Download CURRICULUM VITAE DI

CURRICULUM VITAE OF
LODI STEFANO
Personal Information
Date of birth: 09/03/1963
Place of birth: Bologna, Italy
Nationality: Italian
Address: Viale del Risorgimento 2, 40136 Bologna BO Italy
Phone number: +390512093044
E-mail: [email protected]
Career Objective Summary
My goal is to exploit my knowledge of theoretical computer science and computer engineering to pursue the
following career objectives: (1) attaining a high number of publications in high-performance machine
learning and data mining and (2) teaching systems and algorithm for data science at graduate level.
Education
December 1988: Laurea Degree in Electronic Engineering – Alma Mater Studiorum – University of
Bologna
1993: PhD Degree in Electronic and Computer Engineering – Alma Mater Studiorum – University of
Bologna
Work Experience
July 1995 (start) – September 2005 (end):
Researcher – Alma Mater Studiorum – University of Bologna – Bologna, Italy
• Teacher of the course “Information Systems” (1999-2005)
• Teaching in the course Fundamentals of Informatics (2000-2001, 2002-2004)
• Teacher of the graduate course “Decision-Making Support Information Systems" (2004-2005)
• Teacher of the graduate course “Databases” (2004-2005)
• Member of the Department of Electronic Engineering, Computer Science and Systems
• Member of the Didactic Commission of the Faculty of Statistics
• Member of the Alma Mater Studiorum – University of Bologna research unit of the MIUR national PRIN
project D2I
October 2005 (start) – present:
Associate Professor – Alma Mater Studiorum – University of Bologna – Bologna, Italy
• Teacher of the course “Informatics” (2005-2008, 2015-2017)
www.bbs.unibo.it
• Teacher of the graduate course “Processes of data mining” (2009-2010)
• Teacher of the graduate course “Information Systems Design” (2005-2006)
• Teacher of the course “Information Systems” (2007-2015)
• Teacher of the graduate course “Decision-Making Support Information Systems" (2005-2017)
• Member of the Alma Mater Studiorum – University of Bologna research unit of the MIUR national PRIN
project GenData
• Member of the CINI research unit of the European project “Toreador”
Languages
Italian: Native Speaker
English: Intermediate
German: Basic
Computer Skills
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Programming in Pascal, Prolog, C/C++/CUDA, Java, SQL, R, Python, Wolfram Mathematica
Document editing in Word and LaTeX
OS knowledge: Debian Linux, Ubuntu Linux, Gentoo Linux
Research and Publications (optional)
A list of recent publications of Stefano Lodi follows, by topic.
• Support Vector Models and optimization in distributed and streaming environments
• Frandi, E., Ñanculef, R., Lodi, S., Sartori, C., & Suykens, Johan A. K. (2016). Fast
and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence
Guarantee. Machine Learning 104(2), 195-221. Berlin Heidelberg: Springer.
• Frandi, E., Ñanculef, R., Gasparo, M. G., Lodi, S., & Sartori, C. (2013).Training
Support Vector Machines Using Frank-Wolfe Optimization Methods. International
Journal of Pattern Recognition and Artificial Intelligence 27(3), 1360003. Singapore:
World Scientific.
• Ñanculef, R., Allende, H., Lodi, S., & Sartori, C. Two One-Pass Algorithms for Data
Stream Classification using Approximate MEBs. In A. Dobnikar, U. Lotrič, & B. Šter
(Eds.), Adaptive and Natural Computing Algorithms, 10th International Conference,
ICANNGA'11, Proceedings, Part II, April 14-16, 2011, volume 6594 of Lecture
Notes in Computer Science (pp. 363-372). Berlin Heidelberg: Springer.
• Frandi, E., Gasparo, M. G., Lodi, S., Ñanculef, R., & Sartori, C. (2010). A New
Algorithm for Training SVMs using Approximate Minimal Enclosing Balls. In I. Bloch
& R. M. Cesar (Eds.), Progress in Pattern Recognition, Image Analysis, Computer
Vision, and Applications, 15th Iberoamerican Congress on Pattern Recognition,
CIARP 2010, Sao Paulo, Brazil, November 8-11, 2010, volume 6419 of Lecture
Notes in Computer Science (pp. 87-95). Berlin Heidelberg: Springer. The paper
was awarded as the "Best Paper" of the conference.
• Lodi, S., Ñanculef, R., & Sartori, C. (2010). Single-Pass Distributed Learning of
Multi-class SVMs Using Core-Sets. Proceedings of the Tenth SIAM International
Conference on Data Mining, Columbus, Ohio, April 29-May 1, 2010 (pp. 257-268).
Philadelphia: SIAM.
• Lodi, S., Ñanculef, R., & Sartori, C. (2009). L2-SVM Training with Distributed Data.
www.bbs.unibo.it
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In L. Braubach, W. van der Hoek, P. Petta, & A. Pokahr (Eds.), Multiagent System
Technologies, 7th German Conference, MATES 2009, Hamburg, Germany,
September 9-11, 2009, volume 5774 of Lecture Notes in Computer Science (pp.
208-213). Berlin Heidelberg: Springer.
Parallel and Distributed Outlier Detection
• Angiulli, F., Basta, S., Lodi, S., & Sartori, C. (2016). GPU Strategies for Distancebased Outlier Detection. IEEE Transactions on Parallel and Distributed Systems
27(11), 3256-3268. Los Alamitos, CA: IEEE Computer Society.
• Lodi, S., Angiulli, F., Basta, S., Luiselli, D., Pagani, L., & Sartori, C. (2014).
Distance-based outlier approach to improve the detection of genomic loci
undergoing differentiation in worldwide human populations. Accepted for
publication in a volume of selected papers from Workshop Bringing Maths to Life
(BMTL 2014), Naples, Italy, October 27-29, 2014. Berlin Heidelberg: Springer.
• Angiulli, F., Basta, S., Lodi, S., & Sartori, C. (2014). Accelerating outlier detection
with intra- and inter-node parallelism. HPPD-DM 2014, Special Session on High
Performance Parallel and Distributed Data Mining, International Conference on
High Performance Computing & Simulation (HPCS 2014), Bologna, Italy, July 2125, 2013 (pp. 143-150).
• Angiulli, F., Basta, S., Lodi, S., & Sartori, C. (2013). Fast outlier detection using a
GPU. HPPD-DM 2013, Special Session on High Performance Parallel and
Distributed Data Mining, International Conference on High Performance Computing
& Simulation (HPCS 2013), Helsinki, Finland, July 1-5, 2013 (pp. 143-150). Los
Alamitos, CA: IEEE Computer Society.
• Angiulli, F., Basta, S., Lodi, S., & Sartori, C. (2013). Distributed Strategies for
Mining Outliers in Large Data Sets. IEEE Transactions on Knowledge and Data
Engineering 25(7), 1520-1532. Los Alamitos, CA: IEEE Computer Society.
• Angiulli, F., Basta, S., Lodi, S., & Sartori, C. (2010). A Distributed Approach to
Detect Outliers in Very Large Data Sets. In P. D'Ambra, M. Guarracino, D. Talia
(Eds.), Euro-Par 2010 - Parallel Processing, Proceedings, Part I, 16th International
Euro-Par Conference, Ischia, Italy, August 31-September 3, 2010 (pp. 329-340).
Berlin Heidelberg: Springer.
Distributed Data Clustering
• Costa da Silva, J., Klusch, M., & Lodi, S. (2016). Privacy-awareness of Distributed
Data Clustering Algorithms Revisited. In Henrik Boström, Arno Knobbe, Carlos
Soares, & Panagiotis Papapetrou (eds.), Advances in Intelligent Data Analysis XV,
October 13-15, 2016, Stockholm, volume 9897 of Lecture Notes in Computer
Science (pp. 261-272). Cham: Springer International Publishing.
• Costa da Silva, J., Klusch, M., Lodi, S., & Moro, G. (2006). Privacy-preserving
agent-based distributed data clustering. Web Intelligence and Agent Systems 4,
221-238.
• Bellavia, S., Lodi, S., & Morini, B. (2006). Inferences on Kernel Density Estimates
by Solving Nonlinear Systems. In K. A. Froeschl & W. Grossmann (Eds.),
Proceedings 18th International Conference on Scientific and Statistical Database
Management SSDBM 2006, Vienna, Austria, 3-5 July 2006 (pp. 389-397). Los
Alamitos, California: IEEE Computer Society.
• Costa da Silva, J., Klusch, M., Lodi, S., & Moro, G. (2004). Inference Attacks in
Peer-to-Peer Homogeneous Distributed Data Mining. In R. López de Mántaras & L.
Saitta (Eds.), Proceedings of the 16th Eureopean Conference on Artificial
Intelligence, ECAI 2004, including Prestigious Applicants of Intelligent Systems,
PAIS 2004, Valencia, Spain, August 22-27, 2004 (pp. 450-454). IOS Press.
• Klusch, M., Lodi, S., & Moro, G. (2003). Issues of agent-based distributed data
mining. In Proceedings of the Second International Joint Conference on
Autonomous Agents & Multiagent Systems, AAMAS 2003, July 14-18, 2003,
Melbourne, Victoria, Australia, (pp. 1034-1035). New York, NY: ACM.
www.bbs.unibo.it
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Klusch, M., Lodi, S., & Moro, G. (2003). Agent-Based Distributed Data Mining: The
KDEC Scheme. In M. Klusch, S. Bergamaschi, P. Edwards, & P. Petta (Eds.),
Intelligent Information Agents, The AgentLink Perspective, volume 2586 of Lecture
Notes in Computer Science (pp. 104-122). Berlin Heidelberg: Springer.
• Klusch, M., Lodi, S., & Moro, G. (2003). The Role of Agents in Distributed Data
Mining: Issues and Benefits. IEEE/WIC International Conference on Intelligent
Agent Technology (IAT 2003), 13-17 October 2003 (pp. 211-217). Halifax, Canada:
IEEE Computer Society.
• Klusch, M., Lodi, S., & Moro, G. (2003). Distributed clustering based on sampling
local density estimates. In Proceedings of the 19th International Joint Conference
on Artificial Intelligence (pp. 485-490). Acapulco, Mexico: AAAI Press.
Stream Data Clustering
• Lodi, S., Moro, G., & Sartori, C. (2006). Stream Clustering Based on Kernel Density
Estimation. In G. Brewka, S. Coradeschi, A. Perini, P. Traverso (Eds.), Proceedings
of the 17th Eureopean Conference on Artificial Intelligence, ECAI 2006, Including
Prestigious Applications of Intelligent Systems (PAIS 2006), Riva del Garda, Italy,
August 29 - September 1, 2006 (pp. 799-800). IOS Press.
Data management and mining in massive and self-administered networks
• Lodi, S., Monti, G., Moro, G., & Sartori, C. (2009). Peer-to-Peer Data Clustering in
Self-Organizing Sensor Networks. In A. Cuzzocrea (Ed.), Intelligent Techniques for
Warehousing and Mining Sensor Network Data (pp. 179-212). Hershey, PA:
Information Science Reference.
Awards and Honors (optional)
“A New Algorithm for Training SVMs using Approximate Minimal Enclosing Balls.” was awarded as the
"Best Paper" of the conference at CIARP 2010
I authorize the treatment of my personal data according to D.Lgs 196/2003 e s.m.i.
www.bbs.unibo.it