Rebekah Clements is a historian of Japan, specializing in the Tokugawa period (1600-1868). Her research focuses on language, society, and the characteristics of Japanese early modernity, as understood in the broader context of East Asia. She is currently working on the linguistic and religious networks of exchange that linked seventeenth and early eighteenth century Japanese elites with Chinese and Korean exiles present in Japan following the Imjin War.of 1592-1598 and the fall of the Ming Dynasty. This work takes place within her project funded by the European Research Council, "The Aftermath of the East Asian War of 1592-1598".

My research interests are in experimental soft condensed matter. We study a variety of classical many-body systems that have characteristic energy scales accessible at room temperature and that are internally characterized by mesoscopic length scales in between molecular and macroscopic dimensions. As a result, these soft materials are easily deformable by external stresses and fields, or even by thermal fluctuations, and have microscopic dynamics and structural features that can be directly imaged using optical-microscopy techniques and probed using light scattering; this enables addressing many open questions in equilibrium and non-equilibrium many-body physics. Recent research involves partially ordered fluids, or liquid crystals, colloidal crystals and glasses, and active matter. A recurring theme is the presence of defects in the order and how they sense and respond to the local geometry, the local environment and the system’s inherent activity. We are also interested in fluid mechanics and hydrodynamic instabilities, which we are studying using toroidal droplets.

My research tries to understand how we are as we are. Both answering (i) how we have developed into a massive organism formed by perfectly organized billions of cells from a single-cell zygote during embryo development, and (ii) how we have evolved from our invertebrate ancestors over the last 500 million years of evolution. To address these two interconnected questions, my lab focuses on the role that transcriptome diversification (particularly though alternative splicing and whole genome duplication) plays in vertebrate development and evolution. For that, we combine multiple approaches, from in vivo studies using animal models to high throughput computational analyses.

Ai is a medical doctor and an epidemiologist. Her main research interest concerns health determinants and how they vary by geography or country income level. In particular, mental disorders and their comorbidity with physical disorders are her primary interest. She conducts multi-country epidemiological studies examining the modifiable risk factors for a wide range of mental and physical disorders with high burden at the population level. The goal of her research is to understand why health gaps exist and to provide vital information for the establishment of effective interventions for people to live a long life in full health, and also on how health systems can be improved and disparities be eliminated. 

Dr. Montserrat research is focused in understanding the molecular mechanisms leading to organ regeneration together with the development of basic knowledge in the field of pluripotent stem cells (PSCs) for human disease modeling. Fascinated with the possibility to combine emerging technologies from the field of pluripotent stem cells (i.e., somatic reprogramming, organoids, among others) together with innovative methodologies from the bioengineering field (i.e., 3D bioprinting, organ-on-chip, among others) Montserrat team explores new scenarios of human disease modeling, with a special focus in kidney and heart related fields.

Natasa is a computer scientist working in the area of computational biology and medicine.  Her main research interests lie in designing algorithms for mining systems-level molecular and clinical data, with the ultimate goal to improve biological understanding and therapeutics.  For these purposes, Natasa designs network science and machine learning algorithms that uncover novel molecular mechanisms from large-scale data and give new insight into disease.