January 04, 2021
DataCafé
Season 1
Episode 10

Optimising the Future

DataCafé

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DataCafé

Optimising the Future

Jan 04, 2021
Season 1
Episode 10

DataCafé

As we look ahead to a new year, and reflect on the last, we consider how data science can be used to optimise the future. But to what degree can we trust past experiences and observations, essentially relying on historical data to predict the future? And with what level of accuracy?

In this episode of the DataCafé we ask: how can we optimise our predictions of future scenarios to maximise the benefit we can obtain from them while minimising the risk of unknowns?

Data Science is made up of many diverse technical disciplines that can help to answer these questions. Two among them are mathematical optimisation and machine learning. We explore how these two fascinating areas interact and how they can both help to turbo charge the other's cutting edge in the future.

We speak with **Dimitrios Letsios** from King's College London about his work in optimisation and what he sees as exciting new developments in the field by working together with the field of machine learning.

With interview guest **Dr. Dimitrios Letsios**, lecturer (assistant professor) in the Department of Informatics at King's College London and a member of the Algorithms and Data Analysis Group.

**Further reading**

**Dimirios Letsios' publication list**(https://bit.ly/35vHirH via King's College London)**Paper on taking into account uncertainty in an optimisation model**: Approximating Bounded Job Start Scheduling with Application in Royal Mail Deliveries under Uncertainty (https://bit.ly/3pLHICV via King's College London)**Paper on lexicographic optimisation**: Exact Lexicographic Scheduling and Approximate Rescheduling (https://bit.ly/3rS8Xxk via arXiv)**Paper on combination of AI and Optimisation**: Argumentation for Explainable Scheduling (https://bit.ly/3oobgGF via AAAI Conference on Artificial Intelligence)

*Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.*

*Recording date: 23 October 2020Interview date: 21 February 2020*

*Intro music by Music 4 Video Library** (Patreon supporter)*

Thanks for joining us in the DataCafé. You can follow us on twitter @DataCafePodcast and feel free to contact us about anything you've heard here or think would be an interesting topic in the future.

As we look ahead to a new year, and reflect on the last, we consider how data science can be used to optimise the future. But to what degree can we trust past experiences and observations, essentially relying on historical data to predict the future? And with what level of accuracy?

In this episode of the DataCafé we ask: how can we optimise our predictions of future scenarios to maximise the benefit we can obtain from them while minimising the risk of unknowns?

Data Science is made up of many diverse technical disciplines that can help to answer these questions. Two among them are mathematical optimisation and machine learning. We explore how these two fascinating areas interact and how they can both help to turbo charge the other's cutting edge in the future.

We speak with **Dimitrios Letsios** from King's College London about his work in optimisation and what he sees as exciting new developments in the field by working together with the field of machine learning.

With interview guest **Dr. Dimitrios Letsios**, lecturer (assistant professor) in the Department of Informatics at King's College London and a member of the Algorithms and Data Analysis Group.

**Further reading**

**Dimirios Letsios' publication list**(https://bit.ly/35vHirH via King's College London)**Paper on taking into account uncertainty in an optimisation model**: Approximating Bounded Job Start Scheduling with Application in Royal Mail Deliveries under Uncertainty (https://bit.ly/3pLHICV via King's College London)**Paper on lexicographic optimisation**: Exact Lexicographic Scheduling and Approximate Rescheduling (https://bit.ly/3rS8Xxk via arXiv)**Paper on combination of AI and Optimisation**: Argumentation for Explainable Scheduling (https://bit.ly/3oobgGF via AAAI Conference on Artificial Intelligence)

*Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.*

*Recording date: 23 October 2020Interview date: 21 February 2020*

*Intro music by Music 4 Video Library** (Patreon supporter)*

Thanks for joining us in the DataCafé. You can follow us on twitter @DataCafePodcast and feel free to contact us about anything you've heard here or think would be an interesting topic in the future.