Icaro Augusto Maccari Zelioli

- Support for the tactical and operational planning of the client’s digital transformation initiative. - Technical leadership in data, supporting the client’s Predictive Maintenance system: - Mapping data sources - Mapping the proposed solution and architecture - Understanding algorithms and solutions: - OCR system for identifying process and instrumentation entities (P&ID and PFDs) of FPSO platforms - Text classification system, NLP, and LLMs for extracting failure modes (FMEA) from work orders - Monitoring rule system for FPSO platform assets - Proposing best practices and modeling alternatives - Mapping and supporting technical decisions focusing on speed, scalability, and quality - Worked alongside client's Solutions Architect to design architectures that are scalable, reliable and reprodutible across diverse kinds of industrial assets - Designing and defining processes for evaluating use cases, considering scalability, technical complexity, and value generation. - Built a Dockerized, Kubernetes-orchestrated microservices platform (integrated with Cognite Data Fusion) with Kubernetes-native parallel processing, automated Bash tooling for repeatable deploys and onboarding, and end-to-end test coverage—enabling faster fleet-scale monitoring, reliable CI/CD rollouts, and higher operational confidence.

As a peer reviewer, I support the editorial process by critically evaluating manuscripts that merge environmental chemical engineering with advanced data-driven methods. - Assess research in sustainability, process intensification, water treatment, and advanced materials. - Review studies applying Machine Learning, Deep Learning, and Generative AI to chemical and environmental engineering challenges. - Examine methodology, statistical robustness, chemical analysis, and generalizability to ensure rigor and applicability of findings. - Provide constructive feedback to strengthen clarity, reproducibility, and impact within the global research community.

I teach 6 subjects in bootcamps and post-graduation levels courses - Artificial Intelligence Algorithms for Classification - Artificial Intelligence Algorithms for Clusterization - Neural Networks with Tensorflow - Deep Learning with Tensorflow - Fundamentals of Machine Learning with Scikit-Learn - Model Operationalization with MLOps

I help to manage and technically lead Analytics, Hybrid AI and Generative AI projects for process and general manufacturing industries. - Developed LLM agent flows using RAG techniques to answer queries related to telemetry data, with a focus on operational efficiency and dynamic analysis through interactive dashboards. - Led technical teams by prioritizing and delegating tasks, managing stakeholder expectations, and conducting progress monitoring meetings. - Implemented managerial and tactical reports, identified and corrected data inconsistencies, leading to global recognition for innovative and accurate approaches. - Executed tasks related to prompt engineering, process flow construction, and SQL queries to support quality management and product traceability, contributing to industrial innovation. - Created applications that combine process automation and RAG for document management to enhance the creation and monitoring of agile projects, overseeing the entire project development from technology selection to testing. - Developed methodologies for data management and governance, aiming for greater democratization and scalability in the use of industrial data. - Use of CI/CD tools to deploy APIs using Docker - Built administrative tools automation solutions to increase efficiency and productivity - Business processes mapping (BPMN) focusing on the identification of relevant databases for AI application - Built automated Analytics Engineering scripts of mining industrial data, which reduced significantly the time spent of multiple industrial databases integration and insight generation

I teach in two Specialization Courses in the School of Chemical Engineering; - Artificial Intelligence Applied to Process Engineering - Statistical Tools for Industrial Data Analysis - Process Analysis and Simulation - Digital Transformation in Industry - Process Operation and Supervisory Platforms - Digital Twins and Simulations

Great focus in the industrial data analysis, and process monitoring (especially oil and gas) 1. New-business development support, having direct contact with national and international customers 2. Team mentorship and people training in Data Science 3. Technical proposals development, concerning data science, machine learning and artificial intelligence to industrial applications 4. End-to-end data projects development (extraction, data preprocessing, exploratory data analysis, modeling, validation and deploy) 5. Data Leader of the Disabled People Program for Training in Technology 6. Solution requirement analysis, business cases development to justify new project investments, project’s techinical and economic viability analysis studies. 7. Team leadership of multiple and distinct industrial data science projects, management of several stakeholders’ expectations and demands; task attribution and code reviews from junior team members - I have had the opportunity to mentor several process and reliability engineers at a leading multinational company in the oil and gas sector in developing data solutions focused on digitizing routine processes and decision-making. - In another project, I led and provided technical guidance to the entire team in creating algorithms for managing energy and utilities in a chemical industry setting. - This involved real-time automated control charts, neural network-based forecasting algorithms, algorithms for detecting and identifying multivariate anomalies, and real-time equipment optimization using genetic algorithms. - I lead and implement technology and data solutions for global corporate areas of the business, including capital investments, reliability, and operational efficiency. This includes ETL pipelines, real-time monitoring models, and dashboards.

1. Data pipelines (extraction, processing, modeling and deployment) 2. Data Technical Leadership 3. Mentorship and support to junior teams 4. Leadership in knowledge management 5. Responsible by creating trainings in statistics and data science 6. Internal consulting of best coding practices, project management and statistical and AI/ML modeling techniques 7. Support member for multiple project teams of industrial predictive maintenance and soft sensor development Hybrid model development with potential gains estimated in USD 1.1 MM / year, reduction of fuel demand between 2.5 - 3.0 GJ / h and reduction of carbon emissions between 2 - 5 kt / year. ROI approximately of 200 %. 8. Technical support member for the commercial team in elaborating services proposals for domestic and international clients. The focus is in strategy and architecture development for the data projects 9. Volunteer member of the Training Program for people with disabilities

1. Implementation of Machine Learning models for industry data 2. Data preprocessing to ensure modeling quality 3. Exploratory and statistical data analysis for business problem understanding 4. Non-supervised learning and dimensionality reduction for process anomalies detection and identification 5. Training of classification models using unbalanced databases to predict very uncommon process anomalies 6. Medium leadership matter, tactical and operational project management 7. Communication management between key stakeholders and technical project teams from multiple initiatives 8. Knowledge management and "in-office learning" initiatives leadership

Assistant professor in the following modules: - Databases I - Machine Learning I - Non Structured Data I - Computer Vision and Audio Processing - Non Structured Data II - Natural Language Processing - Introduction to Programming Logic - Python Main teacher in the folowing modules - Machine Learning II - Machine Learning III - Programming Techniques I - Databases I

Mediation between students, professors and Extension coordinator for main doubts about the course and its administrative processes. Management and organization of course files repository. General assistance to course teachers during classes. Student technical assist related to course contents and subjects

I teach the Non-Supervised Machine Learning subjects, with the focus in Clustering Analysis (KMeans, DBSCAN, Hierarchical and Gaussian Mixture Models), Factorial Analysis (EFA), Principal Component Analysis (PCA and KernelPCA), Discriminant Analysis

Introductory course on data science for undergraduate students of the Superior School of Information Technology of Infnet Institute. In this course, we will talk about concepts in data science projects, data processing, exploratory analysis and modeling.

My PhD project involves combining knowledge from chemical engineering and Generative AI. The idea is to build a product that enables a process engineer to interact with an intelligent agent capable of performing scenario analyses, uncertainty quantification, and facilitating decision-making related to everyday industrial activities.

The purpose of the work is to present a novel architecture for model hybridization for first principle approaches along with data-driven models. The work also presents a real application of this model to petrochemical process simulation and optimization.

Capstone Project: Application of hybrid machine-learning and first-principles model to predictive maintenance of heat exchangers (Grade received: 10 / 10) - Introduction to Chemical Processes - Thermodynamic Relations and Phase Equilibrium - Momentum Transfer Phenomena - Non-Newtonian Fluid Flow - Pump and Compressors Design and Selection - Fluidization, Cyclones and Pneumatic Transport - Chemical Reaction Engineering - Heat Exchangers without combustion - Project Management and Economic Assessment - Instrumentation and Control of Chemical Processes - Vessels, Tanks and Materials for Process Equipments - Safety Dispositives and NR-13 - Chemical Industry Utilities - Refining and Petrochemical Elements - Process Risk Management at Chemical Industry - Separation Processes: Distillation, Extraction and Absorption

- Basic Statistics - Quality Management Systems implantation (foccused on ISO 9001: 2015; ISO 14001: 2015 and ISO 45001: 2018) - Quality Improvement Tools (QFD, Canvas Business Model, etc.) - Quality Audits - Project Management - Advanced Quality Management Systems (TPM, Lean Manufacturing, Strategic Planning techniques) - Communication Effective Techniques - Process Improvement - Sic Sigma - Integration of Environmental Management System, Health and Occupational Safety and Quality Management

- XVI Chemical Engineering Week - Ago / 2013 - School of Chemical Engineering - UNICAMP - XVII Chemical Engineering Week - Ago / 2014 - School of Chemical Engineering - UNICAMP - 2nd of 47 students - Particular professor of several disciplines, such: Physics, Chemistry, Thermodynamis, Calculus, Linear Algebra, Statistics, Engineering specific-disciplins