Academic CV Pro Hugo Theme

New Analysis in the Preliminary Design for LNG and LPG Ships

Wed, 01 Oct 2025 00:00:00 +0000

HADAD: Hexagonal A-Star with Differential Algorithm Designed for Weather Routing

Sat, 01 Mar 2025 00:00:00 +0000

HADAD: Hexagonal A-Star with Differential Algorithm Designed for Weather Routing

Thu, 05 Dec 2024 00:00:00 +0000

Hybrid Search Method for Zermelo's Navigation Problem

Sat, 01 Jun 2024 00:00:00 +0000

Classification of Exceptional Jacobi Polynomials

Mon, 01 Jan 2024 00:00:00 +0000

ICMS Mini-Symposium 2024

Mon, 01 Jan 2024 00:00:00 +0000

🎉 Easily create your own simple yet highly customizable blog

Fri, 27 Oct 2023 00:00:00 +0000

Welcome 👋

Overview

The Wowchemy website builder for Hugo, along with its starter templates, is designed for professional creators, educators, and teams/organizations - although it can be used to create any kind of site
The template can be modified and customised to suit your needs. It’s a good platform for anyone looking to take control of their data and online identity whilst having the convenience to start off with a no-code solution (write in Markdown and customize with YAML parameters) and having flexibility to later add even deeper personalization with HTML and CSS
You can work with all your favourite tools and apps with hundreds of plugins and integrations to speed up your workflows, interact with your readers, and much more

Get Started

👉 Create a new site
📚 Personalize your site
💬 Chat with the Wowchemy community or Hugo community
🐦 Twitter: @wowchemy @GeorgeCushen #MadeWithWowchemy
💡 Request a feature or report a bug for Wowchemy
⬆️ Updating Wowchemy? View the Update Guide and Release Notes

Crowd-funded open-source software

To help us develop this template and software sustainably under the MIT license, we ask all individuals and businesses that use it to help support its ongoing maintenance and development via sponsorship.

As a token of appreciation for sponsoring, you can unlock these awesome rewards and extra features 🦄✨

Ecosystem

Bibtex To Markdown: Automatically import publications from BibTeX

Inspiration

Learn what other creators are building with this template.

Features

Page builder - Create anything with no-code blocks and elements
Edit any type of content - Blog posts, publications, talks, slides, projects, and more!
Create content in Markdown, Jupyter, or RStudio
Plugin System - Fully customizable color and font themes
Display Code and Math - Code syntax highlighting and LaTeX math supported
Integrations - Google Analytics, Disqus commenting, Maps, Contact Forms, and more!
Beautiful Site - Simple and refreshing one-page design
Industry-Leading SEO - Help get your website found on search engines and social media
Media Galleries - Display your images and videos with captions in a customizable gallery
Mobile Friendly - Look amazing on every screen with a mobile friendly version of your site
Multi-language - 35+ language packs including English, 中文, and Português
Multi-user - Each author gets their own profile page
Privacy Pack - Assists with GDPR
Stand Out - Bring your site to life with animation, parallax backgrounds, and scroll effects
One-Click Deployment - No servers. No databases. Only files.

Themes

Wowchemy and its templates come with automatic day (light) and night (dark) mode built-in. Visitors can choose their preferred mode by clicking the sun/moon icon in the header.

Choose a stunning theme and font for your site. Themes are fully customizable.

License

Released under the MIT license.

📈 Communicate your results effectively with the best data visualizations

Thu, 26 Oct 2023 00:00:00 +0000

Wowchemy is designed to give technical content creators a seamless experience. You can focus on the content and Wowchemy handles the rest.

Use popular tools such as Plotly, Mermaid, and data frames.

Charts

Wowchemy supports the popular Plotly format for interactive data visualizations. With Plotly, you can design almost any kind of visualization you can imagine!

Save your Plotly JSON in your page folder, for example line-chart.json, and then add the {{< chart data="line-chart" >}} shortcode where you would like the chart to appear.

Demo:

You might also find the Plotly JSON Editor useful.

Diagrams

Wowchemy supports the Mermaid Markdown extension for diagrams.

An example flowchart:

```mermaid
graph TD
A[Hard] -->|Text| B(Round)
B --> C{Decision}
C -->|One| D[Result 1]
C -->|Two| E[Result 2]
```

renders as

graph TD A[Hard] -->|Text| B(Round) B --> C{Decision} C -->|One| D[Result 1] C -->|Two| E[Result 2]

An example sequence diagram:

```mermaid
sequenceDiagram
Alice->>John: Hello John, how are you?
loop Healthcheck
John->>John: Fight against hypochondria
end
Note right of John: Rational thoughts!
John-->>Alice: Great!
John->>Bob: How about you?
Bob-->>John: Jolly good!
```

renders as

sequenceDiagram Alice->>John: Hello John, how are you? loop Healthcheck John->>John: Fight against hypochondria end Note right of John: Rational thoughts! John-->>Alice: Great! John->>Bob: How about you? Bob-->>John: Jolly good!

An example class diagram:

```mermaid
classDiagram
Class01 <|-- AveryLongClass : Cool
Class03 *-- Class04
Class05 o-- Class06
Class07 .. Class08
Class09 --> C2 : Where am i?
Class09 --* C3
Class09 --|> Class07
Class07 : equals()
Class07 : Object[] elementData
Class01 : size()
Class01 : int chimp
Class01 : int gorilla
Class08 <--> C2: Cool label
```

renders as

classDiagram Class01 <|-- AveryLongClass : Cool Class03 *-- Class04 Class05 o-- Class06 Class07 .. Class08 Class09 --> C2 : Where am i? Class09 --* C3 Class09 --|> Class07 Class07 : equals() Class07 : Object[] elementData Class01 : size() Class01 : int chimp Class01 : int gorilla Class08 <--> C2: Cool label

An example state diagram:

```mermaid
stateDiagram
[*] --> Still
Still --> [*]
Still --> Moving
Moving --> Still
Moving --> Crash
Crash --> [*]
```

renders as

stateDiagram [*] --> Still Still --> [*] Still --> Moving Moving --> Still Moving --> Crash Crash --> [*]

Data Frames

Save your spreadsheet as a CSV file in your page’s folder and then render it by adding the Table shortcode to your page:

{{< table path="results.csv" header="true" caption="Table 1: My results" >}}

renders as

Table 1: My results
customer_id	score
1	0
2	0.5
3	1

🧠 Sharpen your thinking with a second brain

Thu, 26 Oct 2023 00:00:00 +0000

Create a personal knowledge base and share your knowledge with your peers.

Wowchemy web framework empowers you with one of the most flexible note-taking capabilities out there.

Create a powerful knowledge base that works on top of a local folder of plain text Markdown files.

Use it as your second brain, either publicly sharing your knowledge with your peers via your website, or via a private GitHub repository and password-protected site just for yourself.

Mindmaps

Wowchemy supports a Markdown extension for mindmaps.

With this open format, can even edit your mindmaps in other popular tools such as Obsidian.

Simply insert a Markdown code block labelled as markmap and optionally set the height of the mindmap as shown in the example below.

Mindmaps can be created by simply writing the items as a Markdown list within the markmap code block, indenting each item to create as many sub-levels as you need:


```markmap {height="200px"}
- Hugo Modules
- wowchemy
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal
```

renders as

- Hugo Modules
- wowchemy
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal

Anh here’s a more advanced mindmap with formatting, code blocks, and math:


```markmap
- Mindmaps
- Links
- [Wowchemy Docs](https://docs.hugoblox.com/)
- [Discord Community](https://discord.gg/z8wNYzb)
- [GitHub](https://github.com/HugoBlox/hugo-blox-builder)
- Features
- Markdown formatting
- **inline** ~~text~~ *styles*
- multiline
text
- `inline code`
-
```js
console.log('hello');
console.log('code block');
```
- Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$
```

renders as

- Mindmaps
- Links
- [Wowchemy Docs](https://docs.hugoblox.com/)
- [Discord Community](https://discord.gg/z8wNYzb)
- [GitHub](https://github.com/HugoBlox/hugo-blox-builder)
- Features
- Markdown formatting
- **inline** ~~text~~ *styles*
- multiline
text
- `inline code`
-
```js
console.log('hello');
console.log('code block');
```
- Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$

Highlighting

Highlight important text with mark:

<mark>Highlighted text</mark>

Callouts

Use callouts (aka asides, hints, or alerts) to draw attention to notes, tips, and warnings.

By wrapping a paragraph in {{% callout note %}} ... {{% /callout %}}, it will render as an aside.

{{% callout note %}}
A Markdown aside is useful for displaying notices, hints, or definitions to your readers.
{{% /callout %}}

renders as

A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

Or use the warning callout type so your readers don’t miss critical details:

A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

👩🏼‍🏫 Teach academic courses

Tue, 24 Oct 2023 00:00:00 +0000

Hugo Blox Builder is designed to give technical content creators a seamless experience. You can focus on the content and the Hugo Blox Builder which this template is built upon handles the rest.

Embed videos, podcasts, code, LaTeX math, and even test students!

On this page, you’ll find some examples of the types of technical content that can be rendered with Hugo Blox.

Video

Teach your course by sharing videos with your students. Choose from one of the following approaches:

Youtube:

{{< youtube w7Ft2ymGmfc >}}

Bilibili:

{{< bilibili id="BV1WV4y1r7DF" >}}

Video file

Videos may be added to a page by either placing them in your assets/media/ media library or in your page’s folder, and then embedding them with the video shortcode:

{{< video src="my_video.mp4" controls="yes" >}}

Podcast

You can add a podcast or music to a page by placing the MP3 file in the page’s folder or the media library folder and then embedding the audio on your page with the audio shortcode:

{{< audio src="ambient-piano.mp3" >}}

Try it out:

Test students

Provide a simple yet fun self-assessment by revealing the solutions to challenges with the spoiler shortcode:

{{< spoiler text="👉 Click to view the solution" >}}
You found me!
{{< /spoiler >}}

renders as

👉 Click to view the solution

You found me 🎉

Math

Hugo Blox Builder supports a Markdown extension for $\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.yaml file.

To render inline or block math, wrap your LaTeX math with {{< math >}}$...${{< /math >}} or {{< math >}}$$...$${{< /math >}}, respectively.

We wrap the LaTeX math in the Hugo Blox math shortcode to prevent Hugo rendering our math as Markdown.

Example math block:

{{< math >}}
$$
\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}
$$
{{< /math >}}

renders as

$$\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}$$

Example inline math {{< math >}}$\nabla F(\mathbf{x}_{n})${{< /math >}} renders as $\nabla F(\mathbf{x}_{n})$ .

Example multi-line math using the math linebreak (\\):

{{< math >}}
$$f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\
1-p_{0}^{*} & \text{if }k=0.\end{cases}$$
{{< /math >}}

renders as

$$ f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\ 1-p_{0}^{*} & \text{if }k=0.\end{cases} $$

Code

Hugo Blox Builder utilises Hugo’s Markdown extension for highlighting code syntax. The code theme can be selected in the config/_default/params.yaml file.

```python
import pandas as pd
data = pd.read_csv("data.csv")
data.head()
```

renders as

import pandas as pd
data = pd.read_csv("data.csv")
data.head()

Inline Images

{{< icon name="python" >}} Python

renders as

Python

✅ Manage your projects

Mon, 23 Oct 2023 00:00:00 +0000

Easily manage your projects - create ideation mind maps, Gantt charts, todo lists, and more!

Ideation

Wowchemy supports a Markdown extension for mindmaps.

Simply insert a Markdown code block labelled as markmap and optionally set the height of the mindmap as shown in the example below.

Mindmaps can be created by simply writing the items as a Markdown list within the markmap code block, indenting each item to create as many sub-levels as you need:


```markmap {height="200px"}
- Hugo Modules
- wowchemy
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal
```

renders as

- Hugo Modules
- wowchemy
- blox-plugins-netlify
- blox-plugins-netlify-cms
- blox-plugins-reveal

Diagrams

Wowchemy supports the Mermaid Markdown extension for diagrams.

An example Gantt diagram:

```mermaid
gantt
section Section
Completed :done, des1, 2014-01-06,2014-01-08
Active :active, des2, 2014-01-07, 3d
Parallel 1 : des3, after des1, 1d
Parallel 2 : des4, after des1, 1d
Parallel 3 : des5, after des3, 1d
Parallel 4 : des6, after des4, 1d
```

renders as

gantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d

Todo lists

You can even write your todo lists in Markdown too:

- [x] Write math example
 - [x] Write diagram example
- [ ] Do something else

renders as

Write math example
- Write diagram example
Do something else

Research Features Coverage on Tun-AI

Fri, 22 Sep 2023 00:00:00 +0000

Thresholding Methods in Non-Intrusive Load Monitoring

Fri, 01 Sep 2023 00:00:00 +0000

Mathematical Optimization for Safer and Decarbonized Maritime Transport

Fri, 07 Jul 2023 00:00:00 +0000

NeoCam: An Edge-Cloud Platform for Non-Invasive Real-Time Monitoring in Neonatal Intensive Care Units

Thu, 01 Jun 2023 00:00:00 +0000

Mathematical Optimization of Maritime Shipping Routes

Tue, 30 May 2023 00:00:00 +0000

CITIC-UDC Coverage on NPI Effectiveness Results

Mon, 17 Apr 2023 00:00:00 +0000

Effectiveness of Non-Pharmaceutical Interventions in Nine Fields of Activity to Decrease SARS-CoV-2 Transmission (Spain, September 2020--May 2021)

Sat, 01 Apr 2023 00:00:00 +0000

Green Navigation: Optimización de Rutas Marítimas

Sun, 01 Jan 2023 00:00:00 +0000

Simons Center Workshop 2023

Sun, 01 Jan 2023 00:00:00 +0000

Tun-AI: Using Echosounder Buoy Technology to Study Tuna Behaviour at Sea

Sun, 01 Jan 2023 00:00:00 +0000

Smart Shipping, Weather Routing and WASP Simulation

Mon, 26 Sep 2022 00:00:00 +0000

Exceptional Gegenbauer Polynomials via Isospectral Deformation

Mon, 01 Aug 2022 00:00:00 +0000

TUN-AI: Tuna Biomass Estimation with Machine Learning Models Trained on Oceanography and Echosounder FAD Data

Wed, 01 Jun 2022 00:00:00 +0000

Toward Automated Feature Extraction for Deep Learning Classification of Electrocardiogram Signals

Sat, 01 Jan 2022 00:00:00 +0000

OpenCV Blog Episode 32 Interview on NeoCam

Thu, 04 Nov 2021 00:00:00 +0000

Diario de Cadiz Coverage of NeoCam International Award

Wed, 08 Sep 2021 00:00:00 +0000

Complete Classification of Rational Solutions of A2-Painlevé Systems

Thu, 01 Jul 2021 00:00:00 +0000

IEEE PowerTech 2021 Presentation on NILM

Mon, 28 Jun 2021 00:00:00 +0000

Non-Intrusive Load Monitoring Using Multi-Output CNNs

Tue, 01 Jun 2021 00:00:00 +0000

Interview for RSME on the COVID-19 Response

Fri, 09 Apr 2021 00:00:00 +0000

Exceptional Legendre Polynomials and Confluent Darboux Transformations

Mon, 01 Feb 2021 00:00:00 +0000

Fall Detection from Electrocardiogram (ECG) Signals and Classification by Deep Transfer Learning

Mon, 01 Feb 2021 00:00:00 +0000

Ladder Operators and Rational Extensions

Fri, 01 Jan 2021 00:00:00 +0000

Spectral Theory of Exceptional Hermite Polynomials

Fri, 01 Jan 2021 00:00:00 +0000

Corrigendum on the Proof of Completeness for Exceptional Hermite Polynomials

Fri, 01 May 2020 00:00:00 +0000

Radio 3 Interview on Big Data and Coronavirus Privacy

Sun, 19 Apr 2020 00:00:00 +0000

Interview in eldiario.es on Data Transparency and COVID-19

Fri, 17 Apr 2020 00:00:00 +0000

Fundacion BBVA Feature on COVID-19 Prediction Models

Tue, 07 Apr 2020 00:00:00 +0000

Cyclic Maya Diagrams and Rational Solutions of Higher Order Painlevé Systems

Wed, 01 Apr 2020 00:00:00 +0000

Fundacion Descubre Note on ICU Occupancy Predictive Model

Wed, 01 Jan 2020 00:00:00 +0000

Las Matematicas frente a la COVID-19

Wed, 01 Jan 2020 00:00:00 +0000

Rational Solutions of Painlevé Systems.

Sun, 01 Dec 2019 00:00:00 +0000

BIDAS 4 Presentation on NILM

Thu, 07 Nov 2019 00:00:00 +0000

Assessing the Effect of Advertising Expenditures upon Sales: A Bayesian Structural Time Series Model

Wed, 01 May 2019 00:00:00 +0000

A Bochner Type Characterization Theorem for Exceptional Orthogonal Polynomials

Mon, 01 Apr 2019 00:00:00 +0000

Big data: conceptos, tecnologías y aplicaciones

Tue, 01 Jan 2019 00:00:00 +0000

JURIX 2019 IberLegal Industry Session Talk

Tue, 01 Jan 2019 00:00:00 +0000

Opponent Aware Reinforcement Learning

Tue, 01 Jan 2019 00:00:00 +0000

Validating Ocean General Circulation Models via Lagrangian Particle Simulation and Data from Drifting Buoys

Tue, 01 Jan 2019 00:00:00 +0000

Durfee Rectangles and Pseudo-Wronskian Equivalences for Hermite Polynomials

Thu, 01 Nov 2018 00:00:00 +0000

Shape Invariance and Equivalence Relations for Pseudo-Wronskians of Laguerre and Jacobi Polynomials

Wed, 01 Aug 2018 00:00:00 +0000

Bayesian Factorization Machines for Risk Management and Robust Decision Making

Mon, 01 Jan 2018 00:00:00 +0000

On the Performance of Blind-Infotaxis under Inaccurate Modeling of the Environment

Sat, 01 Jul 2017 00:00:00 +0000

Recurrence Relations for Exceptional Hermite Polynomials

Fri, 01 Apr 2016 00:00:00 +0000

EL PAIS Coverage on Anti-Fraud Mathematics

Thu, 30 Jul 2015 00:00:00 +0000

ICMAT Coverage of BBVA-Funded Banking Fraud Detection Project

Wed, 29 Jul 2015 00:00:00 +0000

Oscillation Theorems for the Wronskian of an Arbitrary Sequence of Eigenfunctions of Schrödinger's Equation

Wed, 01 Apr 2015 00:00:00 +0000

Extended Krein-Adler Theorem for the Translationally Shape Invariant Potentials

Tue, 01 Apr 2014 00:00:00 +0000

Geometry-Induced Fluctuations of Olfactory Searches in Bounded Domains

Tue, 01 Apr 2014 00:00:00 +0000

Multifractality and Long Memory of a Financial Index

Wed, 01 Jan 2014 00:00:00 +0000

Rational Extensions of the Quantum Harmonic Oscillator and Exceptional Hermite Polynomials

Wed, 01 Jan 2014 00:00:00 +0000

A Conjecture on Exceptional Orthogonal Polynomials

Thu, 01 Aug 2013 00:00:00 +0000

Asymptotic and Interlacing Properties of Zeros of Exceptional Jacobi and Laguerre Polynomials

Fri, 01 Mar 2013 00:00:00 +0000

Scaling, Stability and Distribution of the High-Frequency Returns of the Ibex35 Index

Fri, 01 Mar 2013 00:00:00 +0000

Understanding Complex Dynamics by Means of an Associated Riemann Surface

Wed, 01 Aug 2012 00:00:00 +0000

Two-Step Darboux Transformations and Exceptional Laguerre Polynomials

Thu, 01 Mar 2012 00:00:00 +0000

On Orthogonal Polynomials Spanning a Non-Standard Flag

Sun, 01 Jan 2012 00:00:00 +0000

Exceptional Orthogonal Polynomials and the Darboux Transformation

Fri, 01 Oct 2010 00:00:00 +0000

Integrability and Nonlinear Phenomena

Fri, 01 Oct 2010 00:00:00 +0000

An Extension of Bochner's Problem: Exceptional Invariant Subspaces

Sat, 01 May 2010 00:00:00 +0000

An Extended Class of Orthogonal Polynomials Defined by a Sturm--Liouville Problem

Sun, 01 Nov 2009 00:00:00 +0000

Towards a Theory of Chaos Explained as Travel on Riemann Surfaces

Thu, 01 Jan 2009 00:00:00 +0000

Asymptotically Isochronous Systems:

Tue, 01 Jan 2008 00:00:00 +0000

Nonlinear Evolution Equations and Dynamical Systems 2007: 17th NEEDS Workshop, L'Ametlla de Mar, Spain

Tue, 01 Jan 2008 00:00:00 +0000

Quasi-Exact Solvability in a General Polynomial Setting

Mon, 01 Oct 2007 00:00:00 +0000

A New Class of Solvable Many-Body Problems with Constraints, Associated with an Exceptional Polynomial Subspace of Codimension 2

Sun, 01 Jul 2007 00:00:00 +0000

Two Novel Classes of Solvable Many-Body Problems of Goldfish Type with Constraints

Tue, 01 May 2007 00:00:00 +0000

Dynamical Systems on Infinitely Sheeted Riemann Surfaces

Thu, 01 Mar 2007 00:00:00 +0000

Quasi-Exact Solvability beyond the Sl(2) Algebraization

Thu, 01 Mar 2007 00:00:00 +0000

Structure Theorems for Linear and Non-Linear Differential Operators Admitting Invariant Polynomial Subspaces

Mon, 01 Jan 2007 00:00:00 +0000

Group Theory and Numerical Analysis

Sat, 01 Oct 2005 00:00:00 +0000

The Transition from Regular to Irregular Motions, Explained as Travel on Riemann Surfaces

Sat, 01 Oct 2005 00:00:00 +0000

Quasi-Exact Solvability and the Direct Approach to Invariant Subspaces

Tue, 01 Mar 2005 00:00:00 +0000

Periods of the Goldfish Many-Body Problem:

Sat, 01 Jan 2005 00:00:00 +0000

A Survey of Quasi-Exactly Solvable Systems and Spin Calogero-Sutherland Models

Mon, 01 Nov 2004 00:00:00 +0000

Supersymmetry and Algebraic Darboux Transformations

Fri, 01 Oct 2004 00:00:00 +0000

Reply to Comment on The Darboux Transformation and Algebraic Deformations of Shape-Invariant Potentials

Sun, 01 Aug 2004 00:00:00 +0000

New Many-Body Problems in the Plane with Periodic Solutions

Sun, 01 Feb 2004 00:00:00 +0000

The Darboux Transformation and Algebraic Deformations of Shape-Invariant Potentials

Sun, 01 Feb 2004 00:00:00 +0000

Dunkl Operators and Calogero-Sutherland Models

Thu, 01 Jan 2004 00:00:00 +0000

On the Sutherland Spin Model of B N Type and Its Associated Spin Chain

Sat, 01 Feb 2003 00:00:00 +0000

Quasi-Exact Solvability And Calogero-Sutherland Models

Wed, 01 Jan 2003 00:00:00 +0000

New Spin Calogero--Sutherland Models Related to BN-type Dunkl Operators

Mon, 01 Oct 2001 00:00:00 +0000

A N -Type Dunkl Operators and New Spin Calogero-Sutherland Models

Wed, 01 Aug 2001 00:00:00 +0000

Exact Solutions of a New Elliptic Calogero--Sutherland Model

Fri, 01 Jun 2001 00:00:00 +0000

Quasi-Exactly Solvable Generalizations of Calogero--Sutherland Models

Fri, 01 Jun 2001 00:00:00 +0000

Técnicas Algebraicas En Sistemas Cuánticos de Muchas Partículas

Mon, 01 Jan 2001 00:00:00 +0000

New Algebraic Quantum Many-Body Problems

Sun, 01 Oct 2000 00:00:00 +0000

Symmetry Classification of Systems of Differential-Difference Equations

Thu, 01 Jun 2000 00:00:00 +0000

Symmetries of Discrete Dynamical Systems Involving Two Species

Tue, 01 Jun 1999 00:00:00 +0000

Applied Machine Learning

Mon, 01 Jan 0001 00:00:00 +0000

This research project delves into various aspects of machine learning, including supervised and unsupervised learning techniques, neural networks, and deep learning architectures. The focus is on developing innovative algorithms that can improve predictive accuracy and efficiency in real-world applications such as image recognition, natural language processing, and autonomous systems.

ATENEA for Aerospace Manufacturing

Mon, 01 Jan 0001 00:00:00 +0000

Overview

ATENEA: systems based in artificial intelligence to support manufacturing engineering Contract Art. 83 between AIRBUS D&S and Universidad de Cádiz (CDTI Interconnecta) PI: David Gómez-Ullate (UCA), 01/04/2019 – 31/10/2019, Sum: 90.000 EUR.

Modern aerospace manufacturing demands extremely high quality standards, especially in composite components where defects can be costly and difficult to detect. Within the context of Industry 4.0, ATENEA was a research and innovation project funded by CDTI and developed in collaboration with Airbus, with the goal of bringing data science and artificial intelligence directly into the production and inspection of fan cowls for the Airbus A320/A330 Neo.

The project focused on transforming large volumes of heterogeneous industrial data—machine logs, sensor measurements, manufacturing records, and inspection images—into actionable insights. By integrating predictive analytics, computer vision, and real-time monitoring, ATENEA aimed to improve quality inspection, anticipate manufacturing deficiencies, and reduce non-conformities before they propagated through the production line.

A key outcome of the project was the development of intelligent tools to support both process monitoring and quality assurance, enabling earlier detection of structural defects, better traceability, and more consistent inspection criteria. ATENEA demonstrates how advanced analytics can be embedded into real production environments to enhance reliability, efficiency, and decision-making in aerospace manufacturing.

From a methodological perspective, the project combined predictive modeling and computer vision with industrial data pipelines. Machine-learning models were developed to estimate the probability of non-conformities related to delamination and porosity using data from composite layup machines, environmental sensors, tooling information, and SAP production records. In parallel, image-processing algorithms based on ultrasound inspection data were designed to automatically detect and segment potentially defective regions, producing an objective quality score for inspection reliability. These results were integrated into a real-time dashboard architecture, allowing continuous monitoring of production variables and inspection performance across the manufacturing process.

This was a challenging Industry 4.0 collaboration between Airbus D&S and UCA Datalab at University of Cádiz. I led a team of 4 data scientists and software developers to complete our work package. We made frequent visits to the Airbus production plant at CBC - Puerto de Santa María to work in situ with Airbus personnel, deliver formation courses, become familiar with the whole Fan Cowl production process, etc. Unfortunately, confidentiality requirements due to the sensitive nature of the data and production process did not allow the publication of the results in scientific journals.

Climate Risk and the Future of Insurance

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Climate change is no longer a distant concern for insurers: it is already reshaping mortality patterns, life expectancy, and long-term risk. Rising temperatures, more frequent extremes, and uneven adaptation across populations pose fundamental challenges to how life and health insurance products are priced, managed, and regulated.

This project, developed in collaboration between IE University and Vienna Insurance Group (VIG), translates cutting-edge climate and health research into tools that insurers can actually use. We study how temperature and other climate-related risk factors affect mortality across age groups, regions, and future climate scenarios, and we embed these effects directly into actuarial quantities such as death probabilities, life expectancy, and life tables.

The outcome is a new generation of climate-adjusted life tables that allow insurers to explore how different climate pathways may impact portfolios over time. By combining public data, scientific projections, and proprietary information, the project supports more informed decisions around pricing, capital requirements, and long-term risk management in a changing climate.

Methodologically, the project builds on exposure–response functions linking temperature to mortality, combined with high-resolution climate data and future climate scenarios (Shared Socioeconomic Pathways, SSPs). These relationships are propagated through an actuarial pipeline to produce climate-adjusted mortality rates and life tables by age, sex, and region. The framework integrates results from state-of-the-art epidemiological studies with demographic and actuarial modeling, enabling scenario-based projections of longevity and climate risk relevant for insurance portfolios.

To learn more about the project, visit the project website. We are organizing the 3rd edition of the Climate Change and Insurance conference series (CCI 26) at IE University’s campus in Segovia, from 2-4 September 2026.

COVID-19 Impact of NPIs

Mon, 01 Jan 0001 00:00:00 +0000

Overview

The COVID-19 pandemics was a singular event where scientific activity proved to be instrumental in fighting against the disease and better decision making. Scientists worked round the clock from their homes during lockdown to establish networks, gather and process data, elaborate models and draft reports to help decision makers.

In this context, the main mathematical spanish society CEMAT (Comité Español de Matemáticas) established a Committee of experts called “Acción Matemática contra el Coronavirus” from the 4 main societies (SEMA, RSME, SCM and SEIO) whose role was to elaborate a mathematical response to the challenges posed by the pandemics.

The Committee elaborated a meta-prediction model where many modeling groups participated to predict short term prevalence and spread of the disease.

The Committee started working with technical experts from the Ministry of Health and the Ministry of Economy, and it was tasked with modeling the effect of Non-Pharmaceutical Interventions (NPIs) both from a public health and an economic point of view. This was quite important since decision makers were often faced with the choice of adopting more restrictive measures with a considerable economic impact, whose effectiveness had to be predicted.

Our study analyzed the effectiveness of non-pharmaceutical interventions (NPIs) implemented in Spain during the second wave of COVID-19 (September 2020 to May 2021). Researchers compiled detailed provincial and municipal data on restrictions across nine areas of activity and constructed a daily “restriction intensity index” ranging from 0 to 1 to quantify the strength of measures over time. Using statistical modeling under the framework of the Spanish Committee of Mathematics’ “Mathematical Action against Coronavirus” initiative, the team evaluated how changes in restriction intensity affected virus transmission.

The results showed that increasing the overall intensity of measures by 34% was associated with a 22% reduction in transmission within one week. Interventions related to social distancing and indoor hospitality were found to be particularly effective, while measures affecting leisure, cultural activities, places of worship, religious celebrations, and indoor sports showed less clear effects—though these differences should be interpreted cautiously, as many measures were implemented simultaneously. The project also made all collected data publicly available to support transparency and future research, highlighting the critical role of mathematical modeling and data analysis in managing public health crises.

My role in this project was mainly involved in coding and processing the NPIs into useful variables for the statistical model that matched the NPI intensity time series to incidence metrics. On a separate project, we made a predictive tool to assist hospitals in planning for extra beds in ICUs, leveraging what was known on disease dynamics and observed infected individuals.

Media Coverage

Interview in eldiario.es “To fight the pandemic, we need transparency and access to good data.” (17/04/20) link
Interview for Real Sociedad Matemática Española (09/04/21) link
Las matemáticas frente a la Covid-19, Fundación Ramón Areces en colaboración con Real Sociedad Matemática Española video
M. Salomone “Spanish mathematicians look for a model to predict how the pandemic will evolve”, Fundación BBVA (07/04/20) link
“Big Data contra el coronavirus y ¿nuestra privacidad?” , Fallo de Sistema, Radio 3 (19/04/20) link
“Desarrollan un modelo predictivo de ocupación de camas en las UCI de los hospitales andaluces” Fundación Descubre, Junta de Andalucía link
Acción Matemática contra la COVID confirma que el incremento de las restricciones redujo la transmisión del virus en un 22% a la semana. CITIC-UDC (17/04/23) link

Financial Mathematics

Mon, 01 Jan 0001 00:00:00 +0000

Content

Fraud Detection in Electronic Payments

Mon, 01 Jan 0001 00:00:00 +0000

Overview

This research project applied advanced techniques from artificial intelligence (AI) and data science to the problem of detecting fraud in electronic payment systems, with a particular focus on credit card transactions.

In those days, commercial AI-based tools were still in their infancy, and many anti-fraud systems were still a combination of rule based and very basic statistical filters. Our work involved analyzing over 150 million real transactions collected over one year by a first tier bank, to identify statistical traces and behavioural patterns associated with fraudulent activity. By leveraging AI-driven models, the research aimed to improve the ability of financial institutions to decide in real time whether a transaction should be blocked.

The study tackled significant challenges typical of fraud detection—such as extremely imbalanced data (with approximately one fraudulent transaction per 6 000 legitimate ones), evolving fraud strategies that change over time, and the need to model the decision-making utility for both the bank and the fraudster. The project developed new algorithms that substantially improved model efficiency compared to existing approaches.

In addition to its technical contributions, the project emphasised training early-stage researchers in statistical learning and data science, responding to strong market demand for these skills and the lack of formal academic programmes in this area at the time.

Due to confidentiality clauses, we were unable to publish publicly available results on this research.

Media Coverage

“Matemáticas antirrobo y otras cuatro ideas para mejorar el mundo”, Diario EL País (30/07/2015)
“La Fundación BBVA financia un proyecto basado en matemáticas que permitirá adelantarse al fraude bancario” ICMAT (29/07/2015) link

Projects and Contracts

Artificial Intelligence and Data Science: Applications in Payment Fraud Detection, Leonardo Scholarship, Fundación BBVA. link Red Leonardo
Sum: 40.000 EUR (2015-2016)

Contract and Funding

Learning for fraud detection in electronic payments.
Contract Art. 83 between Evendor Engineering SL and Univ. Complutense de Madrid.
PI: David Gómez-Ullate (UCM-ICMAT), 01/06/2015 - 31/12/2015, Sum: 20.000 EUR.

Green Navigation

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Projects and Contracts

Mathematical optimization for a more efficient, safer and decarbonized maritime transport. Fundación BBVA - Matemáticas 2021, 01/09/2022 – 30/06/2025. PI: David Gómez-Ullate (IE University), Team: 23 researchers, Sum: 150.000 EUR.
Optimización de rutas marítimas con información oceanográfica en tiempo real. AEI Transición Ecológica y Digital (TED2021-129455B-I00), 01/12/2022 – 30/11/2024. PI: David Gómez-Ullate (IE University), Team: 12 researchers, Sum: 116.380 EUR.

Presentations

Mathematical optimization for a more efficient, safer and decarbonized maritime transport, Royal Academy of Sciences, Madrid, Jul 7, 2023 video.
Smart Shipping: weather routing and WASP simulation, Session on Wind Propulsion at Sea Tech Week, Brest, France, Sep 26-30, 2022.
Mathematical optimization of maritime shipping routes, IE-RSME Workshop on Applied Mathematics in Sustainability and Climate Change, IE Tower (Madrid), May 30, 2023 link.

Technical Overview

Maritime transport accounts for more than 80% of global trade, yet it faces growing regulatory and environmental pressure to reduce fuel consumption and greenhouse gas emissions. At the same time, the increasing availability of high‑resolution oceanographic and meteorological data has opened new opportunities for smarter, data‑driven navigation.

The Green Navigation project was born at this intersection: how can rigorous mathematical optimization transform real‑time weather and ocean data into safer, more efficient, and lower‑emission shipping routes?

Funded by Fundación BBVA and the Spanish Ministry of Science, and developed in collaboration with Navantia and multiple academic partners, the project aimed to design a next‑generation weather routing system capable of integrating ocean currents, wind forecasts, fuel consumption models, vessel dynamics, and regulatory constraints into a unified optimization framework.

The result is a fully operational routing platform that combines advanced mathematical theory with industrial‑grade software. The system computes optimized maritime routes based on departure date, vessel characteristics, cruise speed, and meteorological forecasts. It has been validated in real pilot studies with Boluda Shipping achieving average fuel savings of approximately 5-10% while maintaining operational constraints. These reductions translate directly into lower CO₂ emissions, improved economic performance, and enhanced navigational safety.

From a methodological perspective, Green Navigation integrates multiple layers of applied mathematics, optimization, and computational science:

Variational methods and geometric integration extend the classical Zermelo navigation problem using structure‑preserving discretizations with proven convergence properties. Parallel GPU implementations ensure scalability for large‑scale computations.
Graph‑based optimization reformulates maritime routing as a time‑dependent shortest‑path problem. The HADAD algorithm (Hexagonal A‑Star with Differential refinement) introduces a hierarchical hexagonal spatial discretization, enabling multi‑scale exploration of the ocean surface while maintaining computational efficiency.
Hybrid global–local search strategies combine fast heuristic exploration with variational refinement, ensuring both near‑global optimality and local numerical accuracy.
Robust optimization under uncertainty incorporates ensemble weather forecasts and sensitivity analyses to quantify the impact of meteorological prediction errors on route performance.
Data‑driven consumption and emissions models link vessel characteristics and sea‑state conditions to fuel consumption and pollutant emissions. Artificial neural networks trained on thousands of simulated hull geometries enable rapid prediction of hydrodynamic loads and seakeeping behavior with high accuracy.

A key contribution of the project is the creation of Weather Routing Bench 1.0, the first open benchmark for maritime weather routing. By providing standardized datasets, evaluation metrics, and reference implementations, this platform promotes reproducibility, objective comparison, and cumulative progress in a field that previously lacked a common evaluation framework.

The technological maturity and industrial validation of the project ultimately led to the creation of a spin‑off company, Green Navigation. The startup builds on the algorithms, software architecture, and optimization methodologies developed during the research project, translating them into scalable commercial solutions for the maritime sector.

This transition from academic research to entrepreneurial initiative reflects the project’s dual ambition: advancing frontier mathematical research while delivering measurable environmental and economic impact in real shipping operations.

Infotaxis

Mon, 01 Jan 0001 00:00:00 +0000

Content

Integrable and Chaotic Dynamical Systems

Mon, 01 Jan 0001 00:00:00 +0000

Content

Lie Groups in Mathematical Physics

Mon, 01 Jan 0001 00:00:00 +0000

Content

NeoCam

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Neocam was a beautiful project done in collaboration between many different members from UCA Datalab. It was originally motivated by the first OpenCV AI Competition in 2021, which asked for projects based on the new edge computing camera Oak-D from Luxonis, which has 3 objectives (depth field) and a built-in chip that runs CV models on the device itself.

Our project involved developing a complete edge-cloud platform for real-time monitoring of newborns in neonatal intensive care units. Premature babies require continuous observation, yet much clinically relevant information — such as motor activity, stress indicators, environmental conditions, and subtle behavioral cues — is either assessed subjectively or not captured systematically. By embedding intelligent devices directly at the incubator level, the project enables continuous collection and analysis of multimodal data (video, audio, vital signs, and environmental signals) while preserving privacy and minimizing intrusiveness. The goal is to transform raw, heterogeneous sensor streams into structured clinical information that supports more informed and timely medical decisions.

Technically, the system combines optimized deep learning models deployed on edge devices with cloud-based infrastructure for secure storage, visualization, and integration into hospital workflows. In 3 months a team of 8 researchers developed NeoCam, a device able to measure the baby’s limbs motion, infer emotional status to detect pain and measure breathing rate in a contactless fashion. The whole Proof of Concept, which involved also a cloud platform and a mobile app, was deployed at the Neonate Intensive Care Unit at Hospital Puerta del Mar in Cádiz.

The project won the first regional prize (Europe) in the 2021 OpenCV AI Competition and the second global prize, out of more than 1400 submitted projects. It also led to a publication in the high reputation journal IEEE Journal of Biomedical and Health Informatics.

The PI of this project was my colleague Lionel Cervera. My role was to be responsible for the development of the three AI algorithms that were incorporated into the NeoCam.

Media Coverage

OpenCV Blog Episode 32 (04/11/21) Real-time tele-monitoring of preterm neonates with NeoCam - Interview with Satya Mallick, CEO of OpenCV. link
A UCA project wins first place in an international Artificial Intelligence competition, Diario de Cádiz (8/09/21) link

Videos

Spot: https://www.youtube.com/watch?v=58KHGucW0dQ
Motion detector: https://www.youtube.com/watch?v=CGLl9O9GtEg
Breath rate detector: https://www.youtube.com/watch?v=ZsHf2NaaHW8

Non-Intrusive Load Monitoring

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Understanding how electricity is used inside homes is essential for improving energy efficiency, reducing emissions, and designing smarter power systems. Non-Intrusive Load Monitoring (NILM) aims to identify the activity of individual appliances, such as fridges, washing machines, or dishwashers, using only the total electricity consumption measured by a smart meter. By avoiding the need for dedicated sensors on each device, NILM enables scalable and privacy-preserving energy analytics. Reliable appliance-level information can support demand response, personalized energy feedback, fault detection, and more effective integration of renewable energy into the grid.

This work addresses a fundamental but often overlooked modeling choice in NILM: how to define when an appliance is considered ON or OFF. Since real datasets usually provide power consumption but not appliance states, this requires introducing thresholding rules that transform a regression problem into a classification task. We show that different thresholding methods lead to substantially different learning problems and performance outcomes, even when using the same deep learning architectures. By systematically comparing thresholding strategies and proposing objective criteria based on signal reconstruction error, the paper highlights the importance of problem formulation in NILM. In addition, a multi-task learning approach is explored, showing that jointly learning appliance status and power consumption can improve performance for certain types of devices through transfer learning between regression and classification tasks.

This was the first paper we wrote with Daniel Precioso when he joined UCA Datalab at University of Cádiz to start his industrial PhD. He was already interested in this topic because he had done an internship at Foqum analytics. We extended his work and addressed a relevant conceptual problem in how NILM problems are usually framed. The work was presented in a few conferences , among which:

BIDAS 4 - Fourth Bilbao Data Science Workshop, BCAM (Nov 7-10 2019).
IEEE Powertech Madrid 2021, (Jun 28-Jul 2 2021).

It also led to the publication of these papers:

Thresholding Methods in Non-Intrusive Load Monitoring. The Journal of Supercomputing 79 14039–14062 (2023).
Non-Intrusive Load Monitoring Using Multi-Output CNNs. 2021 IEEE Madrid PowerTech, Madrid, Spain (2021).

Orthogonal Polynomials

Mon, 01 Jan 0001 00:00:00 +0000

Exceptional orthogonal polynomials came as a little surprise to the community of mathematicians working on special functions and approximation theory. Classical orthogonal polynomials are known since the mid XIX century, and they appear in numerous applications in physics (optics, quantum mechanics, electromagnetism, stochastic processes), engineering (signal processing, compression), numerical methods (Galerkin methods, FEM, etc.) and Machine Learning (PINNs, kernel methods, KANs, dimensionality reduction, etc.).

Classical orthogonal are, so to say, the building bricks one uses to write and approximate many complex functions.One of the main reasons for this ubiquitous presence is the fact that they constitute polynomial bases of Sturm-Liouville problems, i.e. they span a complete Hilbert space of polynomial eigenfunctions to a second order differential eigenvalue problem with suitable boundary conditions.

Since the work of Solomon Bochner, almost 100 years ago, it was believed that the orthogonal polynomial families of Hermite, Laguerre and Jacobi (including all the subcases like Chebyshev, Legendre, etc.) were the only polynomial bases of Sturm-Liouville problems, and indeed this was often quoted a way to characterize and single out classical orthogonal polynomials. Around 2008, working with my colleagues Niky Kamran at McGill and Robert Milson at Dalhousie, we discovered that the class of polynomial Sturm-Liouville problems could be enlarged, provided one was ready to relax one typical assumption mathematicians always took for granted: that the infinite basis contains a polynomial for every degree.

We originally coined the name exceptional orthogonal polynomials for this new class because we believed that they were very rare, and indeed we first found them via rather involved group theoretic arguments. Soon after we published our article, Christianne Quesne (ULB) showed that our new examples could be obtained by applying Darboux transformations to classical families, and this was a seminal discovery, since iterated Darboux transformations were the key tool to generate larger families of exceptional orthogonal polynomials, as initiated shortly after by Odake and Sasaki.

Together with many collaborators, but especially with my friend Rob Milson, I have devoted the past 20 years of my life to developing the theory of exceptional orthogonal polynomials, which has also attracted many talented researchers worldwide. We are now close to providing a complete classification of all such families, in much the same way as Bochner did in 1929. Yet many of the novel applications that these new building blocks unravel are yet to be developed.

Selected publications

Recent talks

ICMS Mini-Symposium 2024
Slides and recording hosted on the talk page.

Simons Center Workshop 2023
Visit the presentation page for details and downloads.

Predicting Drifting Buoys

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Satlink is one of the largest world manufacturers of smart buoys for the tuna fishing industry. They approached me while I was working at University of Cádiz with an initial project that involved predicting the drifting trajectory of a FAD (Fish Aggregating Device) to which their buoys are attached. Besides other sensors, these smart buoys contain an echosounder that measures biomass and detect tuna presence, and they send this information every hour to a satellite. The buoys are deployed by ships that drop them in the ocean, and once released they drift with the ocean currents. The company found that, despite having an operation life of 6-12 months, many buoys were lost within 2-3 weeks by collision with the coast. They wanted to identify the best spots to drop the buoys so that the drift would keep them circulating in the ocean.

Satlink provided us with a very valuable dataset: the daily positions of more than 40.000 drifting buoys in the Indian Ocean.

We developed a Lagrangian numerical integration scheme which used the ocean currents predictions from an European provider (Copernicus) and an American one (HYCOM).

As a side project, we used their data to validate predictions from these models, comparing the true trajectories of the buoys with those predicted by the Lagrangian model. This is an example of a synergistic and positive by-product between industry and research. The largest publicly funded program to gather data from drifting buoys is the NOAA Global Drifter Program, which contains around 1000 buoys from a collaboration between 19 countries. By way of contrast, we had access to a proprietary dataset of more than 20 years of operation for ca. 40.000 buoys in the three major oceans, providing daily (sometimes hourly) positions. Of course these buoys were deployed for fishing purposes, but the data they gathered can be used for many other scientific purposes, in this case, validation of Ocean General Circulation Models.

My role in this project was Principal Investigator and responsible for the contract. It was a good opportunity to work with Karan Bedi, a visiting MSc student from IIT Roorkee (India), who visited UCA Datalab in Cádiz during that period.

Projects and Contracts

Prediction of drifting objects in the ocean. Contract Art. 83 between Satlink SL and Universidad de Cádiz. PI: David Gómez-Ullate (UCA), 01/04/2019 – 01/07/2019, Sum: 70.000 EUR.

Quantum Many-Body Problems

Mon, 01 Jan 0001 00:00:00 +0000

Content

Smart Advertising Decisions

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Machine learning for precision marketing.

Projects and Contracts

Contract Art. 83 between Omnicom Media Group S.A. and Univ. Complutense de Madrid. PI: David Gómez-Ullate (UCM-ICMAT), 01/12/2016 - 30/11/2017, Sum: 27.600 EUR.
AI on geodata applied to conversion and CTR prediction in precision marketing. Contract Art. 83 between Omnicom Media Group S.A. and Univ. Complutense de Madrid. PI: David Gómez-Ullate (UCM-ICMAT), 01/12/2017 - 30/09/2018, Sum: 20.400 EUR.

One of the central problems in marketing and advertising is to answer the following question:

How much do advertising campaigns actually increase sales? And which channels are worth the investment?

In this project, we study real sales and advertising data from a large fast-food franchise to understand how different advertising channels (TV, online, radio, outdoor, etc.) influence weekly sales. Instead of relying on simple correlations, we use a data-driven approach that separates long-term trends, seasonal effects (such as holidays), and external factors like weather or major events.

The result is a practical decision-support tool that helps managers answer questions such as “Where should I spend my advertising budget next week?” and “What trade-off am I making between expected sales and risk?” The model not only forecasts future sales accurately, but also provides clear guidance on how to allocate advertising budgets more effectively across channels.

From a methodological perspective, the analysis is based on a Bayesian dynamic linear (state-space) model that builds on the classic Nerlove–Arrow framework for advertising response. This approach models advertising as a stock with delayed and decaying effects over time, while naturally incorporating trends, seasonality, and uncertainty. The Bayesian formulation allows prior information to be included and provides full predictive distributions, making it especially well suited for forecasting and risk-aware budget allocation.

This work shows how modern statistical methods can turn complex business data into actionable insights for real-world decision making. In addition, we explored how different advertising plans can be compared using multi-objective optimization, highlighting trade-offs between expected sales, risk, and innovation.

This project was an Art.83 collaboration between my group at Universidad Complutense and Omnicom Media Group. Besides providing an actionable model for the advertising company, it led to a couple of academic publications:

Smart Retrieval and Structuring of Legal Documents

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Natural Language Processing with Deep Learning for retrieval of legal documents

This project was developed in the early days of Deep Learning NLP, before the transformer architecture was built into commercial products.

In 2018, Lefebvre (Spain’s leading provider of legal information) sought to modernize the way it managed judicial content. The goal was to automatically classify, label, and extract relevant information from a corpus of over one million court rulings and legal documents — a repository that continues to grow daily.

Beyond structuring the data, the project aimed to enable advanced search capabilities that would support faster and more informed legal decision-making.

The project began with entirely unlabeled data. Due to the highly specialized nature of legal language — particularly within the Spanish legal system — off-the-shelf pre-trained NLP models were not suitable.

Addressing this required the development of a dedicated annotation pipeline, the design of domain-specific labeling strategies, and the implementation of an active learning framework to efficiently guide expert annotation. At the time (2018), this meant deploying state-of-the-art NLP methodologies adapted specifically to the legal domain.

The project delivered a hierarchical classification system capable of organizing judgments and legal documents across multiple levels of legal categories, achieving accuracy rates above 90%. In addition, the implementation of semantic search capabilities improved information retrieval performance. Compared to the previous system, the new solution was 22 times more efficient, significantly reducing operational workload and increasing productivity and service responsiveness.

Due to confidentiality agreements, the team could not write a publication in this project, but the results were explained in the specialized conference JURIX 2019 - IberLegal with a talk in the industry session.

I was Principal Investigator of the project and responsible for delivery of results under the contract between UCA Datalab and Quantum Analytics.

Contract Art. 83 between Quantum Analytics and Universidad de Cádiz

PI: David Gómez-Ullate (UCA), 02/07/2018 – 01/07/2019, Sum: 72.600 EUR.

SNOMED-CT Coding for Pathology Reports

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Tun-AI

Mon, 01 Jan 0001 00:00:00 +0000

Overview

After successful delivery of the project on dFAD drift prediction, Satlink wanted to develop an AI algorithm to automate and improve the conversion of raw echosounder signal to presence of tuna, which lies at the core of their business. Before, this information with complex spatio-temporal patterns had to be often interpreted by an expert biologist, and often had strong bias, besides being expensive and lacking scalability to a fleet of thousands of vessels. As supervised signal, we received data from 20 years of tuna fishing captures from OPAGAC, and we harvested echosounder data and oceanographic variables on the days prior to the capture as predictor variables to build the first tuna presence detector to be deployed at commercial scale by Satlink.

The Tun-AI project develops an AI-driven pipeline to transform raw data from echosounder buoys attached to drifting Fish Aggregating Devices (dFADs) into meaningful insights about tuna presence and biomass at sea. These buoys — widely deployed across tropical oceans as part of tuna purse-seine fisheries — continuously transmit location and acoustic backscatter information that can indicate the presence of fish under the buoy. By combining buoy data with satellite-derived oceanographic information (e.g., temperature, currents, chlorophyll) and historical catch records, the Tun-AI system uses machine learning models to estimate tuna biomass with high accuracy, effectively turning industrial monitoring infrastructure into a global biological sensor network.

Beyond improving biomass estimation for fishery operations, Tun-AI opens new avenues for scientific research into tuna behaviour and ecology. Traditional ecological studies of highly migratory species like tuna are costly and geographically limited; by harnessing data from thousands of buoys over long time periods, the project offers a cost-effective way to observe patterns of tuna aggregation and movement on an ocean scale. This collaborative effort between industry (buoy manufacturers and fleets) and researchers demonstrates how AI can add value to existing maritime technologies and support more sustainable, data-informed fishery management.

The project allowed Stalink to automate and serve predictions in real time with higher accuracy than the human predictions in use before the project. The AI model is currently in use by more than 1500 fishing vessels. My role in the project was principal investigator and responsible for the contract, which was executed by Komorebi AI, where I supervised a team of 4 data scientists who built, evaluated and deployed the model in production.

Media Coverage

Tun-AI: Using echosounder buoy technology to study tuna behaviour at sea, Research Features Issue 149 (22/09/23)

Academic CV Pro Hugo Theme

New Analysis in the Preliminary Design for LNG and LPG Ships

HADAD: Hexagonal A-Star with Differential Algorithm Designed for Weather Routing

HADAD: Hexagonal A-Star with Differential Algorithm Designed for Weather Routing

Hybrid Search Method for Zermelo's Navigation Problem

Classification of Exceptional Jacobi Polynomials

ICMS Mini-Symposium 2024

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Research Features Coverage on Tun-AI

Thresholding Methods in Non-Intrusive Load Monitoring

Mathematical Optimization for Safer and Decarbonized Maritime Transport

NeoCam: An Edge-Cloud Platform for Non-Invasive Real-Time Monitoring in Neonatal Intensive Care Units

Mathematical Optimization of Maritime Shipping Routes

CITIC-UDC Coverage on NPI Effectiveness Results

Effectiveness of Non-Pharmaceutical Interventions in Nine Fields of Activity to Decrease SARS-CoV-2 Transmission (Spain, September 2020--May 2021)

Green Navigation: Optimización de Rutas Marítimas

Simons Center Workshop 2023

Tun-AI: Using Echosounder Buoy Technology to Study Tuna Behaviour at Sea

Smart Shipping, Weather Routing and WASP Simulation

Exceptional Gegenbauer Polynomials via Isospectral Deformation

TUN-AI: Tuna Biomass Estimation with Machine Learning Models Trained on Oceanography and Echosounder FAD Data

Toward Automated Feature Extraction for Deep Learning Classification of Electrocardiogram Signals

OpenCV Blog Episode 32 Interview on NeoCam

Diario de Cadiz Coverage of NeoCam International Award

Complete Classification of Rational Solutions of A2-Painlevé Systems

IEEE PowerTech 2021 Presentation on NILM

Non-Intrusive Load Monitoring Using Multi-Output CNNs

Interview for RSME on the COVID-19 Response

Exceptional Legendre Polynomials and Confluent Darboux Transformations

Fall Detection from Electrocardiogram (ECG) Signals and Classification by Deep Transfer Learning

Ladder Operators and Rational Extensions

Spectral Theory of Exceptional Hermite Polynomials

Corrigendum on the Proof of Completeness for Exceptional Hermite Polynomials

Radio 3 Interview on Big Data and Coronavirus Privacy

Interview in eldiario.es on Data Transparency and COVID-19

Fundacion BBVA Feature on COVID-19 Prediction Models

Cyclic Maya Diagrams and Rational Solutions of Higher Order Painlevé Systems

Fundacion Descubre Note on ICU Occupancy Predictive Model

Las Matematicas frente a la COVID-19

Rational Solutions of Painlevé Systems.

BIDAS 4 Presentation on NILM

Assessing the Effect of Advertising Expenditures upon Sales: A Bayesian Structural Time Series Model

A Bochner Type Characterization Theorem for Exceptional Orthogonal Polynomials

Big data: conceptos, tecnologías y aplicaciones

JURIX 2019 IberLegal Industry Session Talk

Opponent Aware Reinforcement Learning

Validating Ocean General Circulation Models via Lagrangian Particle Simulation and Data from Drifting Buoys

Durfee Rectangles and Pseudo-Wronskian Equivalences for Hermite Polynomials