About
I am a Ph.D. Candidate in Mechanical Engineering at the University of California, Merced, where my research focuses on Explainable Artificial Intelligence (XAI) empowered sensors and smart sensing. My work bridges advanced AI with real-world applications in both human health and planetary health—ranging from explainable cardiovascular disease prediction and prevention to satellite- and drone-based Earth remote sensing. In addition to my academic research, I worked as a Mechanical Engineer subcontractor at NASA’s Jet Propulsion Laboratory. There, I designed, analyzed, simulated, and built next-generation instruments that support planetary science missions on Earth and beyond. My academic journey began with a Bachelor’s degree in Mechanical Engineering, specializing in Mechatronics and Robotics, at Northern Illinois University—where I served as Vice President of the Mars Rover Team and was inducted into the Honors Society. I continued on to earn my Master of Science in Mechanical Engineering with a focus on Dynamical Systems and Control before pursuing my current Ph.D. I am passionate about developing intelligent sensing systems and digital twins that solve complex, real-world problems impacting both people and the planet.
- CAE Software: Solidworks, NASTRAN, ANSYS, COMSOL
- Languages: Python, MATLAB, C, C++, Bash
- Databases: MongoDB
- Libraries: NumPy, CuPy, Pandas, OpenCV
- Frameworks: Flask, Django, Keras, TensorFlow, PyTorch, ROS/ROS2
Pursuing a challenging position that integrates mechanical engineering, controls, embedded systems, and AI, with strong opportunities for professional growth.
Experience
- Performed FEA thermal and structural analysis on a flight instrument destined for the ISS to verify performance and survivability under launch conditions.
- Designed, built, and operated a tunable laser spectrometer to quantify total water concentration in lunar regolith for NASA’s Artemis program.
- Built a digital twin (Level-II) of a miniature tunable laser spectrometer for ISS water quality monitoring and validated it in an environmental chamber.
- Improved 3D mechanical designs and simulation workflows for instruments mounted on UAVs for planetary science missions.
- Designed and tested power distribution circuitry for gas sensors mounted on drones.
- Integrated methane gas sensors with robotic platforms using ROS.
- Collaborated with Chevron to enhance and field-test a robotic H₂S sensor with wireless telemetry.
- Worked with NASA AFRC on mechanical substantiation of a next-gen fire detection instrument.
- Performed thermal analysis of airborne radar systems for planetary Earth science missions.
Projects
A physics-informed neural network that simulates steady-state heat transfer across a PCB board with localized power sources.
- Tools: TensorFlow, NumPy, Matplotlib, Python
- Implements a 2D physics-informed neural network constrained by the heat equation ∇²T + Q = 0.
- Models real-world boundary conditions for power-dissipating electronic components and fixed ambient edges.
- Trained with automatic differentiation to satisfy the PDE residuals and boundary conditions.
- Produces visual heatmaps and temperature profiles across the PCB domain.
A Raspberry Pi-powered thermal imaging system that visualizes real-time heat distribution across surfaces using an infrared sensor and touchscreen interface.
- Tools: Raspberry Pi 4B, Adafruit MLX90640, Python, OpenCV, NumPy
- Real-time thermal imaging system using a 32×24 infrared sensor with a 55° field of view.
- Displays temperature heatmaps on a 7" capacitive touchscreen with optional user interaction.
- Captures and visualizes temperature distributions for electronics, surfaces, and environments.
- Optimized for portable benchtop use with touchscreen controls and live interpolation.
Detecting and quantifying methane super-emitter plumes from Sentinel-2 satellite imagery using deep learning.
- Tools: Python, PyTorch, Rasterio, NumPy, Matplotlib
- Utilizes Sentinel-2 L2A satellite data, specifically SWIR bands (B11 & B12) sensitive to methane absorption.
- Applies a deep learning model to segment and quantify high-concentration methane plumes from point sources.
- Addition of embedding synthetic methane plumes using gaussian plume model with Ornstein–Uhlenbeck process.
- Implements Grad-CAM for model interpretability, visualizing the spatial features driving the detection.
An octorotor drone custom-built for high-resolution multispectral imaging to monitor environmental and agricultural health.
- Tools: Pixhawk Orange Cube+, Mission Planner, MapIR Cameras, Python
- Features an 8-rotor (octorotor) configuration for heavy lift capacity and flight redundancy.
- Powered by a Pixhawk Orange Cube+ flight controller for robust autonomous navigation.
- Equipped with MapIR multispectral cameras to capture data for planetary health and vegetation indices (e.g., NDVI).
- Utilizes Mission Planner for flight planning, real-time telemetry, and data logging.
A digital twin framework for modeling and controlling prescribed fires using UAVs as integrated sensors and actuators.
- Tools: Python, Physics-Based Modeling, UAV Control Systems, Multispectral & Thermal Sensors
- Proposes a hybrid digital twin framework for real-time fire behavior prediction.
- Integrates physics-based fire propagation models with simulated UAV sensor data (thermal & multispectral).
- Utilizes UAVs as both sensors (for data collection) and actuators (for control) in a closed loop.
- Aims to improve the safety, precision, and effectiveness of prescribed fire management.
A novel signal processing algorithm using fractional calculus to detect methane plumes in noisy hyperspectral satellite data (AVIRIS-ng).
- Tools: Python, Fractional Calculus, AVIRIS-ng, NumPy
- Proposes a Fractional-Order Matched Filter (FOMF) to overcome the limitations of standard filters.
- Designed to handle the heavy-tailed, non-Gaussian noise common in real-world hyperspectral data.
- Successfully identified methane plumes from AVIRIS-ng imagery where traditional integer-order filters failed.
- Achieved a superior ROC Area Under the Curve (AUC) of 0.72, validating its improved sensitivity and accuracy.
An explainable digital-twin framework that analyzes ECG data to predict cardiovascular status using machine learning.
- Tools: Python, NeuroKit2, Heartpy, Scikit-learn, SHAP
- Presents an explainable digital twin (XAI) framework for cardiovascular health monitoring.
- Extracts key features (e.g., heart rate variability) from ECG signals using NeuroKit2 and Heartpy.
- Employs machine learning to predict cardiovascular status, achieving 90.08% accuracy.
- Uses SHAP (Shapley Additive Explanations) to provide transparent insights into the model's predictions.
A PhD research framework using Explainable AI (XAI) and IoT to interpret physiological data from edge devices.
- Tools: Python, TensorFlow Deep Neural Networks (DNN), XAI (SHAP), Edge Devices, IoT
- Developed a XAIoT (Explainable AI + IoT) framework for health monitoring.
- Applies DNNs on edge devices for portable, real-time analysis of physiological data (e.g., ECG, HRV).
- Uses XAI to provide transparent, interpretable insights into model predictions for patients and clinicians.
- This research includes the 'XCardio-Twin' digital twin framework for cardiovascular health.
An XAI framework using Fractional-Order SGD and multi-task learning to monitor planetary health from EuroSAT imagery.
- Tools: Python, TensorFlow, Quantus, XAI (GradCAM, SmoothGrad)
- Implements a novel Fractional-Order Stochastic Gradient Descent (FOSGD) optimizer, achieving 96% accuracy.
- Uses multi-task learning on EuroSAT data to classify land use and map environmental indices (NDVI, NDWI).
- Applies XAI techniques (GradCAM, SmoothGrad) to provide interpretable insights into the model's decisions.
- Evaluates the reliability of the XAI methods using the Quantus framework.
A UAV platform using a digital twin and observability Gramian to optimally sense and estimate methane emission sources.
- Tools: sUAS, Python, Gaussian Plume Model, Tikhonov Regularization
- Develops a low-cost sUAS for mobile methane sensing and source estimation.
- Uses a Gaussian plume model as a digital twin for real-time simulation of methane dispersion.
- Implements an optimal sensing path based on the observability Gramian to maximize estimation accuracy.
- Estimates emission source location and strength using a Source Receptor Matrix (SRM) method.
A cognitive digital twin that monitors battery health (SOC, RUL) to ensure safe landings for long-endurance VTOL drones.
- Tools: Digital Twin, Python, VTOL sUAS, Coulomb Counting
- Develops a Cognitive Battery Monitoring System (CBMS) for a hybrid VTOL fixed-wing sUAS.
- Employs a Digital Twin model to behavior-match battery discharge curves.
- Estimates battery State of Charge (SOC) and Remaining Useful Life (RUL) in real-time.
- Provides early warning recommendations for safe landings on long-endurance missions.
A UAV-based archaeological survey mission using multispectral imaging to uncover and map historical features.
- Tools: Octorotor UAV, Mission Planner, Multispectral Cameras, Agisoft Metashape, Python
- Conducted autonomous, grid-based flight patterns over an archaeological site to ensure complete data capture.
- Utilized multispectral sensors to detect subtle variations in vegetation health and soil composition.
- Processed imagery into orthomosaics and vegetation indices (like NDVI) to reveal subsurface structures and buried features invisible to the naked eye.
- Provided high-resolution, non-invasive data to archaeologists for site mapping and analysis.
Publications
Google Scholar ORCID- R. Krzysiak and C. Yu, “Tiered IoT Device Strategy for Supraventricular Tachycardia Detection with Explainable AI and Patient Engagement,” Abstract Submitted, AIMed, 2025.
- S. Giri, D. Hollenbeck, R. Krzysiak and Y. Chen, “PHANTOM: Physics-informed Hyperspectral Adversarial Network for Transformer-Optimized Methane Detection,” Submitted, American Control Conference (ACC 2026).
- R. Krzysiak, J. Ramirez and Y. Chen, “Thermo-TwinX: A Glass-Box Approach to Intelligent Thermal Monitoring with Digital Twins,” Submitted, International Conference on Mechanical and Electrical Engineering (ICMEE 2025).
- R. Krzysiak, S. Giri and Y. Chen, “Optimal Methane Plume Extraction of Hyperspectral Imagery Using Fractional Order Matched Filter,” Submitted, 2025.
- F. Winiberg, M. Fradet, K. Schwarm, I. Sanders, M. Bryk, V. Cretu, R. Krzysiak, K. Mansour, N. Tallarida, J. Wallace, P. Dodd, A. Noell, and L. Christensen, “Tunable Laser Spectrometer for the Miniaturized Total Organic Carbon Analyzer,” Acta Astronautica.
- D. Hollenbeck, R. Krzysiak, et al., “Developing An Optimal Mobile Measurement sUAS using Digital Twins and the Observability Gramian,” ICCMA 2025.
- R. Krzysiak, et al., “Modeling and Control of a Prescribed Fire with UAVs as Sensors and Actuators,” ICCMA 2025.
- S. Giri, R. Krzysiak, et al., “Aviris-Ng-Like Smart Virtual Remote Sensing via Spectra-Aware Physics Informed Gans,” IDETC, 2025.
- R. Krzysiak et al., “Advancing Multi-Task Learning With Fractional Order Sgd and Quantus-Assessed Explainability for Planetary Health Monitoring,” IDETC, 2025.
- R. Krzysiak et al., “Explainable Multi-task Learning for Improved Land Use Classification in Planetary Health Monitoring,” DTPI, 2024.
- F. Winiberg, M. Fradet, R. Krzysiak et al., “Design and Performance of Indium Seals for Size-Constrained Tunable Laser Spectrometers,” Review of Scientific Instruments, 2024.
- R. Krzysiak et al., “Human prior knowledge estimation from movement cues for information-based control of mobile robots during search,” ACM Transactions on Human-Robot Interaction (THRI), 2024.
- R. Krzysiak et al., “Thermally conductive-radiative driven digital twin of miniature tunable laser spectrometer in micro-gravity,” Submitted, Applied Thermal Engineering, 2023.
- D. Hollenbeck, D. An, R. Krzysiak et al., “Towards Cognitive Battery Monitoring on Hybrid VTOL Fixed-Wing sUAS with Maximized Safe Endurance,” ICCMA, 2023.
- D. An, R. Krzysiak et al., “A Proximal Point Sensing System for Mapping Soil Moisture Using A Miniaturized Spectrometer,” ICCMA, 2023.
- R. Krzysiak, et al., “XCardio-Twin: An Explainable Framework to Aid in Monitoring and Analysis of Cardiovascular Status,” DTPI, 2023.
- D. An, R. Krzysiak, et al., “Long Endurance Site-Specific Management of Biochar Applications Using Unmanned Aircraft Vehicle and Unmanned Ground Vehicle,” IFAC-PapersOnLine, 56.2 (2023): 8908-8913.
- D. An, R. Krzysiak, et al., “Battery-health-aware UAV mission planning using a cognitive battery management system,” International Conference on Unmanned Aircraft Systems (ICUAS), IEEE, 2023.
- R. Krzysiak et al., “XAI – The future of wearable Internet of Things,” IEEE/ASME MESA, 2022.
- R. Krzysiak, “Human-aware information-theoretic control of robotic swarms," Master's Thesis, Northern Illinois University, 2021.
- R. Krzysiak and S. Butail, “Information based control of robots in search and rescue missions with human prior knowledge,” IEEE Transactions on Human-Machine Systems, 2021.
Skills
CAE Software
Solidworks
NASTRAN
ANSYS
COMSOL
Languages and Databases
Python
MATLAB
C++
C
Shell Scripting
Libraries
NumPy
Pandas
OpenCV
scikit-learn
matplotlib
Frameworks
Django
Flask
Keras
TensorFlow
PyTorch
Education
University of California, Merced
Merced, USA
Degree: Ph.D. in Mechanical Engineering
GPA: 4.0/4.0
Expected Graduation: December 2025
- Explainable AI for smart sensing
- Remote sensing and planetary science
- Digital twins for health and Earth monitoring
Research Focus:
DeKalb, USA
Degree: Master of Science in Mechanical Engineering
GPA: 4.0/4.0
Degree: Bachelor of Science in Mechanical Engineering
GPA: 3.8/4.0
- Dynamical Systems and Control
- Mechatronics and Robotics
- Swarm robotics and ROS
Focus Areas: