# Seminars Spring 2011

We have two Seminar Series: Brown Bag Seminars and RUTCOR Colloquia.

Brown Bag Seminars are usually held on Thursdays, 12:00-1:00 PM, in
the RUTCOR lounge. Pizza is served but feel free to bring your own
lunch. The Brown Bag Seminar Series usually hosts speakers from the
RUTCOR community. Unless otherwise noted, all the brown bag seminars
listed below will be held at 12:00 Noon in the RUTCOR lounge.

RUTCOR Colloquia are usually held on Thursdays, 1:30-2:30 PM, in
the Seminar Room or Room 139. The RUTCOR Colloquia Series features
speakers from outside the RUTCOR community. Unless otherwise noted, all
the RUTCOR colloquia listed below will be held at 1:30 PM in Room 139
in the RUTCOR building.

### 03/03/11 RUTCOR Colloquium - 3:00PM
(Note change of time)

Speaker:Elad Cohen

Affiliation:University of Haifa

Title: Intersection Graphs of Paths on a Grid

Abstract: We investigate the class of vertex
intersection graphs of paths on a
grid, and specifically consider the
subclasses that are obtained when each path in the representation
has
at most k bends (turns). We call
such a subclass the Bk-VPG graphs, k ≥
0. In chip manufacturing, circuit layout is modeled
as paths (wires) on
a grid, where it is natural to constrain the number
of bends per wire
for reasons of feasibility and to
reduce the cost of the chip.

This is joint work with Andrei Asinowski, Martin
Charles Golumbic,Vincent
Limouzy, Marina Lipshteyn, and Michal Stern.

### 03/24/11 RUTCOR Colloquium - 1:30PM

Speaker:Dr. Giovanni Felici

Affiliation:Istituto di Analisi
dei Sistemi ed Informatica

Consiglio
Nazionale
delle
Ricerche

Title: Integer and Logic Programming for Knowledge Extraction
from Large Data
Sets

Abstract: Knowledge Extraction and Data Mining techniques are used
to
identify relevant patterns and models in large amount of data. Several
methods have been developed and successfully applied for a vast range
of applications. In this talk we focus on techniques that make use of
Integer and Logic Programming to formulate and solve two central and
strongly related problems: Feature Selection and Supervised Learning.

The method proposed for Feature Selection is based
on integer
formulations that represent the retained information using linear
constraints associated with the observed data. Several variants of such
model will be discussed; we analyze some formal properties of such
variants, compare them with other methods proposed in the literature
and describe an effective randomized heuristic algorithm that has been
developed and successfully applied for the solution of large scale
feature selection problems.

The Supervised Learning method is based on the
extraction of separating
logic formulas from training data and converts the learning problem
into a sequence of instances of a Minimum Cost Satisfiability problem
(MinSat), a well studied and hard optimization problem. These two
methods, when combined, are capable of extracting very synthetic
information with a high semantic value.

We conclude the talk discussing several additional
characteristics of
the proposed approach, and then describing their applications to two
problems that arise in the analysis of DNA and genomic sequences:
Micorarray Analysis, for which we show results on a recent study on
neurodegeneration of transgenic mice, and Barcode Analysis, a recent
project supported by the Barcode of Life Consortium funded by the Sloan
Foundation, that aims at classifying the living species through a small
fragment of mitochondrial DNA.

### 03/31/11 RUTCOR Colloquium - 1:30PM

Speaker:Dr. Lisa Hellerstein

Affiliation:Brooklyn Polytechnic University

Title: Min-Cost and Max-Throughput Boolean Function Evaluation

Abstract: We consider the problem of evaluating a fixed Boolean
function
f(x1,...,xn) on a given input vector (x1,...,xn), when evaluating each
variable xi incurs a cost (or takes time) ci, and each variable xi is
equal to 1 with probability pi.

Boolean function evaluation problems arise in a
number

of fields, including databases (parallel filter ordering), artificial
intelligence (satisficing search), and VLSI testing. We survey selected
results on both Min-cost and Max-throughput versions of the Boolean
Function Evaluation problem.

### 04/14/11 RUTCOR BrownBag - 12:00 Noon

Speaker: Dr. Michael Katehakis

Affiliation: MSIS, Rutgers School of Business

http://www.rci.rutgers.edu/~mnk/

Title: NON-PARAMETRIC UP-AND-DOWN EXPERIMENTATION
REVISITED

Abstract: Let Y(x) be a random variable such that

P(Y(x) = 1) = F(x) and
P(Y(x) = 0) = 1- F(x)

where F(x) is a distribution function. It is sometimes of interest to
estimate a given $\alpha-$ quantile of F(x) with observations
distributed like Y(x) where the choice of x is under control.

Two approaches for obtaining solutions
to this problem are the Robbins-Monro stochastic approximation and
Derman’s non-parametric up-and-down procedure.

In recent work on the non-parametric
up-and-down procedure we have modified the method by establishing a
stopping criterion with predetermined confidence probability of correct
detection. Further we have established worst case performance bounds
and convergence rates.

In this talk we also discuss
applications in inventory control under unknown demand distribution,
tailored testing, drug dosage determination.

This is joint work with Cyrus Derman,
Columbia University