● Teach in the course of Industrial Optimization with Bioinspired Computing, in the Diploma of Specialization in Development of Application with Artificial Intelligence, where we cover topics related to collective intelligence algorithms, genetic algorithms and neuroevolution, and their applications in optimization problems.

● Implemented a pilot project where I developed a RAG system using langchain and LLMs, such as llama3.1:8b (Ollama) and gpt-4o-mini (OpenAI), to analyze and extract key information from glossaries of terms and user manuals of the reports and dashboards provided by the IT Department, improving the information queries and the resolution of doubts.● Developed and deployed ETL and ML pipelines in AWS cloud, resulting in automated detection of student dropout with 89% recall. I defined the project template on Amazon S3, facilitating efficient storage and retrieval of the datasets. I used AWS Glue for the data extraction and preparation, and Amazon Sagemaker with ML frameworks such as pandas and scikit-learn for the data preprocessing, model training and model evaluation.● Developed ETL pipelines in AWS Glue and created dashboards in Power BI, which automated the monitoring of the services provided by the IT Department, increasing satisfied users by 35% according to surveys.● Developed data pipelines in Python and created data quality control reports and dashboards in Power BI, which helped in the management of IT Department projects and reduced data anomalies by 90%.● Created dashboards in Power BI, which centralized information on the student life cycle at the university, making it more accessible and complete, reducing the creation of ad-hoc reports from various sources by 60%.● Created dashboards in Microstrategy, which helped in the accreditation process of university careers, reducing the time spent manually creating reports and KPIs requested by the accrediting agencies by 70%.● Migrated ETL pipelines from Pentaho and DataStage 7 to DataStage 11, reducing the data processing time by 30%.● Provided technical guidance and mentoring to junior data analysts, enhancing their technical skills and productivity, and fostering a collaborative team environment.

● Developed a platform for telephone surveys of graduates, whose friendly interface and the automated saving of responses increased the speed of the survey and ensured the consistency and availability of the data.● Developed an Android mobile application for public voting for the best films of the Lima Cultural Film Festival, which reduced by 50% the time spent on the manual logistics process carried out by the event's collaborators.● Developed a monitoring platform to ensure students answer the mandatory psychological tests of the Health Service, resulting in a response rate of nearly 100%.

● Developed various features of a specialized survey system using Java and JavaScript, and Oracle database.● Documented various processes of the IT Department.

● Collaborated with data scientists and electronic engineers to develop an air quality monitoring system in urban areas with low-cost measurement modules and AI techniques. I made a comparative study of ML models, such as XGBoost and LightGBM, to estimate the concentration level of PM2.5 in urban areas not registered by the measurement modules, reaching a 92% R2 score. My research was presented at the SimBig 2020 international conference and published by Springer.● Identified the most discussed topics by Twitter users related to Fujimorism and referents of politics in Peru. I used tweepy to extract their tweets, I pre-processed them using NLP frameworks such as NLTK, spaCy and Gensim, and I detected the most discussed topics about Peruvian politics using the Latent Dirichlet Allocation (LDA) model.● Evaluated ML models for credit card fraud detection, such as Logistic Regression, Random Forest, k-NN and Naive Bayes. I evaluated the impact of the various data balancing techniques, such as SMOTE. I reached 83% recall and 82% precision; the relevance of the metrics depends on the use case of the bank.

● Taught practical classes, and reviewed and graded the student’s assessments on the following courses: Databases [INF246], Business Intelligence [INF357], Programming Fundamentals [1INF01], Introduction to Computing [INF117].