Slides: Annual Paris-Saclay Center for Data Science Pitching Day

Here is an interesting initiative: Data Science for Science. During a discussion, Balazs reminded me of the very interesting Annual CDS Pitching Day that occured last month. It was a "meeting... to officially launch the Paris-Saclay Center for Data Science 2.0." The CDS ".. gather(s) data providers and data analysts around the common theme of Data Science. There will be five introductory talks presenting the tools and platforms built by the CDS, and 13 talks given by members of the CDS, covering a wide spectrum of topics on both domain sciences and data science. The aim is to draw an overall picture about the demand for CDS tools and expertise in Saclay and to help PIs building their projects.  "

 

9 novembre 2016

• 08:30 - 09:30 Welcome coffee
• 09:30 - 10:45 CDS tools and platforms

  Responsable:

  Dr. Sarah COHEN-BOULAKIA (Laboratoire de Recherche en Informatique)

  • 09:30 Introduction 15'

    Intervenant:	Balázs Kégl (LAL)
    Documents:	Slides

  • 09:45 RAMPs and Data Challenges 15'

    Intervenant:	Balázs Kégl (LAL)
    Documents:	Slides

  • 10:00 The linked data platform 15'

    Intervenant:	Mrs. Karima Rafes (LRI)
    Documents:	Slides more information

  • 10:15 The open software initiative 15'

    Intervenant:	Alexandre Gramfort (Telecom ParisTech, CNRS)
    Documents:	Slides

  • 10:30 Ultrawalls and interactive visualization 15'

    Intervenant:	Emmanuel Pietriga (INRIA)
    Documents:	Slides summary

• 10:45 - 11:15 Coffee break
• 11:15 - 13:05 Life sciences

  Responsable:

  Alexandre Gramfort (Telecom ParisTech, CNRS)

  • 11:15 Apprentissage automatique pour une aide au codage PMSI 22'

    Intervenant:	Namik Taright (APHP)
    Documents:	Slides summary

  • 11:37 Managing and analyzing analytical chemistry data sets 22'

    Intervenant:	Ali Tfayli (LIPSYS / UPSud)
    Documents:	Slides summary

  • 11:59 Secure cytotoxic administration by non-invasive spectroscopy 22'

    Intervenant:	Laetitia Le (LIPSYS / UPSud)
    Documents:	Slides summary

  • 12:21 Efficiently Ranking big biological and biomedical data sets using rank aggregation techniques 22'

    Intervenant:	Dr. Sarah COHEN-BOULAKIA (Laboratoire de Recherche en Informatique)
    Documents:	Slides summary

  • 12:43 Reconstructing the past: deep learning for population genetics 22'

    Intervenant:	Guillaume Charpiat (TAO Team/ INRIA)
    Documents:	Transparents summary

• 13:05 - 14:05 Lunch
• 14:05 - 15:35 Tools and social sciences

  Responsable:

  Dr. Gael Varoquaux (INRIA)

  • 14:05 Causal discovery for CDS practitioners 22'

    Intervenant:	Ms. Isabelle Guyon (UPSud / INRIA / ChaLearn)
    Documents:	Slides summary

  • 14:27 Multi-level data fusion 22'

    Intervenant:	Dr. Kaouthar Benameur (ONERA)
    Documents:	Slides summary

  • 14:49 Global scientific positioning system for CDS 22'

    Intervenant:	Philippe Caillou (LRI / UPSud)
    Documents:	Slides summary

  • 15:11 Heterogeneous social data management and mining 22'

    Intervenant:	Nacéra Bennacer (LRI)
    Documents:	Paper Slides summary

• 15:35 - 16:05 Coffee break
• 16:05 - 17:35 Physical sciences

  Responsable:

  Ms. Isabelle Guyon (UPSud / INRIA / ChaLearn)

  • 16:05 A platform for decoding and recoding turbulence 22'

    Intervenant:	Dr. Bérengère Podvin (CNRS)
    Documents:	Slides summary

  • 16:27 A framework for sensor data management and analysis 22'

    Intervenant:	Karine Zeitouni (DAVID / UVSQ)
    Documents:	Slides summary

  • 16:49 Generic stereoscopic tools for planetary topography 22'

    Intervenant:	Mr. Frédéric Schmidt (GEOPS / UPSud)
    Documents:	Slides summary

  • 17:11 Tracking machine learning challenge 22'

    Intervenant:	Dr. David Rousseau (ATLAS)
    Documents:	Transparents summary

 
 
 
 
 

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Job: Data Scientist Position, Paris-Saclay Center for Data Science (CDS), France

Balazs mentioned this to me a while ago, here is a position of Data Scientist in his group:

 

Data Scientist Position

Paris-Saclay Center for Data Science (CDS)
The Paris-Saclay CDS for has opened data science positions to reinforce the data science ecosystem and to build and support a data science platform. The data scientist will work on (in order of priority):

• Data-science support:  The CDS has many collaborations between scientists and engineers across scientific disciplines. The candidate will work on a small number of data-science projects with different scientific partners. The candidate should be interested in a variety of scientific applications of data processing and able to understand an application, to communicate with scientists of a different culture, and to deploy data-processing solutions for their needs. Current projects include: 
  
  • Galaxy/star classification for preparing the LSST data processing pipeline.
    
  • Particle tracking for the ATLAS/LHC upgrade.
    
  • Segmenting and classifying Solar wind time series data.
    
  • Classifying hospital stays (PMSI coding) with APHP.
    
  • Laser spectrometry to improve the safety of intravenous drug administration.
    
  • Improving search in large biological and biomedical data sets.
    
  • Analyzing sensor data for tracking pollution and its health effects (Polluscope).
    
  • Classifying crowdsourced ecology data (Spipoll).
    
• Software engineering: The objective to enable researchers to do better science thanks to better software tools. It means bringing state of the art data science research software into high quality toolboxes (as scikit-learn). Getting involved with open source development. Assisting data scientists (students, postdoctoral fellows, permanent researchers) to develop their software engineering skills and to get them involved with open source development.
  
• Training:  Accompany domain scientists in their data analysis efforts. Accompany data scientists in their methodological research. Designing training sprints and practical material for the courses (cf. based on software carpentry)
  

Team

• The data scientist will work with scikit-learn core developers such as Gaël Varoquaux, Olivier Grisel, Loic Estève, Alexandre Gramfort and others. Possible collaboration with data and domain science researchers such as Balazs Kegl, Isabelle Guyon, Sarah Cohen-Boulakia, Karine Zeitouni, David Rousseau, and others.
  

Qualifications

• M.S. / Ph.D. in Computer Science, Statistical Machine Learning

  
• Good understanding of the data science workflow. Experience with data challenges is a plus.
  
• Strong programming experience with one or more data science languages (Python, R, Matlab)
  
• Experience with open source development
 (desired but not required).
  

The applicant should send a CV, a statement of purpose, and up to three letters of recommendations to cdsupsay@gmail.com.

Position is open now and will be open until it is filled.

Description:
Duration: 18 months
Gross salary per month in €: 2500-2817

 
 
 
 

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Internship: Signal and image classification with invariant descriptors (scattering transforms), IFPEN Rueil Malmaison, France

Laurent posted an internship position on his site, let me relay it here:

 

Here is a data science internship position at IFP Energies nouvelles (5 months, Spring/Summer 2017), in the field of :

• [English] Signal and image classification with invariant descriptors (scattering transforms)
• [Français] Classification de signaux et d’images par descripteurs invariants (scattering transforms)

Application and additional details

http://www.laurent-duval.eu/lcd-2017-intern-invariant-descriptor.html

Description

The field of complex data analysis (data science) is interested in the extraction of suitable indicators used for dimension reduction, data comparison or classification. Initially based on application-dependent, physics-based descriptors or features, novel methods employ more generic and potentially multiscale descriptors, that can be used for machine learning or classification. Examples are to be found in SIFT-like (scale-invariant feature transform) techniques (ORB, SURF), in unsupervised or deep learning. The present internship focuses on the framework of scattering transform (S. Mallat et al.) and the associated classification techniques. It yields signal, image or graph representations with invariance properties relative to data-modifying transformations: translation, rotation, scale… Its performances have been well-studied for classical data (audio signals, image databases, handwritten digit recognition).

This internship aims at dealing with lesser studied data: identification of the closest match to a template image inside a database of underground models, extraction of suitable fingerprints from 1D spectrometric signals from complex chemical compounds for macroscopic chemometric property learning. 

The stake in the first case resides in the different scale and nature of template and model images, the latter being sketch- or cartoon-like versions of the templates. In the second case, signals are composed of a superposition of hundreds of (positive) peaks. Their nature differs from standard information processed by scattering transforms. A focus on one of the proposed applications can be considered, depending on success or difficulties met. 

References

ENS Ulm, Scattering transforms

 
 

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Job: Data scientist: deep learning approaches to microbial genomics, Institut Pasteur, Paris

David Bikard posted the following job at the intersection of two explosive fields: 

The Synthetic Biology group is looking for a data scientist to develop deep learning approaches applied to bacterial genomics data. In particular the candidate will work in close collaboration with biologists to analyse high-throughput data generated using CRISPR tools. Applicants should ideally be trained in statistics, machine learning or computer science. No previous knowledge in Biology is required, but you do need to be curious and motivated to learn. Masters, PhDs, Engineers and Postdocs are all welcome to apply. Funding is guaranteed through an ERC starting grant. Applications should be send to david.bikard@pasteur.fr 

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Job: Postdoc Position in Machine Learning for Health Sensing, MIT

Dina just sent me the following:

Hi Igor,

I hope all is well. I was wondering whether it is possible to list a postdoc opening on your blog. We have a large MIT project the involves medical applications, and we are looking for postdocs with some experience in Machine Learning and/or related fields.  Any help in reaching out to suitable candidates will be highly appreciated. I included the announcement below. 

Thanks a lot and happy Holidays.  -Dina

Sure Dina, here it is:



--

Job Announcement -- Postdoc Position in Machine Learning for Health Sensing

This postdoc position involves working with MIT faculty, MIT students, and medical doctors to develop ML algorithms and software systems to analyze new sensor data and infer disease progression and medication efficacy.  It will build on our prior work on a technology for extracting health metrics by analyzing how human bodies interact with the surrounding wireless signals. The new research will focus on how such new data type can be used in medical applications. In particular, the postdoc will lead a sub-project that deploys this sensor with patients with a particular condition (e.g., Parkinson's), and in collaboration with a medical team and MIT researchers, develop ML algorithms and systems to extract disease markers from the collected data. The research is interdisciplinary; the research outcomes will span ML conferences (ICML, NIPS), wireless system conferences (MobiCom, Sensys), and medical journals.

The position is for a minimum of one year that can be extended to two years. The candidate should have completed (or nearly completed) a PhD in Computer Science, with a focus on machine learning or related fields such as computer vision, speech, or data mining. In particular, expertise in deep neural networks and transfer learning is highly desirable. The candidate should also have expertise in programming and software systems. No medical background is required. Also no background in wireless signals is required. Interested candidates should email Professor Katabi at dk@mit.edu. Please include the word "postdoc2017" in the email title.  

 

 

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Linearly Convergent Randomized Iterative Methods for Computing the Pseudoinverse -implementation -

Following up on Robert's thesis, here is his latest RandNLA preprint: Linearly Convergent Randomized Iterative Methods for Computing the Pseudoinverse by Robert M. Gower, Peter Richtárik

We develop the first stochastic incremental method for calculating the Moore-Penrose pseudoinverse of a real rectangular matrix. By leveraging three alternative characterizations of pseudoinverse matrices, we design three methods for calculating the pseudoinverse: two general purpose methods and one specialized to symmetric matrices. The two general purpose methods are proven to converge linearly to the pseudoinverse of any given matrix. For calculating the pseudoinverse of full rank matrices we present additional two specialized methods which enjoy faster convergence rate than the general purpose methods. We also indicate how to develop randomized methods for calculating approximate range space projections, a much needed tool in inexact Newton type methods or quadratic solvers when linear constraints are present. Finally, we present numerical experiments of our general purpose methods for calculating pseudoinverses and show that our methods greatly outperform the Newton-Schulz method on large dimensional matrices.

 an implementation is on Github.
 
 

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Thesis: Sketch and Project: Randomized Iterative Methods for Linear Systems and Inverting Matrices - implementation -

Congratulation Dr. Gower !

On top of his thesis, Robert has a few implementations connected to the thesis, here is an excerpt:

InvRand - download, paper

Inverse Random is suite of randomized methods for inverting positive definite matrices implemented in MATLAB

RandomLinearLab - download, paper

Random Linear Lab is a lab for testing and comparing randomized methods for solving linear systems all implemented in MATLAB

Here is the thesis, with the arxiv abstract, the real abstract and the lay person's abstract !
Sketch and Project: Randomized Iterative Methods for Linear Systems and Inverting Matrices by Robert M. Gower

Probabilistic ideas and tools have recently begun to permeate into several fields where they had traditionally not played a major role, including fields such as numerical linear algebra and optimization. One of the key ways in which these ideas influence these fields is via the development and analysis of randomized algorithms for solving standard and new problems of these fields. Such methods are typically easier to analyze, and often lead to faster and/or more scalable and versatile methods in practice.
This thesis explores the design and analysis of new randomized iterative methods for solving linear systems and inverting matrices. The methods are based on a novel sketch-and-project framework. By sketching we mean, to start with a difficult problem and then randomly generate a simple problem that contains all the solutions of the original problem. After sketching the problem, we calculate the next iterate by projecting our current iterate onto the solution space of the sketched problem.

 Here is the real abstract of the thesis:

Probabilistic ideas and tools have recently begun to permeate into several fields where they had traditionally not played a major role, including fields such as numerical linear algebra and optimization. One of the key ways in which these ideas influence these fields is via the development and analysis of randomized algorithms for solving standard and new problems of these fields. Such methods are typically easier to analyze, and often lead to faster and/or more scalable and versatile methods in practice.
This thesis explores the design and analysis of new randomized iterative methods for solving linear systems and inverting matrices. The methods are based on a novel sketch-and-project framework. By sketching we mean, to start with a difficult problem and then randomly generate a simple problem that contains all the solutions of the original problem. After sketching the problem, we calculate the next iterate by projecting our current iterate onto the solution space of the sketched problem.
The starting point for this thesis is the development of an archetype randomized method for solving linear systems. Our method has six different but equivalent interpretations: sketch-and-project, constrain-and-approximate, random intersect, random linear solve, random update and random fixed point. By varying its two parameters – a positive definite matrix (defining geometry), and a random matrix (sampled in an i.i.d. fashion in each iteration) – we recover a comprehensive array of well known algorithms as special cases, including the randomized Kaczmarz method, randomized Newton method, randomized coordinate descent method and random Gaussian pursuit. We also naturally obtain variants of all these methods using blocks and importance sampling. However, our method allows for a much wider selection of these two parameters, which leads to a number of new specific methods. We prove exponential convergence of the expected norm of the error in a single theorem, from which existing complexity results for known variants can be obtained. However, we also give an exact formula for the evolution of the expected iterates, which allows us to give lower bounds on the convergence rate.
We then extend our problem to that of finding the projection of given vector onto the solution space of a linear system. For this we develop a new randomized iterative algorithm: stochastic dual ascent (SDA). The method is dual in nature, and iteratively solves the dual of the projection problem. The dual problem is a non-strongly concave quadratic maximization problem without constraints. In each iteration of SDA, a dual variable is updated by a carefully chosen point in a subspace spanned by the columns of a random matrix drawn independently from a fixed distribution. The distribution plays the role of a parameter of the method. Our complexity results hold for a wide family of distributions of random matrices, which opens the possibility to fine-tune the stochasticity of the method to particular applications. We prove that primal iterates associated with the dual process converge to the projection exponentially fast in expectation, and give a formula and an insightful lower bound for the convergence rate.
We also prove that the same rate applies to dual function values, primal function values and the duality gap. Unlike traditional iterative methods, SDA converges under virtually no additional assumptions on the system (e.g., rank, diagonal dominance) beyond consistency. In fact, our lower bound improves as the rank of the system matrix drops. By mapping our dual algorithm to a primal process, we uncover that the SDA method is the dual method with respect to the sketch-and-project method from the previous chapter. Thus our new more general convergence results for SDA carry over to the sketch-and-project method and all its specializations (randomized Kaczmarz, randomized coordinate descent...etc). When our method specializes to a known algorithm, we either recover the best known rates, or improve upon them. Finally, we show that the framework can be applied to the distributed average consensus problem to obtain an array of new algorithms. The randomized gossip algorithm arises as a special case.
In the final chapter, we extend our method for solving linear system to inverting matrices, and develop a family of methods with specialized variants that maintain symmetry or positive definiteness of the iterates. All the methods in the family converge globally and exponentially, with explicit rates. In special cases, we obtain stochastic block variants of several quasi-Newton updates, including bad Broyden (BB), good Broyden (GB), Powell-symmetric-Broyden (PSB), Davidon-Fletcher-Powell (DFP) and Broyden-Fletcher-Goldfarb-Shanno (BFGS). Ours are the first stochastic versions of these updates shown to converge to an inverse of a fixed matrix.
Through a dual viewpoint we uncover a fundamental link between quasi-Newton updates and approximate inverse preconditioning. Further, we develop an adaptive variant of the randomized block BFGS (AdaRBFGS), where we modify the distribution underlying the stochasticity of the method throughout the iterative process to achieve faster convergence. By inverting several matrices from varied applications, we demonstrate that AdaRBFGS is highly competitive when compared to the well established Newton-Schulz and approximate preconditioning methods. In particular, on large-scale problems our method outperforms the standard methods by orders of magnitude. The development of efficient methods for estimating the inverse of very large matrices is a much needed tool for preconditioning and variable metric methods in the big data era.

 There is even a Lay Summary

Lay Summary
This thesis explores the design and analysis of methods (algorithms) for solving two common problems: solving linear systems of equations and inverting matrices. Many engineering and quantitative tasks require the solution of one of these two problems. In particular, the need to solve linear systems of equations is ubiquitous in essentially all quantitative areas of human endeavour, including industry and science. Specifically, linear systems are a central problem in numerical linear algebra, and play an important role in computer science, mathematical computing, optimization, signal processing, engineering, numerical analysis, computer vision, machine learning, and many other fields. This thesis proposes new methods for solving large dimensional linear systems and inverting large matrices that use tools and ideas from probability.
The advent of large dimensional linear systems of equations, based on big data sets, poses a challenge. On these large linear systems, the traditional methods for solving linear systems can take an exorbitant amount of time. To address this issue we propose a new class of randomized methods that are capable of quickly obtaining approximate solutions. This thesis lays the foundational work of this new class of randomized methods for solving linear systems and inverting matrices. The main contributions are providing a framework to design and analyze new and existing methods for solving linear systems. In particular, our framework unites many existing methods. For inverting matrices we also provide a framework for designing and analysing methods, but moreover, using this framework we design a highly competitive method for computing an approximate inverse of truly large scale positive definite matrices. Our new method often outperforms previously known methods by several orders of magnitude on large scale matrices 

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Saturday Morning Videos: Learning, Algorithm Design and Beyond Worst-Case Analysis workshop, Simons Institute, Berkeley

 

All the videos of the Learning, Algorithm Design and Beyond Worst-Case Analysis workshop at the Simons Institute at Berkeley are available by following the links of each talks.

Learning, Algorithm Design and Beyond Worst-Case Analysis

Nov. 14 – Nov. 18, 2016

• Click on the titles of individual talks for abstract, slides and archived video (available approximately one week after the conclusion of the workshop).

Monday, November 14th, 2016

9:00 am – 9:20 am		Coffee and Check-In
9:20 am – 9:30 am		Opening Remarks
9:30 am – 10:10 am		A Brief Intro to Analysis Beyond the Worst Case Avrim Blum, Carnegie Mellon University
10:10 am – 10:50 am		Learning Probabilistic Models for Graph Partitioning in the Presence of Noise Aravindan Vijayaraghavan, Northwestern University
10:50 am – 11:20 am		Break
11:20 am – 12:00 pm		Clustering Under Perturbation Resilience Colin White, Carnegie Mellon University
12:00 pm – 2:00 pm		Lunch
2:00 pm – 2:40 pm		On the Effect of Randomness on Planted 3-Coloring Models Uriel Feige, Weizmann Institute
2:40 pm – 3:20 pm		Analyzing Algorithms on Real World Data Cliff Stein, Columbia University
3:20 pm – 3:50 pm		Break
3:50 pm – 4:30 pm		Applied Mixed Integer Programming: Beyond 'The Optimum' Pawel Lichocki, Google
4:40 pm – 5:00 pm		Impromptu Talks Session
5:00 pm – 6:00 pm		Reception

Tuesday, November 15th, 2016

9:00 am – 9:30 am		Coffee and Check-In
9:30 am – 10:10 am		Distribution-Specific Analysis of Nearest Neighbor Search and Classification Sanjoy Dasgupta, UC San Diego
10:10 am – 10:50 am		Spectral Approaches to Nearest Neighbor Search Robert Krauthgamer, Weizmann Institute
10:50 am – 11:20 am		Break
11:20 am – 12:00 pm		Timing Matters: Online Dynamics in Broadcast Games Shuchi Chawla, University of Wisconsin - Madison
12:00 pm – 2:00 pm		Lunch
2:00 pm – 2:40 pm		A Theoretical Approach to Semantic Coding and Hashing Sanjeev Arora, Princeton University
2:40 pm – 3:20 pm		Automatic Resource Bound Analysis and Linear Optimization Jan Hoffmann, Carnegie Mellon University
3:20 pm – 3:50 pm		Break
3:50 pm – 4:30 pm		Beyond Big-O: Statistical Analysis of Performance Scaling Holger Hoos, University of British Columbia
4:40 pm – 5:00 pm		Impromptu Talks Session

Wednesday, November 16th, 2016

9:00 am – 9:30 am		Coffee and Check-In
9:30 am – 10:10 am		Self-Improving Algorithms for Sorting and Geometric Problems Ken Clarkson, IBM Almaden
10:10 am – 10:50 am		A PAC Approach to Application-Specific Algorithm Selection Rishi Gupta, Stanford University
10:50 am – 11:20 am		Break
11:20 am – 12:00 pm		Learning the Best Agorithm for Max-Cut, Clustering, and Other​ ​Partitioning Problems Vaishnavh Nagarajan, Carnegie Mellon University
12:00 pm – 12:40 pm		Learning as a Tool for Algorithm Design and Beyond-Worst-Case Analysis Kevin Leyton-Brown, University of British Columbia
12:40 pm – 2:00 pm		Lunch
2:00 pm – 3:20 pm		Breakout Groups
3:20 pm – 4:00 pm		The Computational Benefit of Correlated Instances Shafi Goldwasser, Massachusetts Institute of Technology
4:00 pm – 5:00 pm		Discussion

Thursday, November 17th, 2016

9:00 am – 9:30 am		Coffee and Check-In
9:30 am – 10:10 am		Recovery Guarantee of Non-Negative Matrix Factorization via Alternating Updates Yingyu Liang, Princeton University
10:10 am – 10:50 am		Characterizing the Typical Case Complexity of Formal Verification and Synthesis Sanjit Seshia, UC Berkeley
10:50 am – 11:20 am		Break
11:20 am – 12:00 pm		Beyond Worst Case: When Complex Feedback Can Improve the Label Complexity of Active Learning Kamalika Chaudhuri, UC San Diego
12:00 pm – 2:00 pm		Lunch
2:00 pm – 2:40 pm		Exploiting Myopic Prediction Models in Reinforcement Learning Craig Boutilier, Google and University of Toronto
2:40 pm – 3:20 pm		Follow the Leader with Dropout Perturbations Manfred Warmuth, UC Santa Cruz
3:20 pm – 3:50 pm		Break
3:50 pm – 4:30 pm		When Does Clustering Become Easy, and Should We Care About Other Cases? Shai Ben-David, University of Waterloo
4:30 pm – 5:10 pm		Sketching and Randomization for Distributed Submodular and Coverage Optimization Vahab Mirrokni, Google
5:10 pm – 5:30 pm		Impromptu Talks Session

Friday, November 18th, 2016

9:00 am – 9:30 am		Coffee and Check-In
9:30 am – 10:10 am		Automated Scientific Discovery Using Insights from Problem Structure Bart Selman, Cornell University
10:10 am – 10:50 am		How Hard Is Inference for Structured Prediction? Tim Roughgarden, Stanford University
10:50 am – 11:20 am		Break
11:20 am – 12:00 pm		Monotone Estimation Framework and its Applications for Scalable Analytics of Large Data Sets Edith Cohen, Google Research
12:00 pm – 2:00 pm		Lunch
2:00 pm – 2:40 pm		Above Average-Case Analysis? Ankur Moitra, Massachusetts Institute of Technology
2:40 pm – 3:20 pm		When Existing Techniques in Linear Regression Preserve Differential Privacy Or Sheffet, University of Alberta
3:20 pm – 3:50 pm		Break
3:50 pm – 4:30 pm		The Power of Predictions in Online Optimization Adam Wierman, California Institute of Technology
4:40 pm – 5:00 pm		Impromptu Talks Session

 

 
 
 
 

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