Valentino Crespi
Assoc. Professor
CSULA
College of Engineering,
Computer Science and Technology (ECST)
5151 State University Drive
Los Angeles, CA 900328150
Office: ETA318
Phone: (323) 3434596
Fax: (323) 3436672


Education
 1992: Laurea in Scienze dell'Informazione (summa cum Laude),
University of Milan, Milan, Italy.
 1997: Ph.D. in Computer Science, Universities of Milan and Turin, Italy.
Experience
 199596: International
Computer Science Institute (ICSI), Berkeley, CA
Visiting Graduate Student
 199697: Istituto di Matematica
Computazionale (now IITCNR), Pisa, Italy
Visiting Graduate Student
 19982000: Eastern Mediterranean
University (EMU), Famagusta, Cyprus
Assistant Professor
 20002003: Thayer
School of Engineering  Dartmouth College, Hanover, NH
Research Faculty
 2003present: CSULA, Los Angeles, CA
Associate Professor (since 2009)
Research
 Combinatorial Optimization, Matrix Computation and Graph Theory
Involved in the study of the computational complexity of the Permanent
function of sparse circulant matrices and of the Lovász theta
function of sparse circular graphs (19942004).
Established fast algorithms and exact formulas published on Linear
Algebra and its Applications and on the SIAM Journal on Discrete
Mathematics.
 Distributed Control, Surveillance, Sensor Networks and UAVs
In charge of
the DARPA TASK
research project (20002003), developed at Dartmouth College.
Main contributions: development of a novel design methodology for
provably performant distributed control systems. Applications to
problems of multiagent UAV navigation, distributed sensor
registration (in a sensor network it is the problem for all the
individual sensors to establish their geographic position), and
tracking with networks of minimalistic sensors.
Results published in the Journal of Autonomous Robots and in the
proceedings of the World congress on Artificial Intelligence, of the
International Joint Conference on Neural Networks, of the KIMAS
conference, of the AAMAS conference, of the ACM SenSys conference, and
of several SPIE conferences.
 MultiTarget Tracking, Process Query Systems and Stochastic Modeling
Among the initiators of
the DHS PQS
research project (20032008), developed at Dartmouth College and at the
Institute for Security
Technology Studies, Hanover, NH.
Contributions: development of a Process Query System, a novel
revolutionary software system capable of accepting process
descriptions as queries and then performing standing queries and
searches against databases and data streams for evidence that
instances of the queried processes exist in the data. This technology
was successfully applied to build one of the best intrusiondetection
systems for coordinated computer attacks.
Results on surveillance applications published in SPIE
conferences. Established results on estimating the entropy rate of
Hidden Markov models published on IEEE Transactions on Information Theory.
 Trackability, Complexification and Machine Learning of Hidden Markov Models (HMMs)
Principal Investigator of the AFOSR Engineering Awareness
research project (20072009), developed at Dartmouth College and at Pasadena, CA.
Goal: establish fundamental scientific results that allow to
monitor environments effectively.
Contributions: a) developed a rigorous quantitative notion of
trackability of processes/behaviors which allows to determine the
"complexity" of estimating state trajectories of a target process
based on a discretetime sequence of noisy "observations"; b)
developed a novel algorithm to machine learn HMMs from observed data
based on the nonnegative matrix factorization (NMF) of higher order
Markovian statistics, structurally different from the classical
BaumWelch and associated approaches; c) developed a technique to
attack and defend covert channels through machine learning certain
behavioral models.
Results on Trackability Theory published on ACM Transactions on Sensor
Networks (special edition, 2008), results on machine learning HMMs
using the NMF published on IEEE Transactions on Information Theory
(June, 2011). See the most recent work
on Attacking and Defending
Covert Channels and Behavioral Models.
See also special topics classes
on machine learning languages and
processes offered at CSULA.
 The Modeling Component and the CEaSCREST center
Current Leader of the Modeling Component of
the NSF Center for Energy
and Sustainability (CEaSCREST).
Recent Talks
Recent Students
 Gail Casburn, M.S., distributed algorithms for sensor registration in
3D, defended in 2011.
 Natalya Shatokhina, M.S., parameter optimization for SOLiD next
generation sequencers, defended in 2011.
 Sanmit Narvekar (undergraduate), classification problems in
computer security and social networks.
 David Gilbert, B.S., machine learning Hidden Markov Models (supported by
CEaSCREST).
BINF
 Multidisciplinary Minor in Bioinformatics and Computational Biology
at CSULA
CoPI of the
NIH Center for
Interdisciplinary Quantitative Analysis (CINQA) and, together
with Dr. Jamil
Momand,
author of BINF.
Designer
of BINF
403 "Process Estimation & Detection in Cellular Biology"
(prerequisites: CS 202, BINF 400, one of the listed Statistics
course).
 Announcements for the Students
12/2011: All students interested in BINF need to
complete the following prerequisites by the end of Spring quarter
prior to taking BINF 400 in Fall 2012: Biol 100A, Biol
100B. Furthermore at least one of the following courses in
statistics is required by the end of Fall 2012: MATH 270, MATH 474,
ECON 209 and BIOL 300. Remember that Biol 100A is offered in Winter
2012 and is a prerequisite for Biol 100B, whereas the statistics
classes, if offered, may be taken also concurrently with BINF 400 in
Fall 2012.
