reliability by deploying Zabbix and developing monitoring scripts for proactive issue detection
- Delivered responsive technical support to 250+ clients and staff, troubleshooting diverse IT issues and implementing solutions to minimize downtime in a fast-paced production environment
Intern
July 2019 - Aug. 2019
Arendai Inc.
· Łodz, PL
July 2019 - Aug. 2019
- Researched deep neural network applications for autonomous vehicle cybersecurity applications
Open Source Contributions
A collection of efforts to which I contributed, but did not create.
touchHLE – Open Source iOS Emulator
An iOS emulator written in Rust that aims to run iPhone OS apps on modern platforms.
- Implemented low-level system interfaces in Rust to improve emulator accuracy and performance
- Ported Apple's ld64 linker for Windows to support native build compatibility across platforms
Rust
iOS
Emulation
Low-level
Cross-platform
Publications
Research and published work I've been involved in, spanning machine learning security, energy-efficient computing, and biomedical signal processing.
Validation of Three-Dimensional Thoracic Electrical Impedance Tomography of Horses During Normal and Increased Tidal Volumes
Physiological Measurement
·
February 2024
Led data analysis for validating 3D electrical impedance tomography for equine lung health assessment, enabling more complete visualization of respiratory physiology.
- Developed signal processing algorithms for analyzing electrical impedance tomography data
- Processed and validated 3D reconstruction of lung ventilation patterns across multiple anatomical slices
Signal Processing
Data Analysis
Medical Research
Python
Biomedical Engineering
Extracting neural network models via contention-based side channel attacks on shared memory system-on-chips
Master's Thesis, Colorado School of Mines
·
August 2022
Demonstrated the feasibility of extracting neural network architectures through memory contention side-channel attacks on shared hardware platforms.
- Engineered an RNN-based sequence-to-sequence model, achieving 80% accuracy in mapping memory contention patterns to specific neural network layer executions
- Optimized network training pipeline using TensorFlow and TensorRT, generating a diverse synthetic dataset of neural networks
- Enhanced data collection precision by configuring a low-latency Linux kernel and developing a high-precision C program
Machine Learning
Security
Side-Channel Attacks
TensorFlow
TensorRT
AxoNN: energy-aware execution of neural network inference on multi-accelerator heterogeneous SoCs
59th ACM/IEEE Design Automation Conference (DAC)
·
July 2022
Developed an energy-efficient model for neural network execution on heterogeneous System-on-Chips, enabling optimal execution strategies under energy constraints.
- Addressed energy and latency demands of critical workloads like object detection in embedded systems
- Optimized execution flow across multiple accelerators with diverse power and performance characteristics
- Evaluated on NVIDIA Xavier AGX platform for autonomous and mobile SoC applications
Machine Learning
Energy Efficiency
Heterogeneous Computing
NVIDIA Xavier
Education
May 2020
B.S. in Electrical Engineering
Minor: Computer Science
GPA: 3.91
May 2020