Advancing antenna, microwave imaging, and electric machine design through deep learning and AI — where electromagnetic theory meets neural networks to push the boundaries of intelligent sensing and design.
Bridging the gap between physical electromagnetics and data-driven intelligence.
Magnetic field optimization and electromagnetic design for high-efficiency propulsion systems.
Developing bespoke neural network architectures for predictive microwave imaging and antenna performance forecasting.
Next-gen mmWave antenna arrays and miniaturized designs for space-constrained applications.
Design and optimization of high-frequency front-ends and propagation modeling.
Theoretical analysis and simulation of electromagnetic wave interaction with complex structures.
Deep learning across computer vision, generative AI, and time-series forecasting.
CNN to classify gender from 48x48 grayscale facial images using UTKFace with TensorFlow/Keras.
Fine-tuned CLIP on Food-101 using LoRA for parameter-efficient adaptation.
ML models for stock price prediction with SHAP feature importance analysis.
Key projects in antenna design, RF measurement, and material characterization.
Designed a miniaturized Vivaldi antenna with -10 dB bandwidth from 3.3 to 8.6 GHz using Ansys HFSS. Fabricated and measured far-field patterns in an anechoic chamber.
Designed flexible PCBs using polyimide substrates at mmWave frequencies. Developed Multiline TRL calibration using HFSS and MATLAB.
Simulated and fabricated a magnetic coaxial connector up to 10 GHz. Measured reflection coefficient and insertion loss via VNA.
Awarded for measuring broadband transmission loss and permittivity of FCCL films at mmWave frequencies up to 40 GHz.
Conferences, lab work, campus life, and more.
Interested in collaboration, have questions, or just want to say hello?