Inference, COmputation and Numerics for Insights into Cities (ICONIC)
Lead Research Organisation:
Imperial College London
Department Name: Mathematics
Abstract
There are many interesting open questions at the interface between applied mathematics, scientific computing and applied statistics.
Mathematics is the language of science, we use it to describe the laws of motion that govern natural and technological
systems. We use statistics to make sense of data. We develop and test computer algorithms that make these ideas concrete. By bringing these concepts together in a systematic way we can validate and sharpen our hypothesis about the underlying science, and make predictions about future behaviour. This general field of Uncertainty Quantification is a very active area of research, with many challenges; from intellectual questions about how to define and measure uncertainty to very practical issues concerning the need to perform intensive computational experiments as efficiently as possible.
ICONIC brings together a team of high profile researchers with the appropriate combination of skills in modeling, numerical analysis, statistics and high performance computing. To give a concrete target for impact, the ICONIC project will focus initially on Uncertainty Quantification for mathematical models relating to crime, security and resilience in urban environments. Then, acknowledging that urban analytics is a very fast-moving field where new technologies and data sources emerge rapidly, and exploiting the flexibility built into an EPSRC programme grant, we will apply the new tools to related city topics concerning human mobility, transport and infrastructure. In this way, the project will enhance the UK's research capabilities in the fast-moving and globally significant Future Cities field.
The project will exploit the team's strong existing contacts with Future Cities laboratories around the world, and with nonacademic stakeholders who are keen to exploit the outcomes of the research. As new technologies emerge, and as more people around the world choose to live and work in urban environments, the Future Cities field is generating vast quantities of potentially valuable data. ICONIC will build on the UK's strength in basic mathematical sciences--the cleverness needed to add value to these data sources--in order to produce new algorithms and computational tools. The research will be conducted alongside stakeholders--including law enforcement agencies, technical IT and infrastructure providers, utility companies and policy-makers. These external partners will provide feedback and challenges, and will be ready to extract value from the tools that we develop. We also have an international Advisory Board of committed partners with relevant expertise in academic research, policymaking, law enforcement, business engagement and public outreach. With these structures in place, the research will have a direct impact on the UK economy, as the nation competes for business in the global Future Cities marketplace. Further, by focusing on crime, security and resilience we will directly improve the lives of individual citizens.
Mathematics is the language of science, we use it to describe the laws of motion that govern natural and technological
systems. We use statistics to make sense of data. We develop and test computer algorithms that make these ideas concrete. By bringing these concepts together in a systematic way we can validate and sharpen our hypothesis about the underlying science, and make predictions about future behaviour. This general field of Uncertainty Quantification is a very active area of research, with many challenges; from intellectual questions about how to define and measure uncertainty to very practical issues concerning the need to perform intensive computational experiments as efficiently as possible.
ICONIC brings together a team of high profile researchers with the appropriate combination of skills in modeling, numerical analysis, statistics and high performance computing. To give a concrete target for impact, the ICONIC project will focus initially on Uncertainty Quantification for mathematical models relating to crime, security and resilience in urban environments. Then, acknowledging that urban analytics is a very fast-moving field where new technologies and data sources emerge rapidly, and exploiting the flexibility built into an EPSRC programme grant, we will apply the new tools to related city topics concerning human mobility, transport and infrastructure. In this way, the project will enhance the UK's research capabilities in the fast-moving and globally significant Future Cities field.
The project will exploit the team's strong existing contacts with Future Cities laboratories around the world, and with nonacademic stakeholders who are keen to exploit the outcomes of the research. As new technologies emerge, and as more people around the world choose to live and work in urban environments, the Future Cities field is generating vast quantities of potentially valuable data. ICONIC will build on the UK's strength in basic mathematical sciences--the cleverness needed to add value to these data sources--in order to produce new algorithms and computational tools. The research will be conducted alongside stakeholders--including law enforcement agencies, technical IT and infrastructure providers, utility companies and policy-makers. These external partners will provide feedback and challenges, and will be ready to extract value from the tools that we develop. We also have an international Advisory Board of committed partners with relevant expertise in academic research, policymaking, law enforcement, business engagement and public outreach. With these structures in place, the research will have a direct impact on the UK economy, as the nation competes for business in the global Future Cities marketplace. Further, by focusing on crime, security and resilience we will directly improve the lives of individual citizens.
Planned Impact
The advances in fundamental research arising from the programme will impact academic researchers in applied/computational
mathematics, statistics and computer science; in particular those working in numerical analysis, statistical inference,
uncertainty quantification, machine learning, data science, network science, stochastic simulation, mathematical modelling, data assimilation and high performance computing. Moreover, the application-oriented results will impact colleagues in geography, urban design, architecture, transport, crime studies, politics and policy making.
Outside academia, because the project is embedded within a Future Cities framework, with emphasis on security, crime and resilience, immediate beneficiaries will include
- SMEs who develop bespoke data analytics applications for clients in local and national government, who will gain access to more insightful data analytics tools that can be customized to their needs,
- larger hi-tech companies developing complex IT solutions across multiple platforms,
- clients of these companies, including those in transport, sensors, satellite imaging, construction, energy and other
utilities, charities and governments, who will benefit from the extra efficincies and insights through improved products and services,
- companies working in tourism, event planning, sports and retail who will benefit from quantified city-level information,
- policy makers and law enforcement officers, who will be better placed to make informed decisions,
- user-communities living, working or spending leisure time in a city environment, who will experience better and safer
services.
A key aspect of the impact delivery plan is to exploit our existing, strong links with important players at the
academic/external interface. In particular, we have in-house connections with Imperial's Digital City Exchange, the Oxford Martin School's programme in Cities, in Future Technology, and in Science and Society, and Strathclyde's Institute for Future Cities. We also have very close working relationships with the UK's Future Cities Catapult, Dublin's Trinity Centre for Smart and Sustainable Cities, New York's Center for Urban Science and Progress (CUSP) and Chicago's Urban Centre for Computation and Data. These partners give an excellent mechanism for outreach to relevant stakeholders and for public engagement, with a level and scope that would not be possible from a single, isolated programme.
Girolami and D. Higham have been drivers in the ATI continuing development of an Urban Analytics research theme. ICONIC will be completely distinct from this and fully complementary, it is driven by the urgent need for fundamental research in mathematical sciences, driven by mathematical and statistical modelling challenges that form an holistic UQ pipeline. ATI is viewed as a UK organisation that can exploit and translate the novel tools emerging from ICONIC and co-engage in additional outreach and impact generation. Similarly, D. Higham's Digital Economy Fellowship, which employs the PDRA Dr Francesca Arrigo as a Data Scientist and has a focus on dynamic networks of digital interactions, has external partners such as Bloom Agency, Capita, CountingLab & Siemens, who can make rapid use of ICONIC's research outputs.
Relevant new links to external partners have also been established: see letters of support from representatives of UK Government Home Office, Metropolitan Police Service, New York Police Department, Police Scotland & West Midlands Police. In addition to shaping and stress-testing the research, these partners will also provide routes to rapid and high-value deployment.
Further, by training PDRAs, the project will deliver skilled, outward facing, future-leaders with experience of interacting with external partners. These individuals will be well placed to act as agents-for-change in shaping the UK demand for Future City Analytics in way that strengthens UK competitiveness.
mathematics, statistics and computer science; in particular those working in numerical analysis, statistical inference,
uncertainty quantification, machine learning, data science, network science, stochastic simulation, mathematical modelling, data assimilation and high performance computing. Moreover, the application-oriented results will impact colleagues in geography, urban design, architecture, transport, crime studies, politics and policy making.
Outside academia, because the project is embedded within a Future Cities framework, with emphasis on security, crime and resilience, immediate beneficiaries will include
- SMEs who develop bespoke data analytics applications for clients in local and national government, who will gain access to more insightful data analytics tools that can be customized to their needs,
- larger hi-tech companies developing complex IT solutions across multiple platforms,
- clients of these companies, including those in transport, sensors, satellite imaging, construction, energy and other
utilities, charities and governments, who will benefit from the extra efficincies and insights through improved products and services,
- companies working in tourism, event planning, sports and retail who will benefit from quantified city-level information,
- policy makers and law enforcement officers, who will be better placed to make informed decisions,
- user-communities living, working or spending leisure time in a city environment, who will experience better and safer
services.
A key aspect of the impact delivery plan is to exploit our existing, strong links with important players at the
academic/external interface. In particular, we have in-house connections with Imperial's Digital City Exchange, the Oxford Martin School's programme in Cities, in Future Technology, and in Science and Society, and Strathclyde's Institute for Future Cities. We also have very close working relationships with the UK's Future Cities Catapult, Dublin's Trinity Centre for Smart and Sustainable Cities, New York's Center for Urban Science and Progress (CUSP) and Chicago's Urban Centre for Computation and Data. These partners give an excellent mechanism for outreach to relevant stakeholders and for public engagement, with a level and scope that would not be possible from a single, isolated programme.
Girolami and D. Higham have been drivers in the ATI continuing development of an Urban Analytics research theme. ICONIC will be completely distinct from this and fully complementary, it is driven by the urgent need for fundamental research in mathematical sciences, driven by mathematical and statistical modelling challenges that form an holistic UQ pipeline. ATI is viewed as a UK organisation that can exploit and translate the novel tools emerging from ICONIC and co-engage in additional outreach and impact generation. Similarly, D. Higham's Digital Economy Fellowship, which employs the PDRA Dr Francesca Arrigo as a Data Scientist and has a focus on dynamic networks of digital interactions, has external partners such as Bloom Agency, Capita, CountingLab & Siemens, who can make rapid use of ICONIC's research outputs.
Relevant new links to external partners have also been established: see letters of support from representatives of UK Government Home Office, Metropolitan Police Service, New York Police Department, Police Scotland & West Midlands Police. In addition to shaping and stress-testing the research, these partners will also provide routes to rapid and high-value deployment.
Further, by training PDRAs, the project will deliver skilled, outward facing, future-leaders with experience of interacting with external partners. These individuals will be well placed to act as agents-for-change in shaping the UK demand for Future City Analytics in way that strengthens UK competitiveness.
Organisations
- Imperial College London (Lead Research Organisation)
- West Midlands Police (Project Partner)
- New York Police Department (Project Partner)
- MathWorks (United Kingdom) (Project Partner)
- Smith Institute (Project Partner)
- Metropolitan Police Service (Project Partner)
- New York University (Project Partner)
- Police Scotland (Project Partner)
- Future Cities Catapult (United Kingdom) (Project Partner)
Publications
Dunlop Matthew M.
(2018)
How Deep Are Deep Gaussian Processes?
in JOURNAL OF MACHINE LEARNING RESEARCH
Ellam L
(2018)
Stochastic Modelling of Urban Structure
Ellam L
(2017)
A determinant-free method to simulate the parameters of large Gaussian fields
in Stat
Ellam L
(2018)
Stochastic modelling of urban structure.
in Proceedings. Mathematical, physical, and engineering sciences
Enderlein J
(2022)
Modeling charge separation in charged nanochannels for single-molecule electrometry
in The Journal of Chemical Physics
Fang W
(2018)
Monte Carlo and Quasi-Monte Carlo Methods
Fang W
(2019)
Multilevel Monte Carlo method for ergodic SDEs without contractivity
in Journal of Mathematical Analysis and Applications
Fang W
(2022)
Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information.
in Medical decision making : an international journal of the Society for Medical Decision Making
Fang W
(2021)
Importance Sampling for Pathwise Sensitivity of Stochastic Chaotic Systems
in SIAM/ASA Journal on Uncertainty Quantification
Fang W
(2020)
Adaptive Euler-Maruyama method for SDEs with nonglobally Lipschitz drift
in The Annals of Applied Probability
Fasi M
(2021)
Generating Extreme-Scale Matrices With Specified Singular Values or Condition Number
in SIAM Journal on Scientific Computing
Fasi M
(2023)
CPFloat: A C Library for Simulating Low-precision Arithmetic
in ACM Transactions on Mathematical Software
Fasi M
(2021)
Matrices with Tunable Infinity-Norm Condition Number and No Need for Pivoting in LU Factorization
in SIAM Journal on Matrix Analysis and Applications
Fasi M
(2023)
Computing the Square Root of a Low-Rank Perturbation of the Scaled Identity Matrix
in SIAM Journal on Matrix Analysis and Applications
Fasi M
(2023)
Matrix Multiplication in Multiword Arithmetic: Error Analysis and Application to GPU Tensor Cores
in SIAM Journal on Scientific Computing
Fasi M
(2019)
An Arbitrary Precision Scaling and Squaring Algorithm for the Matrix Exponential
in SIAM Journal on Matrix Analysis and Applications
Fasi M
(2021)
Algorithms for Stochastically Rounded Elementary Arithmetic Operations in IEEE 754 Floating-Point Arithmetic
in IEEE Transactions on Emerging Topics in Computing
Fasi M
(2018)
Multiprecision Algorithms for Computing the Matrix Logarithm
in SIAM Journal on Matrix Analysis and Applications
Fasi M
(2021)
Numerical behavior of NVIDIA tensor cores.
in PeerJ. Computer science
Georgescu D
(2017)
Explicit Solutions to Correlation Matrix Completion Problems, with an Application to Risk Management and Insurance
in SSRN Electronic Journal
Georgescu D
(2018)
Explicit solutions to correlation matrix completion problems, with an application to risk management and insurance
in Royal Society Open Science
Giles M
(2019)
Multi-level Monte Carlo methods for the approximation of invariant measures of stochastic differential equations
in Statistics and Computing
Giles M
(2022)
Analysis of Nested Multilevel Monte Carlo Using Approximate Normal Random Variables
in SIAM/ASA Journal on Uncertainty Quantification
Giles M
(2019)
Random bit multilevel algorithms for stochastic differential equations
in Journal of Complexity
Giles M
(2023)
Approximating Inverse Cumulative Distribution Functions to Produce Approximate Random Variables
in ACM Transactions on Mathematical Software
Giles M
(2019)
An Adaptive Random Bit Multilevel Algorithm for SDEs
Giles, MB
(2020)
Multivariate Algorithms and Information-Based Complexity
GILMOUR C
(2021)
Modelling Burglary in Chicago using a self-exciting point process with isotropic triggering
in European Journal of Applied Mathematics
Girolami M
(2021)
The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions
in Computer Methods in Applied Mechanics and Engineering
Glyn-Davies A
(2022)
Anomaly detection in streaming data with gaussian process based stochastic differential equations
in Pattern Recognition Letters
Gong X
(2023)
Generative hypergraph models and spectral embedding.
in Scientific reports
Gong X
(2021)
Directed Network Laplacians and Random Graph Models
Gong X
(2021)
Directed network Laplacians and random graph models.
in Royal Society open science
Gong X
(2022)
Generative Hypergraph Models and Spectral Embedding
Gong X
(2024)
Higher-order connection Laplacians for directed simplicial complexes
in Journal of Physics: Complexity
Gregory A
(2019)
The synthesis of data from instrumented structures and physics-based models via Gaussian processes
in Journal of Computational Physics
Grindrod P
(2018)
High Modularity Creates Scaling Laws.
in Scientific reports
Grindrod P
(2023)
Estimating Network Dimension When the Spectrum Struggles
Haidar A
(2020)
Mixed-precision iterative refinement using tensor cores on GPUs to accelerate solution of linear systems.
in Proceedings. Mathematical, physical, and engineering sciences
Higham C
(2022)
Core-periphery Partitioning and Quantum Annealing
Higham C
(2018)
Deep Learning: An Introduction for Applied Mathematicians
Higham C
(2019)
Deep Learning: An Introduction for Applied Mathematicians
in SIAM Review
Higham D
(2022)
Mean Field Analysis of Hypergraph Contagion Models
in SIAM Journal on Applied Mathematics
Description | Mathematics of Adversarial Attacks |
Amount | £202,126 (GBP) |
Funding ID | EP/V046527/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 01/2021 |
End | 12/2022 |
Title | MATLAB Software for Deep Learning |
Description | Scientific Computing software to accompany an expository article on the mathematics of deep learning |
Type Of Material | Improvements to research infrastructure |
Year Produced | 2018 |
Provided To Others? | Yes |
Impact | Accompanying expository article has been used in several universities as the basis for classes, tutorials and reading groups on deep learning. |
URL | http://personal.strath.ac.uk/d.j.higham/algfiles.html |
Title | Data for Fluid simulations accelerated with 16 bits: Approaching 4x speedup on A64FX |
Description | Dataset for M Kloewer, S Hatfield, M Croci, PD Dueben and TN Palmer, 2021. Fluid simulations accelerated with 16 bits: Approaching 4x speedup on A64FX by squeezing ShallowWaters.jl into Float16, in review. This dataset contains data from simulations with ShallowWaters.jl with varying number formats and with or without a compensated time integration. All other parameters are shared between simulations. All .tar.gz are packed folders of the same name that contain netCDF files presenting velocities u,v, sea surface height eta, and tracer sst (sea surface temperature) run0002. Float16 simulation with compensated summation in the time integration. run0003. Float16 simulation without compensated summation in the time integration. run0004. Float64 reference simulation (without compensated summation in the time integration). run0005. Float16/32 mixed-precision simulation. No compensated time integration. Additionally, parameter.txt in each run summarizes all model parameters and progress.txt was created to monitor the progress of the data output during simulation. The file benchmarking.jld2 stores data for the benchmarking of the different runs as Julia's JLD2 format (a subset of HDF5). This dataset was created using the Isambard UK National Tier-2 HPC Service operated by GW4 and the UK Met Office, and funded by the Engineering and Physical Sciences Research Council EPSRC. For more details, see ShallowWaters.jl or the preprint Kloewer et al, 2021. |
Type Of Material | Database/Collection of data |
Year Produced | 2021 |
Provided To Others? | Yes |
URL | https://zenodo.org/record/5705162 |
Title | Supplementary Summary from Beyond non-backtracking: non-cycling network centrality measures |
Description | Walks around a graph are studied in a wide range of fields, from graph theory and stochastic analysis to theoretical computer science and physics. In many cases it is of interest to focus on non-backtracking walks; those that do not immediately revisit their previous location. In the network science context, imposing a non-backtracking constraint on traditional walk-based node centrality measures is known to offer tangible benefits. Here, we use the Hashimoto matrix construction to characterize, generalize and study such non-backtracking centrality measures. We then devise a recursive extension that systematically removes triangles, squares and, generally, all cycles up to a given length. By characterizing the spectral radius of appropriate matrix power series, we explore how the universality results on the limiting behaviour of classical walk-based centrality measures extend to these non-cycling cases. We also demonstrate that the new recursive construction gives rise to practical centrality measures that can be applied to large-scale networks. |
Type Of Material | Database/Collection of data |
Year Produced | 2020 |
Provided To Others? | Yes |
URL | https://rs.figshare.com/articles/Supplementary_Summary_from_Beyond_non-backtracking_non-cycling_netw... |
Title | Supplementary Summary from Beyond non-backtracking: non-cycling network centrality measures |
Description | Walks around a graph are studied in a wide range of fields, from graph theory and stochastic analysis to theoretical computer science and physics. In many cases it is of interest to focus on non-backtracking walks; those that do not immediately revisit their previous location. In the network science context, imposing a non-backtracking constraint on traditional walk-based node centrality measures is known to offer tangible benefits. Here, we use the Hashimoto matrix construction to characterize, generalize and study such non-backtracking centrality measures. We then devise a recursive extension that systematically removes triangles, squares and, generally, all cycles up to a given length. By characterizing the spectral radius of appropriate matrix power series, we explore how the universality results on the limiting behaviour of classical walk-based centrality measures extend to these non-cycling cases. We also demonstrate that the new recursive construction gives rise to practical centrality measures that can be applied to large-scale networks. |
Type Of Material | Database/Collection of data |
Year Produced | 2020 |
Provided To Others? | Yes |
URL | https://rs.figshare.com/articles/Supplementary_Summary_from_Beyond_non-backtracking_non-cycling_netw... |
Title | matlab files updated from Beyond non-backtracking: non-cycling network centrality measures |
Description | Walks around a graph are studied in a wide range of fields, from graph theory and stochastic analysis to theoretical computer science and physics. In many cases it is of interest to focus on non-backtracking walks; those that do not immediately revisit their previous location. In the network science context, imposing a non-backtracking constraint on traditional walk-based node centrality measures is known to offer tangible benefits. Here, we use the Hashimoto matrix construction to characterize, generalize and study such non-backtracking centrality measures. We then devise a recursive extension that systematically removes triangles, squares and, generally, all cycles up to a given length. By characterizing the spectral radius of appropriate matrix power series, we explore how the universality results on the limiting behaviour of classical walk-based centrality measures extend to these non-cycling cases. We also demonstrate that the new recursive construction gives rise to practical centrality measures that can be applied to large-scale networks. |
Type Of Material | Database/Collection of data |
Year Produced | 2020 |
Provided To Others? | Yes |
URL | https://rs.figshare.com/articles/matlab_files_updated_from_Beyond_non-backtracking_non-cycling_netwo... |
Title | matlab files updated from Beyond non-backtracking: non-cycling network centrality measures |
Description | Walks around a graph are studied in a wide range of fields, from graph theory and stochastic analysis to theoretical computer science and physics. In many cases it is of interest to focus on non-backtracking walks; those that do not immediately revisit their previous location. In the network science context, imposing a non-backtracking constraint on traditional walk-based node centrality measures is known to offer tangible benefits. Here, we use the Hashimoto matrix construction to characterize, generalize and study such non-backtracking centrality measures. We then devise a recursive extension that systematically removes triangles, squares and, generally, all cycles up to a given length. By characterizing the spectral radius of appropriate matrix power series, we explore how the universality results on the limiting behaviour of classical walk-based centrality measures extend to these non-cycling cases. We also demonstrate that the new recursive construction gives rise to practical centrality measures that can be applied to large-scale networks. |
Type Of Material | Database/Collection of data |
Year Produced | 2020 |
Provided To Others? | Yes |
URL | https://rs.figshare.com/articles/matlab_files_updated_from_Beyond_non-backtracking_non-cycling_netwo... |
Description | LMS/IMA |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | Invited talk at London Mathematical Society/Society for Industrial and Applied Mathematics joint meeting on 30th September and 1st October. Hosted by the ICMS (Edinburgh), addressing the theme of 'Mathematics in Human Society'. |
Year(s) Of Engagement Activity | 2021 |
URL | https://ima.org.uk/17272/lms-ima-joint-meeting-2021-maths-in-human-society/ |
Description | Pint of Science talk in Glasgow, 22 May 2019 |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Public/other audiences |
Results and Impact | Pint of Science is a regular public engagement initiative, with selected science speakers. |
Year(s) Of Engagement Activity | 2019 |
URL | https://pintofscience.co.uk/city/glasgow |
Description | Research talk at Skolkovo |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Invited research talk and round table panel membership at Trustworthy AI 5-7 July Skolkovo, Moscow My attendance was virtual. |
Year(s) Of Engagement Activity | 2012,2021 |
URL | https://events.skoltech.ru/ai-trustworthy#content |
Description | Turing mtg |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Invited research talk at Interpretability, safety and security in AI conference 13-15th December, Alan Turing Institute (virtual attendance) |
Year(s) Of Engagement Activity | 2021 |
URL | https://www.turing.ac.uk/events/interpretability-safety-and-security-ai |
Description | Workshop on Data Science and Crime |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | One day workshop involving police data scientists from West Midlands, Police Scotland and ondon Met, and academics with relevant skills. Knowledge exchange and highlighting of current challenges were the key aims. |
Year(s) Of Engagement Activity | 2018 |
URL | https://iconicmath.org/ds-crime-workshop/ |
Description | Workshop on Data Science and Soaicl Media |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Industry/Business |
Results and Impact | An afternoon Workshop on Data Science and Social Media, held as part of a wekk ling Engage with Strathclyde series of events. About 30 people attended from social media, policymaking, economics, to share best practice and learn about new developments at the intersection of Data Science and Social media. |
Year(s) Of Engagement Activity | 2018 |
URL | https://www.engage.strath.ac.uk/ |
Description | faculty lecture |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Postgraduate students |
Results and Impact | Invited faculty lecture (online) Deep Learning: what could go wrong April 2021 |
Year(s) Of Engagement Activity | 2020,2021 |
URL | https://www.youtube.com/watch?v=yVXtoizLl8U |
Description | research worksop |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Dagstuhl Seminar on 'Higher-Order Graph Models: From Theoretical Foundations to Machine Learning' (21352) August 29- Sep 1, 2021 |
Year(s) Of Engagement Activity | 2021 |
URL | https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=21352 |