Posters

2016

1. “A high performance computing web service for the analysis of local field potentials in Neurodata Without Borders-fomatted datasets." Sean Mackesey, Prabhat, Fritz Sommer. Neuroscience 2016. Poster. (accepted)

2015

1. Implementing Randomized Matrix Algorithms on Spark, Jiyan Yang, Jey Kottalam, Mohit Singh, Oliver Ruebel, Curt Fischer, Ben Bowen, Michael Mahoney, Prabhat XLDB 2015.
2. “Spark @ NERSC”, Prabhat, Michael Mahoney, Jey Kottalam, Jiyan Yang, Venkat Krishnamurthy, AMPLab Retreat 2015, Poster
3. “Data Intensive Supercomputing”, Shreyas Cholia, Prabhat, Yushu Yao, Lisa Gerhardt, Joaquin Correa, Dani Ushizima, Annette Greiner, Wahid Bhimji, Oliver Ruebel, Burlen Loring, Jeff Porter, Michael Urashka. 2nd BIDS Data Science Faire, 2015.
4.  “Supporting Experimental Neuroscience @ NERSC”, Prabhat, Kris Bouchard, Annette Greiner, Oliver Ruebel, Peter Denes, Alex Bujan, Sean Mackesey, Jesse Livezey, Jeff Teeters, Fritz Sommer, Eddie Chang. Poster. MSRI workshop on Computational Neuroscience, Berkeley, 2015.
5. “Scalable Analytic Methods for Data Driven Discovery in Neuroscience”, Kris Bouchard, Jesse Livezey, Alex Bujan, Prabhat, Sharmodeep Bhattacharyya, Fritz Sommer, Peter Denes, Eddie Chang. Poster. MSRI workshop on Computational Neuroscience, Berkeley, 2015.
6. “A high performance computing web service for local field potential analysis”, Sean Mackesey, Prabhat, Gyorgi Buzsaki, Fritz Sommer. Neuroscience 2015. Poster.
7.  “Deep Learning for Climate Pattern Detection”, AGU 2015. Poster.
8. "Scalable Bayesian Optimization using Deep Neural Networks”, Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan Adams, Invited Poster, 1st Deep Learning Symposium at NIPS 2015. 
9. "Celeste: Variational inference for a generative model of astronomical images", Jeffrey Regier,  Andrew Miller, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, Prabhat. International Conference on Machine Learning, 2015.
10. "A deep generative model for astronomical images of galaxies", Jeffrey Regier, Jon McAuliffe, Prabhat. NIPS 2015 workshop: Advances in approximate inference.

2014

1. Prabhat, Jon McAuliffe, Michael Mahoney, Ryan Adams, Aydin Buluc, Fritz Sommer, Ben Bowen, Brenton Partridge, Jeffrey Regier, Oliver Rubel, Urs Koster, Albert Wu, Jiyan Yang, Peter Nugent, David Schlegel, Craig Tull, Michael Wehner, “MANTISSA: Massive Acceleration of New Techniques in Science using Scalable Algorithms”, LBL Machine Learning workshop, Nov 2013.
2. Prabhat, Jon McAuliffe, Michael Mahoney, Ryan Adams, Aydin Buluc, Fritz Sommer, Ben Bowen, Brenton Partridge, Jeffrey Regier, Oren RIppel, Oliver Rubel, Urs Koster, Albert Wu, Jiyan Yang, Peter Nugent, David Schlegel, Craig Tull, Michael Wehner, Yushu Yao, “Scalable Statistics and Machine Learning for data-centric science”, UC Berkeley, 1st Annual Data Science Faire, Dec 2013.
3. "Celeste: Scalable variational inference for a generative model of astronomical images", Jeffrey Regier,  

   Brenton Partridge, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, Prabhat. NIPS 2014 workshop: Advanced in variational inference.

4.  

   "Celeste: Scalable variational inference for a generative model of astronomical images", Jeffrey Regier,  Brenton Partridge, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel, Prabhat. Berkeley Statistics Annual Research Symposium, 2014.