School of Physical Sciences

Abstract not available

• Speaker: Prof. Sir David Clary, FRS (Cambridge & Oxford)
• Thursday 15 May 2025, 14:00-15:30
• Venue: TCM Seminar Room.
• Series: Theory of Condensed Matter; organiser: Bo Peng.

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In this talk, I will show how a gauge-theoretic approach to Jackiw–Teitelboim (JT) gravity naturally yields a two-dimensional Henneaux–Teitelboim (HT) unimodular theory, applicable to both flat and curved spacetimes. Under a mini-superspace reduction, the Wheeler–DeWitt equation becomes a Schrödinger-like equation admitting a consistent, unitary quantum description. The resulting wavefunction describes a quantum distribution for the scale factor, illuminating cosmic expansion and contraction, and allowing topology change at a=0.

• Speaker: Farbod Rassouli, University of Nottingham
• Friday 07 February 2025, 13:00-14:00
• Venue: Venue to be confirmed.
• Series: DAMTP Friday GR Seminar; organiser: Xi Tong.

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Abstract not available

• Speaker: Shohei Koide, NYU Langone Health
• Monday 16 June 2025, 12:30-13:30
• Venue: CRUK CI Lecture Theatre.
• Series: Seminars on Quantitative Biology @ CRUK Cambridge Institute ; organiser: .

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Abstract not available

• Speaker: David Savage, UC Berkeley
• Monday 19 May 2025, 12:30-13:30
• Venue: CRUK CI Lecture Theatre.
• Series: Seminars on Quantitative Biology @ CRUK Cambridge Institute ; organiser: Kate Davenport.

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Abstract not available

• Speaker: Oliver Hantschel, University of Marburg
• Monday 28 April 2025, 12:30-13:30
• Venue: CRUK CI Lecture Theatre.
• Series: Seminars on Quantitative Biology @ CRUK Cambridge Institute ; organiser: .

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In the first part of the talk, we focus on the conceptual development of alternative treatment strategies that leverage the principles of evolution to mitigate treatment resistance. We introduce this broad class of drug scheduling strategies known as evolutionary therapies and explain how mathematical modeling can aid by providing patient-specific predictions as a decision-support tool for providing clinical insight. Next, we explore the practical implementation of an evolutionary therapy steering strategy within an in vivo model of non-small-cell lung cancer treated with ALK inhibitors. Treatment-naïve tumors are associated with more convex exposure-response curves (low doses provide sufficient responses) while evolved-resistance tumors are generally more concave (requiring high doses for equivalent responses). Resistance to ALK inhibitors in vivo occurs gradually, as tumors acquire cooperating genetic and epigenetic adaptive changes. Thus, we hypothesized the existence of a critical point in the time-evolution of ALK -positive tumors where it is optimal to switch from continuous treatment to high-dose / low-dose to mitigate the onset of gradual resistance. In vivo validation provides evidence that evolutionary steering is a viable strategy for predicting the onset of resistance and developing resistance management treatment strategies. Thus far, we neglect the fact that cancer growth can be described as a caricature of the renewal process of the tissue of origin, where the tissue architecture and spatial correlations have a strong influence on the evolutionary dynamics within a tumor. To incorporate these characteristics, we introduce agent-based modeling methods that integrate clinical spatial data to make inferences on the role of microenvironmental mechanism of immune escape, and define implications on therapy.

• Speaker: Jeffrey West, Ph.D. Assistant Member, Integrated Mathematical Oncology H. Lee Moffitt Cancer Center & Research Institute
• Monday 03 February 2025, 12:30-13:30
• Venue: CRUK CI Lecture Theatre.
• Series: Seminars on Quantitative Biology @ CRUK Cambridge Institute ; organiser: Kate Davenport.

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The field of cellular biology has long sought to understand the intricate mechanisms that govern cellular responses to various perturbations, be they chemical, physical, or biological. Traditional experimental approaches, while invaluable, often face limitations in scalability and throughput, especially when exploring the vast combinatorial space of potential cellular states. Enter generative machine learning that has shown exceptional promise in modeling complex biological systems. This talk will highlight recent successes, address the challenges and limitations of current models, and discuss the future direction of this exciting interdisciplinary field. Through examples of practical applications, we will illustrate the transformative potential of generative ML in advancing our understanding of cellular perturbations and in shaping the future of biomedical research.

• Speaker: Mohammad Lotfollahi
• Monday 24 February 2025, 12:30-13:30
• Venue: CRUK CI Lecture Theatre.
• Series: Seminars on Quantitative Biology @ CRUK Cambridge Institute ; organiser: .

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In this talk, closed-form expressions are presented for the variation of the expectation of a given function due to changes in the probability measure used for the expectation. Two immediate applications are found in statistical machine learning and game theory. The former leads to closed-form expressions for the generalization error of machine learning algorithms. The latter leads to exact characterizations of the gain/loss of players involved in a zero-sum game in which a player allows its opponent to observe its action up to certain distortion. In both cases, these characterizations reveal interesting connections with Gibbs probability measures, mutual information, and lautum information.

• Speaker: Dr Samir Perlaza, INRIA
• Wednesday 26 February 2025, 14:00-15:00
• Venue: MR5, CMS Pavilion A.
• Series: Information Theory Seminar; organiser: Dr Amir R. Asadi.

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I will discuss a class of diffusion-based algorithms to draw samples from high-dimensional probability distributions given their unnormalized densities. Ideally, the method can transport samples from a Gaussian distribution to a specified target distribution in finite time. The stochastic interpolants framework used to derive a diffusion process, and also involves solving certain Hamilton-Jacobi-Bellman PDEs. These are solved using the theory of forward-backward stochastic differential equations (FBSDE) together with machine learning-based methods. Numerical experiments illustrating that the algorithm will also be discussed. This is joint work with Anand Jerry George.

• Speaker: Dr Nicolas Macris, EPFL
• Wednesday 12 February 2025, 14:00-15:00
• Venue: MR5, CMS Pavilion A.
• Series: Information Theory Seminar; organiser: Prof. Ramji Venkataramanan.

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Sea ice dynamics are highly complex and generally poorly resolved by sea ice models. This is problematic, as they modulate the amount of momentum exchanged between the atmosphere and the ocean in polar regions, as well as play a key role in heat and light fluxes through the opening/closing of sea ice leads. A solution to improve simulated sea ice dynamics is to use a brittle rheology to represent the mechanical behaviour of sea ice. Such rheology is included in the sea ice model neXtSIM, and we demonstrated its ability to capture the observed characteristics and complexity of fine-scale sea ice deformations.Here, we present two cases where we coupled this sea ice model to better understand the role of ice dynamics in ice-ocean interactions.

In the first case, we set up a 12km resolution ocean—sea-ice coupled model, using OPA , the ocean component of NEMO . We investigate the sea ice mass balance of the model for the period 2000-2018. We estimate the contribution of leads and polynyas to winter ice production. We find this contribution to add up from 25% to 35% of the total ice growth in pack ice in winter, showing a significant increase over the 18 years covered by the model simulation.

In the second case, we focus on the marginal ice zone (MIZ) and couple neXtSIM with the wave model WAVEWATCH III . We investigate how wave-induced breakup impacts sea ice dynamics in the MIZ . We show how, using the “damage” quantity that is at the core of the brittle rheology framework, we can represent the loss of ice strength associated with wave-induced breakup, and how breakup can increase the mobility of the thickest ice in the MIZ after storms. For both cases, we will also discuss briefly how using a brittle sea ice model could impact the modelling of Antarctic sea ice using preliminary results from a new configuration.

• Speaker: Guillaume Boutin (Nansen Environmental and Remote Sensing Center)
• Wednesday 29 January 2025, 14:00-15:00
• Venue: BAS Seminar Room 1; https://ukri.zoom.us/j/97264211524.
• Series: British Antarctic Survey - Polar Oceans seminar series; organiser: Dr Birgit Rogalla.

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Aerosols are unique microcompartments central to areas as diverse as climate and air pollution, disease transmission, and chemical synthesis. Resolving their roles in each of these areas is challenging. For instance, the surface composition of aerosol droplets is key to predicting cloud droplet number concentrations, understanding atmospheric pollutant transformation, and interpreting observations of accelerated droplet chemistry. However, direct measurement of the surface properties of aerosol droplets is challenging, even though such measurements are necessary, as surface-bulk partitioning is strongly affected by the droplet’s surface area-to-volume ratio. In this presentation, we will discuss new advances to characterise the equilibrium and dynamic surface properties of picolitre volume droplets, gaining important insights that bear directly on our understanding of how cloud droplets form in the atmosphere and how chemical reactions may proceed in finite-volume systems. We will also describe a new mass spectrometry approach enabling sensitive, high throughput chemical analysis of picolitre droplets, facilitating more robust studies of the factors governing chemical reactivity in microcompartments.

Bio: Bryan Bzdek is Proleptic Associate Professor in the School of Chemistry, University of Bristol. He earned a B.S. degree in Chemistry at Bucknell University and a Ph.D. in Chemistry at the University of Delaware. He performed postdoctoral studies with Jonathan P. Reid, and then began his independent career at the University of Bristol in 2017 as a NERC Independent Research Fellow. His research on the physical and analytical chemistry of aerosols spans applications in atmospheric science and health. He is a recipient of the Kenneth T. Whitby (2024) Award from the American Association for Aerosol Research, the Marlow Prize (2023) from the Royal Society of Chemistry, and the Philip Leverhulme Prize (2022) from the Leverhulme Trust. During the COVID -19 pandemic, his research altered UK government guidance in the performing arts and the NHS infection prevention and control manual. He also gave many print and radio interviews about aerosols and COVID -19 to organisations including US public radio, BBC , CBS, and CNN .

RSC 2023 Faraday Early Career Prize: Marlow Prize Winner

• Speaker: Dr Bryan Bzdek, University of Bristol
• Tuesday 25 February 2025, 14:00-15:00
• Venue: Pfizer Lecture Theatre, Department of Chemistry.
• Series: Physical Chemistry Research Interest Group; organiser: David Madden.

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Combinatorial optimisation (CO) is a sub-area of discrete mathematics. Basic examples for CO problems are finding a shortest path or a minimum spanning tree in a graph. So-called network flows or variations of matching would be more advanced problems. There are also abstract concepts like matroids that offer an algebraic point of view and a uniform foundation for some of the more concrete problems.

Since the considered structures are finite, it is a natural aim to compute a solution efficiently. That implies an overlap with the theory of algorithms, especially running time analysis.

This talk is mainly about the Isabelle/HOL formalisation of a specific CO problem, namely, minimum cost flows, which are a subtype of network flows. Among others, this includes Orlin’s Algorithm, which is a most efficient method to compute a minimum cost flow in general networks. Also, the running time argument for this advanced algorithm and some reductions among flow problems were formalised.

=== Hybrid talk ===

Join Zoom Meeting https://cam-ac-uk.zoom.us/j/87143365195?pwd=SELTNkOcfVrIE1IppYCsbooOVqenzI.1

Meeting ID: 871 4336 5195

Passcode: 541180

• Speaker: Thomas Ammer (King's College London)
• Thursday 06 February 2025, 17:00-18:00
• Venue: MR14 Centre for Mathematical Sciences.
• Series: Formalisation of mathematics with interactive theorem provers ; organiser: Jonas Bayer.

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We establish the local well-posedness of the Bartnik static metric extension problem for arbitrary Bartnik data that perturb that of any sphere in a Schwarzschild $\{t=0\}$ slice. Our result in particular includes spheres with arbitrary small mean curvature. We introduce a new framework to this extension problem by formulating the governing equations in a geodesic gauge, which reduce to a coupled system of elliptic and transport equations. Since standard function spaces for elliptic PDEs are unsuitable for transport equations, we use certain spaces of Bochner-measurable functions traditionally used to study evolution equations. In the process, we establish existence and uniqueness results for elliptic boundary value problems in such spaces in which the elliptic equations are treated as evolutionary equations, and solvability is demonstrated using rigorous energy estimates. The precise nature of the expected difficulty of solving the Bartnik extension problem when the mean curvature is very small is identified and suitably treated in our analysis.

• Speaker: Ahmed Ellithy (Uppsala)
• Monday 03 February 2025, 14:00-15:00
• Venue: MR13.
• Series: Partial Differential Equations seminar; organiser: Amelie Justine Loher.

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Sea ice plays a key role in Earth’s climate system and exhibits significant seasonal variability as it advances and retreats across the Arctic and Antarctic every year. The production of sea ice forecasts provides great scientific and practical value to stakeholders across the polar regions, informing shipping, conservation, logistics, and the daily lives of inhabitants of local communities. Machine learning offers a promising means by which to develop such forecasts, capturing the nonlinear dynamics and subtle spatiotemporal patterns at play as effectively—if not more effectively—than conventional physics-based models. In particular, the ability of deep generative models to produce probabilistic forecasts which acknowledge the inherent stochasticity of sea ice processes and represent uncertainty by design make them a sensible choice for the task of sea ice forecasting. Diffusion models, a class of deep generative models, present a strong option given their state-of-the-art performance on computer vision tasks and their strong track record when adapted to spatiotemporal modelling tasks in weather and climate domains. In this talk, I will present preliminary results from a IceNet-like [1] diffusion model trained to autoregressively forecast daily, 6.25 km resolution sea ice concentration in the Bellingshausen Sea along the Antarctic Peninsula. I will also touch on the downstream applications for these forecasts, from conservation to marine route planning, which are under development at the British Antarctic Survey (BAS). I welcome ideas and suggestions for improvement and look forward to discussing opportunities for collaboration within and beyond BAS .

[1] Andersson, Tom R., et al. “Seasonal Arctic sea ice forecasting with probabilistic deep learning.” Nature communications 12.1 (2021): 5124. https://www.nature.com/articles/s41467-021-25257-4

• Speaker: Andrew McDonald, University of Cambridge and British Antarctic Survey
• Wednesday 12 February 2025, 14:00-15:00
• Venue: BAS Seminar Room 2.
• Series: British Antarctic Survey - Polar Oceans seminar series; organiser: Dr Birgit Rogalla.

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Air pollution assessment in urban and rural areas is really challenging due to high spatio-temporal variability of aerosols and pollutants and the uncertainties in measurements and modelling estimates. Nevertheless, accurate determination of the pollution sources and distribution of PM2 .5 concentrations is especially important for source apportionment and mitigation strategies. This study provides estimates of PM2 .5 concentrations across Malaysia in high spatial resolution, based on multi-satellite data and machine learning (ML) models, namely Random Forest (RF), Support Vector Regression (SVR) and extreme Gradient Boosting (XGBoost), also covering remote areas without measurement networks. The study aims to develop ML models that are simpler than previous works and demonstrate computational efficiency. Six sub-models were developed to represent different locations and seasons in Malaysia. Model 1 includes all data from 65 air-quality stations, Models 2 and 3 characterize urban/industrial and suburban sites, respectively, while Models 4 to 6 correspond to dry, wet, and inter-monsoon seasons, respectively. The RF technique exhibited slightly better performance compared to the XGBoost and SVR approaches. More specifically, for model 1, it exhibited a high correlation with a coefficient of determination (R2) of 0.64 and RMSE of 12.17 μg m−3, while similar results were obtained for models 3, model 4 and model 5. The lower performance (R2 = 0.16-0.94) observed in the wet and inter-monsoon seasons is due to fewer numbers of data used in model calibration. Integration of two Aerosol Optical Depth products from the Advanced Himawari Imager and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors together with gases pollutants from Sentinel 5P enabled seamless seasonal PM2 .5 mapping over Malaysia, even for a short period of time. However, usage of data with insufficient information during the model training procedure, and lack of satellite data due to cloud contamination, can limit the PM2 .5 prediction accuracy.

https://teams.microsoft.com/l/meetup-join/19%3ameeting_MTQ5N2Q5ZDYtODRmYi00MzJhLTg0ZjctNjc2NGVlZDUzYmUx%40thread.v2/0?context=%7b%22Tid%22%3a%2249a50445-bdfa-4b79-ade3-547b4f3986e9%22%2c%22Oid%22%3a%228b208bd5-8570-491b-abae-83a85a1ca025%22%7d

• Speaker: Kasturi Kanniah, Centre for Environmental Sustainability and Water Security (IPASA), Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia
• Tuesday 04 March 2025, 11:00-12:00
• Venue: Chemistry Dept, Unilever Lecture Theatre and Zoom.
• Series: Centre for Atmospheric Science seminars, Chemistry Dept.; organiser: Dr Megan Brown.

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Stratospheric aerosol injection (SAI) is a proposed method of cooling the planet and reducing the impacts of climate change by adding a layer of small particles to the high atmosphere where they would reflect a fraction of incoming sunlight. While it is likely that SAI could reduce global temperature, it has many serious risks and would not perfectly offset climate change. For SAI to be effective, injection would need to take place in the stratosphere. The height of the transition to the stratosphere decreases with latitude, from around 17km near the equator to 8km near the poles. The required injection height would therefore also decrease for higher latitude injection. In this talk, I will present simulations of SAI in an earth system model, UKESM , which quantify how impacts would vary with the injection location and timing, focusing on low-altitude high-latitude injection strategies. Our results suggest that SAI could meaningfully cool the planet even if limited to using existing large jets and injecting at around 13km altitude, if this injection is in the high latitudes during spring and summer. However, relative to a more optimal deployment with novel aircraft at 20km, this strategy requires three times more sulphur dioxide injection and so would strongly increase some side-effects.

https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZjVmYTU2YmItNmMyZC00NGYzLTllZmMtNGU5OWJiMjlhNDAy%40thread.v2/0?context=%7b%22Tid%22%3a%2249a50445-bdfa-4b79-ade3-547b4f3986e9%22%2c%22Oid%22%3a%228b208bd5-8570-491b-abae-83a85a1ca025%22%7d

• Speaker: Alistair Duffey, PhD Student at University College London, Earth Sciences
• Tuesday 04 February 2025, 11:00-12:00
• Venue: Chemistry Dept, Unilever Lecture Theatre and Teams.
• Series: Centre for Atmospheric Science seminars, Chemistry Dept.; organiser: Yao Ge.

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Abstract not available

• Speaker: Andrzej Wereszynski (Jagiellonian U., Krakow)
• Thursday 06 February 2025, 16:30-17:30
• Venue: CMS, MR11.
• Series: Mathematical Physics Seminar; organiser: Professor Maciej Dunajski.

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Abstract not available

• Speaker: Adam Kmec (Oxford)
• Tuesday 18 February 2025, 16:00-17:00
• Venue: CMS, MR11.
• Series: Mathematical Physics Seminar; organiser: Professor Maciej Dunajski.

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Abstract not available

• Speaker: Peter Cameron (Imperial)
• Tuesday 11 February 2025, 16:00-17:00
• Venue: CMS, MR11.
• Series: Mathematical Physics Seminar; organiser: Professor Maciej Dunajski.

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Abstract not available

• Speaker: Nick Manton (DAMTP)
• Tuesday 04 March 2025, 16:00-17:00
• Venue: CMS, MR11.
• Series: Mathematical Physics Seminar; organiser: Professor Maciej Dunajski.

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