7 citations · 7 across the 6 of their papers we have counts for
8 papers
Data-driven Optimal Sensor Placement for Aerosol Monitoring in the UK
Talha Ansar, Waqar Muhammad Ashraf, Alejandro Coca-Castro +3
We develop a data-driven sensor-placement framework and apply it to aerosol monitoring in the United Kingdom (UK). A trained convolutional Gaussian neural process is embedded withi…
Data-driven Bi-level Optimization of Thermal Power Systems with embedded Artificial Neural Networks
Talha Ansar, Muhammad Mujtaba Abbas, Ramit Debnath +2
Industrial thermal power systems have coupled performance variables with hierarchical order of importance, making their simultaneous optimization computationally challenging or inf…
Code, Capital, and Clusters: Understanding Firm Performance in the UK AI Economy
Waqar Muhammad Ashraf, Diane Coyle, Ramit Debnath
The UK has established a distinctive position in the global AI landscape, driven by rapid firm formation and strategic investment. However, the interplay between AI specialisation,…
Neural Network-enabled Domain-consistent Robust Optimisation for Global CO Reduction Potential of Gas Power Plants
Waqar Muhammad Ashraf, Talha Ansar, Abdulelah S. Alshehri +3
We introduce a neural network-driven robust optimisation framework that integrates data-driven domain as a constraint into the nonlinear programming technique, addressing the overl…
Domain-Informed Operation Excellence of Gas Turbine System with Machine Learning
Waqar Muhammad Ashraf, Amir H. Keshavarzzadeh, Abdulelah S. Alshehri +3
The domain-consistent adoption of artificial intelligence (AI) remains low in thermal power plants due to the black-box nature of AI algorithms and low representation of domain kno…
When Large Language Models are More PersuasiveThan Incentivized Humans, and Why
Jiacheng Liu, Francesco Salvi, Philipp Schoenegger +37
Large Language Models (LLMs) have been shown to be highly persuasive, but when and why they outperform humans is still an open question. We compare the persuasiveness of two LLMs (…
Human Preferences for Constructive Interactions in Language Model Alignment
Yara Kyrychenko, Jon Roozenbeek, Brandon Davidson +2
As large language models (LLMs) enter the mainstream, aligning them to foster constructive dialogue rather than exacerbate societal divisions is critical. Using an individualized a…
Domain Consistent Industrial Decarbonisation of Global Coal Power Plants
Waqar Muhammad Ashraf, Vivek Dua, Ramit Debnath
Machine learning and optimisation techniques (MLOPT) hold significant potential to accelerate the decarbonisation of industrial systems by enabling data-driven operational improvem…