http://talks.cam.ac.uk/show/rss/5408

Abstract

The growing accessibility of solar photovoltaic (PV) systems offers a promising pathway for homeowners to decarbonize their buildings. However, determining the appropriate size of a PV system and battery storage remains a complex task, influenced by household energy demand, daily activity patterns, and local solar potential. This decision becomes more complex with the increasing adoption of electric vehicles (EVs), as commute patterns and charging strategies, including bidirectional charging, significantly influence electricity demand profiles. Conventional approaches to sizing PV and battery systems rely on detailed simulations that, while accurate, are computationally intensive and often take several minutes to hours to complete. This latency reduces interactivity and limits users’ ability to explore different scenarios, such as varying EV charging policies or desired levels of energy self-sufficiency. In this work, we introduce SolarFit, an application that delivers instant, high-accuracy sizing recommendations based on simple user-provided inputs. SolarFit leverages a neural network-based surrogate model, which generates results within milliseconds. By drastically reducing computation time, our approach enables users to efficiently evaluate a range of scenarios and identify system configurations that best match their needs.

Bio

Julia Gschwind is a visiting Master’s student at the University of Cambridge from ETH Zurich. She is supervised by Prof. Srinivasan Keshav and her research focuses on using neural networks to predict the optimal sizing of photovoltaic systems.

• Speaker: Julia Gschwind ETH Zurich, University of Cambridge
• Thursday 17 July 2025, 13:00-14:00
• Venue: Room GS15 at the William Gates Building and on Zoom: https://cl-cam-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=addon .
• Series: Energy and Environment Group, Department of CST; organiser: lyr24.

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In the talk we review the need to revisit performance evaluation in Machine Learning, as long as the existing mainstream options (accuracy, f-measure, MSE /MAE) provide a too narrow insight on method performance. Some of the topics discussed in the talk relate to the interpretation and modelling of data in a dataset, including multiple ground truth, classes of equivalence, and area-based interpretation of input population. A later part of the talk reviews alternatives to accuracy and f-measure in literature, mostly leaning towards inclusion of explainability or truthworthiness.

• Speaker: Speaker to be confirmed
• Monday 07 July 2025, 15:50-17:20
• Venue: Computer Laboratory, William Gates Building, Room FW26.
• Series: Foundation AI; organiser: Pietro Lio.

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Abstract not available

• Speaker: Speaker to be confirmed
• Monday 07 July 2025, 15:50-17:20
• Venue: Computer Laboratory, William Gates Building, Room FW26.
• Series: Foundation AI; organiser: Pietro Lio.

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Abstract not available

• Speaker: Speaker to be confirmed
• Monday 07 July 2025, 15:50-17:20
• Venue: Computer Laboratory, William Gates Building, Room FW26.
• Series: Foundation AI; organiser: Pietro Lio.

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Abstract not available

• Speaker: Speaker to be confirmed
• Monday 07 July 2025, 15:50-17:20
• Venue: Computer Laboratory, William Gates Building, Room FW26.
• Series: Foundation AI; organiser: Pietro Lio.

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Abstract not available

• Speaker: Speaker to be confirmed
• Monday 07 July 2025, 15:50-17:20
• Venue: Computer Laboratory, William Gates Building, Room FW26.
• Series: Foundation AI; organiser: Pietro Lio.

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This free event is open only to members of the University of Cambridge (and affiliated institutes). Please be aware that we are unable to offer consultations outside clinic hours.

If you would like to participate, please sign up as we will not be able to offer a consultation otherwise. Please sign up through the following link: https://forms.gle/m6g2KuZY4LNwQHA6A. Sign-up is possible from Sep 11 midday (12pm) until Sep 15 midday or until we reach full capacity, whichever is earlier. If you successfully signed up, we will confirm your appointment by Sep 17 midday.

• Speaker: Speaker to be confirmed
• Wednesday 17 September 2025, 16:30-18:00
• Venue: MR14.
• Series: Cambridge Statistics Clinic; organiser: tm681.

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This free event is open only to members of the University of Cambridge (and affiliated institutes). Please be aware that we are unable to offer consultations outside clinic hours.

If you would like to participate, please sign up as we will not be able to offer a consultation otherwise. Please sign up through the following link: https://forms.gle/Wj8YcAW6a5MKDtHr8. Sign-up is possible from Aug 21 midday (12pm) until Aug 25 midday or until we reach full capacity, whichever is earlier. If you successfully signed up, we will confirm your appointment by Aug 27 midday.

• Speaker: Speaker to be confirmed
• Wednesday 27 August 2025, 16:30-18:00
• Venue: MR14.
• Series: Cambridge Statistics Clinic; organiser: tm681.

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This free event is open only to members of the University of Cambridge (and affiliated institutes). Please be aware that we are unable to offer consultations outside clinic hours.

If you would like to participate, please sign up as we will not be able to offer a consultation otherwise. Please sign up through the following link: https://forms.gle/jmaJSKySQL1yicNMA. Sign-up is possible from July 31 midday (12pm) until Aug 4 midday or until we reach full capacity, whichever is earlier. If you successfully signed up, we will confirm your appointment by Aug 6 midday.

• Speaker: Speaker to be confirmed
• Wednesday 06 August 2025, 16:30-18:00
• Venue: MR14.
• Series: Cambridge Statistics Clinic; organiser: tm681.

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This free event is open only to members of the University of Cambridge (and affiliated institutes). Please be aware that we are unable to offer consultations outside clinic hours.

If you would like to participate, please sign up as we will not be able to offer a consultation otherwise. Please sign up through the following link: https://forms.gle/7egUWjAAQnS5g4Pt7. Sign-up is possible from July 10 midday (12pm) until July 14 midday or until we reach full capacity, whichever is earlier. If you successfully signed up, we will confirm your appointment by July 16 midday.

• Speaker: Speaker to be confirmed
• Wednesday 16 July 2025, 16:30-18:00
• Venue: MR14.
• Series: Cambridge Statistics Clinic; organiser: tm681.

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In my group we build minimal cytoskeletal systems using purified components which allow us to study the dynamic, force-generating and self-organizing properties of (for example) minimal mitotic spindles in (artificial) confinement. In addition, we are interested in designing and building a minimal DNA segregation system for synthetic cells based on prokaryotic cytoskeletal filaments. In my talk I will give an overview of current interests, work in progress and challenges that we face.

• Speaker: Marileen Dogterom - TU Delft, Delft University of Technology
• Monday 07 July 2025, 11:00-12:00
• Venue: In person in the Max Perutz Lecture Theatre (CB2 0QH) and via Zoom link https://mrc-lmb-cam-ac-uk.zoom.us/j/95584842300?pwd=V2DAKEE0bQXA4bbbSUryiRPELlhmzP.1.
• Series: MRC LMB Seminar Series; organiser: Scientific Meetings Co-ordinator.

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The seminar “From Score to Sound: Music Production in the AI Era” will explore the evolution of AI-driven methods for automatic music generation, from the early use of Recurrent Neural Networks to the latest Foundational Models. The talk will examine the shift from symbolic score modelling to audio-centric and multimodal approaches. It will also highlight the technical and conceptual advancements that have enabled the development of transformer and diffusion-based models, paving the way for today’s prompt-to-music systems.

• Speaker: Francesco Bardozzo (University of Salerno)
• Thursday 10 July 2025, 15:50-17:20
• Venue: Computer Laboratory, William Gates Building, Room FW26.
• Series: Foundation AI; organiser: Pietro Lio.

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Artificial Intelligence (AI) is expected to have a transformative impact on the natural sciences by enhancing modeling capabilities and improving the prediction of natural phenomena across multiple spatial and temporal scales. This talk will highlight the urgency of coordinated scientific and regulatory initiatives to ensure the sustainable development of our planet. It will also provide an overview of recent advances in AI-driven approaches within the environmental domain, with a particular focus on solutions for coastal and marine ecosystem monitoring. Finally, the presentation will offer ideas on potential future developments from a modeling perspective, underscoring emerging directions and opportunities for interdisciplinary research.

• Speaker: Prof. Antonino Staiano
• Tuesday 08 July 2025, 15:50-17:20
• Venue: Computer Laboratory, William Gates Building, Room FW26.
• Series: Foundation AI; organiser: Pietro Lio.

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AI governance is increasingly shaped by a complex interplay of normative approaches. While high-level principles such as fairness, transparency, accountability, and safety are widely recognized across governance frameworks, their implementation varies significantly. The growing geopolitical significance of AI has driven governments to develop distinct strategies and policies, giving rise to 3 main models of AI governance. The Neoliberal Model, championed by the United States, prioritizes market-driven innovation, industry self-regulation, and minimal government intervention. Digital Sovereignty, exemplified by China, reflects a state-controlled and security-driven approach that emphasizes data localization and algorithmic transparency tailored to government priorities, particularly in information control and social stability. The European Union’s Digital Constitutionalism model embeds fundamental rights and democratic oversight into AI regulation, aiming for human-centric, trustworthy, and accountable AI governance. However, the boundaries between these governance paradigms are increasingly blurring. Under the Biden administration, the U.S. briefly moved closer to the EU model before reverting to a neoliberal stance, leveraging Big Tech as proxies of power and security actors. The EU struggles to balance its ambition to lead in Trustworthy AI with competitiveness and security concerns. China, while maintaining strict state control, has introduced selective innovation incentives and consumer rights protections with distinct “Chinese characteristics.” Rather than fostering a cross-fertilization of these models, these shifting boundaries appear to reflect escalating geopolitical tensions, making international consensus on AI governance increasingly difficult to achieve.

• Speaker: Dr. Nicola Palladino, University of Salerno
• Wednesday 09 July 2025, 09:00-10:30
• Venue: Computer Laboratory, William Gates Building, Room FW26.
• Series: Foundation AI; organiser: Pietro Lio.

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AI governance is increasingly shaped by a complex interplay of normative approaches. While high-level principles such as fairness, transparency, accountability, and safety are widely recognized across governance frameworks, their implementation varies significantly. The growing geopolitical significance of AI has driven governments to develop distinct strategies and policies, giving rise to 3 main models of AI governance. The Neoliberal Model, championed by the United States, prioritizes market-driven innovation, industry self-regulation, and minimal government intervention. Digital Sovereignty, exemplified by China, reflects a state-controlled and security-driven approach that emphasizes data localization and algorithmic transparency tailored to government priorities, particularly in information control and social stability. The European Union’s Digital Constitutionalism model embeds fundamental rights and democratic oversight into AI regulation, aiming for human-centric, trustworthy, and accountable AI governance. However, the boundaries between these governance paradigms are increasingly blurring. Under the Biden administration, the U.S. briefly moved closer to the EU model before reverting to a neoliberal stance, leveraging Big Tech as proxies of power and security actors. The EU struggles to balance its ambition to lead in Trustworthy AI with competitiveness and security concerns. China, while maintaining strict state control, has introduced selective innovation incentives and consumer rights protections with distinct “Chinese characteristics.” Rather than fostering a cross-fertilization of these models, these shifting boundaries appear to reflect escalating geopolitical tensions, making international consensus on AI governance increasingly difficult to achieve.

• Speaker: Dr. Nicola Palladino, University of Salerno
• Wednesday 09 July 2025, 09:00-10:30
• Venue: Lecture Theatre 2, Computer Laboratory, William Gates Building.
• Series: Foundation AI; organiser: Pietro Lio.

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Abstract not available

• Speaker: Prof. Dr. Ryan Gilmour, University of Münster,
• Wednesday 29 October 2025, 14:00-15:00
• Venue: Dept. of Chemistry, Wolfson Lecture Theatre.
• Series: Synthetic Chemistry Research Interest Group; organiser: Matt Gaunt.

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• Speaker: Prof. Syuzanna Harutyunyan, University of Groningen.
• Monday 20 October 2025, 14:00-15:00
• Venue: Dept. of Chemistry, Wolfson Lecture Theatre.
• Series: Synthetic Chemistry Research Interest Group; organiser: Ruth Webster.

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• Speaker: Prof. Harry Anderson, University of Oxford
• Monday 17 November 2025, 14:00-15:00
• Venue: Dept. of Chemistry, Wolfson Lecture Theatre.
• Series: Synthetic Chemistry Research Interest Group; organiser: Chris Hunter.

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Abstract not available

• Speaker: Speaker to be confirmed
• Friday 04 July 2025, 15:50-17:20
• Venue: Lecture Theatre 2, Computer Laboratory, William Gates Building.
• Series: Foundation AI; organiser: Pietro Lio.

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Abstract not available

• Speaker: Speaker to be confirmed
• Friday 04 July 2025, 15:50-17:20
• Venue: Lecture Theatre 2, Computer Laboratory, William Gates Building.
• Series: Foundation AI; organiser: Pietro Lio.

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