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Cristovão Freitas Iglesias Junior

Cristovão Freitas Iglesias Junior

(Alumni) PhD

Personal Email email
  • Research Associate
  • Ph.D. in Computer Science (Machine Learning), University of Ottawa
  • Member of CARG Health-Devices

Bio

I am a ML Engineer with a robust background in software engineering. My experience spans across various projects, from molecular dynamics simulations to digital twins development. Currently, I completed a Ph.D. in Computer Science (Machine Learning) at the University of Ottawa. With over 10 years of experience in ML, I bring expertise in deep learning, Bayesian modeling, federated learning, and data fusion for time series analysis, regression, classification, forecasting and clustering tasks. I excel in both independent and collaborative environments, effectively bridging technical and business domains by translating complex technical concepts into actionable insights. My strengths lie in innovative problem-solving, optimizing data pipelines and algorithms with advanced statistical techniques, and maintaining a commitment to continuous learning and staying at the forefront of emerging analytics technologies.

Research Interests

  • Data Science & Machine Learning: deep learning, Bayesian modeling, NLP, LLM, data fusion, scientific machine learning, nonlinear Kalman filters, time-series analysis, uncertainty quantification and federated learning
  • Programming & Development: Python, SQL (PostgreSQL, MySQL), R, Julia, Ruby on Rails, Docker, AWS Stack, TensorFlow, PyTorch, JAVA, C++.

List of Papers (Google Scholar )

  • RITA: Automatic Framework for Designing of Resilient IoT Applications (LAFUSION 2024)
    • Code
    • Skills: Python, LLM, Fine-tuning
  • Two Students: Enabling Uncertainty Quantification in Federated Learning Clients (BDU – NeurIPS 2024)
    • Code
    • Skills: Python, Julia, Federated Learning
  • Limitations of Joint and Dual Nonlinear Kalman Estimators in Low-Cost Bioprocess Monitoring (LatinX-ICML 2024)
    • Code
    • Skills: Julia, Global Optimization, Nonlinear Kalman Estimators, Bayesian Inference
  • Batch Bayesian Auto-Tuning for Nonlinear Kalman Estimators (under review for Nature Scientific Reports 2024)
    • Code
    • Skills: Julia, Global Optimization, Genetic Algorithm, Bayesian Optimization, Bayesian Inference
  • Hybrid Nonlinear Kalman Estimators for Low-Cost Bioprocess Monitoring (under review)
    • Code
    • Skills: Julia, Optimization, Bayesian Inference, Filters, Deep Learning
  • How Not to Make the Joint Extended Kalman Filter Fail with Unstructured Mechanistic Models (Sensors Journal – 2024)
    • Code
    • Skills: Julia, Optimization, Bayesian Inference
  • Automated Extraction of IoT Critical Objects from IoT Storylines, Requirements and User Stories via NLP (SDS – 2023)
    • Code
    • Skills: NLP, Python, TensorFlow, PyTorch, BERT, Transformers, ElMo
  • An Architectural Design Decision Model for Resilient IoT Application (arXiv Preprint – 2023)
  • DEMDE: Decision Making Design based on Bayesian Network for Personalized Monitoring System (FUSION – 2023)
    • Code
    • Skills: Bayesian Network
  • rAAV Manufacturing: The Challenges of Soft Sensing during Upstream Processing (Bioengineering Journal – 2023)
  • A Real-Time Respiration Monitoring and Classification System Using a Depth Camera and Radars (Frontiers – 2022)
    • Skills: Python, Classification, Data Fusion
  • Monitoring the Recombinant Adeno-Associated Virus Production Using Extended Kalman Filter (Processes Journal – 2022)
    • Code
    • Skills: Julia, Bayesian Inference for Optimization, EKF, NODE
  • Handling Massive Proportion of Missing Labels in Multivariate Long-Term Time Series Forecasting (IC-MSQUARE 2021)
    • Code
    • Skills: Python, TensorFlow, LSTM, Deep Learning
  • A Domain Model for Personalized Monitoring System Based on Context-Aware Data Fusion (FUSION – 2019)
  • Agile Software Development Learning Through Open Hardware Project (WBMA – 2015)
Visit his webpage for a deeper dive or to have a chat!