AI Colloquium
The AI Colloquium is a series of lectures dedicated to cutting-edge research in the field of machine learning and artificial intelligence, coorganized by the Lamarr Institute for Machine Learning and Artificial Intelligence (Lamarr Institute), the Research Center Trustworthy Data Science and Security (RC Trust), and the Center for Data Science & Simulation at TU Dortmund University (DoDas).
Programme
Distinguished researchers deliver captivating lectures followed by vibrant discussions. However, unlike traditional colloquia, the AI Colloquium prioritizes interactive dialogue, fostering international collaboration. Conducted primarily in English, these 90-minute sessions feature hour-long lectures and 30-minute Q&A sessions. Join every Thursday at 10 AM c.t. for a stimulating exploration of cutting-edge topics. Whether in-person at our Lecture Room on Fraunhofer Strasse 25 or via Zoom, our hybrid format ensures accessibility for all.
| Day (usually) | Thursday |
| Start and end time | 10 AM c.t. - 12 AM |
| Duration of Presentation | 60 Minutes |
| Location (usually) | Lecture Room 303 3. Floor Fraunhofer Strasse 25 Dortmund |
Upcomming Events
Lessons from Synthetic Health Data Generation: Fidelity, Privacy, Augmentation & Time
- RC Trust
Abstract: Primary healthcare care data offers huge value in modelling disease and illness. However, this data holds extremely private information about individuals and privacy concerns continue to limit the wide-spread use of such data, both by public research institutions and by the private health-tech sector. One possible solution is the use of synthetic data which mimics the underlying correlational structure and distributions of real data but avoids many of the privacy concerns. Brunel University London has been working in a long-term collaboration with the Medicine and Health Regulatory Authority in the UK to construct a high-fidelity synthetic data generator using probabilistic models with complex underlying latent variable structures. This work has led to multiple releases of synthetic data on a number of diseases including covid and cardiovascular disease, which are available for state-of-the-art AI research. Two major issues that have arisen from our synthetic data work are issues with bias, even when working with comprehensive national data, and with concept drift where subsequent batches of data move away from current models and what impact this may have on regulation.
In this talk I will discuss some of the key results of the collaboration: on our experiences of synthetic data generation, on the detection of bias and how to better represent the true underlying UK population, and how to handle concept drift when building models of healthcare data that evolves over time.
Prof. Allan Tucker
Bio: Allan Tucker is Professor of Artificial Intelligence in the Department of Computer Science at Brunel University London, where he heads the Intelligent Data Analysis Group consisting of 17 academic staff, 15 PhD students and 4 post-docs. He has been researching Artificial Intelligence and Data Analysis for 28 years and has published 120 peer-reviewed journal and conference papers on data modelling and analysis. His research work includes long-term projects with Moorfields Eye Hospital where he has been developing pseudo-time models of eye disease (EPSRC - £320k) and with DEFRA on modelling fish population dynamics using state space and Bayesian techniques (NERC - £80k). Currently, he has projects with Google, the University of Pavia Italy, the Royal Free Hospital, UCL, Zoological Society of London and the Royal Botanical Gardens at Kew. He was academic lead on an Innovate UK, Regulators’ Pioneer Fund (£740k) with the Medical and Health Regulatory Authority on benchmarking AI apps for the NHS, and another on detecting significant changes in Adaptive AI Models of Healthcare (£195k). He recently acted as academic lead on two Pioneer Funds on Explainability of AI (£168k) and In-Silico Trials (£750k). He is currently CI on a Centre of Excellence for Regulatory Sciences and Innovation. He serves regularly on the PC of the top AI conferences (including IJCAI, AAAI, and ECML) and is on the editorial board for the Journal of Biomedical Informatics. He hosted a special track on "Explainable AI" at the IEEE conference on Computer Based Medical Systems in 2019 and was general chair for AI in Medicine 2021. He has been widely consulted on the ethical and practical implications of AI in health and medical research by the NHS, and the use of machine learning for modelling fisheries data by numerous government thinktanks and academia.
Prof. Edward A. Lee
Bio: Edward A. Lee is Professor of the Graduate School and Distinguished Professor Emeritus in Electrical Engineering and Computer Sciences (EECS) at the University of California at Berkeley, where he has been on the faculty since 1986. He is also co-founder of Xronos Inc. and BDTI, Inc. He is the author of seven books, some with several editions and translations, including two for a general audience, and hundreds of papers and technical reports. Lee has delivered hundreds of keynotes and other invited talks at venues worldwide and has graduated 40 PhD students.
Professor Lee's research studies cyber-physical systems, which integrate physical dynamics with software and networks. His focus is on the use of deterministic models as a central part of the engineering toolkit for such systems. He is the director of iCyPhy, the Berkeley Industrial Cyber-Physical Systems Research Center. From 2005-2008, he served as Chair of the EE Division and then Chair of the EECS Department at UC Berkeley. He has led the development of several influential open-source software packages, notably Ptolemy and Lingua Franca.
Past Events
Lessons from Synthetic Health Data Generation: Fidelity, Privacy, Augmentation & Time
- RC Trust
Abstract: Primary healthcare care data offers huge value in modelling disease and illness. However, this data holds extremely private information about individuals and privacy concerns continue to limit the wide-spread use of such data, both by public research institutions and by the private health-tech sector. One possible solution is the use of synthetic data which mimics the underlying correlational structure and distributions of real data but avoids many of the privacy concerns. Brunel University London has been working in a long-term collaboration with the Medicine and Health Regulatory Authority in the UK to construct a high-fidelity synthetic data generator using probabilistic models with complex underlying latent variable structures. This work has led to multiple releases of synthetic data on a number of diseases including covid and cardiovascular disease, which are available for state-of-the-art AI research. Two major issues that have arisen from our synthetic data work are issues with bias, even when working with comprehensive national data, and with concept drift where subsequent batches of data move away from current models and what impact this may have on regulation.
In this talk I will discuss some of the key results of the collaboration: on our experiences of synthetic data generation, on the detection of bias and how to better represent the true underlying UK population, and how to handle concept drift when building models of healthcare data that evolves over time.
Prof. Allan Tucker
Bio: Allan Tucker is Professor of Artificial Intelligence in the Department of Computer Science at Brunel University London, where he heads the Intelligent Data Analysis Group consisting of 17 academic staff, 15 PhD students and 4 post-docs. He has been researching Artificial Intelligence and Data Analysis for 28 years and has published 120 peer-reviewed journal and conference papers on data modelling and analysis. His research work includes long-term projects with Moorfields Eye Hospital where he has been developing pseudo-time models of eye disease (EPSRC - £320k) and with DEFRA on modelling fish population dynamics using state space and Bayesian techniques (NERC - £80k). Currently, he has projects with Google, the University of Pavia Italy, the Royal Free Hospital, UCL, Zoological Society of London and the Royal Botanical Gardens at Kew. He was academic lead on an Innovate UK, Regulators’ Pioneer Fund (£740k) with the Medical and Health Regulatory Authority on benchmarking AI apps for the NHS, and another on detecting significant changes in Adaptive AI Models of Healthcare (£195k). He recently acted as academic lead on two Pioneer Funds on Explainability of AI (£168k) and In-Silico Trials (£750k). He is currently CI on a Centre of Excellence for Regulatory Sciences and Innovation. He serves regularly on the PC of the top AI conferences (including IJCAI, AAAI, and ECML) and is on the editorial board for the Journal of Biomedical Informatics. He hosted a special track on "Explainable AI" at the IEEE conference on Computer Based Medical Systems in 2019 and was general chair for AI in Medicine 2021. He has been widely consulted on the ethical and practical implications of AI in health and medical research by the NHS, and the use of machine learning for modelling fisheries data by numerous government thinktanks and academia.
Prof. Edward A. Lee
Bio: Edward A. Lee is Professor of the Graduate School and Distinguished Professor Emeritus in Electrical Engineering and Computer Sciences (EECS) at the University of California at Berkeley, where he has been on the faculty since 1986. He is also co-founder of Xronos Inc. and BDTI, Inc. He is the author of seven books, some with several editions and translations, including two for a general audience, and hundreds of papers and technical reports. Lee has delivered hundreds of keynotes and other invited talks at venues worldwide and has graduated 40 PhD students.
Professor Lee's research studies cyber-physical systems, which integrate physical dynamics with software and networks. His focus is on the use of deterministic models as a central part of the engineering toolkit for such systems. He is the director of iCyPhy, the Berkeley Industrial Cyber-Physical Systems Research Center. From 2005-2008, he served as Chair of the EE Division and then Chair of the EECS Department at UC Berkeley. He has led the development of several influential open-source software packages, notably Ptolemy and Lingua Franca.
Lessons from Synthetic Health Data Generation: Fidelity, Privacy, Augmentation & Time
- RC Trust
Abstract: Primary healthcare care data offers huge value in modelling disease and illness. However, this data holds extremely private information about individuals and privacy concerns continue to limit the wide-spread use of such data, both by public research institutions and by the private health-tech sector. One possible solution is the use of synthetic data which mimics the underlying correlational structure and distributions of real data but avoids many of the privacy concerns. Brunel University London has been working in a long-term collaboration with the Medicine and Health Regulatory Authority in the UK to construct a high-fidelity synthetic data generator using probabilistic models with complex underlying latent variable structures. This work has led to multiple releases of synthetic data on a number of diseases including covid and cardiovascular disease, which are available for state-of-the-art AI research. Two major issues that have arisen from our synthetic data work are issues with bias, even when working with comprehensive national data, and with concept drift where subsequent batches of data move away from current models and what impact this may have on regulation.
In this talk I will discuss some of the key results of the collaboration: on our experiences of synthetic data generation, on the detection of bias and how to better represent the true underlying UK population, and how to handle concept drift when building models of healthcare data that evolves over time.
Prof. Allan Tucker
Bio: Allan Tucker is Professor of Artificial Intelligence in the Department of Computer Science at Brunel University London, where he heads the Intelligent Data Analysis Group consisting of 17 academic staff, 15 PhD students and 4 post-docs. He has been researching Artificial Intelligence and Data Analysis for 28 years and has published 120 peer-reviewed journal and conference papers on data modelling and analysis. His research work includes long-term projects with Moorfields Eye Hospital where he has been developing pseudo-time models of eye disease (EPSRC - £320k) and with DEFRA on modelling fish population dynamics using state space and Bayesian techniques (NERC - £80k). Currently, he has projects with Google, the University of Pavia Italy, the Royal Free Hospital, UCL, Zoological Society of London and the Royal Botanical Gardens at Kew. He was academic lead on an Innovate UK, Regulators’ Pioneer Fund (£740k) with the Medical and Health Regulatory Authority on benchmarking AI apps for the NHS, and another on detecting significant changes in Adaptive AI Models of Healthcare (£195k). He recently acted as academic lead on two Pioneer Funds on Explainability of AI (£168k) and In-Silico Trials (£750k). He is currently CI on a Centre of Excellence for Regulatory Sciences and Innovation. He serves regularly on the PC of the top AI conferences (including IJCAI, AAAI, and ECML) and is on the editorial board for the Journal of Biomedical Informatics. He hosted a special track on "Explainable AI" at the IEEE conference on Computer Based Medical Systems in 2019 and was general chair for AI in Medicine 2021. He has been widely consulted on the ethical and practical implications of AI in health and medical research by the NHS, and the use of machine learning for modelling fisheries data by numerous government thinktanks and academia.
Prof. Edward A. Lee
Bio: Edward A. Lee is Professor of the Graduate School and Distinguished Professor Emeritus in Electrical Engineering and Computer Sciences (EECS) at the University of California at Berkeley, where he has been on the faculty since 1986. He is also co-founder of Xronos Inc. and BDTI, Inc. He is the author of seven books, some with several editions and translations, including two for a general audience, and hundreds of papers and technical reports. Lee has delivered hundreds of keynotes and other invited talks at venues worldwide and has graduated 40 PhD students.
Professor Lee's research studies cyber-physical systems, which integrate physical dynamics with software and networks. His focus is on the use of deterministic models as a central part of the engineering toolkit for such systems. He is the director of iCyPhy, the Berkeley Industrial Cyber-Physical Systems Research Center. From 2005-2008, he served as Chair of the EE Division and then Chair of the EECS Department at UC Berkeley. He has led the development of several influential open-source software packages, notably Ptolemy and Lingua Franca.