Archive for travel award

Lawrence D. Brown PhD Student Award

Posted in Kids, Statistics, Travel, University life with tags , , , , , , on June 3, 2019 by xi'an

[Reproduced from the IMS Bulletin, an announcement of a travel award for PhD students in celebration of my friend Larry Brown!]

Lawrence D. Brown (1940-2018), Miers Busch Professor and Professor of Statistics at The Wharton School, University of Pennsylvania, had a distinguished academic career with groundbreaking contributions to a range of fields in theoretical and applied statistics. He was an IMS Fellow, IMS Wald Lecturer, and a former IMS President. Moreover, he was an enthusiastic and dedicated mentor to many graduate students. In 2011, he was recognized for these efforts as a recipient of the Provost’s Award for Distinguished PhD Teaching and Mentoring at the University of Pennsylvania.

Brown’s firm dedication to all three pillars of academia — research, teaching and service — sets an exemplary model for generations of new statisticians. Therefore, the IMS is introducing a new award for PhD students created in his honor: the IMS Lawrence D. Brown PhD Student Award.

This annual travel award will be given to three PhD students, who will present their research at a special invited session during the IMS Annual Meeting. The submission process is now open and applications are due by July 15th, 2019 for the 2020 award. More details, including eligibility and application requirements, can be found at:

Donations are welcome as well, through under “IMS Lawrence D. Brown Ph.D. Student Award Fund”


Posted in Statistics, Travel, University life with tags , , , , , , , , , on September 2, 2014 by xi'an

[An announcement from ISBA about sponsoring young researchers at NIPS that links with my earlier post that our ABC in Montréal proposal for a workshop had been accepted and a more global feeling that we (as a society) should do more to reach towards machine-learning.]

The International Society for Bayesian Analysis (ISBA) is pleased to announce its new initiative *ISBA@NIPS*, an initiative aimed at highlighting the importance and impact of Bayesian methods in the new era of data science.

Among the first actions of this initiative, ISBA is endorsing a number of *Bayesian satellite workshops* at the Neural Information Processing Systems (NIPS) Conference, that will be held in Montréal, Québec, Canada, December 8-13, 2014.

Furthermore, a special ISBA@NIPS Travel Award will be granted to the best Bayesian invited and contributed paper(s) among all the ISBA endorsed workshops.

ISBA endorsed workshops at NIPS

  1. ABC in Montréal. This workshop will include topics on: Applications of ABC to machine learning, e.g., computer vision, other inverse problems (RL); ABC Reinforcement Learning (other inverse problems); Machine learning models of simulations, e.g., NN models of simulation responses, GPs etc.; Selection of sufficient statistics and massive dimension reduction methods; Online and post-hoc error; ABC with very expensive simulations and acceleration methods (surrogate modelling, choice of design/simulation points).
  2.  Networks: From Graphs to Rich Data. This workshop aims to bring together a diverse and cross-disciplinary set of researchers to discuss recent advances and future directions for developing new network methods in statistics and machine learning.
  3. Advances in Variational Inference. This workshop aims at highlighting recent advancements in variational methods, including new methods for scalability using stochastic gradient methods, , extensions to the streaming variational setting, improved local variational methods, inference in non-linear dynamical systems, principled regularisation in deep neural networks, and inference-based decision making in reinforcement learning, amongst others.
  4. Women in Machine Learning (WiML 2014). This is a day-long workshop that gives female faculty, research scientists, and graduate students in the machine learning community an opportunity to meet, exchange ideas and learn from each other. Under-represented minorities and undergraduates interested in machine learning research are encouraged to attend.

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