Matias Aiskovich

I'm a

About

I am a Machine Learning Engineer specialized in Neural Networks, Computer Vision, Natural Language Processing, and the application of Machine Learning in healthcare.


I have worked at IBM Research as a Machine Learning Engineer Researcher, primarily collaborating with IBM’s Yorktown Heights research lab. I also co-founded a start-up that develops research-backed cognitive games for the elderly, which was a provider for an Uruguayan government program. Additionally, I have consulted as a Machine Learning Engineer in dozens of companies ranging from start-ups to Fortune 500 companies.


I hold a graduate certificate in Data Science from Harvard University - Extension School and a Master’s Degree in Data Science from Universidad Austral, with my thesis focused on Computer Vision. I possess advanced skills in NLP, Computer Vision, and various Medical EHR standards such as HL7, FHIR, and SMART on FHIR.


Machine Learning Engineer

Testimonials

Matías joined my team as a Senior Machine Learning Engineer in a contract position. His impact was immediate and Matías brought great value to our team, both from a technical standpoint with his expert knowledge in computer vision and deep convolutional neural networks, but also from a leadership and managerial standpoint. Similarly impressive was Matías' ability to adapt and learn new technologies with ease, quickly becoming an SME on our team in areas he'd recently picked up. Lastly, Matías' emotional intelligence and soft skills are exemplary and brought real benefits to our team. He makes those around him more productive, and for this reason alone I wouldn't hesitate to hire him again. Very best of luck going forwards Matías.

Benjamin Jones

Head of Machine Learning at Motorway

Matias has worked with us on a particularly challenging machine learning project that required multiple simultaneous skills, both technical and soft. He quickly implemented a 3D CNN that reached state of the art performance, following best practices for validation, implementing different data augmentation techniques, etc. But before that he designed a flexible database to accommodate data from disparate sources into a homogeneous structure for training/testing, he designed code flexible enough to move training from different clusters and HCP systems, and enabled non-standard applications of the resulting models. He accomplished all of this in mere 6 months, following quite ill-posed specifications on our part, which speaks to his adaptability. He is a great communicator and our daily interactions were always fluid and smooth. We already miss him in our team. I can't recommend him enough!

Pablo Polosecki

Research Staff Member at IBM

I worked with Matias on quite a few CV/ML research projects while I was a research staff member in IBM Research. I am confident to say that Matias is the best person that I had been working with in IBM. He is very proactive in making progress on the projects and humble to learn new things. He is independent and always able to get things done beyond expectations. Matias also has a solid background in machine learning such that he can come up with deep learning models for different tasks. He is also strong in engineering with rich experience in implementing and evaluating deep learning models. I would recommend him to any company. If you are a hiring manager, be prepared to be "pushed" by him. He will be finishing tasks fast and always asking for more :-)

Bing Zhou

Senior Research Engineer at Snap Inc.

Matías is one of the most hardworking and talented engineers I've ever worked with. He's able to apply not just his technical prowess in machine learning, but also a refreshing amount of creativity to solve complex challenges. I've particularly enjoyed his insatiable level of curiosity, and his responsiveness – he will always rise up to a challenge at lightning speed. Throughout his time at Wevat, we've had many positive, meaningful interactions.

Raphael Chow

CEO & Co-founder at Wevat | Forbes 30 Under 30

I hired Matias for the Machine Learning position at Wevat and worked alongside him during his time at Wevat. Although my knowledge in this area is limited, Matias was always very humble and explained things in a way for the team to understand. I found that he is an extremely passionate person regarding his work in Data Science and Machine Learning, and also very talented. He also has a great "learn it all" attitude which I find really impressive. His thirst for learning new things is admirable, I have definitely taken some inspiration from him in this regard! Matias also worked well in regards to remote working and timezone issues. He has no trouble with being vocal about any issues and blockers, and always gave good visibility of his work. I would recommend for future companies for sure!

Harry Bloom

Mobile engineering in tech for good

I had the pleasure of working with Matias on some machine learning projects at Wevat and found him to be an exceptionally dedicated and talented individual. We were based in different time zones yet his great communication and reliability made it feel like we were working side-by-side. Matias was able to deliver effective data solutions in very tight timeframes, often suggesting clever workarounds to maximise impact whilst minimising cost. I was particularly impressed by his curiosity and empathy – he went out of his way to understand the broader commercial context of the projects and to work with non-technical team members, making him a real asset to our cross-functional team.

Susan Ren

VP Operations at Stacker

Matias was the first machine learning engineer to join our product team of 8 people (distributed across multiple countries). After joining, he quickly figured out how each part of the company works and suggested areas he could work on that would bring the most value. As most of the team was not very familiar with machine learning, Matias organised a lunch and learn in which he explained what kind of problems it can be used solve. He then helped to set the direction of his work, proposing several projects which we then prioritised. Whenever Matias worked on a project, he would provide clear and regular progress updates, clarifying any questions and ambiguity. This made it really easy to work with Matias as he proved to be a manager of one. Matias was accommodating to the time difference with others in the team and easy to communicate with, both in real time and async. While working with him, I also noticed that he’s great at taking on feedback and trying out new ideas. I really enjoyed working with Matias and was impressed by his attitude to work, communication and ability to get things done.

Dmitry Ivanov

Co-Founder at WeVat

I worked with Matias in a project building sensors that could give early alerts in case of floodings. His ingenuity and willingness to learn by himself everything that was needed for the project was only matched by his dedication to work. I highly recommend Matias to to any future employer.

Gabriel Weitz

COO at AdGoat

Matias is one the most dedicated professionals I know. I had the pleasure of working with him for 2 years at Morsum, collaborating on several projects. He is not only a brilliant and independent machine learning engineer but also an inspiring person. He is an energetic perfectionist, his knowledge is vast and thorough. He is an excellent team player with amazing leading abilities. Matias would be an asset to any company.

Valeria Bo, PhD

Senior Data Scientist at Abbott

Skills

Natural Language Processing (NLP):

  • Large Language Model (LLM)
  • Generative Pre-trained Transformers (GPT)
  • BERT
  • Transformers
  • Word2Vec
  • Hugging Face
  • Natural Language Toolkit (NLTK)
  • Spacy
  • LangChain

Computer Vision (CV):

  • Stable Diffusion
  • Point Clouds
  • Object Detection
  • Object Tracking
  • Semantic Segmentation
  • 3D Reconstruction
  • Image Processing
  • Image Recognition
  • 3D Image Processing
  • LiDAR
  • Depth Prediction
  • Facial Recognition
  • Detectron2

Programming Languages

  • Python
  • Java
  • R
  • JavaScript

Frameworks/Libraries

  • PyTorch
  • TensorFlow
  • Flask
  • Pandas
  • NumPy
  • Keras
  • Scikit-learn
  • XGBoost
  • Apache Beam
  • Apache Spark

Healthcare:

  • Fast Healthcare Interoperability Resources (FHIR)
  • HL7
  • DICOM
  • NIfTI images
  • Picture Archiving & Communication Systems (PACS)
  • OpenEMR
  • Genomics
  • Medical Imaging
  • Spatial Transcriptomics
  • Biology
  • Biopython
  • Mirth Connect

Cloud:

  • Amazon Web Services (AWS)
  • AWS SageMaker
  • Google Cloud Platform (GCP)
  • GCP AI Platform
  • GCP BigQuery
  • GCP Dataflow
  • GCP Pub/Sub

Infra:

  • Linux
  • Data Warehousing
  • ETL
  • Machine Learning Operations (MLOps)
  • Docker
  • Kubernetes
  • CI/CD
  • Jenkins

Others:

  • MySQL
  • PostgreSQL
  • SQL
  • Artificial Neural Networks (ANN)
  • Data Analysis
  • Data Analytics
  • Data Mining
  • Data Modeling
  • Data Science
  • Deep Neural Networks
  • Machine Learning
  • Convolutional Neural Networks
  • Data Engineering
  • Association Rule Learning

Resume

Professional Experience

Machine Learning Engineer Consultant

Mar. 2019 - Present

Multiple Clients

  • Collaborated with the Computational Omics Huang Lab at the Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, where I contributed to the development of computer vision Variational Autoencoder (VAE) models. The primary objective of our research was to identify immunotherapy target genes within spatial transcriptomics data.
  • As the NLP subject matter expert, led the creation of an ML pipeline for the automatic processing of legal documents for IFF-DuPont RD team; this project included the use of open-source (Google’s T5) and proprietary (GPT-3) LLM.
  • As a principal ML engineer, audited, improved, and coached team members on a computer vision pipeline for object detection and semantic segmentation to detect small defects in car manufacturing plants.
  • In the same project, responsible for an end-to-end Detectron 2 ML training and deployment pipeline, in Python combined with DVC for data versioning and W&B for tracking and evaluation.
  • Developed SMART on FHIR app for healthtech start-up to be published in the Epic (EHR vendor) app store and coached client’s team members on FHIR and SMART on FHIR technologies.
  • Integrated telemedicine company with different EHR’s, using Mirth Connect for routing HL7 message, and built a custom Python FHIR API for interoperability between platforms.
  • Developed XGBoost gradient boosting machine learning models for predicting DNA sequences’ manufacture timeline for a biotech start-up, and serve them using Docker and Kubernetes.
  • Developed NLP machine learning sentiment classification models based on Transformers and BERT, rebuilt client’s platform architecture and designed long-term roadmap for a marketing start-up’s platform.

Machine Learning Research Engineer

Aug. 2020 - Feb. 2022

IBM

  • Developed computer vision machine learning models (3D CNN based in PyTorch) for brain age prediction (predicting age given an MRI image of the brain) and led the curation of a large dataset of brain MRI images for Yorktown Heights IBM Exploratory Life Science Sector (neuroscience team).
  • Led machine learning experimentation in natural language processing project for detection of security threats in software packages for IBM Research in collaboration with IBM TSS team–which had several Fortune 500 companies as intended customers–as the NLP subject matter expert of the team, which included the usage of models such as LDA and BERT.
  • Coached Software Engineers in ML and NLP.
  • Co-authored two research papers, "Sparse Depth Completion with Semantic Mesh Deformation Optimization" and "Acoustic Sensing-based Hand Gesture Detection for Wearable Device Interaction" (patent pending).

Co-Founder and Full Stack Developer

Mar. 2013 - Dec. 2021

Caretronics

  • Developed an app that was chosen to take part in the Uruguayan governmental project, Ibirapita. It was downloaded by more than 65,000 people.
  • Developed a web platform based on medical research for improving the quality of life of people with cognitive diseases; at the moment is being used by several patients with Alzheimer’s disease.

Machine Learning Engineer

Aug. 2019 - Jul. 2020

Wevat Tax Refund

  • As the first hire in the Machine Learning team, led the planning of the machine learning roadmap, ensuring that stakeholders not familiar with ML capabilities were included in the decision-making process.
  • Responsible for end-to-end ML modeling, developed computer vision models with TensorFlow to confirm receipt images were compliant with UK legal norms and serve them with Google Cloud ML Engine.
  • Built machine learning models with XGBoost (gradient boosting) to predict the volume of customers.

Senior Data Engineer

Oct. 2017 - Aug. 2019

Morsum, LLC

  • Designed and led the implementation of an ETL into Google Cloud Platform (Pub/Sub, Dataflow, BigQuery).
  • Developed machine learning market basket analysis recommendation models for food ordering.
  • Responsible for the design and implementation of the inpatient food ordering project for hospitals (based on SMART on FHIR to connect with EHR’s).

Data Engineer

Oct. 2016 - Oct. 2017

Morsum, LLC

  • Developed a statistical tool for customers to get insights about their nutritional consumption.
  • Developed Python API’s to act as an interface between web and mobile apps and machine learning models.
  • Worked closely with Data Scientists, providing support in the optimization of Python code, review of Machine Learning models, and providing data sources to be utilized in their products.

Full Stack Developer and Sysadmin

Jul. 2012 - Sept. 2016

Gumma SRL

  • Improved the reliability and speed of company infrastructure with the virtualization of the physical servers in the regional branch offices in Argentina, Uruguay, and Brazil.
  • Changed company internal processes, with the development of In-house software; simplifying and automating several tasks, resulting in more productivity, better communication between sectors, and precise business forecasts for the management department.

Publications

Sparse Depth Completion with Semantic Mesh Deformation Optimization

2021

Acoustic Sensing-based Hand Gesture Detection for Wearable Device Interaction

2021

Cognitive Stimulation of Autobiographic and Emotional Memory in a Patient with Alzheimer’s Disease

2020

Education

Master's Degree in Data Science

2020 - 2022

Universidad Austral, Buenos Aires, Argentina

Key areas of study: Data Mining, Natural Language Processing (NLP), Computer Vision (CV), Neural Networks, Descriptive & Inferential Statistics, Data Architecture
Thesis in Computer Vision: Controlling bias with explainability techniques.

Data Science Graduate Certificate

2016 - 2018

Harvard University - Extension School

Key areas of study: Data Science, Big Data in Healthcare

Commercial Pilot

2014 - 2015

ETAP, Buenos Aires, Argentina

Courses & Certifications

Introduction to the Biology of Cancer

John Hopkins - Coursera (2023)

Human Research

CITI Program (2022)

Data or Specimens Only Research

CITI Program (2022)

Clinical Genomics

Universidad de Buenos Aires - (2022)

Introduction to Biology - The Secret of Life

MIT - EDX - (2022)

NLP Specialization

DeepLearning.AI (2021)

Fundamentals of Reinforcement Learning

University of Alberta - Coursera (2020)

Deep Learning Specialization

DeepLearning.AI (2020)

Data Science in Stratified Healthcare and Precision Medicine

The University of Edinburgh - Coursera (2020)

Fundamentals of GIS

UCDAVIS - Coursera (2020)

Image processing for Artificial Vision

Universidad CAECE (2019)

Deep Learning Nanodegree

Udacity (2019)

Understanding Clinical Research: Behind the Statistics

University of Cape Town - Coursera (2018)

Data Engineering for Google Cloud Platform

ROI Training, San Jose, California (2017)

Senior Web Developer Nanodegree

Udacity (2015)

Portfolio

Computer Vision Machine Learning (VAE): Find novel genes for immunotherapy treatment

Computational Omics Huang Lab at the Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai

Built Variational Auto Encoder (VAE) models using several 10XGenomics Spatial Transcriptomics datasets for different cancer types/tissues. Compared euclidian distance of genes in each tissue latent space and across tissues latents spaces for each gene.

  • Programming Languages & Software: Python, OpenCV, NumPy, PyTorch, Git.
  • Machine Learning: Tested different VAE architectures and hyperparameters.
  • Data preprocessing: Reconstructed spatial data for each tissue as a tensor input for the network. Standardised dimensions across different datasets.
  • Results processing: Alignment of latent spaces across different cancer types, found closest novel genes to known genes targeted by immunomodulators.

2023

Large Language Model (Chat-GPT) end-to-end pipeline: Automation of document processing

IFF - DuPont

Deployment of a Python Web-App to aid in the processing, metadata extraction, and creation of legal documents.

  • Programming Languages & Software: Python, HuggingFace, Git.
  • Machine Learning: Exploration of different LLMs, including open source ones (using HuggingFace for Google T5) and proprietary LLMs (Chat-GPT, GPT-3).
  • Web-App Design: Web-App designed completely from scratch using Streamlit Python package.

2022-2023

Object Detection Computer Vision pipeline: Detection of defects in car manufacturing production lines

UK-based Company

Creation of an object detection pipeline.

  • Programming Languages & software: Python, Detectron2, PyTorch, Weights & Biases (W&B), GitHub Actions, DVC, Kubernetes, Docker.
  • Machine Learning: Tested several object detection models, including YOLO (PyTorch implementation) and most models present in the Detectron2 library.
  • MLOPS: Created guidelines for artifacts and experiment tracking in W&B.
  • Model deployment: Exported selected model from Detectron2 to TorchScript in order to serve the model with Nvidia Triton.
  • Leadership: Coached client's Machine Learning Engineers in model development and experimentation best practices.

2022

Machine Learning (Decision Trees) and App Development: SMART on FHIR app for healthtech start-up

US-based Start-up

Development of a SMART on FHIR app with a Python back-end serving a decision tree classifier for a health tech start-up to be published in the Epic electronic health record (EHR) vendor app gallery.

  • Programming Languages & Software: Python, Flask, XGBoost, JavaScript, Git.
  • Machine Learning: Tested several gradient boosting algorithms (AdaBoost, XGBoost, and LightGBM) and ended up using XGBoost classifier with its hyperparameters optimized with Optuna.
  • App development: Built a SMART on FHIR front-end app using JavaScript.
  • Back end: Built an API with Flask and Python to act as an interface between the front-end app and the classifier model.
  • Leadership: Coached company's Software Engineers in SMART on FHIR technology.

2021

Computer Vision Machine Learning: Brain MRI Age Prediction

IBM Thomas J. Watson Research Center

Developed computer vision machine learning models (3D CNN based in PyTorch) for brain age prediction (predicting age given an MRI image of the brain) and led the curation of a large dataset of brain MRI images.

  • Programming Languages & Software: Python, PyTorch, FreeSurfer, Git, HPC cluster, PostgreSQL, Flask.
  • Computer vision architectures: Tested several common convolutional neural network architectures (ResNet50, EfficientNet) and adapted them to process 3D images (MRI slices).
  • Data preprocessing: Compiled various datasets from different institutions and standardized their terms and ontologies under a master structure that I created. Stored and served the standardized data under a PostgreSQL DB and Flask API. Aligned Brain MRI images using FreeSurfer.
  • Results processing: Compared prediction age error for control individuals against individuals with neurodegenerative diseases.

2021

NLP & Machine Learning: Early detection of security threats in software packages

IBM Thomas J. Watson Research Center

Created different Machine Learning models for classifying security-related reports.

  • Programming Languages & Software: Python, scikit-learn, Flask, PyTorch, HuggingFace, XGBoost, Spacy.
  • Machine Learning: Tested several algorithms: extracted embeddings using BERT and used them as input for Gradient Boosting classifiers; LDA models for unsupervised data.
  • Data preprocessing: Preprocessed text with Spacy.
  • Leadership: Coached Software Engineers in Machine Learning and NLP.

2020

Services

These are just some examples of areas in which I can help you in the Machine Learning and Data Engineering space. If you have a need that is not mentioned here, please feel free to reach out, and I will let you know if I can help; if not, I will do my best to put you in contact with someone who can.

Machine Learning Strategy

Help businesses define their machine learning goals, develop a roadmap, and create a strategy for implementing machine learning solutions.

Data Analysis and Modeling

Assist in data analysis, data preprocessing, feature engineering, and building predictive models using various machine learning algorithms.

Computer Vision

Develop computer vision solutions for tasks such as object detection, image classification, facial recognition, and video analysis.

Natural Language Processing (NLP)

Assist in the seamless integration and optimization of Large Language Models (LLMs) tailored to meet the specific requirements and workflows of your company. I can also construct Natural Language Processing (NLP) models for a wide range of tasks, including sentiment analysis, text classification, named entity recognition, text generation, and language translation, among others.

Healthcare ML pipelines

Develop a comprehensive healthcare data pipeline encompassing various stages. I am proficient in handling data extraction through HL7 or FHIR protocols, as well as managing images through PACS systems. Additionally, I excel at preprocessing data, constructing machine learning models, and integrating with electronic health record (EHR) systems using the SMART on FHIR framework.

Training and Workshops

Conduct training sessions and workshops to educate teams or individuals on machine learning concepts, best practices, and tools.

Contact

Location:

Buenos Aires, Argentina (GMT-3)