Nahuel Passano Portfolio

About

I define myself as a high-performing, intrinsically motivated individual with a strong passion for technology. I thrive in multidisciplinary
and multicultural teams, and I am motivated by projects with social impact, where technical challenges are combined with user needs.

Audio DSP Engineer & Machine Learning Engineer.

Blog: nahue-passano.github.io/blog
City: Buenos Aires, Argentina

Degree: Engineering
Email: nahue.passano@gmail.com

🧠 Currently focused on applying artificial intelligence in the embedded audio industry.

👁️ I also have experience in end-to-end computer vision projects and applying AI in the healthcare sector, particularly for ALS patient analysis and prognosis.

🗣️ In my Acoustic Engineering thesis project, I am developing a voice cloning system for the Rioplatense Spanish dialect, capable of synthesizing high-quality speech in real time with detailed control over pitch and phoneme duration.

📚 Additionally, I have teaching experience in engineering and data science subjects, contributing to the development of skills in these areas.

Download CV

Skills

Throughout my career, I have had the privilege of being a member of high-performance technical teams focused on various topics
within the realm of artificial intelligence. During this projects, I had the opportunity to develop and refine these skills, among others.

Machine Learning and Deep Learning 90%

Audio DSP 90%

Computer Vision 80%

Python 90%

AWS 80%

Docker 75%

Resume

I've had the opportunity to collaborate with captivating interdisciplinary teams, enriching my experience profoundly.
Concurrently, I've engaged in compelling and groundbreaking projects that I take great pride in showcasing

Education

Acoustical Engineering

2016 - 2024

National University of Tres de Febrero, Buenos Aires, Argentina

Engineering’s degree specialized in acoustic measurements, NVH and DSP.

Projects

AIRA: Ambisonics Impulse Response Analyzer

Innovative software for visualizing impulse responses from Ambisonics microphones, featuring an interactive interface that allows for detailed examination of sound reflections using ’hedgehog-type’ graphs

Mi identidad vocal

Development of voice cloning software for the former national deputy Jorge Rivas. Through his sessions in the Chamber of Deputies, his voice was successfully reconstructed, restoring his vocal identity.

Certificates

Cloud Computing (AWS)

CoderHouse

Convolutional Neural Networks

DeepLearning.ai

Data analysis with Python

freeCodeCamp

Docker for the absolute begginer

KodeKloud

MLOps concepts

DataCamp

Sequence Models

DeepLearning.ai

Professional Experience

Ssr Machine Learning Engineer

9/2023 - Present

EverythingALS

Presentation of scientific advancements to high-level executives, emphasizing skills in communication and specialized information management.
Development of signal processing pipelines for the extraction of digital biomarkers from speech and measurements of pressure sensors, accelerometers and gyroscopes.
Training of supervized models to predict subjective metrics of ALS patients.
Design of a proof of concept on voice cloning for ALS patients using Eleven Labs, TortoiseTTS and RVC.

Ssr Machine Learning Engineer

4/2023 - 10/2023

MecanTronic

Technical leadership in an Audio Machine Learning project (Voice Cloning for Argentinian speakers).
Design technical exam for the selection process, perform technical interviews
Machine Learning projects lifecycle managment with MLFlow.
Implementation of CI pipelines with GitHub Actions for automated testing.
Cloud architect with AWS for Machine Learning projects.
Containerization of Machine Learning applications using Docker.

Jr Machine Learning Engineer

2/2022 - 4/2023

MecanTronic

EDA for Object Detection and Instance Segmentation datasets.
Reserch and application of Computer Vision models for object detection in drone imagery.
Adaptation of libraries such as Detectron2 and Anomalib for instance segmentation and anomaly detection tasks in drone imagery.
Full pipeline development of preprocessing, inference and postprocessing in Computer Vision projects.
Training AI models on GPU cloud instances (AWS EC2, Paperspace).