Murray Wiki - User contributions [en]

Lillian Ratliff, 1-2 Feb 2016

2016-02-01T17:59:11Z

Dvij: /* Schedule */

Lillian Ratfliff will be visiting Caltech on 1-2 Feb 2016. Please sign up below if you would like to meet with her.

=== Schedule ===

1 Feb 2016 (Mon)
* Noon: arrive at BUR. Travel to Pasadena (Caltech car)
* 1 pm: Yisong (lunch) - meet at the Ath
* 2 pm: Pietro Perona, 104A Moore
* 2:45 pm: open
* 3:30 pm: open
* 4:00 pm: Seminar - Munzer Dahleh, 105 Annenberg
* 5 pm: done for the day
* 6:30 pm: meet in the lobby of the Ath for dinner with Nik Matni, Vanessa Jonsson, Dj Krishanmurth (location TBD)

2 Feb 2016 (Tue)
* 7:45 am: breakfast with Richard (Ath)
* 8:45 am: open
* 9:30 am: Dj, Annenberg 218
* 10:15 am: Adam W, 215 Annenberg
* 11:00 am: Nikolai M
* 11:45 am: seminar setup
* 12:00 pm: seminar, 104 ANB
* 1 pm: lunch with Richard (Chandler)
* 1:45 pm: Vanessa Jonsson (330 Braun)
* 2:30 pm: Joel Tropp, 307 Annenberg
* 3:15 pm: Niangjun Chen
* 4:00 pm: Ivan Papusha, 231 Annenberg
* 4:45 pm: open
* 5:30 pm: done for the day
* 6:15 pm: depart from Ath for BUR (Caltech car)

=== Seminar: 2 Feb (Wed), 12-1 pm, 105 ANB ===

TITLE:
The Emerging Data Market: Adaptive Incentives for Smart, Connected Infrastructure

ABSTRACT:
The next generation urban ecosystem empowered by the internet of things has at its core a shared economy where physical resources and data are easily aggregated and exchanged. In particular, advances in technology have lead to the proliferation of smart devices that provide access to streaming data and platforms for novel sharing mechanisms. This has, in turn, resulted in an emerging marketplace in which data is a commodity. At the same time, many urban constituents are increasingly becoming aware of the value of their data and its usefulness for operations. In such an environment, new learning and optimization schemes which consider users as strategic data sources and resource seekers are needed. In this talk, we will discuss the emerging data market, its incentive structure (players and their motivations), and tools for learning with strategic data sources. Focusing on the design of adaptive incentive mechanisms under adverse selection, we will construct an algorithm for online utility learning and incentive design and show convergence results for both the case where players are rational (play according to Nash) and myopic. We will see through a tutorial example how the algorithm performs, and conclude with some open questions and future directions.

Lillian Ratliff, 1-2 Feb 2016

2016-01-28T21:55:52Z

Dvij: /* Schedule */

Lillian Ratfliff will be visiting Caltech on 1-2 Feb 2016. Please sign up below if you would like to meet with her.

=== Schedule ===

1 Feb 2016 (Mon)
* Noon: arrive at BUR. Travel to Pasadena (Caltech car)
* 1 pm: Yisong (and John Doyle?) (lunch)
* 2 pm: Pietro Perona
* 2:45 pm: open
* 3:30 pm: open
* 4:00 pm: Seminar - Munzer Dahleh
* 5 pm: done for the day
* 6 pm: optional dinner with graduate students and postdocs

2 Feb 2016 (Tue)
* 7:45 am: breakfast with Richard (Ath)
* 8:45 am: open
* 9:30 am: Dj
* 10:15 am: Adam W
* 11:00 am: Nikolai M
* 11:45 am: seminar setup
* 12:00 pm: seminar, 104 ANB
* 1 pm: lunch with Richard (Chandler)
* 1:45 pm: open
* 2:30 pm: Joel Tropp
* 3:15 pm: open
* 4:00 pm: open
* 4:45 pm: open
* 5:30 pm: done for the day
* 6:15 pm: depart from Ath for BUR (Caltech car)

=== Seminar: 2 Feb (Wed), 12-1 pm, 105 ANB ===

TITLE:
The Emerging Data Market: Adaptive Incentives for Smart, Connected Infrastructure

ABSTRACT:
The next generation urban ecosystem empowered by the internet of things has at its core a shared economy where physical resources and data are easily aggregated and exchanged. In particular, advances in technology have lead to the proliferation of smart devices that provide access to streaming data and platforms for novel sharing mechanisms. This has, in turn, resulted in an emerging marketplace in which data is a commodity. At the same time, many urban constituents are increasingly becoming aware of the value of their data and its usefulness for operations. In such an environment, new learning and optimization schemes which consider users as strategic data sources and resource seekers are needed. In this talk, we will discuss the emerging data market, its incentive structure (players and their motivations), and tools for learning with strategic data sources. Focusing on the design of adaptive incentive mechanisms under adverse selection, we will construct an algorithm for online utility learning and incentive design and show convergence results for both the case where players are rational (play according to Nash) and myopic. We will see through a tutorial example how the algorithm performs, and conclude with some open questions and future directions.

Lillian Ratliff, 1-2 Feb 2016

2016-01-28T21:44:08Z

Dvij: /* Schedule */

Lillian Ratfliff will be visiting Caltech on 1-2 Feb 2016. Please sign up below if you would like to meet with her.

=== Schedule ===

1 Feb 2016 (Mon)
* Noon: arrive at BUR. Travel to Pasadena (Caltech car)
* 1 pm: Yisong (and John Doyle?) (lunch)
* 2 pm: Pietro Perona
* 2:45 pm: open
* 3:30 pm: open
* 4:15 pm: Dj, ANB 218
* 5 pm: done for the day
* 6 pm: optional dinner with graduate students and postdocs

2 Feb 2016 (Tue)
* 7:45 am: breakfast with Richard (Ath)
* 8:45 am: open
* 9:30 am: open
* 10:15 am: Adam W
* 11:00 am: Nikolai M
* 11:45 am: seminar setup
* 12:00 pm: seminar, 104 ANB
* 1 pm: lunch with Richard (Chandler)
* 1:45 pm: open
* 2:30 pm: Joel Tropp
* 3:15 pm: open
* 4:00 pm: open
* 4:45 pm: open
* 5:30 pm: done for the day
* 6:15 pm: depart from Ath for BUR (Caltech car)

=== Seminar: 2 Feb (Wed), 12-1 pm, 105 ANB ===

TITLE:
The Emerging Data Market: Adaptive Incentives for Smart, Connected Infrastructure

ABSTRACT:
The next generation urban ecosystem empowered by the internet of things has at its core a shared economy where physical resources and data are easily aggregated and exchanged. In particular, advances in technology have lead to the proliferation of smart devices that provide access to streaming data and platforms for novel sharing mechanisms. This has, in turn, resulted in an emerging marketplace in which data is a commodity. At the same time, many urban constituents are increasingly becoming aware of the value of their data and its usefulness for operations. In such an environment, new learning and optimization schemes which consider users as strategic data sources and resource seekers are needed. In this talk, we will discuss the emerging data market, its incentive structure (players and their motivations), and tools for learning with strategic data sources. Focusing on the design of adaptive incentive mechanisms under adverse selection, we will construct an algorithm for online utility learning and incentive design and show convergence results for both the case where players are rational (play according to Nash) and myopic. We will see through a tutorial example how the algorithm performs, and conclude with some open questions and future directions.

Soheil Feizi, Oct 2015

2015-10-18T17:57:26Z

Dvij: /* Schedule */

* Visitor: Soheil Feizi (MIT)
* Date: October 19th (Mon)
=== Schedule ===
* 9:15 am - Yutaka Hori (218 ANB; ex 3552)
* 10:15 am - Enrique Mallada (ANB 338)
* 11 am - Vipul Singhal (Keck 139)
* 11:45 am - seminar set up
* 12-1:15 pm - seminar (121 ANB)
* 1:15 pm - Dj (ANB 218)
* 2pm - Michelle Effros (Moore 250)
* 3 pm - Enoch Yeung (Keck 139)
* 4 pm - Richard Murray (Steele 109)
* 4:30 pm - Anandh Swaminathan (Steele 103)
* 5:15 pm - done

=== Seminar ===
Title: Learning (from) networks: fundamental limits, algorithms, and applications

Abstract: Network models provide a unifying framework for understanding dependencies among variables in medical, biological, and other sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging.

In this talk, we illustrate the use of spectral, combinatorial, and statistical inference techniques in several significant network science problems. First, we consider the problem of network alignment where the goal is to find a bijective mapping between nodes of two networks to maximize their overlapping edges while minimizing mismatches. To solve this combinatorial problem, we present a new scalable spectral algorithm, and establish its efficiency theoretically and experimentally over several synthetic and real networks. Next, we introduce network maximal correlation (NMC) as an essential measure to capture nonlinear associations in networks. We characterize NMC using geometric properties of Hilbert spaces and illustrate its application in learning network topology when variables have unknown nonlinear dependencies. Finally, we discuss the problem of learning low dimensional structures (such as clusters) in large networks, where we introduce logistic Random Dot Product Graphs, a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a spectral network clustering algorithm that possesses robust performance under different clustering setups. In all of these problems, we examine underlying fundamental limits and present efficient algorithms for solving them. We also highlight applications of the proposed algorithms to data-driven problems such as functional and regulatory genomics of human diseases, and cancer.

Sonja Glavaski, Oct 2015

2015-10-11T14:22:02Z

Dvij:

Sonja Glavaski will be visiting CDS on 13 Oct 2015 (Tue) and giving an IST lunch bunch talk. If you would like to meet with her, edit this page and put your time into an open slot.

* 11:45 am - seminar setup
* 12:00 pm - IST lunch bunch seminar
* 1:00 pm - lunch with Richard (1-on-1)
* 2:00 pm - meeting with CDS students, 243 Annenberg
* 3:00 pm - Adam Wierman and Steven Low, 215 Annenberg
* 3:45 pm - open (hold for Karina Montilla)
* 4:15 pm - Dj, 218 Annenberg
* 4:45 pm - open
* 5:15 pm - open