Hello! I am Yunong Lin. Second year DPhil student from China.

My current research is on exploring shared and distinct genetic architectures of multiple related traits.

I am DPhil Statistics student working on machine learning based drug development methods.

I am interested in machine learning based drug development methods. These can be used for screening and lead optimization.

Hello! I'm Lucy, a 2nd year DPhil student on the Computational Discovery CDT and working in OPIG. I'm currently working on fragment-based drug discovery. This consists of developing drugs for a given target protein beginning from fragments that are known to weakly bind to it. When elaborating on a fragment in such campaigns, information about known ligands and the protein pocket can both be leveraged to maximise the binding affinity of the end result. However, using information about known ligands has been demonstrated to bias the drug design process. In collaboration with IBM Research, I am investigating methods to extract information directly from protein pockets in a way that can be used in elaboration campaigns. Before this, I attended Durham University, where I did an integrated Masters degree in Physics.

Machine learning for drug design

I am a second year DPhil student at the SABS:R3 CDT, improving the robustness and generalisation of deep learning algorithms in early-stage drug discovery. Before that, I earned a joint degree in chemistry, biology and computer science from ETH Zurich, where I picked up a fondness for all things outdoors.

• small molecule drug discovery
• machine learning

I studied mathematics as an undergraduate, before focusing my doctoral research on population genetics – where we use probabilistic models to help us understand how population movements and selective pressures gave rise to modern-day human genetic variation.

Since then, I’ve developed a general passion for the process of discovery in biomedical science. How can we best design scientific experiments and update our beliefs, based on the resulting data, to help improve public health? Our essential common goal is to investigate and refine scientific hypotheses about the mechanisms of disease, through careful experimentation and observation, followed up by robust, reproducible analyses.

We as statisticians contribute to science by developing statistical models that probabilistically relate data to underlying mechanisms of interest. Methodological tools such as Bayesian networks and Markov chain Monte Carlo allow us to work with arbitrarily complex statistical models. We strive to design and fit models that satisfactorily represent the mechanisms through which data arise.

Modern scientific datasets are often large, highly structured, and multifaceted. They span multiple high-dimensional data types (such as genetic, molecular, clinical, image or audio data), are gathered sequentially and/or spread spatially, can be affected by selection bias and harbour missing data. Such large, multimodal, complex datasets present challenges as well as opportunities. While we are theoretically capable of modelling all data types and generating mechanisms in an all-encompassing model, high computational complexity may mean it is infeasible in practice to fit our model in reasonable time. I’m interested in statistical methods that help us perform inference in this setting, in ways that are computationally efficient yet still probabilistically coherent. Here are some example themes and applications of current focus:

Multivariate methods. Effective modelling of multivariate relationships in high dimensional data can often provide transformative insights. We are developing composable multivariate models, based on sparse factor representations, to extract information from high dimensional phenotypic measurements with missing data. 

Longitudinal data analysis. We are developing methods for multivariate longitudinal analysis of clinical trials data, whereby we can harness information both across clinical endpoints and across time points of an individual patient. We are also interested in inferring longitudinal trajectories from infrequently measured phenotypes in UK biobank data. We recently implemented a susceptible-infectious-recovered (SIR) to model changes in local Covid prevalence over time.

Composable inference. We’re interested in developing composable statistical methods that allow us to extract information from diverse datasets separately and conveniently, and then to synthesise information coherently and pragmatically (e.g., Markov melding). We have employed composable statistical inference in application areas ranging from UK biobank, randomised clinical trials, multivariate phenotyping, and Covid testing data.

Inference under model misspecification. When combining information from multiple data sources, we may want to control the influence of less reliable or poorly modelled sources (e.g., generalised Bayesian inference). I’m interested in computationally efficient ways of doing this. We used this form of inference to obtain unbiased local Covid prevalence estimates: we combined highly accurate randomised testing surveys (REACT) with precise but inaccurate symptoms-ascertained data (Test-and-trace).

I received my PhD in computer science from Columbia University in 2014. I then spent three and a half years working on statistical and population genetics as a postdoctoral fellow at the Harvard Chan School of Public Health, and at the Broad Institute of MIT and Harvard. Prior to that, I obtained a bachelor’s and a master’s degree from Rome’s Sapienza University, and a master’s degree from Columbia University, all in computer science with a focus on artificial intelligence, machine learning, and cognitive robotics.

My research is at the intersection of statistics, computer science, and genetics. I develop methods to enable new types of analyses in statistical and population genetics, with a particular interest in problems that involve modeling and inference in large datasets. Specific areas of research include studying evolutionary parameters in the human genome (natural selection, mutation/recombination rates), reconstructing past demographic events using genetic data (migration, expansion/contraction of populations), studying the heritability and genetic architecture of complex traits (nature vs nurture), and detecting disease-causing variation in the human genome.