Matias Aiskovich
Verified Expert in Engineering
Data Scientist and Developer
Buenos Aires, Argentina
Toptal member since March 25, 2019
Matias is a machine learning engineer who's delivered creative solutions for social impact projects. His past experience includes working at IBM Research as a machine learning engineer (collaborating with IBM's Yorktown Heights research lab), co-founding a startup that develops research-backed cognitive games for the elderly (which was a provider for a Uruguayan government program), and working on several projects that use machine learning to innovate in the healthcare sector.
Portfolio
Experience
- Python - 7 years
- Computer Vision - 6 years
- PyTorch - 6 years
- Natural Language Processing (NLP) - 5 years
- Generative Pre-trained Transformers (GPT) - 5 years
- Medical Imaging - 3 years
- Genomics - 3 years
- Bioinformatics - 2 years
Preferred Environment
Python, Linux, Google Cloud Platform (GCP), Amazon Web Services (AWS), PyTorch
The most amazing...
...thing I've built is a machine learning model for age regression from 3D brain MRI images, using the model's delta to diagnose neurodegenerative diseases.
Work Experience
Machine Learning Engineer Consultant
Toptal Client
- Bioinformatician Technical Lead at Syngenta, managed a team of developers and led the creation of a Bioinformatics R&D data integration tool, providing scientists with a centralized platform that accelerates experimental workflows.
- Led the creation of an ML pipeline for the automatic processing of legal documents for the IFF-DuPont RD team; this project included using open-source (Google's T5) and proprietary (GPT-3) LLM.
- Worked as a principal machine learning engineer, auditing, improving, and developing processes, and coaching team members on a computer vision pipeline for object detection and semantic segmentation to detect small defects in car manufacturing plants.
- Developed SMART on an FHIR app for a healthtech startup to be published in the Epic (EHR vendor) app store.
- Integrated the Medweb (a telemedicine company) platform with different EHRs using HL7 and FHIR data.
- Created gradient boosting machine learning models for predicting DNA sequences' manufacturing timeline for Strandbase (a biotech startup).
- Developed NLP (natural language processing) machine learning sentiment classification models and an end-to-end machine learning pipeline for marketing startups.
Bioinformatician and Machine Learning Engineer
University of Helsinki
- Developed a pipeline with multiple instance learning models applied to whole slide H&E images of ovarian cancer tissues to detect regions of interest related to patient outcome (survival, therapy resistance).
- Developed VAE models with highly multiplexed immunofluorescence images of ovarian cancer tissues.
- Adapt foundation models trained with H&E images to highly multiplexed immunofluorescence images, successfully achieving high classification performance.
Machine Learning Research Engineer
IBM
- Conducted machine learning experimentation in a natural language processing project to detect security threats in software packages for IBM Research in collaboration with the IBM TSS team.
- Developed computer vision models for age regression (predicting age given an MRI image of the brain) and curated a large dataset of brain MRI images as part of my work in the research task for the Exploratory Life Science Sector (a neuroscience team).
- Co-authored two research papers: "Sparse Depth Completion with Semantic Mesh Deformation Optimization" (depth perception for augmented reality) and "Acoustic Sensing-based Hand Gesture Detection for Wearable Device Interaction."
- Coached software engineers on machine learning topics, including NLP and computer vision.
Co-founder
Caretronics
- Created an API using Flask to serve a mobile app that I also deployed on AWS.
- Developed an app chosen to take part in the Uruguayan governmental project, Ibirapita.
- Made a web platform based on research for improving the quality of life of people with cognitive diseases, which resulted in the publication “Cognitive Stimulation of Autobiographic and Emotional Memory in a Patient with Alzheimer’s Disease.”.
- Analyzed the data from patient interactions with the app and tracked patients' progress through time.
Machine Learning Engineer
WeVat
- Developed machine learning computer vision models with TensorFlow to confirm that retailers' receipts in images were compliant with UK legal norms.
- Solved performance and scalability problems in company databases, improving their schemas and the general architecture.
- Designed and implemented the dashboard solution for the company, including carrying out the design and implementation of each dashboard and integrating the data sources that the company uses that formerly were not integrated.
- Built machine learning models with XGBoost (gradient boosting) to predict the company’s volume of customers.
- Detected anomalies and potential fraud in data, leading to changes in the platform.
- Created NLP models to detect receipt features and prevent fraud.
Senior Data Engineer
Morsum
- Designed and led the implementation of an inpatient food ordering project for hospitals based on SMART and FHIR to connect with EHRs.
- Led implementation of ETL into Google Cloud Platform, using Pub/Sub, Google Dataflow (Java SDK), and Google BigQuery.
- Developed Python APIs to interface between the web and mobile apps and machine learning models.
- Created machine learning market basket analysis recommendation models for food ordering.
- Made scripts in Apache Spark to handle the big data for company products. I parallelized NLP-related tasks of matching food ingredients from many different sources using Spark.
Developer | System Administrator
Gumma SRL
- Created in-house software for making quotations with custom company requirements and also inserted it into the SugarCRM.
- Developed an in-house payroll software for construction projects.
- Led the project involving server virtualization using VMware.
- Extended an active directory network to regional offices based in Brazil and Uruguay.
Help Desk
Grupo Estisol
- Provided technical support to internal users and infrastructure support on a wide array of technology solutions, including Zentyal Servers, VMware ESXi, and Active Directory.
- Handled data back-ups and restoration in Windows Server Active Directory systems.
- Led the implementation of Network Attached Storage (NAS) for shared files company-wide.
Experience
Interview About Recuerdos
https://www.youtube.com/watch?v=DhtjRrXo_ScArticle About Caretronics
Harvard CS109A | Final Project
https://harvardfinalproject.wordpress.com/App for Training the Memory of Those in the Uruguayan Elderly Population
Parrot Detector
https://github.com/maiskovich/parrot_2000Education
Master's Degree in Life Science Informatics - Bioinformatics and Systems Medicine
University of Helsinki - Helsinki, Finland
Master of Science Degree in Data Science
Universidad Austral - Buenos Aires, Argentina
Certificate in Data Science
Harvard Extension School - Boston, MA, USA
Commercial Pilot License in Aviation
ETAP - Buenos Aires, Argentina
Certifications
Data Engineering for Google Cloud Platform
ROI Training
Skills
Libraries/APIs
PyTorch, Keras, XGBoost, Scikit-learn, TensorFlow, Pandas, NumPy, SpaCy, OpenCV, Mirth Connect, Natural Language Toolkit (NLTK), Pydantic
Tools
BigQuery, Apache Beam, Cloud Dataflow, SugarCRM, VMware, Biopython, Amazon SageMaker
Languages
SQL, Python, Java, Snowflake, JavaScript, PHP, R
Frameworks
Flask, Apache Spark, Spark, Ionic, LlamaIndex, LangGraph
Paradigms
ETL, Fast Healthcare Interoperability Resources (FHIR)
Platforms
Linux, Docker, Amazon EC2, Google Cloud Platform (GCP), Weights & Biases, Windows Server, Amazon Web Services (AWS), Kubernetes, NiftyNet, EPIC Electronic Health Records (EHR)
Storage
MySQL, Databases, Relational Databases, PostgreSQL, Google Cloud, Amazon S3 (AWS S3)
Industry Expertise
Healthcare, Bioinformatics, Transcriptomics
Other
Computer Vision, Artificial Intelligence (AI), Data Science, Convolutional Neural Networks (CNNs), Deep Learning, Data Analytics, Data Analysis, BERT, Google BigQuery, Machine Learning, Image Recognition, Natural Language Processing (NLP), Data Engineering, Data Warehousing, Detectron2, Word2Vec, Data Mining, Data Modeling, Neural Networks, Deep Neural Networks (DNNs), Image Processing, Artificial Neural Networks (ANN), Generative Pre-trained Transformers (GPT), DICOM, HL7, OpenEMR, Association Rule Learning, Statistics, Medical Imaging, Optical Character Recognition (OCR), 3D Reconstruction, Depth Prediction, Object Tracking, Pub/Sub, Genomics, Object Detection, Machine Learning Operations (MLOps), Semantic Segmentation, Data Reporting, 3D Image Processing, Transformers, Hugging Face, OpenAI, Large Language Models (LLMs), Transformer-XL, Retrieval-augmented Generation (RAG), Natural Language Understanding (NLU), IT Management, Technical Leadership, FastAPI, VMware ESXi, Picture Archiving & Communication Systems (PACS), Biology, Facial Recognition, Data Versioning, Point Clouds, LiDAR, Stable Diffusion, Interviews, Startups, LangChain, scRNA-seq, RNA Sequencing, Bioconductor, Health, Immunofluorescence imaging, WSI, Laboratory Information Management System (LIMS), Oncology, MLflow, Agentic AI, Spatial Proteomics
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