The major focus in the Donnelly group is on the development and application of statistical methods for understanding genetic variation, and its association with phenotypic variation and disease susceptibility. These methods typically combine modern computationally-intensive statistical approaches with insights from population genetics models, and aim to get as much information as possible from the large datasets currently being generated by high-throughput experimental techniques.
Much current work involves genome-wide association studies, with Donnelly leading the Wellcome Trust Case Control Consortium (WTCCC), and a subsequent consortium, WTCCC2. These involve collaborations of several hundred scientists studying a range of common diseases. WTCCC was the largest study of its kind. It was responsible for the discovery of many novel genetic associations, and won several major awards and prizes. WTCCC2 will examine DNA samples from about 60,000 individuals with the goal of understanding the genetic basis of susceptibility to 15 human diseases and conditions.
Another research focus concerns human recombination. It had long been known from pedigree studies that recombination rates vary over large scales across chromosomes. More recently, experimental studies and patterns of human genetic variation suggested that most recombination occurs in small (~2kb) sequence regions called recombination hotspots. In collaboration with the McVean and Myers groups, we developed computational statistical methods and applied these to large surveys of human genetic variation to characterise over 30,000 human recombination hotspots, and to identify DNA sequence motifs associated with hotspot activity.
Experimental work in the group is currently principally focussed on natural variation in several bacterial species, and mechanisms for horizontal gene exchange and vaccine escape.
As Professor of Government and Technology in Residence at Harvard University, my mission is create and use technology to assess and solve societal, political and governance problems, and to teach others how to do the same. On focus area is the scientific study of technology's impact on humankind, and I am the Editor-in-Chief of Technology Science. Another focus area is data privacy, and I am the Director of the Data Privacy Lab at Harvard. There are other foci too. (more)
I was formerly the Chief Technology Officer, also called the Chief Technologist, at the U.S. Federal Trade Commission (FTC). It was a fantastic experience! I thank Chairwoman Ramirez for appointing me. One of my goals was to make it easier for others to work on innovative solutions at the intersection of technology, policy and business. Often, I thought of my past students, who primarily came from computer science or governance backgrounds, and who were highly motivated to change the world. I would like to see society harness their energy and get others thinking about innovative solutions to pressing problems. During my time there, I launched the summer research fellows program and blogged on Tech@FTC to facilitate explorations and ignite brainstorming on FTC-related topics.
Raia Hadsell, a senior research scientist at DeepMind, has worked on deep learning and robotics problems for over 10 years. Her early research developed the notion of manifold learning using Siamese networks, which has been used extensively for invariant feature learning. After completing a PhD with Yann LeCun, which featured a self-supervised deep learning vision system for a mobile robot, her research continued at Carnegie Mellon’s Robotics Institute and SRI International, and in early 2014 she joined DeepMind in London to study artificial general intelligence. Her current research focuses on the challenge of continual learning for AI agents and robotic systems. While deep RL algorithms are capable of attaining superhuman performance on single tasks, they cannot transfer that performance to additional tasks, especially if experienced sequentially. She has proposed neural approaches such as policy distillation, progressive nets, and elastic weight consolidation to solve the problem of catastrophic forgetting and improve transfer learning.
Max Planck Institute for Intelligent Systems
Bernhard Schölkopf's scientific interests are in machine learning and causal inference. He has applied his methods to a number of different application areas, ranging from biomedical problems to computational photography and astronomy. Bernhard has researched at AT&T Bell Labs, at GMD FIRST, Berlin, and at Microsoft Research Cambridge, UK, before becoming a Max Planck director in 2001. He is a member of the German Academy of Sciences (Leopoldina), and has received the J.K. Aggarwal Prize of the International Association for Pattern Recognition, the Max Planck Research Award (shared with S. Thrun), the Academy Prize of the Berlin-Brandenburg Academy of Sciences and Humanities, and the Royal Society Milner Award.