Outline of the programme

All events will take place in Lecture hall F211 (address: Unioninkatu 38), unless otherwise stated.

Sunday, August 7
Monday, August 8
  • 9:00-17:00: Technical sessions (F211)
  • 18:00-20:00: Reception offered by the Rector of the University of Helsinki
    Location: University Main Building, (Room "Lehtisali"), Unioninkatu 34
Tuesday, August 9
  • 9:00-17:00: Technical sessions (F211)
  • 18:15: Sauna and Banquet Dinner at Restaurant Uunisaari.
    Location: Uunisaari Island, (address: Merisatamanranta/Kompassitori)
Wednesday, August 10
  • 9:00-12:00: Technical sessions (F211)

Technical Programme

Sunday, August 7

16:00-16:30 Opening of the Workshop

16:30-17:30 Plenary Talk: Mati Wax

17:30-20:00 Cocktails

Monday, August 8

9:00-10:00 Plenary Talk: Neri Merhav

10:00-10:25 Coffee Break

10:25-12:05 Session 1
  • Vincent Poor: Adaptive Sampling for Sparse Recovery
  • Andrew Barron: Sparse Superposition Codes of Low Complexity and Exponentially Small Error Probability at all Rates below Capacity for the Gaussian Channel
  • Garvesh Raskutti: Minimax Optimal Rates for Sparse Additive Models for Kernel Classes
  • Marcelo Weinberger: Deinterleaving Markov Processes via Penalized Maximum-Likelihood
12:05-14:05 Lunch

14:05-15:20 Session 2
15:20-15:45 Coffee Break

15:45-17:00 Session 3
18:00-20:00 Rector's Reception

Tuesday, August 9

09:00-10:00 Plenary Talk: Veronica Gonzalez-Lopez and Jesus Garcia

10:00-10:25 Coffee Break

10:25-12:05 Session 4 12:05-14:05 Lunch Break

14:05-15:20 Session 5
15:20-15:45 Coffee Break

15:45-17:00 Session 6
18:15-22:00 Sauna and Banquet Dinner

Wednesday, August 10

09:00-10:20 Session 7
10:20-10:45 Coffee Break

10:45-12:00 Session 8
12:00-14:00 Lunch

Plenary speakers

Mati Wax (Wavion Wireless Networks): Position Location by Multipath Fingerprinting
  • Abstract: Position location of a wireless source is a well known and old problem, with both military and commercial applications. Many techniques have been proposed and applied to this problem over the last 70 years. All these techniques are based on the assumption that the wireless signal travels from the source to the receiving antennas along the line-of-sight path connecting them. Unfortunately, in urban canyons and in indoors venues such as warehouses, hospitals, production floors, malls, etc., this is usually never the case. In these cases the propagation from the wireless source to the receiving antennas is usually made through reflections from buildings and walls, referred to as multipath, which may be very different from the line-of-sight path. As a result, the applicability of the classical position location techniques to such scenarios is void.

    This talk presents a new method for position location, specifically tailored to heavy multipath scenarios, which exploits the multipath to its advantage, rather than suffers from it. The basic underlying principle of this method is that in heavy multipath scenarios there is a one-to-one correspondence between the multipath "fingerprint", as captured by an array of receiving antennas, and the location of the source. Based on this premise, the position location problem is casted as a pattern matching problem: the multipath "fingerprint" of the source to be located is extracted, compared to a data base of "fingerprints", which are pre-collected from the target area, and the best match is selected as the source location. We will present various theoretical, computational and practical aspects of the method and discuss its pros and cons.

  • Time: 16:30-17:30, Sunday, August 7
  • Location: Lecture hall F211, Unioninkatu 38
Neri Merhav (Technion): Random Coding and Statistical Physics
  • Abstract: Following a brief introduction of basic background in statistical physics, we will describe relationships and analogies between certain models of spin glasses, in particular - the random energy model (REM), and the behavior of certain ensembles of codes for communication systems. Beyond the purely theoretical aspects of these relations, we will also demonstrate how analysis techniques, rooted in the statistical mechanics of the REM, can be harnessed to obtain sharper and more accurate evaluations of the ensemble performance of these codes. Time permits, we will also point out several extensions of the basic model.
  • Time: 9:00-10:00, Monday, August 8
  • Location: Lecture hall F211, Unioninkatu 38
Antonio Galves (University of Sao Paulo)
Veronica Gonzalez-Lopez and Jesus Garcia (State University of Campinas): Minimal Markov Models
  • Abstract: In this work we introduce a new and richer class of finite order Markov chain models and address the following model selection problem, find the Markov model with the minimal set of parameters (called here minimal Markov model) which is enough to represent a source as a Markov chain of finite order. Let us call M the order of the chain and A the finite alphabet, to determine the minimal Markov model, we define an equivalence relation on the state space AM, such that all the sequences of size M with the same transition probabilities are put in the same part. In this way we have one set of (|A|-1) transition probabilities for each part, obtaining a model with a minimal number of parameters. We show that the model can be selected consistently using the Bayesian information criterion. The application of our model is exemplified in two DNA data sets and on simulated data.
  • Time: 9:00-10:00, Tuesday, August 9
  • Location: Lecture hall F211, Unioninkatu 38