Teaching · Research · Engineering
Professor at PUCP. Researcher.
Artificial intelligence, data science and operations research for decision-making. A career connecting university teaching, research and professional practice.
Engineering, knowledge and professional practice.
01 / Curriculum vitae
Full-time Professor in the Department of Engineering at Pontificia Universidad Católica del Perú, Industrial Engineering Section. Undergraduate and graduate teaching, applied research and thesis supervision.
Coordinator of the Innovation Management Area and member of the Steering Committee for the master’s program in Innovation and Technology Management and Policy.
Problem formulation, research design and evaluation of results in courses and thesis seminars.
Thesis approved with the distinction “Sobresaliente”.
Professional degree thesis with the distinction “Sobresaliente”.
MicroMasters program credential.
More than fifteen years leading ERP implementation projects, specialising in SAP Business One. Information technology consulting and systems integration for business management. General manager of CHANOSA.
02 / Research
A research agenda in artificial intelligence, modelling and decision-making, with an emphasis on validation, interpretability and reproducible evidence.
Machine learning, data mining and statistical modelling. Assessment of assumptions, interpretability and model validation.
Operations research, simulation and systems analysis under constraints and uncertainty.
Data provenance, claim verification and reproducibility. Studies of model limitations and human–AI collaboration.
Artificial intelligence for decision-making under uncertainty, with an emphasis on machine learning, optimisation and trustworthy systems.
Objectives, main findings, scope and status of each project. The research catalog distinguishes work in development, submitted manuscripts and research lines on hold.
View the research catalog2022 · Springer · Book chapter
2022 · Springer · Book chapter
03 / CHANOSA
Each project starts with a business need and establishes clear criteria to assess what changes.
Systems aligned with business needs
Align processes, requirements and digital solutions. Identify friction, prioritise improvements and define how implementation quality will be verified.
Analysis in support of a decision
Translate business questions into analytical problems. Assess data quality, develop modelling alternatives and evaluate their usefulness in the actual operating context.
From an idea to a plan of action
Structure innovation initiatives and develop the capabilities to deliver them. Bring objectives, experimentation and learning together within the organisation.
04 / Professional approach
Technology creates value when it addresses a well-defined problem. The work centres on decisions, evidence and observable outcomes.
Clarify the problem, context and constraints. Identify the decision that needs support.
Compare alternatives and agree on scope, deliverables and evaluation criteria.
Test assumptions through data and pilots. Document results and limitations.
Deliver an understandable solution and a path for adoption, monitoring and improvement.
05 / Contact
Research collaboration, doctoral opportunities, teaching and academic supervision. Technology and consulting projects through CHANOSA.