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Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Research Unit 1735




  Sun, Mar 5 Mon, Mar 6 Tue, Mar 7 Wed, Mar 8 Thu, Mar 9 Fri, Mar 10


  Joel Tropp Joel Tropp Joel Tropp

Lorenzo Rosasco

Lorenzo Rosasco


Joel Tropp

Lorenzo Rosasco

Giovanni Peccati

Giovanni Peccati

Giovanni Peccati




  Participants' Introduction Group Exercises Excursion Group Exercises
16:30-18:00 Arrival Research Unit Talks Lorenzo Rosasco
(Discussion of group exercise)

Giovanni Peccati

(Discussion of group exercise)

    Poster Session      



Lecture Series

This year's lecture series will be given by




Giovanni Peccati

Luxembourg University






Lorenzo Rosasco

Massachusetts Institute of Technology

and University of Genoa



Joel A. Tropp

California Institute of Technology




Preparatory material

  • Giovanni Peccati's lectures will in parts be based on material contained in the edited volume Stochastic Analysis for Poisson Point Processes, G. Peccati and M. Reitzner (eds.), Springer, 2016.                                        A forthcoming monograph by G. Last and M. Penrose with "Lectures on the Poisson Process" also contains important material.
  • Joel Tropp's lectures will be based on a subset of the following articles:  

J.A. Tropp (2015): Convex recovery of a structured signal from independent random linear measurements. in: Sampling Theory, a Renaissance, Birkhäuser.

V. Chandrasekaran et al. (2012): Convex geometry of linear inverse problems. Foundations of Computational Mathematics 12, 805-849.

D. Amelunxen et al. (2014): Living on the edge: Phase transitions in convex programs with random data. Available at https://arxiv.org/abs/1303.6672.

M.B. McCoy and J.A. Tropp (2014): From Steiner formulas for cones to concentration of intrinsic volumes. Discrete and Computational Geometry 51, 926-963.

C. Thrampoulidis, S. Oymak, and B. Hassibi (2015): The Gaussian min-max theorem in the presence of convexity. Available at https://arxiv.org/abs/1408.4837.

S. Oymak and J.A. Tropp (2015): Universality laws for randomized dimension reduction, with applications. Available at https://arxiv.org/abs/1511.09433.

C. Thrampoulidis (2016): Recovering structured signals in high-dimensions via non-smooth convex optimization: Precise performance analysis. PhD thesis, available at http://resolver.caltech.edu/CaltechTHESIS:06032016-144604076.


Lorenzo Rosasco's notes on Regularization methods in large scale machine learning with exercises at the end.


Exercises to Giovanni Peccati's lecturees.


Joel Tropp's slides.


Poster Session

All participants are encouraged to present their work in a poster session.


Participants Short Introduction

All participants briefly introduce themselves and their research interests within about 3 minutes on Monday afternoon, supported by one or at most two slides. Please send your slide(s) by March 1 as a single .pdf-file to for1735.math@lists.uni-hamburg.de.