- This event has passed.
08/12/2021 – AI3SD Autumn Seminar IX: Large Spaces
8th December 2021 @ 2:00 pm - 3:45 pmFree
Eventbrite Link: https://ai3sd-autumn-series-081221.eventbrite.co.uk
This seminar forms part of the AI3SD Online Seminar Series that will run across the autumn (from October 2021 to December 2021). This seminar will be run via zoom, when you register on Eventbrite you will receive a zoom registration email alongside your standard Eventbrite registration email. Where speakers have given permission to be recorded, their talks will be made available on our AI3SD YouTube Channel. The theme for this seminar is Large Spaces.
- 14:00-14:45: Audacity of huge: Machine Learning for the discovery of transition metal catalysts and materials – Professor Heather Kulik (MIT)
- 14:45-15:00: Coffee Break
- 15:00-15:45: Artificial Intelligence for Safer Urban Spaces – Professor Zoheir Sabeur (University of Bournemouth)
Abstracts & Speaker Bios
- Audacity of huge: Machine Learning for the discovery of transition metal catalysts and materials – Professor Heather Kulik: I will discuss our efforts to use machine learning (ML) to accelerate the computational tailoring and design of transition metal complexes and metal-organic framework (MOF) materials. One limitation in a challenging materials space such as open shell, 3d transition metal chemistry is that ML models and ML-accelerated high-throughput screening traditionally rely on density functional theory (DFT) for data generation, but DFT is both computationally demanding and prone to errors that limit its accuracy in predicting new materials. I will describe three ways we’ve overcome these limitations: i) through efficient global optimization to minimize the numbers of calculations carried out to obtain design rules in weeks instead of decades while satisfying multiple objectives; ii) through machine-learned consensus from a family of dozens of functionals to more robustly uncover new materials; and iii) by the use of natural language processing to extract, learn, and directly predict experimental measures of stability on heterogeneous MOF materials.
Bio: Heather J. Kulik is an Associate Professor in Chemical Engineering at MIT. She received her B.E. in Chemical Engineering from Cooper Union in 2004 and her Ph.D. in Materials Science and Engineering from MIT in 2009. She completed postdocs at Lawrence Livermore (2010) and Stanford (2010−2013), prior to returning to MIT as a faculty member in 2013 and receiving tenure in 2021. Her work has been recognized by a Burroughs Wellcome Fund Career Award at the Scientific Interface (2012-2017), Office of Naval Research Young Investigator Award (2018), DARPA Young Faculty Award (2018), AAAS Marion Milligan Mason Award (2019-2020), NSF CAREER Award (2019), the Industrial & Engineering Chemistry Research “Class of Influential Researchers”, the ACS COMP Division OpenEye Award for Outstanding Junior Faculty in Computational Chemistry, the JPCB Lectureship (ACS PHYS), the DARPA Director’s Fellowship (2020), MSDE Outstanding Early-Career Paper Award (2021), and a Sloan Fellowship (2021).
- Artificial Intelligence for Safer Urban Space – Professor Zoheir Sabeur: The ever-growing adoption of big data technologies, smart sensing, data science and artificial intelligence is enabling the development of new intelligent urban spaces with real-time monitoring and advanced cyber-physical situational awareness capabilities. The advancement of cyber-physical situational awareness is experimented for achieving safer smart city spaces in Europe and beyond. The deployment of digital twins leads to understanding real-time situation awareness and risks of potential physical and/or cyber-attacks on urban critical infrastructure specifically. The critical extraction of knowledge using digital twins, which ingest, process and fuse observation data and information, prior to machine reasoning can also be performed. In this cyber behavior detection modules, which identify unusualness in cyber traffic networks can be deployed together with a physical behaviour detection module, based on computer vision and statistical methods. The two modules function within the so-called Malicious Attacks Information Detection System (MAIDS) digital twin.
Bio: Zoheir Sabeur is Professor of Data Science and Artificial Intelligence at Bournemouth University (2019-present). He is also Visiting Professor of Data Science at Colorado School of Mines, Golden, Colorado, USA (2017-present). Zoheir was Science Director at the School of Electronics and Computer Science, IT Innovation Centre, University of Southampton (2009-2019). He led his data science research teams in more than 30 large projects as Principal Investigator (PI). The research was mainly supported with research grants (totalling £8.0M) and awarded by the European Commission (under FP5, FP6, FP7 and H2020), Innovate UK, DSTL, NERC and Industries. Prior to Southampton, Zoheir worked as Director and Head of Research, at BMT Group Limited (1996-2009), where he led his teams in the development of advanced environmental information systems, in particular the PROTEUS system for the UK O&G Industries and UK Government. Prior to that, Zoheir held several academic appointments in Computing as Senior Research Fellow at Oxford Brookes University(1993-1995), SERC Research Fellow at University of Leeds(1991-1993) and University of Strathclyde(1990-1991). He also worked as a Research Scientist in the Intensive Computing Lattice QCD Group at the University of Wuppertal, Germany (1987). Zoheir graduated with a PhD and MSc in Particle Physics from the University of Glasgow (1985-1990). His PhD was on “Lattice QCD at High Density with Dynamical Fermions”. This was in fact his earliest involvement in “Data Science” using vector machines for intensive computing and understanding hadron matter thermodynamics, under the UK Lattice QCD Grand-Challenge. In the last decade or so, Zoheir’s long research career, focussed more on fundamentals of Artificial Intelligence, knowledge extraction for human, natural, or industrial processes behaviour understanding. These are being investigated in context of cyber-physical security, healthcare, industrial, environmental systems, and more. Zoheir has published over 130 papers in scientific journals, conference proceedings and books. He is peer reviewer, member of international scientific committees and editing board of various science and engineering conferences and journals. Zoheir chairs the OGC Digital Global Grid System Specification and Domain Working Groups, and co-chairs the AI and Data Science Task Group at the BDVA. He is Fellow of the British Computer Society; Member of the Institute of Physics; and Fellow of IMaREST.