Junior Fellows by year

- 2016 Junior Fellows
- 2015 Junior Fellows
- 2014 Junior Fellows
- Junior Fellows Alumni

### Y-Lan Boureau

Y-Lan Boureau was appointed as a Junior Fellow in 2014 as a postdoctoral researcher at New York University. She resigned her Fellowship in 2015 after accepting a position at Facebook. She did her undergraduate studies at École Polytechnique in France and completed her Ph.D. at New York University and École Normale Supérieure, working in machine learning and computer vision.

Her doctoral work has yielded theoretical and practical insights into the extraction of mid-level visual features and the so-called pooling operations integral to most modern image recognition systems. She has also worked to clarify the picture of interactions between neuromodulators dopamine and serotonin, which play critical roles in many aspects of affective learning and behavioral shaping. Her current research focuses on self-control, and investigates neural mechanisms for rational arbitration between costs and rewards in a changing environment, by building computational models of optimal control.

### Ailsa Keating

Ailsa Keating is a postdoctoral research scientist in the department of mathematics at Columbia University. She completed her Ph.D. in the mathematics department at the Massachusetts Institute of Technology, working under the direction of Paul Seidel. Prior to coming to MIT, Keating received a B.A. and M.Math. (Part III) from the University of Cambridge.

She is interested in symplectic geometry, notably in relation to mirror symmetry, singularity theory, and low-dimensional topology. Her thesis studies structural properties of the collection of transformations of a symplectic manifold, and mirror symmetry and symplectic topology questions for a family of manifolds coming from singularity theory.

### Boris Leistedt

Boris Leistedt was appointed as a Junior Fellow in 2015 as a postdoctoral researcher at the Center for Cosmology and Particle Physics at New York University. He resigned his Fellowship in 2016 after accepting a NASA Einstein Postdoctoral Fellowship. He completed his Ph.D. with Hiranya Peiris at University College London. He received a master’s degree in physics from University Paris Sud (Orsay), as well as a joint master’s degree in electrical engineering from the University of Mons (Belgium) and Supélec (France).

Leistedt’s research is at the intersection of astrostatistics and observational cosmology: he specializes in analyzing large astronomical data sets to test pivotal questions in astrophysics and high-energy physics. During his thesis, he developed innovative methods to precisely measure the large-scale distribution of quasars. This lead to the tightest constraints on the primordial non-Gaussianity, one of the few signatures of the early universe observable in the galaxy distribution. He also showed that massive sterile neutrinos, exotic particles that could be added to the standard model, are not currently needed to explain cosmological observations. Actively involved in the Dark Energy Survey (DES), Leistedt searches for signatures of fundamental physics in the distribution of galaxies, quasars, cosmic voids and dark matter.

### Jonathan Ullman

Jonathan Ullman was appointed as a Junior Fellow in 2014 as a postdoctoral research scientist in the department of computer science at Columbia University. He resigned his Fellowship in 2015 due to receiving a faculty position. He completed his Ph.D. at Harvard University in 2013 and stayed there for a year as a postdoctoral fellow in the Center for Research on Computation and Society. While at Harvard, he had the good fortune to be advised by Prof. Salil Vadhan. Prior to coming to Harvard, he graduated from Princeton 2008 and spent a year trading stock options on the floor of the American Stock Exchange.

He works on the foundations of privacy-preserving data analysis ‹ how data analysts can (and cannot) safely perform accurate statistical analyses of data containing sensitive information about individuals ‹ and its connections with fields like cryptography, machine learning, and mechanism design. His work has shown how to obtain nearly optimal lower bounds for privacy-preserving data analysis using a cryptographic primitive called a fingerprinting code, which was originally designed for watermarking digital content. More recently, he has been working on using techniques from privacy to understand the problem of false discovery in modern interactive data analysis.

### Omri Weinstein

Omri Weinstein was appointed as Junior Fellow in 2015 as postdoctoral research scientist in the department of computer science at New York University. He resigned his fellowship after accepting a position as an assistant professor in the Theoretical Computer Science group at Columbia University. He completed his Ph.D. at Princeton University in 2015 under the supervision of Mark Braverman, and before that he earned a B.Sc. in mathematics and computer science from Tel Aviv University.

Omri’s primary research focus is communication complexity and applications of information theory to computational complexity, privacy, streaming and economics. His research in information complexity led to significant progress on some of the major open problems in communication and circuit complexity (the direct sum and product conjectures, and the KRW conjecture), and to a better understanding of the limits of parallel computation. His most recent research interest lies in the intersection between communication complexity and algorithmic game theory. His awards include the 2015 Siebel Scholarship and two graduate awards from the Simons Foundation.