Benjamin Guedj is a Principal Research Fellow. He is also a tenured scientist with Inria (France), and holds a visiting position at ATI. He is a co-leader (with Peter Grünwald, ELLIS member) of the joint team between CWI and Inria. He is a scientific leader of the Inria@London hub. His research interests include statistical learning theory and algorithms focused on PAC-Bayes theory, concentration inequalities and generalisation bounds, computational statistics and deep learning. He holds two ANR grants (French agency for research) and an H2020 grant in 2018. He is regularly on the program committees of NeurIPS, COLT, ICML, ICLR, AISTATS. He organised (with Francis Bach, ELLIS member, and Pascal Germain) a NeurIPS 2017 workshop, and a tutorial (with John Shawe-Taylor, ELLIS Fellow) at ICML 2019. Local chair for COLT 2022 (in London). He is a member of the board of ECAS, board of the French Statistical Society, and of Inria’s Evaluation Committee. He is currently supervising 5 PhD students and 2 postdocs. Collaborations with Inria, CWI (the Netherlands), Amazon Cambridge, UCLA, among others.

• Department: Computer Science
• Expertise: TBA
• Website

Brooks Paige is an Associate Professor and a Turing fellow. His research is on interpretable machine learning and methods for scalable Bayesian inference in probabilistic programming systems. He co-supervises two PhD students and is a co-PI on a Turing UKRI-funded project “Machine learning prediction of plant chemical production”. He participates in long-term collaborative projects in ML for applications in chemistry, climate science, with John Innes Centre, the British Antarctic Survey, and University of Cambridge, among others.

• Department: Computer Science
• Expertise: TBA
• Website

David Barber is Director of the University College London Centrebfor AI, Director of the UKRI Centre for Doctoral Training in Foundational AI. Research on probabilistic modelling (22 NeurIPS papers) in AI and ML, for which his textbook on Bayesian Reasoning and ML is one of the standards in the field. Chief Scientific Officer and co-founder re:infer, a London based startup that uses NLP to analyse unstructure text UK government advisor on AI; action editor for JMLR. Grants: £6.7 million from the UKRI to establish a Centre for Doctoral Training in AI, supported by an additional £5 million from industry. Supervises 10 PhD students at UCL and 1 postdoc. He has collaborated extensively with Bert Kappen (Radboud University), Peter Sollich (Goettingen).

• Department: Computer Science
• Expertise: TBA
• Website

Dimitrios Kanoulas is a lecturer. He leads the Robot Perception and Learning (RPL) lab of the Autonomous System Research Group at UCL.They fous on perception and learning for mobile/articulated robots, new estimation and planning algorithms for robots that navigate, locomote, and manipulate in uncertain natural environments. He was a best Student Paper Award Finalist at IEEE ICARCV 2018 and the Best Interactive Paper Award Winner at IEEE-RAS Humanoids 2017. He co-authored an NSF Career Award (500k, 2012-2015) and a member of the 2020 UK-RAS Strategic Task Group on “Legged Robotics and Locomotion Technical Committee”. He is an Associate editor and is in the program committee of highly ranked conferences/journals, such as ICRA, IROS, IJCAI, and Frontiers. He has one PhD student at the Italian Institute of Technology (IIT) and at least 2 more PhD students at UCL in Sept. 2020. He actively collaborates with (IIT) and the ZOARobotics UK-based robotics start-up.

• Department: Computer Science
• Expertise: TBA
• Website

Emine Yilmaz is a Professor and a Turing Fellow. She is also Amazon Scholar at Amazon Research Cambridge. From 2012 - 2019, she was research consultant for Microsoft Research Cambridge, where she was a full time researcher prior to joining UCL. Her research interests include information retrieval, natural language processing and applications of machine learning. She was awarded the Bloomberg Data Science Research Award (2018), Karen Sparck Jones Award (2015), the Google Faculty Research Award (2014) and the Best paper award ACM CHIIR 2017. She is a co-editor-in-chief of the Information Retrieval Journal, a PC Chair for ECIR 2019, ACM SIGIR 2018 and ACM ICTIR 2017, Practice and Experience Chair for ACM WSDM 2017, Doctoral Consortium Chair for ECIR 2017, and an Executive Committee member of ACM SIGIR. She holds funding from the EPSRC fellowship, EU Horizon 2020 grant. She currently supervises one postdoc and 6 PhD students. She holds collaborations University of Glasgow, Amazon, Microsoft Research and Spotify.

• Department: Computer Science
• Expertise: TBA
• Website

Ingemar J. Cox is a Professor at UCL. He is also a part time professor at the University of Copenhagen. His research focuses on the application of AI, ML, and NLP to large data sets of digital footprints, e.g. Web query logs and Twitter, to infer health information for individuals and populations. With these tools, estimate prevalence and virulence of a disease, and effectiveness of national public health interventions (vaccines and changes to law). Estimates of the prevalence of influenza have been adopted by Public Health England as part of its influenza surveillance programme. Co-I and Deputy Director of an £11M EPSRC IRC called i-sense, “Early Warning Sensing Systems for Infectious Diseases”. He is a co-I and Deputy Director of the £4M follow-on funding from EPSRC. He is a Fellow of the ACM, IEEE, IET, and BCS. In 2019 he was awarded the Tony Kent Strix Award for contributions to information retrieval. In 2015 he received an IEEE Signal Proc. Soc. Sustained Impact Paper Award. He is a co-chair of the 2019 “Health on the Web” track, Web Conference (Formerly WWW2019). He currently supervises two postdocs and 3 Ph.D. students. He has strong collaborations with Microsoft Research.

• Department: Computer Science
• Expertise: TBA
• Website

John Shawe-Taylor is a Professor, an Ellis fellow and a Turing Fellow, He helped to drive a fundamental rebirth in ML, with applications including computer vision, document classification, biology, and medicine (brain scan, immunity and proteome analysis). He has published over 250 papers and two books with over 69000 citations. He is a member of the ELLIS Interactive Learning and Interventional Representations theme. He is a leader of the UCL application for ATI membership, and a former member of ATI PC, and a former Head of CS at UCL (2010-19). In 2014 UCL CS was ranked highest in the UK in the Research Evaluation Framework (REF). He has been a UNESCO Chair of AI since 2018, a leading trustee of Knowledge 4 All Foundation. He is a program co-chair and General co-chair of the NeurIPS. He is an associate editor of JMLR, editor of Information and Inference. He coordinated PASCAL and PASCAL2, EU projects including KerMIT (Kernel Methods for Images and Text), ComPLACS (Composing Learning for Artificial Cognitive Systems). He currently grants from X5Gon (Cross Modal, Cross Cultural, Cross Lingual, Cross Domain, and Cross Site Global OER Network) EU project, and is a PI in the joint US/UK MURI grant, ‘Semantic Information Pursuit for Multimodal Data Analysis’. He supervises two postdocs and five PhD students. He has collaborations with Jozef Stefan Institute Slovenia, Aalto University in Finland, U. of Nantes in France, U. of Valencia in Spain, IIT in Italy, UC at Berkeley, John Hopkins University, U. of Maryland, and UCLA in US.

• Department: Computer Science
• Expertise: TBA
• Website

Lourdes Agapito is a Professor of 3D Vision at UCL, co-founder of London-based startup Synthesia Technologies, and ERC Grant holder (2008-14). Her research interests are in computer vision, graphics and machine learning; including 3D reconstruction from video, 3D shape modelling, weakly supervised learning for 3D vision, human pose estimation and video synthesis. She is program Chair for the top computer vision conferences (CVPR'16, ICCV'21) and Workshops Chair for ECCV'14; Area chair for CVPR (3x), ECCV (2x), ICCV (1x), Associate editor of IEEE PAMI and IJCV. She was the keynote speaker at ICRA'17, the top robotics conference. Her current team consists of six, funded by EU H2020 grant SecondHands and industry. She holds Collaborations with Facebook Reality Labs, ATI, University of Adelaide (Australia). She is co-director of the EPSRC Centre for Doctoral Training in Foundational AI at UCL CS.

• Department: Computer Science
• Expertise: TBA
• Website

Professor Marc Deisenroth is the DeepMind Chair of Machine Learning and Artificial Intelligence at University College London and PI of the Statistical Machine Learning Group at UCL. He also holds a visiting faculty position at the University of Johannesburg. Marc's research interests center around data-efficient machine learning, probabilistic modeling, climate science and autonomous decision making.

• Department: Computer Science
• Expertise: Gaussian processes, reinforcement learning, approximate inference, data-efficient machine learning
• Website

Mark Herbster is an Associate Professor. His research interests are in online learning, semi-supervised learning and matrix completion He is regularly on the program committee for ICML, NeurIPS and has acted as an area chair for NeurIPS (2x), AISTATS (1x), PC. He is currently supervising 3 PhD students and 1 postdoc. He has often collaborated with Università degli Studi di Milano and Università degli studi dell'Insubria.

• Department: Computer Science
• Expertise: TBA
• Website

Marta Betcke is an associate professor in the Department of Computer Science at the University College London. She is also a member of Centre for Inverse Problems and Centre for Medical Image Computing. Between 2010 and 2013, she held an EPSRC Postdoctoral Research Fellowship "Image Reconstruction: the Sparse Way" at UCL to investigate the implications of sparsity on image acquisition and reconstruction in different modalities. Before coming to UCL in the end of 2009, she was a PDRA in the School of Mathematics at the University of Manchester working on novel X-ray CT scanners for airport baggage screening. Hey interest is in broad areas of Inverse Problems, Numerical Analysis and Scientific Computing. In particular in numerical solution of inverse problems, tomographic image reconstruction, compressed sensing, sparsity and compression, applied harmonic analysis and machine learning. She enjoys working on novel imaging technologies and their applications with focus on experimental design and reconstruction problem.

• Department: Computer Science
• Expertise: TBA
• Website

Massimiliano Pontil is an Ellis Fellow, a part-time Professor in the Department of Computer Science, and a Senior Researcher at Istituto Italiano di Tecnologia (IIT). His research interests are in machine learning theory and algorithms, with a recent focus on statistical learning theory, meta-learning, hyperparameter optimization and algorithmic fairness. He was awarded the Best Paper Runner Up Award from ICML 2013, an EPSRC Advanced Research Fellowship in 2006-2011, and the Edoardo R. Caianiello Award for the Best Italian PhD Thesis on Connectionism in 2002. He supervises 3 PhD students and 1 postdoc at UCL, and 4 Phd students and 3 postdocs at IIT. He is regularly on the programme committee of the main machine learning conferences (COLT, ICML and NeurIPS), editorial board of the ML Journal, Statistics and Computing, JMLR, and Scientific Advisory Board, MPI for Intelligent Systems.

• Department: Computer Science
• Expertise: TBA
• Website

Matt J. Kusner is an Associate Professor, an Ellis fellow and a Turing Fellow. His research is in simple machine learning models tailored to problem constraints, particularly in causal inference, social justice, privacy preserving learning and chemical design. He was awarded the Turner Dissertation Award for best CS & Engineering PhD. He gives talks at the Royal Society, the London Machine Learning Meetup, and the machine learning podcast Talking Machines. He work is reported in The Guardian, Forbes, and The New Scientist. He is a co-organizer of the NeurIPS 2018 workshop Critiquing and Correcting Trends in Machine Learning, an UAI 2018 Publications Chair, and an invited speaker at the NeurIPS 2017 Press Conference. He supervises 3 PhD students, and has long-term collaborations with NYU, Oxford, Cambridge, MPI for Intelligent Systems Tuebingen, Georgia Tech, Google, BenevolentAI and Frontier Development Lab.

• Department: Computer Science
• Expertise: TBA
• Website

Pontus Stenetorp, a lecturer, leads the University College London NLP group. He is a CO-PI of the European Union Horizon 2020 CLARIFY project (860627). His research is in the intersection between natural language and machine learning, including question answering, information extraction, and semantics. He was awarded the Outstanding paper award at the 2017 Conference of the European Chapter of the Association for Computational Linguistics, a Facebook AI Research (FAIR) award in 2016, and a Japan Society for the Promotion of Science (JSPS) Postdoctoral Fellowship from 2013 to 2015. He supervises five PhD students and one postdoctoral researcher. He holds long-term collaborations with both the University of Tokyo and Tohoku University and is CS Department Ambassador to Japan.

• Department: Computer Science
• Expertise: TBA
• Website

Laura Toni (Lecturer at UCL) studies the area of machine learning for immersive communications, decision-making strategies under uncertainty, and large-scale signal processing for machine learning. Her current research focuses on applying graph signal processing tools to more data-efficient decision making strategies, with a deep focus on recommendations and reinforcement learning for optimal managing of large-scale systems. She recently received the UCL Future Leadership Award, and the Adobe System academic donation, and the Cisco academic grant. She received her PhD degree from the University of Bologna, Italy, followed by postdoc positions at the University of California at San Diego (UCSD) and at the Swiss Federal Institute of Technology (EPFL).

• Department: Department of Electronic and Electrical Engineering
• Expertise: TBA
• Website

Miguel Rodrigues (Professor, Turing Fellow) is a Full Professor at University College London, he is also the Director of the MSc in Integrated Machine Learning Systems at University College London, and he is a Turing Fellow. He has also previously held various appointments with various institutions worldwide including Cambridge University, Princeton University, Duke University, and the University of Porto, Portugal. His research work lies in the general areas of information theory, information processing, and machine learning He currently supervises five postdocs and six phd students. He is also currently funded by UKRI, Innovate UK, Royal Society, EU H2020, industry.

• Department: Department of Electronic and Electrical Engineering
• Expertise: TBA
• Website

Arthur Gretton is a Professor, an Ellis Fellow and Unit Coordinator, and Director, Centre for Computational Statistics and ML (CSML) at UCL).His research interests are in design and training of generative models, both implicit (e.g. GANs) and explicit (high/infinite dimensional exponential family models), nonparametric hypothesis testing, and kernel methods. He was awarded the Best paper award (NeurIPS 2017), Best student paper runner up (NeurIPS 2009), 9 NeurIPS orals. He is an Associate editor, IEEE TPAMI (until 2013), action Editor JMLR, area chair/Senior area chair of NeurIPS (x3), ICML (x2), COLT, and the RSS Research Section Committee (from 2020). He is a program chair with C. Robert of AISTATS 2016, ICML 2018 tutorials chair with R. Salakhudinov, an ICML 2019 workshops chair with H. Lee, program chair of Dali 2019 with S. Mohammed, K. Muandet, and a co-organiser of the Machine Learning Summer School 2019 with Marc Deisenroth. He currently supervises8 students (incl. 2 from Deepmind) and 2 postdocs. He has ongoing long-term collaborations with ISM Japan, Penn State, ENS Paris-Saclay, MSR New England, MPI for Intelligent Systems. He is a Scientific adviser for Babylon Health.

• Department: Gatsby Computational Neuroscience Unit
• Expertise: TBA
• Website

Ferenc Huszár is a Honaray lecturer, and studies the probabilistic foundations and principles of data efficient machine learning. His recent work addresses meta-learning, few-shot classification, and causal inference. He is currently a Senior Research Scientist at Twitter where he leads a research team on data-efficient representation learning, as well as championing the company’s efforts on ethical applications of machine learning. He did his PhD at the Computational and Biological Learning Laboratory at Cambridge University in 2013. He has held multiple positions in the technology sector since then. Most notably, Ferenc was principal research scientist at Magic Pony Technology, a startup focussed on applying deep learning to the problem of image compression and low level computer vision.

• Department: Gatsby Computational Neuroscience Unit
• Expertise: TBA
• Website

Maneesh Sahani is a Professor, and focuses on probabilistic inference and learning in natural and machine settings. His research interests include flexible approximate inference in graphical models, structured gaussian-process models and dynamical systems, neural implementations of probabilistic computation, and models of complex behaviour. He was in the Programme committee for N(eur)IPS (04, 06) and Cosyne (07), workshops chair for CNS (99-03) and N(eur)IPS (08), and programme chair (09) and general chair (10) for Cosyne. He is in editorial boards for Neural Computation, and Network and Frontiers. His advisory/review panels include Germany, the Netherlands, Israel, Japan and the USA. He is Lead advisory panel member for Babylon Health. Currently, he supervises 7 PhD students and 4 postdoctoral fellows. His funding includes the Gatsby Foundation, Simons Foundation, NIH, DARPA, BBSRC and the Wellcome Trust. He has collaborations with Stanford, Princeton, ENS, Champalimaud Centre and Hebrew University.

• Department: Gatsby Computational Neuroscience Unit
• Website

Peter Latham is a Professor. His research interests are mainly in neuroscience; ultimately he is interested in understanding how biologically realistic networks give rise to behavior, and how behavior evolves with learning. He also has strong interests in learning in artificial systems. He is an editor for eLife and PLoS Computational Biology. He currently supervising 6 students (one from DeepMind) and two postdocs. He has ongoing long-term collaborations with Alex Pouget (University of Geneva) and Greg Wayne (DeepMind).

• Department: Gatsby Computational Neuroscience Unit
• Expertise: TBA
• Website

Peter Orbanz is an Associate Professor. He was formerly an Associate Professor at Columbia, and a visiting faculty, MSR New England. His research focuses on network and relational data, Bayesian nonparametrics, and the statistics of highly interdependent variables. He has two graduated PhD students, and currently supervises one PhD and one postdoc. He has been awarded circa one million GBP in funding as a PI from funding bodies in the United Kingdom and the USA. He is regularly an area chair for NeurIPS, ICML, AISTATS. His collaborators include Yee Whye Teh (Oxford), Ismael Castillo (Paris VI), Ryan Adams (Princeton), David Blei (Columbia), and Christian Borgs and Jennifer Chayes (UC Berkeley).

• Department: Gatsby Computational Neuroscience Unit
• Expertise: TBA
• Website

Alexandros Beskos is a Reader in Statistics and a Turing Fellow, H-Index 20. He is a the Leader of the “Stochastic Modelling of Complex Systems” Group. His research includes Methodology & Applications for Monte-Carlo & Optimisation (MCMC, Sequential Monte-Carlo, Hamiltonian Monte-Carlo, Data Assimilation, Inverse Problems, 3D-VAR), Statistical Modelling & Applications in Finance, Epigenetics, Biostatistics, Graphical Models, Atmospheric Sciences, and Econometrics, Copulas. He is Associate Editor of Journal of Statistics & Computing, PI at an EPSRC First Grant (2011-12, £99K), a co-I at a Standard EPSRC Grant (2013-18, £408K) and an ATI Grant (2019-20, £225K). He has been awarded the Leverhulme Trust Prize (2015-20, £100K), 1 of 5 awarded in 2014 in the UK in Mathematics & Statistics. He sas supervised 6 Post-Doctoral Researchers (1 current) and 6 PhD students (3 current). He has collaborations in the USA (Caltech, Harvard), Singapore (NUS), Japan (Osaka University), Spain (Pompeu Fabra); and various UK. He is a visiting Associate Professor at Yale-NUS, Singapore (2020) and was a visitor at the Signal Processing Group, Cambridge (2014-15).

• Department: Department of Statistical Science
• Expertise: TBA
• Website

Francois-Xavier Briol is a Lecturer/Assistant Professor and a Group leader of "Fundamentals of Statistical Machine Learning, ATI. His research includes theory and methodology for computation and learning in large scale and computationally expensive probabilistic models. He has developed tools in kernel methods, Gaussian processes, Stein's method and Monte Carlo. He has a Discussion paper in the journal of Statistical Science, and spotlights/long talks at ICML and NeurIPS. He currently supervises two PhD students and has long-term collaborations with Cambridge, Imperial College London, Oxford, Warwick and Microsoft Research.

• Department: Department of Statistical Science
• Expertise: TBA
• Website

Ioanna Manolopoulou is an Associate Professor and a Turing Fellow. Her research focuses on Bayesian mixture modelling and non-parametric modelling, ML in causal inference, and statistical methods for biased and heterogeneous data. Her work has applications in health data science, ecology, customer science. She was awarded ISBA Mitchell prize (2012), and has published two discussion papers in Bayesian Analysis (2010) and the Journal of the American Statistical Association (2012). She is Associate Editor for Bayesian Analysis, and UCL lead for a cross- institutional PhD programme run by Health Data Research UK and the ATI, funded by the Wellcome Trust. She currently supervises 5 PhD students, and collaborates with Edinburgh University, Arizona State University, and industry (3 PhD students jointly funded by Dunnhumby).

• Department: Department of Statistical Science
• Expertise: TBA
• Website

Jim Griffin is a Professor. His research is on Bayesian nonparametric methods, computation for Bayesian inference, functional data analysis, high-dimensional regression, and time series modelling. His work has found applications in biology, economics, the environment, finance, medicine, and elite sporting performance. He supervises two PhD students at UCL and one PhD student at the University of Kent. He is an associate editor of Statistics and Computing, and Bayesian Analysis, and was an associated editor of the Journal of the Royal Statistical Society, Series B from 2013-7. His main collaborators are Mark Steel (Warwick), Eleni Matechou (Kent), Maria Kalli (Kent) and James Hopker (Kent).

• Department: Department of Statistical Science
• Expertise: TBA
• Website

Jinghao Xue is an Associate Professor and Turing Fellow. His research themes include statistical machine learning, multivariate and high-dimensional data analysis, statistical pattern recognition, and image analysis. He has been/is on editorial boards of Digital Signal Processing, Neurocomputing. He is a Guest Editor, IEEE Transactions on Neural Networks and Learning Systems, Neurocomputing, and Signal Processing Image Communication. He has xix PhD students at UCL, and has collaborations with Tsinghua University, China and Tsukuba University, Japan.

• Department: Department of Statistical Science
• Expertise: TBA
• Website

Petros Dellaportas is a Professor and a Turing Fellow. He is also professor, Athens U. of Economics & Business. His research is on Bayesian theory, MCMC theory, Gaussian processes, reinforcement learning and financial modelling. He has been / is on editorial boards of Bayesian analysis (co-editor), Biometrika (AE, 2012-2019), EJS (AE, 2012-2019), Stats & Comp. (AE, 2001-2012), Statistical modeling (AE, 2001-2007), and JRSS D (1996-2000). He has been awarded grants for Causal inference and RL (Just Eat Ltd), Detecting anomalies in networks (ATI), Forecasting with large macroeconomic and financial data with structural change (ATI). He currently supervises 4 PhD and 2 post-doctoral students. He collaborates with Greek academics, and with Michalis Titsias (Deepmind), Alain Durmus (ENS Paris-Saclay), and Constantinos Daskalakis (MIT).

• Department: Department of Statistical Science
• Expertise: Bayesian statistics, Machine Learnig, Financial econometrics, Dynamic pricing
• Website

Ricardo Silva is an Associate Professor, a Turing Fellow, and a member of the Adjunct Faculty, Gatsby Computational Neuroscience Unit. His main interests are in causal inference, graphical models, and probabilistic machine learning. His research has received funding from organisations such as EPSRC, Innovate UK, the Office of Naval Research, Winton Research and Adobe Research. Ricardo has also served in the senior program committee of several top machine learning conferences, including acting as a Senior Area Chair at the NeurIPS conference and being a Program Chair and Conference Chair for the Uncertainty in Artificial Intelligence conference.

• Department: Department of Statistical Science
• Expertise: TBA
• Website

Serge Guillas is a Professor of Statistics and a Turing Fellow. He is PI of the "Uncertainty Quantification of multi-scale and multi-physics computer models: Applications to hazard and climate models" with Oxford, Warwick and Exeter, and of “Uncertainty Quantification for tsunamis” (EPSRC HPC), and “Real-Time Advanced Data assimilation for Digital Simulation of Numerical Twins on HPC” 2019-2020 with Leeds, Cambridge, and RIKEN. He founded UQ interest group at ATI. He holds invited professorships and fellowships in the USA, France, and Japan, elected Vice-Chair of SIAM UQ (2015-2016), the leading UQ society. He is Associate Editor of the International Journal for Uncertainty Quantification. His articles are number 1 and 3 most read in SIAM/ASA Journal on Uncertainty Quantification, the flagship journal in UQ. He is the UK Chair of the Uncertainty Working Group of the COST action “Accelerating global science in tsunami hazard and risk analysis” (2019-23, 25 countries). He is project lead, 2019-22 if “Future Indonesian Tsunamis: Towards End-to-end Risk quantification”, funded by three foundations. He currently supervises 5 PhD students and two postdocs.

• Department: Department of Statistical Science
• Expertise: TBA
• Website

Tengyao Wang is a Lecturer. He is Theme leader of ‘Multivariate and High Dimensional Data’ research group. His research interests include computationally efficient procedures for high-dimensional problems, while understanding statistical limitations imposed by computation. His work has found applications in sparse PCA, high-dimensional change-point detection and estimation, statistical and computational trade-offs and shape-constrained estimation. He was awarded the Royal Statistical Society Research Prize in 2019, an early-career award for an outstanding published contribution to statistical theory or application. He currently supervises three PhD students. He has collaborations at the University of Cambridge and Fudan University.

• Department: Department of Statistical Science
• Expertise: TBA
• Website

Carlo Ciliberto's research interests focus on foundational aspects of machine learning within the framework of statistical learning theory. He is particularly interested in the role of “structure” (being it in the form of prior knowledge or structural constraints) in reducing the sample complexity of learning algorithms with the goal of making them more sustainable both computationally and financially. He investigated these questions within the settings of structured prediction, multi-task and meta-learning, with applications to computer vision, robotics and recommendation systems.

• Department: Computer Science
• Expertise: Statistical Learning Theory, Kernel methods, Optimal Transport, Multi-Task and Meta-Learning
• Website

Sebastian Riedel is a Professor of NLP and ML at UCL, and a research scientist and manager at Facebook AI Research London. His research focuses on how machines can create, share and leverage knowledge, involving Natural Language Understanding, Knowledge Representation, and Reasoning and Integrity. He is an Allen Distinguished Investigator (an international program by the Paul G. Allen Foundation for high-risk, high-reward ideas, $1M award) and has acquired more than £2M of grant funding. He is a program chair of EMNLP 2017 (a main NLP conferences). He collaborates with the University of Cambridge and Sorbonne Universités, among others.

• Department: Computer Science
• Expertise: TBA
• Website

Victor is an Ellis member, a computer scientist, and a PhD research fellow in machine learning at the UCL Energy Institute. His current research focuses on recognizing patterns from multimodal data and has been applied in several domains and AI real-world problems - affective computing, e-health, Earth sciences, and currently in energy systems. He promotes deep learning and evolutionary computation to steer novel approaches for the benefit of society. His main research interests and expertise are transversal and multidisciplinary. He is a former scientific committee member of ChaLearn Looking At People, where he organised challenges and competitions in machine learning and affective computing. As a member of the ChaLearn organization, he worked in close collaboration with Prof. Isabelle Guyon, the co-inventor of the widely used method Support Vector Machines (SVM). He has been co-organiser of events at ICCV, ICMI, ECCV, ICPR, and CCIA conferences. He has participated in peer-reviewing processes for impact factor journals and conference proceedings in JMLR, IEEE TMultimedia, IEEE TPAMI, IET-CV, TTAC, ECCV, ICCV, CVPR, FG, in areas related to computer vision and machine learning with especial interest in behaviour analysis and social computing applications. He was awarded with the best national B.Sc. thesis in computer science in 2010, whose outcomes were published during the realisation of his interdisciplinary M.Sc. in Artificial Intelligence. He has co-supervised and mentored 5+ students at different universities.

• Department: UCL Energy Institute
• Expertise: Pattern Recognition, Deep Learning, Affective Computing, Behaviour Analysis, Computer Vision, Evolutionary Computation.
• Website