Daniele Zago

Daniele Zago

Data & Decision Scientist

Data Scientist @OPTIT S.r.l., Bologna

Research-oriented data and decision scientist bridging statistics, machine learning, and mathematical optimization. I design and implement end-to-end solutions spanning stochastic optimization algorithms, ML models, and production software on enterprise stacks, with a track record in both academic research and industrial deployment.
Contact

Education

University of Padova

2021 — 2024

Doctor of Philosophy (Ph.D.)

Statistical Sciences

Padova, Italy

  • Advisors: Prof. Giovanna Capizzi (University of Padua), Prof. Peihua Qiu (University of Florida)
  • Research topics: Online outlier detection, stochastic optimization
  • Research stay: University of Florida (Jan 2023 - Dec 2023), supervised by Prof. Peihua Qiu

University of Padova

2019 — 2021

Master of Science (M.Sc.)

Statistical Sciences

Padova, Italy

  • Final grade: 110/110 cum Laude
  • GPA: 29.5/30
  • Thesis topic: Bayesian nonparametric mixture models

University of Padova

2016 — 2019

Bachelor of Science (B.Sc.)

Statistics for Technology and Sciences

Padova, Italy

  • Final grade: 110/110 cum Laude
  • GPA: 29.2/30
  • Thesis topic: Applied Bayesian modelling

Summer Schools

2020 — 2022

Advanced training programs

University of Perugia (July 2020)

  • Summer School in Mathematics
  • Functional analysis and mathematical statistics

INFN, Bertinoro (October 2022)

  • Thirteenth INFN International School on Efficient Scientific Computing
  • Efficient C++ programming
  • GPU programming with CUDA

Experience

Experienced in mathematical optimization, machine learning, and statistical computing, with applications in operations research and industrial statistics.

2024-10 — present

Data Scientist

OPTIT S.r.l

Bologna, Italy

Research and development of mathematical optimization algorithms for vehicle routing and facility location, machine learning pipelines for energy forecasting and anomaly detection.

Data Scientist

OPTIT S.r.l

Bologna, Italy

2024-10 — present
  • Research and development of solutions to the periodic vehicle routing problem with clustered routes for multi-day strategic fleet management, including front-end development for interactive visualization and exploration (Python, Java, Hexaly, Gradle, Docker, TypeScript, Next.js)
  • Improved minimum cost flow approaches for crossdock facility selection when serving customers from warehouses with potential crossdock drop-off of goods (Hexaly, Xpress Mosel, Docker)
  • Developed forecasting and anomaly detection pipelines for power plant energy production/consumption and heat and electricity generation using machine learning methods (Python, scikit-learn, MLFlow)
2021 — 2024

Ph.D. Student in Statistical Sciences

University of Padua

Padova, Italy

Doctoral research in online outlier detection and stochastic optimization, with publications in top-tier journals and real-world consulting applications.

Ph.D. Student in Statistical Sciences

University of Padua

Padova, Italy

2021 — 2024

During my Ph.D. in Statistical Sciences at the University of Padua (supervisors: Prof. Giovanna Capizzi and Prof. Peihua Qiu), I focused on advancing online outlier detection and stochastic optimization methods for statistical process monitoring.

My research resulted in publications in top-tier journals including the Journal of Quality Technology, Statistics & Computing, Quality Engineering, and the Journal of Statistical Software. I also developed the StatisticalProcessMonitoring.jl package, a comprehensive Julia framework for real-time process monitoring.

Additionally, I applied my expertise in real-world contexts through consulting projects on sequential monitoring systems for structural health monitoring, involving algorithm development in R and C++.

Thesis: Advanced Statistical Process Monitoring using Simulation-Based Algorithms (2024)

Research stay: University of Florida (Jan 2023 - Dec 2023), supervised by Prof. Peihua Qiu
2023-07 — 2023-10

Statistical Consultant

Expin S.r.l.

Padova, Italy

Development of sequential monitoring systems and optimal alarm threshold algorithms for structural health monitoring.

Statistical Consultant

Expin S.r.l.

Padova, Italy

2023-07 — 2023-10

Developed a sequential monitoring system in R and C++ for anomaly detection in vibrating string strain gauges, addressing critical challenges in structural health monitoring of civil infrastructure.

Designed a novel algorithm for optimal alarm threshold selection using stochastic optimization techniques, enabling adaptive and efficient detection of structural anomalies while minimizing false alarms.

The project combined statistical process control, computational efficiency (C++ implementation), and modern optimization theory to deliver a production-ready monitoring solution.
2022-10 — 2022-12

Teaching Assistant

University of Padua - Dept. of Developmental Psychology

Padova, Italy

Lectures on factor models and questionnaire analysis, laboratory sessions on R programming for psychology students.

Teaching Assistant

University of Padua - Dept. of Developmental Psychology

Padova, Italy

2022-10 — 2022-12

Delivered lectures on factor models and questionnaire analysis for graduate students in psychology, bridging statistical theory with practical applications in psychological research.

Conducted hands-on R programming laboratory sessions, teaching students statistical computing skills for data analysis and visualization in psychological studies.

Supported students in developing their research projects, providing guidance on statistical methodology and computational implementation.
2017-09 — 2019-01

Academic Tutor

University of Padua - Dept. of Statistical Sciences

Padova, Italy

Calculus lessons and interactive workshops for undergraduate statistics students in preparation for final exams.

Academic Tutor

University of Padua - Dept. of Statistical Sciences

Padova, Italy

2017-09 — 2019-01

Provided calculus lessons and interactive workshops for undergraduate students in the Statistics program, helping students develop strong mathematical foundations for their statistical education.

Conducted regular tutoring sessions covering topics in mathematical analysis, including limits, derivatives, integrals, and series, with emphasis on problem-solving techniques.

Organized guided exercise sessions to prepare students for final examinations, improving pass rates and student confidence in mathematical reasoning.

Publications & Software

Research in statistical computing, mathematical optimization, and decision sciences.

Software

StatisticalProcessMonitoring.jl

StatisticalProcessMonitoring.jl Julia

Journal of Statistical Software

A Julia package for real-time statistical process monitoring, integrating advanced algorithms and control charts to handle complex data types, such as sequential data, functional data, and structured observations.

Click to read more

StatisticalProcessMonitoring.jl

StatisticalProcessMonitoring.jl Julia

Journal of Statistical Software

A Julia package for real-time statistical process monitoring, integrating advanced algorithms and control charts to handle complex data types, such as sequential data, functional data, and structured observations.

Paper Source Code

Published Articles

A doubly-stochastic constrained optimization algorithm

A doubly-stochastic constrained optimization algorithm

Journal of Quality Technology

I designed a novel stochastic optimization algorithm with stochastic constraints, aimed at optimizing control chart tuning parameters with greater efficiency compared to traditional numerical methods.

Click to read more

A doubly-stochastic constrained optimization algorithm

A doubly-stochastic constrained optimization algorithm

Journal of Quality Technology

I designed a novel stochastic optimization algorithm with stochastic constraints, aimed at optimizing control chart tuning parameters with greater efficiency compared to traditional numerical methods.

View Article
An improved bisection-type algorithm for control chart calibration

An improved bisection-type algorithm for control chart calibration

Statistics and Computing

I developed a modified bisection algorithm for computing control limits in complex settings where standard methods are inefficient. The approach removes the need for a predefined search range and scales efficiently to multi-chart scenarios.

Click to read more

An improved bisection-type algorithm for control chart calibration

An improved bisection-type algorithm for control chart calibration

Statistics and Computing

I developed a modified bisection algorithm for computing control limits in complex settings where standard methods are inefficient. The approach removes the need for a predefined search range and scales efficiently to multi-chart scenarios.

View Article
Alternative parameter learning schemes for monitoring process stability

Alternative parameter learning schemes for monitoring process stability

Quality Engineering

I formalized the theoretical behavior of parameter learning schemes in relation to outlier detection performance in control charts. This work enabled the generalization of alternative parameter learning methods, leading to a more efficient and accurate detection scheme compared to traditional approaches.

Click to read more

Alternative parameter learning schemes for monitoring process stability

Alternative parameter learning schemes for monitoring process stability

Quality Engineering

I formalized the theoretical behavior of parameter learning schemes in relation to outlier detection performance in control charts. This work enabled the generalization of alternative parameter learning methods, leading to a more efficient and accurate detection scheme compared to traditional approaches.

View Article
A general framework for monitoring mixed data

A general framework for monitoring mixed data

Journal of Quality Technology

We developed a general methodology to monitor processes involving mixed-type data (continuous, ordinal, categorical), common in real-world applications. The method enables effective sequential monitoring under serial correlation.

Click to read more

A general framework for monitoring mixed data

A general framework for monitoring mixed data

Journal of Quality Technology

We developed a general methodology to monitor processes involving mixed-type data (continuous, ordinal, categorical), common in real-world applications. The method enables effective sequential monitoring under serial correlation.

View Article

Under Review & In Preparation

Monitoring of complex geometrical shapes

Monitoring of complex geometrical shapes Submitted

I developed an innovative statistical quality control method for detecting shape defects in complex geometries obtained via additive manufacturing. I introduced a novel nonparametric control chart based on kurtosis analysis which provides superior defect detection for 3D-printed objects compared to existing approaches.

Click to read more

Monitoring of complex geometrical shapes

Monitoring of complex geometrical shapes Submitted

I developed an innovative statistical quality control method for detecting shape defects in complex geometries obtained via additive manufacturing. I introduced a novel nonparametric control chart based on kurtosis analysis which provides superior defect detection for 3D-printed objects compared to existing approaches.
Likelihood-ratio monitoring of processes with mixed data

Likelihood-ratio monitoring of processes with mixed data In Prep

Technometrics

We developed a likelihood-ratio methodology to monitor processes involving continuous and categorical data. The approach makes use of adaptive kernel density estimation in order to approximate the likelihood ratio, thus providing efficient detection power.

Click to read more

Likelihood-ratio monitoring of processes with mixed data

Likelihood-ratio monitoring of processes with mixed data In Prep

Technometrics

We developed a likelihood-ratio methodology to monitor processes involving continuous and categorical data. The approach makes use of adaptive kernel density estimation in order to approximate the likelihood ratio, thus providing efficient detection power.

Conference Presentations

Selected presentations on stochastic optimization, statistical process monitoring, and decision sciences.

Nov 2025

Efficient algorithms for the calibration of control limits

Università degli Studi di Padova , Padova, Italy

Invited Seminar
Sep 2025

Optimal constrained design of control charts using stochastic approximations

ENBIS-25 Conference , Piraeus, Greece

Invited Talk
Oct 2023

Optimal constrained design of control charts using stochastic approximations

2023 INFORMS Annual Meeting , Phoenix, AZ, USA

Invited Talk
Sep 2022

Profile monitoring based on adaptive parameter learning

Statistical methods and models for complex data , Padova, Italy

Poster
Jun 2022

Bayesian nonparametric multiscale mixture models via Hilbert-curve partitioning

2022 ISBA World Meeting , Montréal, Canada

Poster