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Franco AlcarazElectronics engineer · AI engineer · Systems architect

Franco Alcaraz: disciplines

Discipline

Artificial intelligence

Classical machine learning and deep learning, from computer vision to natural language, taken to production with the discipline of a system that has to work.

Main skills

  • Machine learning
  • Deep learning
  • Computer vision
  • NLP and LLMs
  • Time series
  • High-performance computing
  • Robotics
  • AI for signals and critical systems
Discipline

Signal processing

What happens between the measurement and the decision: spectral analysis, filtering, tracking and noise modeling.

Main skills

  • DSP
  • Software-defined radio
  • Time-frequency analysis
  • Micro-Doppler
  • Filtering and tracking
  • Phase noise
  • Probability and statistics
  • GNU Radio · MATLAB
Discipline

Critical systems

Systems that cannot fail: harsh environments, high reliability, electromagnetic compatibility and end-to-end verification.

Main skills

  • Radar and surveillance
  • Space systems
  • Rad-hard electronics
  • High-reliability design
  • EMI/EMC
  • System verification
  • Real-time embedded systems
Discipline

Systems engineering

Architecture, modeling and verification: making the parts add up to a system, with every requirement traceable.

Main skills

  • Systems architecture
  • MBSE (Arcadia/Capella)
  • Modeling and simulation
  • Design of experiments
  • Design reviews
  • Project management
Discipline

Digital and FPGA

High-speed digital and mixed-signal design, from the FPGA to fixed point and the GPU.

Main skills

  • FPGA and SoC
  • VHDL
  • HLS
  • High speed
  • Mixed signals
  • Embedded and RTOS
  • Fixed point
  • GPGPU · CUDA
Discipline

RF

Analog radio frequency: front-ends, boards and transmitters, from design to commissioning.

Main skills

  • Analog RF
  • Front-ends
  • Power transmitters
  • Commissioning and testing
Bio and contact

Bio

I'm an electronics engineer, and for almost twenty years I've worked on critical systems, where failure is expensive. I started out designing radio-frequency electronics and FPGAs for equipment that had to work under extreme conditions. Then I moved into systems architecture, where I learned to look at the whole before the parts.

Artificial intelligence came first through work; a master's degree at Instituto Balseiro, with a thesis on deep learning applied to radar echoes, made it the center of my career. Today I work as AI Principal Engineer at INVAP, where I lead the company's data science and AI group, and I still work on the implementations, from the architecture down to the low level.

In AI I've worked with classical machine learning and deep learning, computer vision, natural language processing and language models, high-performance computing and robotics, and what interests me most is where all of that meets critical systems and signal processing. I care about it working outside the lab, inside real systems: that's where engineering weighs as much as the model.

I taught computer architecture and FPGAs for five years, I take part in panels on AI in critical infrastructure and I maintain fxpmath, an open-source fixed-point arithmetic library for Python. On the blog I'll be adding articles on research and engineering.

Outside engineering, I compose music and take photos.

Electronics engineer, Universidad Nacional de Tucumán, with the gold medal of the National Academy of Engineering · Master in Engineering, Instituto Balseiro · Native Spanish, C1 English.

Contact

I'm interested in AI projects and related work, and in the people who drive them. If you're on one, or would like to connect, write to me or find me on LinkedIn.