van der Schaar Lab

Research team

Led by Professor Mihaela van der Schaar and based in Cambridge, U.K., the van der Schaar Lab’s research team is one of the most impactful and diverse teams in its field, developing a wide range of ML approaches including deep learning, causal inference, AutoML, time series analysis, ensemble learning, and many more.

The lab’s shared vision is to develop cutting-edge machine learning methods and techniques, with the goal of improving healthcare and medical knowledge.

Prof. van der Schaar

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Mihaela van der Schaar

John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine, University of Cambridge

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA.

Mihaela has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise span signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.

In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).

Read Mihaela’s full bio.

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Postdocs

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Fergus Imrie

Postdoc – joined the lab in 2021

Fergus Imrie is a postdoc at the ECE Department, University of California, Los Angeles (UCLA).

He is excited and motivated by the promise of transforming healthcare and improving medical knowledge through the use of machine learning in combination with clinical experts.

In particular, Fergus is interested in self-supervised learning and methods for understanding clinical decision making.

Prior to joining the lab, Fergus completed his DPhil (PhD) at the University of Oxford in the Department of Statistics, developing deep learning approaches for drug discovery. Fergus values work with a strong translational impact: his research is currently being used by a number of pharmaceutical companies on active drug discovery projects to develop new therapeutics.

PhD students

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Alex Chan

Ph.D. student – joined the lab in 2020

Alex Chan graduated with a BSc in Statistics at University College London before moving to Cambridge for an MPhil in Machine Learning and Machine Intelligence.

Having started early in research, he won an EPSRC funding grant in his second year of undergraduate for a project on Markov chain Monte Carlo mixing times, and has now had work published at all three of the major machine learning conferences: ICML, ICLR, and NeurIPS.

Much of Alex’s research focuses on understanding and building latent representations of human behavior, with a specific emphasis on understanding clinical decision-making (an important new area of focus for the lab’s research) through imitation, representation learning, and generative modeling. In Alex’s own words, replicating and understanding decision-making at a higher level is, in itself, incredibly interesting, but “also being able to apply it healthcare is hugely important, and promises to actually make a difference to people’s lives in the near future.”

He is particularly interested in developing approximate Bayesian methods to appropriately handle the associated uncertainty that naturally arises in this setting and which is vital to understand.

Drawn to the lab’s special focus on healthcare, Alex notes that “No other area promises the same kind of potential for really having an impact with your research, and the lab benefits from the wide diversity of work being done alongside connections everywhere in both academia and industry.”

Alex’s studentship is sponsored by Microsoft Research.

Outside of machine learning, Alex captains the Wolfson College Boat Club and occasionally keeps up with Krav Maga as a trainee instructor.

Alex has a personal website: alexjchan.com/

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Alicia Curth

Ph.D. student – joined the lab in 2020

Alicia Curth, a self-described “full-blooded applied statistician,” recently completed an MSc in Statistical Science at the University of Oxford, where she graduated with distinction and was awarded the Gutiérrez Toscano Prize (awarded to the best-performing MSc candidates in Statistical Science each year). Her previous professional experience includes a data science role for Media Analytics, and a research internship at Pacmed, a healthcare tech start-up.

Alicia also holds a BSc in Econometrics and Operations Research and a BSc in Economics and Business Economics from the Erasmus University Rotterdam.

Since meeting Mihaela van der Schaar at Oxford, Alicia says she’s “been fascinated by the diverse, creative and bleeding edge work of everyone in the lab ever since.”

Alicia hopes to explore ways of making machine learning ready for use in applied statistics, where problems are inferential rather than purely predictive in nature and the ability to give theoretical guarantees is essential. As she sees it, “there is much to gain by replacing linear regression with more flexible machine learning models.” She is particularly excited by potential applications in the areas of personalized and precision medicine, where she hopes machine learning can help healthcare “consider more than just the average patient in the future.”

Alicia is interested in building a better understanding of which algorithms work when and why, and aims to contribute to bridging the gap between theory and practice in machine learning. She is particularly interested in building decision support systems for doctors, and aiding knowledge discovery through next-generation clinical trials as well as analyses of genomics (and other omics) data.

Alicia’s studentship is funded by AstraZeneca.

Alicia has played waterpolo since the age of 12, and was German champion during high school. At Oxford, she represented the university as part of the women’s Blues team.

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Alihan Hüyük

Ph.D. student – joined the lab in 2019

Alihan is a PhD student in the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge. He is supervised by Professor Mihaela van der Schaar.

Prior to attending Cambridge, he completed a BSc in Electrical and Electronics Engineering at Bilkent University. Alihan’s current research focuses on developing interpretable machine learning methods with the purpose of understanding the decision-making process of clinicians.

Previously, he worked on multi-armed bandit problems in combinatorial and multi-objective settings.

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Boris van Breugel

Ph.D. student – joined the lab in 2020

Boris van Breugel most recently completed a MSc in Machine Learning at University College London. His study was supported under a Young Talent Award by Prins Bernhard Cultuurfonds, and a VSBfonds scholarship. Prior to this, he received a MASt in Applied Mathematics from the University of Cambridge.

Reflecting his broad research background, Boris’ current research interests range from model interpretability to learning from missing data, from modelling treatment effects to high-dimensional omics data.

While studying for his MSc in Machine Learning at UCL, Boris developed a model to detect Alzheimer’s disease in different forms of medical imaging data, potentially enabling diagnosis at an earlier stage and thereby aiding the development of more effective treatment plans. He found the healthcare domain uniquely challenging and rewarding, and decided to continue research in the domain.

As a PhD student with the van der Schaar Lab, Boris aims to develop methods for finding meaningful structure in omics data—in essence, he says, “the amount of omics data is increasing at a huge speed, and machine learning methods can allow us to interpret and make sense of all this data.”

Boris’ studentship is funded by the Office of Naval Research (ONR).

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Dan Jarrett

Ph.D. student – joined the lab in 2019

Dan is a third-year Ph.D. student in the machine learning and artificial intelligence research group at the department of mathematics, advised by Professor van der Schaar.

He graduated from Princeton University with a B.A. in economics, and from Oxford with an MSc. in computer science.

He has professional experience in finance, consulting, and technology spaces, and research interests include representation learning and decision-making over time.

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Hao Sun

Ph.D. student – joined the lab in 2021

Hao received his B.Sc. in Physics from Peking University, and M.Phil. from the Chinese University of Hong Kong’s Multimedia Lab (MMLab).

His M.Phil. research focused on sample-efficient deep reinforcement learning (DRL) algorithms for continuous control. He proposed several sample-efficient algorithms for sparse reward goal-conditioned tasks, general continuous control locomotion tasks, diversity-seeking exploration and safe reinforcement learning tasks.

Hao’s previous work on self-supervised sample-efficient reinforcement learning algorithms has already been featured in spotlight presentation at NeurIPS 2019. As Hao points out, “the area of supervised-learning based RL is thriving nowadays, and is definitely a promising under-explored direction worthy of further investigation!”

Although DRL has achieved great success in computer games and similar applications, deploying prevailing DRL algorithms in real-world applications is still an open question. In his own words, Hao believes that “we should be able to take DRL beyond simply working in simulations; the lab is in a unique position to apply it in real-world scenarios where we can help people making better decisions.”

Hao believes that healthcare provides a natural scenario where high-quality decision-making policy is needed, but that there is still a pressing need to address the interpretability, transparency, and reliability issues of modern reinforcement learning and inverse reinforcement learning algorithms.

Hao’s research is supported by funding from the Office of Naval Research (ONR).

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Ioana Bica

Ph.D. student – joined the lab in 2018

Ioana Bica is a second year PhD student at the University of Oxford and at the Alan Turing Institute. She has previously completed a BA and MPhil in Computer Science at the University of Cambridge where she has specialised in machine learning and its applications to biomedicine.

Ioana’s PhD research focuses on building machine learning methods for causal inference and individualised treatment effect estimation from observational data. In particular, she has developed methods capable of estimating the heterogeneous effects of time-dependent treatments, thus enabling us to determine when to give treatments to patients and how to select among multiple treatments over time.

Recently, Ioana has started working on methods for understanding and modelling clinical decision making through causality, inverse reinforcement learning and imitation learning.

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Jeroen Berrevoets

Ph.D. student – joined the lab in 2020

Jeroen Berrevoets joined the van der Schaar Lab from the Vrije Universiteit Brussel (VUB). Prior to this, he analyzed traffic data at 4 of Belgium’s largest media outlets and performed structural dynamics analysis at BMW Group in Munich.

As a PhD student in the van der Schaar Lab, Jeroen plans to explore the potential of machine learning in aiding medical discovery, rather than simply applying it to non-obvious predictions. His main research interests involve using machine learning and causal inference to gain understanding of various diseases and medications.

Much of this draws from his firmly-held belief that, “while learning to predict, machine learning models captivate some of the underlying dynamics and structure of the problem. Exposing this structure in fields such as medicine, could prove groundbreaking for disease understanding, and consequentially drug discovery.”

Jeroen’s studentship is supported under the W. D. Armstrong Trust Fund. He will be supervised jointly by Mihaela van der Schaar and Dr. Eoin McKinney.

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Jonathan Crabbé

Ph.D. student – joined the lab in 2020

Jonathan Crabbé’s academic passions range from black boxes to black holes. He joins the lab following a MASt in in theoretical physics and applied mathematics at Cambridge, which he passed with distinction, receiving the Wolfson College Jennings Price. Before this, he received an M.Sc. from Ecole Normale Superieure of Paris’ Department of Physics, with his studies fully funded under the LABEX-ICFP Scholarship (awarded based on academic excellence).

Jonathan’s work focuses on the development of explainable artificial intelligence (XAI), which he believes to be one of the biggest challenges in machine learning. Through his research over the coming years, he hopes to help deploy of state-of-the-art machine learning models, meeting the expectations of (possibly non-expert) users by supplementing the predictions made by models with informative and actionable explanations.

Jonathan describes explainability as “crucial in numerous domains of application, such as healthcare, where life-impacting decisions might be taken based on machine learning models.” He points out that “the impact of XAI goes well beyond state-of-the-art methods, as progress in machine learning will need to be based on a better understanding of model’s architecture.”

Jonathan’s studentship is supported by funding from Aviva.

In his time off, Jonathan enjoys hiking, swimming, diving and crafting cocktails for his friends.

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Kamile Stankeviciute

Ph.D. student – joined the lab in 2021

Before joining the van der Schaar lab, Kamile did her undergraduate studies in Computer Science at the University of Cambridge, followed by an MSc in the same field at the University of Oxford, where she explored the applications of machine learning to computational neuroscience and time series uncertainty estimation problems.

As a three-time software engineering intern at Google, Kamile has additional experience in large scale system capacity planning, search ranking, statistical experimentation and machine learning frameworks. Kamile’s impact on diversity, demonstrated leadership, and academic excellence have been recently recognized by the Generation Google Scholarship 2021 award, and she has the rare distinction of having a paper published at NeurIPS before starting her PhD!

As part of the van der Schaar lab, Kamile aspires to use machine learning to meaningfully integrate structured medical domain expert knowledge with the clinical observation data. Her goal is to reduce the gap between machine learning and clinical practice by enhancing the existing models and making new medical discoveries.

When she is not in the lab, Kamile is probably at the gym or on a run, working on her strength and endurance goals.

Kamile’s research is supported by funding from AstraZeneca.

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Krzysztof Kacprzyk

Ph.D. student – joined the lab in 2021

Krzysztof graduated with an M.Sc. in Mathematical Sciences at the University of Oxford, where he studied statistics, geometry and quantum computing. His dissertation was devoted to optimization algorithms for the generalized multi-armed bandit problem. Before moving to Oxford, he completed his B.Sc. in Mathematics at University College London.

In his second year of undergraduate study, Krzysztof was awarded funding from EPSRC to conduct research in mathematical modelling, during which he designed a biomechanical model for rice seedling emergence. Later, as an Oxford AI Society member, he investigated bias, fairness and privacy issues in computer vision algorithms and presented his findings at the ICLR workshop on Synthetic Data Generation.

Krzysztof is driven to explore ways for machine learning to aid scientific discovery, especially in pharmacology. He is particularly interested in combining data from clinical trials with data from electronic health records to suggest better treatments for patients.

In his leisure time, Krzysztof enjoys playing the piano, building robots, juggling and performing yo-yo tricks.

Krzysztof’s research is supported by funding from Roche.

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Nabeel Seedat

Ph.D. student – joined the lab in 2021

Before joining the van der Schaar Lab, Nabeel received a merit scholarship for a master’s degree at Cornell University, researching Bayesian deep learning and uncertainty estimation for high stakes applications. In addition, he holds a master’s degree from the University of the Witwatersrand (South Africa), where he was awarded a National Research Foundation grant for his work applying signal processing and machine learning to Parkinson’s disease diagnostics in low-resource settings.

Professionally, Nabeel has worked as a machine learning engineer in the United States and South Africa. The computer vision and natural language processing models he worked on are currently deployed and serving millions of customers on a daily basis.

Nabeel is keenly aware that taking methods from the lab to the bedside “requires a unique focus beyond just high-performance predictive models; it requires the development of a toolkit of methods for transfer learning across domains and locations, learning on smaller datasets, understanding model biases and quantifying model reliability and uncertainty are fundamentally needed to bridge this divide.”

Nabeel’s research is supported by funding from the Cystic Fibrosis Trust.

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Sam Holt

Ph.D. student – joined the lab in 2021

Sam holds a M.Eng. in Engineering Science from the University of Oxford, where he graduated with a first-class degree and numerous awards for academic excellence (placing 2nd in the year for his master’s thesis).

In the course of his studies, Sam undertook two machine learning research internships at the University of Oxford: one researching detecting and tracking cars in noisy radar data (for a self-driving car), and the other in economic time-series forecasting. He also initiated his own original research into noise reduction on propellers, developing a propeller design that emits 44% less noise.

Upon graduating, Sam worked for an Oxford spin out, Mind Foundry, investigating dialogue systems for an industrial client. He has also authored and taught an online machine learning course covering recent work, and shared his passion for machine learning through teaching students in a classroom at a London-based tech-MBA program. Previously, he has collaborated and led three quantitative financial research projects, alongside working in data science and software engineering for a quantitative finance startup, Fifth Row Technologies. He has also created proof-of-concept automation ML tools to help doctors in GP practices.

Sam’s research is supported by funding from AstraZeneca.

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Yao Zhang

Ph.D. student – joined the lab in 2019

Yao Zhang is a second-year PhD student in the van der Schaar Lab.

Prior to this, he studied Mathematics, Statistics and Machine Learning (BA and MPhil Hons.) at the University of Cambridge and the University of Birmingham.

His primary research interest is Causal Inference.

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Yuchao Qin

Ph.D. student – joined the lab in 2020

Yuchao Qin joined the van der Schaar Lab from Tsinghua University, where he received an BS in Automation and MS in Control Science and Engineering.

In 2019, Yuchao won 1st prize in the Oral Presentation category of the Beijing University Artificial Intelligence Academic Forum. He was awarded a highly competitive national scholarship in same year, while pursuing his master’s degree.

Yuchao’s prior research primarily focused on control and optimization methods for smart power systems with joint utilization of control theory and machine learning techniques. He has published a number of papers at leading conferences and in journals in intelligent power and energy systems.

His recent research interests are reinforcement learning, and inverse reinforcement learning. He explains that during the course of his research at Tsinghua he learned that “there’s a strong connection between optimal control theory and reinforcement learning,” and that “reinforcement learning is definitely one of the most promising methods to achieve higher level artificial intelligence as it allows machine to learn itself via interacting with the environment.” He believes that these techniques will ultimately contribute to the intelligence revolution in healthcare.

Yuchao describes the van der Schaar Lab as “one of the world’s leading labs in the field of machine learning, including reinforcement learning,” and hopes to use his studentship to “further explore the possibility of reinforcement learning methods, and their applications in understanding decision-making strategies of clinicians and other healthcare professionals.”

Yuchao’s studentship is funded by the U.K. Cystic Fibrosis Trust.

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Zhaozhi Qian

Ph.D. student – joined the lab in 2019

After obtaining a MSc in Machine Learning at UCL, Zhaozhi Qian started his career as a data scientist in the largest mobile gaming company in Europe. Three years later, he found it might be more fulfilling to apply AI to cure cancer than to make the gamers hit the purchase button 1% more often.

He thus joined the group in 2019 as a PhD student focusing on robust and interpretable learning for longitudinal data. So far, his work has included inferring latent disease interaction networks from Electronic Health Records, uncovering the causal structure between events that unfold over time, and calibrating the predictive uncertainty under domain shift.

Zhaozhi also worked as a contractor in the NHS during the COVID-19 pandemic contributing his analytical skills to the national response to the pandemic.

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Research engineers

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Bogdan Cebere

Research engineer – joined the lab in 2021

Bogdan is one of the lab’s research engineers, having joined the team in 2021. He received his bachelor’s degree in computer science in 2012 and his master’s degree in distributed systems in 2014, both from the University of Bucharest.

Prior to joining the van der Schaar Lab, Bogdan worked for roughly 10 years at a cybersecurity company. During this time, he contributed to a range of research projects related to network security, cryptography, and data privacy, which required high-performance solutions in embedded or cloud environments.

Bogdan has also made substantial contributions to open-source projects, mostly focused on privacy preserving techniques for machine learning. Some of his key contributions in this space have been for the OpenMined community; he and his collaborators published this work in workshops at the prominent NeurIPS and ICLR conferences.

Bogdan is driven to keep learning new things every day, and to keep improving—that’s his main reason for joining the van der Schaar lab.

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Evgeny Saveliev

Research engineer & part-time Ph.D. student – joined the lab in 2020

Evgeny is one of the lab’s research engineers, and has been a part-time Ph.D. student since 2021. His educational background is Natural Sciences at the University of Cambridge, followed by postgraduate study in Computer Science at University of Southampton.

Evgeny was an AI Resident at Microsoft Research Cambridge before joining the lab, where he worked on projects covering meta-learning and reinforcement learning as applied to recommender systems. He also has experience in computational finance, having worked in a fintech start-up and commodities trading.

Evgeny facilitates turning the lab’s research code into robust production quality code, making it more scalable, applying software engineering best practices; he also collaborates with our PhD students on some research topics.

He is particularly interested in working on AutoML and time-series modelling, as well as machine learning for time series, and synthetic data.

Finally, Evgeny is a hobbyist photographer, and a Karate practitioner, having a black belt in two different Karate styles.

Communications

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Nick Maxfield

Communications & project coordination – joined the lab in 2020

Nick oversees the van der Schaar Lab’s communications, including media relations, content creation, and maintenance of the lab’s online presence.

Nick studied Japanese (BA Hons.) at the University of Oxford, graduating in 2012. Nick previously worked in HQ communications roles at Toyota (2013-2016) and Nissan (2016-2020).

Given his humanities/languages background and experience in communications, Nick is well-positioned to highlight and explain the real-world impact of research that can often be quite esoteric. Thankfully, he is comfortable asking almost endless questions in order to understand a topic.

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Recent alumni

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Ahmed Alaa

2015-2019 (Ph.D); 2019-2021 (postdoc)

Ahmed joined the van der Schaar Lab as a Ph.D. student in 2015 at the University of California, Los Angeles, and completed his doctoral research (supervised by Mihaela van der Schaar) in December 2019. His dissertation, entitled “Discovering Data-Driven Actionable Intelligence for Clinical Decision Support,” is available here.

Subsequently, Ahmed remained with the lab as a postdoctoral scholar at UCLA and an affiliated postdoctoral researcher at the University of Cambridge (COVID-19 task force). He received the 2021 Edward K. Rice Outstanding Doctoral Student Award from UCLA.

Ahmed is now a Postdoctoral Associate at the Broad Institute of MIT and Harvard, and the MIT Computer Science & Artificial Intelligence Laboratory (CSAIL).

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Alexis Bellot

2017-2021 (Ph.D.)

Alexis joined the lab as a Ph.D. student in 2017, under the supervision of Mihaela van der Schaar and affiliated with the University of Cambridge and The Alan Turing Institute.

Alexis’ research consistently focused on causal inference, hypothesis testing, and its applications, most notably in healthcare. He awarded the G-research PhD competition prize in 2019.

In June 2021, Alexis was awarded his doctorate by the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge following a successful defense of his thesis, entitled “Hypothesis testing and causal inference with heterogeneous medical data.”

Alexis is now a postdoctoral research scientist at Columbia University, under the direction of Prof. Elias Bareinboim in the Computer Science Department. His research continues to focus on causal inference, hypothesis testing, and its applications.

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Changhee Lee

2016-2021 (Ph.D.)

Changhee joined the lab as a Ph.D. student in 2016 (supervised by Mihaela van der Schaar) at the University of California, Los Angeles.

His research has focused on deep learning approaches for addressing challenges associated with modeling, predicting, and interpreting in time-to-event analysis and time-series analysis.

Changhee’s thesis, entitled “Machine Learning Frameworks for Data-Driven Personalized Clinical Decision Support and the Clinical Impact,” is available here.

Changhee is now an assistant professor in Chung-Ang University’s School of Software and Computer Engineering (Department of Artificial Intelligence).

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James Jordon

2017-2021 (Ph.D.)

James joined the lab as a Ph.D. student in 2017 under Mihaela van der Schaar’s supervision at the University of Oxford.

Much of his research with the lab focused on the use of generative adversarial networks in solving supervised, unsupervised and private learning problems including estimation of individualised treatment effects, feature selection, private synthetic data generation, data imputation, and transfer learning.

James graduated on the basis of an integrated thesis comprised of multiple papers: GANITESCIGANGAINKnockoffGANPATEGAN, and DPBag.

James is now at The Alan Turing Institute, where he is pursuing a postdoc on synthetic data.

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Trent Kyono

2018-2021 (Ph.D.)

Motivated by the desire to build cutting-edge machine learning models that can transform healthcare, Trent first joined the lab at the University of California, Los Angeles, under the supervision of Mihaela van der Schaar. He pursued a research agenda at the confluence of machine learning, computer vision, and causality.

Trent is a senior machine learning researcher within the Ads Ranking Team at Meta (Facebook) where he works on machine learning methods for ad personalization.

Trent’s dissertation, entitled “Towards Causally-Aware Machine Learning” focuses on leveraging cause and effect relationships for improving several aspects of machine learning, such as regularization, missing data, synthetic data, and domain adaptation.

Alumni (graduated pre-2020)

Post-docs
  • Dr. William Whoiles
    • Now Post-doc at the Department of Electrical and Electronics Engineering, University of British Columbia, Canada
  • Dr. Cem Tekin
    • Now Assistant Professor at the Department of Electrical and Electronics Engineering, Bilkent University, Turkey
  • Dr. Luca Canzian
    • Now Post-doc at the Computer Science Department, University of Birmingham, UK
Ph.D.
Affiliated Ph.D. Alumni
  • Dr. Cong Shen
    • Now Assistant Professor at University of Science and Technology China (USTC), China
  • Dr. Omar A. Nasr
    • Now Assistant Professor at Cairo University, Egypt
M.Sc.
Past visiting students
  • Minhae Kwon (2016) (Email)
  • Sabrina Muller (2015) (Email)
  • Suoheng Li (2014-2015) (Email)
  • Zhenlong Yuan (2014-2015) (Email)
  • Meier Yannick (2015) (Email)
  • Jonas Braun (2014) (Email)
  • SaiDhiraj Amuru (2014) (Email)
  • Mahnoosh Alizadeh (2013) (Email)
  • Cuiling Lan (2013) (Email)
  • Byung-Gook Kim (2012) (Email)
  • Luca Canzian (2012) (Email)
  • Saeede Parsaee Fard (2011) (Email)
  • Oussama Habachi (2011) (Email)
  • Ulrich Berthold (2008)(Email)
  • Elodie Heslouis (2005)
  • Aymeric Larcher (2004)