Title: Patterns in Multi-dimensional Permutations

Date: Thursday 2nd October 2025, 2.00pm

Venue: LT907

Abstract:  In this talk, I will present a general framework that extends the theory of permutation patterns to higher dimensions and unifies several combinatorial objects studied in the literature. The approach introduces the concept of a level for an element in a multi-dimensional permutation, which can be defined in various ways. I will discuss two natural definitions of level, each establishing connections to combinatorial sequences listed in the Online Encyclopedia of Integer Sequences (OEIS).

Our framework provides combinatorial interpretations for numerous OEIS sequences, many of which previously lacked such interpretations. As a notable example, we offer an elegant interpretation of the Springer numbers: they count weakly increasing 3-dimensional permutations under a level definition based on maximal entries.

This is joint work with Shaoshi Chen, Hanqian Fang, and Candice X.T. Zhang, all from the Chinese Academy of Sciences.

Title: Essential spectra of Sturm-Liouville operators and their indefinite counterpart

Date: Thursday 9th October 2025, 2.00pm

Venue: LT907

Abstract:  The difference to classical Sturm-Liouville expressions is here that the weight may change its sign.  In this situation the associated maximal operators become self-adjoint with respect to indefinite inner products and their spectral properties differ essentially from the Hilbert space situation.  We investigate the essential spectra and accumulation properties of nonreal and real discrete eigenvalues.

Title: A tractable framework for modelling hydrophilic large-swelling gels

Date: Wednesday 1st October 2025, 1.00pm

Venue: LT908

Abstract:  TBA

Title: Patient-Specific Multiscale Modelling of Glioblastoma: Targeted Modulation of
Interstitial Fluid Flow Using Electric Field

Date: Wednesday 15th October 2025, 1.00pm

Venue: LT907

Abstract:  TBA

Title: Patient- The Lean Azimuthal Flame (LEAF) combustor concept: exhaust emissions and
flame topology

Date: Wednesday 29th October 2025, 1.00pm

Venue: LT907

Abstract:  TBA

Title: Patient- TBC

Date: Wednesday 5th November 2025, 1.00pm

Venue: LT907

Abstract:  TBA

Title: Patient- TBC

Date: Wednesday 26th November 2025, 1.00pm

Venue: LT907

Abstract:  TBA

Title: Patient- TBC

Date: Wednesday 3rd December 2025, 1.00pm

Venue: LT907

Abstract:  TBA

Title: AI for Decision Support to Smallholder Farming

Date: Tuesday 16th September 2025, 2.00pm

Venue: LT908

Abstract:  Pests and diseases contribute to an estimated 40% reduction in global crop yields annually, necessitating scalable, data-driven interventions to help smallholder farmers. Recent advances in artificial intelligence (AI) and machine learning (ML) have demonstrated significant potential in augmenting plant health diagnostics and decision support at field scale. A forthcoming initiative, the AgriLLM Project, scheduled for launch at COP30 (Brazil, November 2025), integrates Large Language Models (LLMs) with CGIAR’s domain expertise to generate context-specific, real-time agronomic advisories. This innovation addresses persistent gaps in extension delivery across the Global South, where resource-constrained systems limit access to expert recommendations during critical phenological stages. Among operational examples, the Plantix platform has emerged as a large scale AI application, capable of automatically diagnosing over 700 biotic and abiotic stresses across 45 crops. Advisories were offered for over 150 million targeted farmer problems using plant damage symptoms. Complementing this, ICRISAT’s Plant Health Detector employs supervised ML classifiers to automate recognition of 50 crop damages. An Intelligent Agricultural Systems Advisory Tool is being piloted to provide climate smart pre-season and in-season advisories in India and Africa. Parallel advances in computer vision have facilitated spatially explicit tracking of invasive pests such as Spodoptera frugiperda, while citizen science models enhance surveillance. Collectively, these AI-enabled systems exemplify transformative pathways for resilient, data-driven crop advisory management for smallholders in Asia and Africa.

Title: Identifying the Effect of Climate Warming on Ocean Chlorophyll from Satellite Records Using Deep Learning and Multiple Regression Methods

Date: Wednesday 15th October 2025, 2.00pm

Venue: LT908

Abstract:  Anthropogenic climate warming is expected to cause long-term changes in global marine phytoplankton, affecting the marine ecosystem and fishery production. However, it is challenging to distinguish the effects of long-term climate warming from natural variability in satellite chlorophyll-a (Chl-a, a proxy for phytoplankton biomass) records—the only global-scale observations of phytoplankton—due to limited data length and interference from natural fluctuations. As a result, we still cannot confidently determine whether observed changes in global Chl-a from limited satellite records are caused by long-term climate warming or how quickly warming-induced Chl-a changes occur.

In this talk, I will introduce our recent two studies on this topic. First, we developed a deep learning model and trained it using an ensemble of Earth System Models’ simulations, which successfully detected the climate warming signal from satellite observations of global Chl-a. This validates the model’s predictions and highlights the emerging anthropogenic impacts on global marine phytoplankton. Second, using multiple regression models, we reduced the influence of natural climate variability on trend analysis of satellite Chl-a in the tropical Pacific from 1997–2023, revealing a long-term decline in Chl-a at a rate of about −4‰/yr. Global warming contributed to this decline at a rate of −14.5%/℃. This finding offers solid estimates of the global warming-driven trend in tropical marine phytoplankton biomass, helping to forecast future changes in marine ecosystems.

Title: Application of GAM in Exploring Long-Term Productivity-Biodiversity Relationships in Marine Microzooplankton Assemblages

Date: Wednesday 22nd October 2025, 2.00pm

Venue: LT511

Abstract:  Although the productivity-biodiversity relationship (PBR) has been a hot topic, few studies have considered how anthropogenic pressures affect PBRs in marine microzooplankton. Here, we applied GAMs to provide the first insights into PBRs in tintinnid assemblages using 18-year data from Jiaozhou Bay, a typical coastal bay in the Yellow Sea. We hypothesized and verified that PBRs vary across contrasting anthropogenic nutrient inputs and that functional and phylogenetic diversity would provide more information than conventional species richness. High productivity promotes higher diversity under low to medium rather than high anthropogenic nutrient inputs. Compared to species richness, functional and phylogenetic diversity reveal more PBR patterns and respond more rapidly to varying anthropogenic inputs. A concave PBR is revealed for functional diversity in the ecozone with highly active water exchange. Our study contributes to the understanding of PBRs in marine unicellular secondary producers and their responses to anthropogenic nutrient inputs in coastal ecosystems.

Title: Long time behavior for SIS model driven by pure-jump noise with Markov switching  

Wednesday 17th September 2025, 3.00-4.00pm

Venue: LT907

Abstract: In this talk, we focus on long time behavior for SIS epidemic model driven by an alpha-stable process with Markov switching. Necessary and sufficient conditions for recurrence or extinction have been established. To analyze the ergodic behavior, we establish the well-posedness of a nonlocal Dirichlet problem with regime switching and derive a strong maximum principle. Our results disclose heavy-tailed fluctuations and stationary distribution of Markov chain significantly affect the epidemic threshold and asymptotic dynamics. 

Title: A Journey Through Academia: Some Personal Reflections as a Member of an Under-Represented Group(s)

Date: Wednesday 29th October 2025, 3.00pm

Venue: LT908

Abstract: In this talk I will present a brief overview of my career, discussing why I became interested in Statistics and how this led to my academic journey to date, including several critical turning points. I will present personal reflections in the context of being an individual from an under-represented group through different career stages and in particular how this has subsequently informed how I now approach situations and in particular decision-making. I will also aim to discuss some aspects of leadership (at least as I see it) including some challenges this brings, as well as opportunities to support diversity.