About
Update:
This workshop will run from Monday, October 21 to Thursday, October 24. There will be no Friday session. All talks will take place in Sibley Auditorium, Bechtel Engineering Center, UC Berkeley.
Recent years have seen dramatic changes in the architectures underlying both large-scale and small-scale data analysis environments. For example, distributed data centers consisting of clusters of a large number of commodity machines, so-called cloud-computing platforms, and parallel multi-core architectures are all increasingly common. This, coupled with the computations that are often of interest in large-scale analytics applications, presents fundamental challenges to the way we think about efficient and meaningful computation in the era of large-scale data. For example, when data are stored in a distributed manner, computation is often relatively inexpensive, and communication, i.e., actually moving the data, is often the most precious computational resource. Another example is the observation that suboptimal solutions to large-scale optimization problems often lead to better behavior in downstream applications than optimal solutions. This workshop will address the state-of-the-art as well as novel future directions in parallel and distributed algorithms for large-scale data analysis applications. In addition to focusing on algorithmic questions, e.g., whether and how particular computations can be parallelized, the workshop will take a coordinated approach to exploring the many ties between large-scale learning and distributed optimization.
Enquiries may be sent to the organizers at this address.
Chairs/Organizers
Invited Participants
Alekh Agarwal (Microsoft Research New York), Deepak Agarwal (LinkedIn), Haim Avron (IBM T.J. Watson Research Center), Nina Balcan (Georgia Institute of Technology), Grey Ballard (Sandia National Laboratories), Leonid Barenboim (Ben-Gurion University of the Negev), Ivona Bezáková (Rochester Institute of Technology), Peter Bickel (UC Berkeley), Guy Blelloch (Carnegie Mellon University), Josh Bloom (UC Berkeley), Sebastien Bubeck (Princeton University), Aydin Buluç (Lawrence Berkeley National Laboratory), Amit Chakrabarti (Dartmouth College), Xi Chen (Carnegie Mellon University), Jim Demmel (UC Berkeley), Petros Drineas (Rensselaer Polytechnic Institute), Noureddine El Karoui (UC Berkeley), Michael Friedlander (University of British Columbia), Rainer Gemulla (Max Planck Institute, Saarbrücken), John Gilbert (UC Santa Barbara), David Gleich (Purdue University), Joseph Gonzalez (UC Berkeley), Michael Goodrich (UC Irvine), Alex Gray (Georgia Institute of Technology), Moritz Hardt (IBM Almaden), Martin Jaggi (École Polytechnique), Michael Jordan (UC Berkeley), Sagar Kale (Dartmouth College), Ravi Kannan (Microsoft Research India), Valerie King (University of Victoria), Mladen Kolar (Carnegie Mellon University), Jakub Konečný (University of Edinburgh), Tim Kraska (Brown University), John Langford (Microsoft Research New York), Jian Li (Tsinghua University), Yi Li (University of Michigan), Han Liu (Princeton University), Michael Mahoney (Stanford University), Andrew McGregor (University of Massachusetts), Michael Mitzenmacher (Harvard University), Muthu Muthukrishnan (Microsoft Research India), Angelia Nedich (University of Illinois, Urbana-Champaign), Jennifer Neville (Purdue University), Rob Nowak (University of Wisconsin-Madison), Sang-Yun Oh (Stanford University), Cynthia Phillips (Sandia National Libraries), Ali Pinar (Sandia National Laboratories), Ely Porat (Bar-Ilan University), Eric Price (Massachusetts Institute of Technology), Michael Rabbat (McGill University), Chris Ré (Stanford University), Ben Recht (UC Berkeley), Peter Richtarik (University of Edinburgh), Ed Rothberg (Gurobi Optimization), Richard Samworth (University of Cambridge), Leonard Schulman (California Institute of Technology), Devavrat Shah (Massachusetts Institute of Technology), Shai Shalev-Shwartz (Hebrew University of Jerusalem), Or Sheffet (Carnegie Mellon University), Harsha Vardhan Simhadri (Carnegie Mellon University), Nikhil Srivastava (Microsoft Research India), Daniel Štefankovič (University of Rochester), Mario Szegedy (Rutgers University), Kanat Tangwongsan (IBM), Justin Thaler (Harvard University), Sivan Toledo (Tel Aviv University), Joel Tropp (California Institute of Technology), David Tse (UC Berkeley), Caroline Uhler (IST Austria), Sergei Vassilvitskii (Google), Suresh Venkatasubramanian (University of Utah), Martin Wainwright (UC Berkeley), Ermin Wei (Massachusetts Institute of Technology), David Woodruff (IBM Almaden), Bin Yu (UC Berkeley), Tong Zhang (Rutgers University).