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UCL, a global leader in AI and machine learning, is a core component of the ELLIS network through its ELLIS Unit. ELLIS is a European AI network of excellence comprising Units within 30 research institutions. It focuses on fundamental science, technical innovation and societal impact. The ELLIS Unit at UCL spans across multiple departments (Gatsby Computational Neuroscience Unit, Department of Computer Science, Department of Statistical Science and Department of Electronic and Electrical Engineering).
“Some of the most effective learning algorithms are those that combine perspectives from many different models or parameters. This has always seemed a fitting metaphor for effective research. And now ELLIS will provide a new architecture to keep our real-life committee machine functioning --- reinforcing, deepening and enlarging the channels that connect us to colleagues throughout Europe At UCL we're excited to be a part of this movement to grow together. We look forward to sharing new collaborations, workshops, exchanges, joint studentships and more, and to the insight and breakthroughs that will undoubtedly follow. ”
Prof Maneesh Sahani
Director, Gatsby Computational Neuroscience Unit
“Advances in AI that benefit people and planet require global cooperation across disciplines and sectors. The ELLIS network is a vital part of that effort and UCL is proud to be a contributor. ”
Prof Geraint Rees
UCL Pro-Vice-Provost (AI)
News
Events
People
Unit Directors
Computer Science
Gatsby Computational Neuroscience Unit
Department of Statistical Science
Department of Electronic and Electrical Engineering
Mathematics
UCL Energy Institute
Division of Psychology and Language Sciences
Benjamin Guedj (Co-director and Fellow, Computer Science)
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.
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TBA - Website
Brooks Paige
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.
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Computer Science - Expertise:
TBA - Website
David Barber (Fellow)
David Barber is Director of the University College London Centre for 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).
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Computer Science - Expertise:
TBA - Website
Dimitrios Kanoulas
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.
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Computer Science - Expertise:
TBA - Website
Emine Yilmaz (Fellow)
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.
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Computer Science - Expertise:
TBA - Website
Ingemar Cox
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.
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Computer Science - Expertise:
TBA - Website
John Shawe-Taylor (Fellow)
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.
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Computer Science - Expertise:
TBA - Website
Lourdes Agapito (Fellow)
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.
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Computer Science - Expertise:
TBA - Website
Marc Deisenroth (Fellow)
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.
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Computer Science - Expertise:
Gaussian processes, reinforcement learning, approximate inference, data-efficient machine learning - Website
Mark Herbster
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.
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Computer Science - Expertise:
TBA - Website
Marta Betcke
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.
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Computer Science - Expertise:
TBA - Website
Massimiliano Pontil
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.
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Computer Science - Expertise:
TBA - Website
Matt Kusner (Scholar)
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.
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Computer Science - Expertise:
TBA - Website
Pontus Stenetorp (Fellow)
Pontus Stenetorp 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.
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Computer Science - Expertise:
TBA - Website
Laura Toni (Fellow)
Laura Toni an associate professor of Electronic and Electrical Engineering at UCL. She 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 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).
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Department of Electronic and Electrical Engineering - Expertise:
TBA - Website
Miguel Rodrigues (Fellow)
Miguel Rodrigues is a Full Professor at University College London, where he is also the Director of the MSc in Integrated Machine Learning Systems. Miguel 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.
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Department of Electronic and Electrical Engineering - Expertise:
TBA - Website
Arthur Gretton (Co-director and Fellow, Gatsby)
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.
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TBA - Website
Maneesh Sahani (Fellow)
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.
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Gatsby Computational Neuroscience Unit - Website
Peter Latham
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).
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Gatsby Computational Neuroscience Unit - Expertise:
TBA - Website
Peter Orbanz
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).
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Gatsby Computational Neuroscience Unit - Expertise:
TBA - Website
Alexandros Beskos
Alexandros Beskos is a Professor in Statistics within UCL Statistical Science. 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.
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Department of Statistical Science - Expertise:
TBA - Website
François-Xavier Briol (Co-director and Scholar, Statistical Science)
François-Xavier Briol is a Professor of Statistics and Machine Learning within UCL Statistical Science, where he leads the "Fundamentals of Statistical Machine Learning" (https://fsml-ucl.github.io) research group. His research focuses on building statistical and machine learning methods which enable the use of large-scale models in the physical, environmental and engineering sciences.
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TBA - Website
Ioanna Manolopoulou
Ioanna Manolopoulou is a Professor of Statistical Science at the Department of Statistical Science at University College of London, as well as Associate Director of the HDRUK-Turing PhD programme. 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.
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Department of Statistical Science - Expertise:
TBA - Website
Jim Griffin
Jim Griffin is a Professor of Statistical Science. Jim is interested in a wide-range of areas in Bayesian statistics. He is particularly interested in the areas of Bayesian nonparametric modelling, high-dimensional regression modelling and time series modelling, and the computational methods needed to estimate these models. He has worked on applications of these methods in biology, ecology, economics, finance, medicine and sports science.
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Department of Statistical Science - Expertise:
TBA - Website
Jinghao Xue
Jinghao Xue is a Professor of Statistical Pattern Recognition. His research themes include statistical machine learning, multivariate and high-dimensional data analysis, statistical pattern recognition, and image analysis.
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Department of Statistical Science - Expertise:
TBA - Website
Petros Dellaportas
Petros Dellaportas has a joint appointment as a professor in Statistical Science in the department of Statistical Science, University College London, and as a professor of Statistics in the department of Statistics, Athens University of Economics and Business. His research is on Bayesian theory, MCMC theory, Gaussian processes, reinforcement learning and financial modelling.
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Department of Statistical Science - Expertise:
Bayesian statistics, Machine Learnig, Financial econometrics, Dynamic pricing - Website
Ricardo Silva
Ricardo Silva is a Professor of Statistical Machine Learning and Data Science in the UCL Department of Statistical Science, and a member of the Adjunct Faculty, Gatsby Computational Neuroscience Unit. His main interests are in causal inference, graphical models, and probabilistic machine learning.
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Department of Statistical Science - Expertise:
TBA - Website
Serge Guillas
Serge Guillas is a Professor of Statistics. He is currently Met Office Joint Chair in Data Sciences for Weather and Climate, leading the UCL Met Office Academic Partnership and Associate Director, UCL Centre for Advanced Research Computing (ARC). His work focuses on environmental statistics, and uncertainty quantification of complex computer models, with applications to tsunami and climate models.
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Department of Statistical Science - Expertise:
TBA - Website
Carlo Ciliberto
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.
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Computer Science - Expertise:
Statistical Learning Theory, Kernel methods, Optimal Transport, Multi-Task and Meta-Learning - Website
Sebastian Riedel (Fellow)
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.
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Computer Science - Expertise:
TBA - Website
Victor Ponce Lopez
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.
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UCL Energy Institute - Expertise:
Pattern Recognition, Deep Learning, Affective Computing, Behaviour Analysis, Computer Vision, Evolutionary Computation. - Website
Ilija Bogunovic
Ilija Bogunovic serves as an Assistant Professor in Machine Learning Systems Engineering. His research is centered around algorithmic decision-making within the realms of reliable, aligned, and safe AI. Specifically, he delves into the algorithmic challenges of efficiently making decisions and discovering policies, designs, and solutions that can withstand distributional shifts (sim-to-real gap), corruptions, adversaries, and model misspecifications. His work explores how to harness the potential of foundation models to facilitate robust and aligned decision-making. Additionally, within the context of real-world machine learning, he investigates how reliability and robustness considerations can be integrated into interactive data-driven algorithms, encompassing areas such as RL and RLHF, Bayesian optimization/experiment design (AI for science), and multi-agent learning.
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Department of Electronic and Electrical Engineering - Expertise:
TBA. - Website
Federica Sarro
Federica Sarro is a Full Professor of Software Engineering at University College London, where she is the Head of the Software System Engineering group and she has established the SOLAR team within the CREST centre. Professor Sarro has extensive academic and industrial expertise in Software Analytics , Search-Based Software Engineering, and Empirical Software Engineering, with a focus on automated software management, optimisation, testing and repair of functional and non-functional properties of modern software systems, including AI-enabled and mobile systems. On these topics she has published over 100 peer-reviewed scholarly articles, and she has given several invited talks at academic and industrial international events. She has also worked in collaboration with several companies including Bloomberg, Google, Meta, and Microsoft. Professor Sarro has obtained numerous awards and generous funding for her research, including the LERO Partnership Fellowship in 2023 and the IEEE TCSE Rising Star Award in 2021 in recognition of her “excellence in Software Engineering research with scholarly and real-world impact. Professor Sarro is a member of the IEEE TCSE Executive Committee, a member of the ELLIS Society, and an ACM Distinguished Speaker.
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Computer Science - Expertise:
TBA. - Website
Andrew Saxe
Andrew Saxe is a Professorial Research Fellow at the Gatsby Computational Neuroscience Unit and the Sainsbury Wellcome Centre. He serves as the Principal Investigator of the UCL Theory of Learning Lab. His research is concentrated on the theory of deep learning and its applications to phenomena in neuroscience and psychology.
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Gatsby Computational Neuroscience Unit - Website
Jeremias Knoblauch
Jeremias Knoblauch is an Associate Professor of Statistical Science. Jeremias' research revolves around extending the paradigm of Bayesian inference to cope with the challenges posed by modern large-scale data, simulator models, and machine learning techniques. In this context, he is particularly interested in generalised and Post-Bayesian inference, model misspecification and robustification strategies, computational challenges involving intractability, and variational methods.
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Department of Statistical Science - Expertise:
TBA - Website
Mario Giulianelli
Mario is an Associate Professor in the UCL Division of Psychology and Language Sciences. His research investigates the computational principles that enable the understanding, production, learning, and use of language in interaction—both in natural and artificial cognitive systems. Until recently at the AI Security Institute, Mario is also strongly committed to advancing evaluation standards for AI systems, both within the domain of language and across the broader AI landscape.
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Division of Psychology and Language Sciences - Expertise:
TBA - Website
Leena Chennuru Vankadara
Leena Chennuru Vankadara is a Lecturer at the Gatsby unit. Her research aims to develop efficient, reliable, and trustworthy machine learning models by understanding the theoretical foundations of deep learning and causal learning. Previously, she was an Applied Scientist at the AGI Foundations Lab at Amazon, where she worked on fundamental research on the theory and science of scaling large language models.
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Gatsby Computational Neuroscience Unit - Expertise:
TBA - Website
Alessandro Barp
Alessandro Barp is a Lecturer in the Statistical Science department. His research focuses on developing interpretable deep learning architectures, as well as building methodologies for sampling and inference with theoretical guarantees, typically via geometric tools. Previously, Alessandro was part of the Turing-Roche partnership.
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Department of Statistical Science - Expertise:
TBA - Website
Siddharth Swaroop
Siddharth Swaroop is a Lecturer in Artificial Intelligence at the Department of Computer Science. His research focusses on understanding and adapting knowledge in machine learning models. This focus has two parts: designing new, more efficient, machine learning (ML) methods, and investigating the human and social impact of adapting ML models. More specifically, (1) he uses a probabilistic framework to unify different knowledge-adaptation tasks (such as continual learning and federated learning), in order to design improved algorithms; and (2) he improves performance of human-AI teams by adapting ML systems to different humans in human-computer interaction (HCI), as well as translates AI policies into technical requirements for ML systems.
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Computer Science - Expertise:
TBA. - Website
