MLATCL.GITHUB.IO - Key Persons

Aditya Ravuri

Job Titles:
  • Student, Cambridge University

Annabelle Carrell

Job Titles:
  • Student, University of Cambridge

Bianca Dumitrascu

Biography Bianca works at the intersection of machine learning and genetics. Her main research interest is understanding how local molecular rules give raise to emergent spatial patterns in the context of biological dynamical systems. To this end, she uses techniques from statistical optimization, statistical physics and domain adaptation to identify contextual phenotypes in spatial transcriptomic data and to understand the identity of single cells and their interactions in early development. She is also interested in active learning and graphical neural networks as models to study the effects and side-effects of drug cocktails.

Carl Henrik Ek

Job Titles:
  • Senior Lecturer, Cambridge University
Biography Carl Henrik is a Senior Lecturer in Machine learning in the Department of Computer Science and Technology at the University of Cambridge. Learning is the task of creating structure of uncertainty by making assumptions of the world. The science of machine learning is concerned with how to formulate assumptions into mathematics (modelling) and how to related them to observed data (inference). Carl Henrik's research spans both these areas, in specific he is interested in how we can create data efficient and interpretable assumptions that allows us to learn from small amounts of data. Before joining the group in Cambridge Carl Henrik was a Senior Lecturer at the University of Bristol, prior to this he was an Assistant Professor at the Royal Institute of Technology (KTH) in Stockholm. He did my postdoctoral research at University of California at Berkeley and his PhD is from Oxford Brookes University. His undergraduate degree is a MEng degree in Vehicle Engineering from the Royal Institutie of Technology in Stockholm.

Challenger Mishra

Biography Challenger is developing machine-driven approaches to problems in String Theory and related Calabi-Yau geometries, studying the vast landscape of String Theory solutions using a combination of tools and techniques from machine learning and mathematical physics. His work seeks to deepen understandings of the map between String Theory models and the Standard Model of particle physics.

Christian Cabrera Jojoa

Job Titles:
  • Research Associate, Cambridge University
Biography Christian's research focuses on the intersection of machine learning and systems design. He explores how systems perspectives can help develop safe and reliable machine learning technologies, combining data-oriented architectures with techniques from service-oriented computing and self-adaptive systems.

Diana Robinson

Job Titles:
  • Student, Cambridge University
Biography Diana Robinson is a PhD candidate in computer science at the University of Cambridge, specialising in human-computer interaction. Her focus is on interaction design of clinical decision support tools, looking at ways of representing and working with uncertainty in this context. Diana is also a Student Fellow at the Leverhulme Centre for the Future of Intelligence where she was previously a research assistant exploring ethical questions relating to AI in applications ranging from immigration to medicine. Diana was a Visiting Scholar at the MIT Media Lab in the Opera of the Future group in 2017, working on the Philadelphia City Symphony. Prior to that, she worked as a Commodity Risk Analyst in BP's Integrated Supply and Trading business. She was a Princeton Project 55 Fellow in 2012/13. Diana holds an MBA from the Cambridge Judge Business School and a BA in philosophy from Princeton University.

Eric Meissner

Job Titles:
  • Student, Cambridge University
Biography Eric is a Ph.D. Candidate at the University of Cambridge supervised by Neil Lawrence. His research interests span probabilistic programming languages, algorithmic fairness and privacy, and building better ways for AI, traditional software systems, and humans to interact. Previously, he worked at Amazon for 3 years building distributed systems, developing probabilistic programming languages, and deploying machine learning in Alexa and in the supply chain domain.

Ferenc Huszár

Job Titles:
  • Senior Lecturer, Cambridge University
Biography Ferenc finished his PhD in Machine Learning at the Cambridge University Engineering Department in 2013. At that time he worked on Bayesian inference, nonparametric and kernel methods. Since then, he has worked in the London technology sector: in various tech startups and briefly in venture capital. He served as Principal Research Scientist at deep learning startup Magic Pony Technology, focussed on applying machine learning to the problem of lossy image and video compression. Following the acquisition of Magic Pony by Twitter in 2016, he served as Senior Machine Learning Researcher at Twitter where he worked on a range of ML-related projects including computer vision, recommender systems and ML ethics and fairness. He joined the Department of Computer Science and Technology in 2020 as a Senior Lecturer and continues to advise Twitter's ML Ethics, Transparency and Accountability (META) team as a staff research scientist.

Francisco Vargas

Job Titles:
  • Student, Cambridge University
Biography Francisco is a PhD student in the Computer Laboratory. He is Interested in the duality between optimisation and sampling with a focus on applications. In particular, exploring stochastic control based methodologies (e.g. Schrödinger Bridges) in practical contexts such as Bayesian machine learning as well as generative modelling, for example developing better samplers for Bayesian Deep Learning. Overall, he aims to focus on dynamical formulations of different learning tasks to explore physically motivated efficient algorithms, always keeping the practical/application component as the main focus.

Friedrich Weberling

Biography Friedrich is a visiting student from the Technical University Munich for writing his M.Sc. thesis supervised by Carl Henrik Ek and Markus Kaiser. Friedrich is interested in probabilistic machine learning focussing on interpreting deep generative inference schemes such as Variational Autoencoders using Bayesian non-parametric models. He is also interested in computational biology.

Han-Bo Li

Job Titles:
  • Student, University of Cambridge

Jess Montgomery

Job Titles:
  • Executive Director of the Accelerate Programme for Scientific Discovery
  • Executive Director, Accelerate Science, Cambridge University
Biography Jess is Executive Director of the Accelerate Programme for Scientific Discovery, and Director of the Data Trusts Initiative. She has a range of collaborations in areas where AI is being used to tackle real-world challenges. These explore the roles that technological advances, scientific evidence, policy development and public dialogue can play in sharing the benefits of AI technologies across society. Her interests in AI and its consequences for science and society stem from her policy career, in which she worked with senior parliamentarians, leading researchers and civil society organisations to bring scientific evidence to bear on major policy issues.

Justin Tan

Job Titles:
  • Student, Cambridge University
Biography Justin's research lies at the intersection of geometry and machine learning, searching for interesting structure in geometries which feature in string theory and other areas of mathematical physics. Previously he worked in experimental particle physics at the Belle II experiment.

Katie Green

Job Titles:
  • Student, Cambridge University
Biography Katie is a PhD student and member of the AI4ER CDT and is supervised across Computer Science and British Antarctic Survey. She is interested in the application of machine learning in ecology and how different methodologies can be leveraged to learn about the underlying dynamics of ecosystems.

Mala Virdee

Job Titles:
  • Student, Cambridge University
Biography Mala is a PhD student with the AI4ER Centre for Doctoral Training. Her research uses probabilistic machine learning tools to study risk from future climate extremes.

Markus Kaiser

Biography Markus's research seeks to encode expert knowledge into hierarchical probabilistic models to formulate informative prior assumptions. At Siemens, he works with domain experts to create reliable machine learning systems that are insightful for engineers. In his research, he explores how Bayesian non-parametric models can be composed to enforce abstract constraints, yield principled reasoning under uncertainty, and enable scalable and reliable inference.

Morine Amutorine

Job Titles:
  • Data Science Africa Fellow, UN Global Pulse

Neil D. Lawrence

Job Titles:
  • Professor of Machine Learning at the University of Cambridge
Biography Neil Lawrence is the inaugural DeepMind Professor of Machine Learning at the University of Cambridge. He has been working on machine learning models for over 20 years. He recently returned to academia after three years as Director of Machine Learning at Amazon. His main interest is the interaction of machine learning with the physical world. This interest was triggered by deploying machine learning in the African context, where ‘end-to-end' solutions are normally required. This has inspired new research directions at the interface of machine learning and systems research, this work is funded by a Senior AI Fellowship from the Alan Turing Institute. Neil is also visiting Professor at the University of Sheffield and the co-host of Talking Machines.

Oluwatomisin Dada

Job Titles:
  • Student, University of Cambridge

Paula Bibby

Job Titles:
  • Data Trusts Initiative 's Project Manager
Biography Paula is the Data Trusts Initiative's Project Manager. She oversees coordination of the Initiative's research programmes, projects and engagement activities.

Pierre Thodoroff

Job Titles:
  • Student, Cambridge University

Ryan Daniels

Job Titles:
  • Machine Learning Engineer, Cambridge University

Sam Nallaperuma

Job Titles:
  • Senior Research Associate, Accelerate Programme, Cambridge University

Samuel J. Bell

Job Titles:
  • Student, University of Cambridge

Sarah Morgan

Biography Sarah's research applies machine learning, network science and Natural Language Processing to better understand and predict mental health conditions. A main focus is using brain connectivity derived from MRI to predict disease trajectories for patients with schizophrenia. Sarah is also interested in using transcribed speech data to perform similar prediction problems.

Siyuan Guo

Job Titles:
  • Student, Cambridge University
Biography Siyuan is a PhD student with Ferenc Huszár at University of Cambridge and Bernhard Schölkopf at Max Planck Institute for Intelligent Systems. She is a fellow under Cambridge-Tübingen fellowship and funded by Premium Research Studentship. Her research interest lies in the intersection of causal inference and machine learning. She is interested in exploring how to transfer from traditional prediction-based ML algorithms to non i.i.d ML tasks, such that we can have more robust and fair ML algorithms. Previously, she studied Machine Learning (MSc) at UCL and Mathematics (BA + MSc) at Cambridge. She also worked as a quantitative strategist in Goldman Sachs International.

Soumya Banerjee

Job Titles:
  • Senior Research Associate, Accelerate Programme for Scientific Discovery, Cambridge University
Biography Soumya analyses complex problems and implement new statistical and machine learning techniques for deriving insights from large amounts of data. He works closely with people from other domains, especially experimentalists and clinicians. Soumya worked in industry before completing a PhD in applying computational techniques to interdisciplinary topics. He has worked closely with domain experts in finance, healthcare, immunology, virology, and cell biology. Recently he collaborated with clinicians and patients on using patient and public involvement to build trust in AI algorithms. Soumya's research uses data science for social good and answer questions about complex systems. Complex systems are all around us, from social networks to transportation systems, cities, economies and financial markets. He is also very passionate about outreach, science communication.

Tabitha Goldstaub

Job Titles:
  • Executive Director, Innovate Cambridge
Biography Tabitha is an advisor to government, academia and business on all things AI. Tabitha is the author of How To Talk To Robots - A Girl's guide to a World Dominated by AI.

Vidhi Lalchand

Job Titles:
  • Research Associate, Cambridge University
Biography Vidhi is a Ph.D. student at the Cavendish Laboratory, a Turing Scholar and a member of Christ's College, University of Cambridge. Since Feb, 2022 she is also postdoctoral research associate working on the Human Cell-ATLAS project supervised by Prof. Neil Lawrence. Her PhD is supervised by Prof. Carl Rasmussen and Dr Christopher Lester. Her research interests are in Bayesian Non-parametrics, Gaussian Processes and Hierarchical Modelling. She is interested in applications of probabilistic machine learning to problems in contemporary sciences like computational biology, high energy physics and astronomy. Her Ph.D. is funded by the Alan Turing Institute and Qualcomm Innovation Fellowship (Europe).

Vincent Dutordoir

Job Titles:
  • Student, Cambridge University

Yonatan Gideoni

Job Titles:
  • Student, Cambridge University
Biography Yonatan is an MPhil student working on problems in theoretical deep learning through the lens of symmetry. He previously worked as an algorithm developer at Mobileye and as a high-school teacher at the Israeli Arts and Sciences Academy. He holds a bachelor in physics from the Hebrew University of Jerusalem and is currently a Rhodes Scholar elect.