Alejandro Amat Payá
Research Associate at Carnegie Mellon University
Hi! I'm a Research Associate in the Human Sensing Lab at Carnegie Mellon University's Robotics Institute, working under Prof. Fernando De la Torre. I'm also taking graduate courses (ML & Graphics) as staff. I received my dual Bachelor's degree in Electrical Engineering and Computer Science from CFIS-UPC Barcelona.
My work focuses on real-time differentiable rendering, GPU-accelerated 3D vision, human modeling, and neural scene reconstruction. I combine research in graphics and machine learning with high-performance engineering, developing methods that run in real time on modern hardware. By leveraging GPU programming with CUDA, Vulkan, and modern graphics APIs, I translate research prototypes into production-ready systems for 3D vision and rendering applications.
Publications

Location-based real-time utilization of reconfigurable intelligent surfaces for mmWave integrated communication and sensing
IET Advanced Metaverse Wireless Communication Systems, 2025
Projects

Vulkan 3D Gaussian Splatting Renderer
Cross-platform neural rendering engine breaking CUDA vendor lock-in with Vulkan and GLSL compute shaders. Built complete tile-based rasterization, multi-stage radix sort, and ImGui keyframe animation system achieving stable 60 fps on Nvidia 3060 Ti and 24-45 fps on Apple Silicon. It also has a Python library through PyBind bindings.

Real-time Differentiable Rendering with Slang
Built a full C++ rendering system and shader pipeline leveraging Slang’s automatic differentiation. Designed custom optimizers and real-time GPU kernels, achieving 116× speedup over NVIDIA Falcor for inverse texturing and 4× faster performance in 2D Gaussian Splatting, enabling practical real-time inverse rendering.

Monte Carlo Path Tracer
Implemented a high-performance CPU Monte Carlo path tracer in C++ with cosine-weighted hemisphere sampling, global illumination, and multi-threaded rendering for photorealistic images.

Museum VR Simulation (Unity)
Developed a cross-platform Unity VR simulation for a museum experience in collaboration with researchers from IESE, MIT, and LBS. Implemented gizmo-style controls, gyroscope/accelerometer sensor fusion, crowd simulation, and immersive spatial audio, with Firebase for real-time synchronization and persistent user action storage. Deployed with Google Cardboard for accessible mobile VR.

High-Performance CUDA Optimization Suite
Collection of GPU-accelerated algorithms with massive performance improvements. Sobel Edge Detection achieving 224733× speedup over NumPy, CUDA-JPEG with 120× speedup vs CPU, and optimized Multi-Scale SSIM for real-time image quality assessment.

Custom x86 Operating System Kernel
Complete OS kernel implementation in C and assembly featuring process scheduling, virtual memory management with paging, system call interface, hardware interrupt handling, and basic file system operations. Built from scratch for systems programming coursework.

mmWave Radar & IRS Simulator
Developed a MATLAB simulator for mmWave radar heatmap generation and Intelligent Reflecting Surface (IRS) optimization, enabling multi-user tracking and signal homogeneity analysis for immersive VR environments.

Custom Rendering Engines (OpenGL & Vulkan)
Developed real-time rendering engines in OpenGL and Vulkan, featuring point, directional, and spotlight lighting, shadow mapping (with Vulkan subpasses), normal mapping, texturing, and subsampling for efficient and high-quality rendering.
Experience
Research Associate
Carnegie Mellon University
Feb 2024 – Present
Conducting research on real-time neural rendering and 3D computer vision at the Human Sensing Lab, bridging graphics, machine learning, and high-performance computing.
AI Researcher
i2CAT Foundation
May 2023 – Dec 2023
Developed real-time 3D imaging algorithms with MIMO FMCW mmWave radar, published 2 papers on intelligent surface optimization.
Unity VR Developer
IESE Business School
Oct 2023 – Apr 2024
Built cross-platform VR application for IESE/MIT/LBS collaboration with Firebase integration and multi-platform deployment.
Big Data Engineer Intern
Mango
May 2022 – Sep 2022
Developed automated data pipelines using Jenkins CI/CD, Airflow, and AWS for large-scale retail analytics.
Education
Visiting Research Student - Bachelor's Thesis
Carnegie Mellon University, Robotics Institute
Feb 2024 – July 2024
Thesis: "Real-Time Differentiable Rendering with Slang" - Developed high-performance differentiable rendering pipelines under supervision of Dr. Fernando de la Torre. Grade: 10/10
Dual Bachelor of Engineering
CFIS-UPC Barcelona
2019 – 2024
Electrical Engineering & Computer Science - Graduated with honors, recognized as 6th best student of 2024 graduating class.