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Ignacio Ponzoni

Referee for: I.2.6, I.2.8, I.5.3, J.3, J.6, G.2.2
Institution: Universidad Nacional del Sur
Address: Instituto de Ciencias e Ingeniería de la Computación (UNS-CONICET)
Universidad Nacional del Sur
San Andrés 800, Campus Palihue UNS
CP 8000, Bahía Blanca
Home Page: http://cs.uns.edu.ar/~ip/

Curriculum Vitae:

Ignacio Ponzoni received the B.Sc. degree in Computer Science from the Universidad Nacional del Sur, Bahía Blanca, Argentina, in 1996. He obtain a fellowship from the National Council of Scientific and Technological Research of Argentina (CONICET) in 1996 and received a Ph.D. on Computer Science from the Universidad Nacional del Sur in 2001. His Ph.D. thesis was focused in computer-aided instrumentation design of industrial processes using artificial intelligence, parallel computing and graph theory.

At the present time, he is an Associate Professor (equivalent to Reader in UK system) at the Department of Computer Science and Engineering of Universidad Nacional del Sur, and a Scientific Researcher of Instituto de Ciencias e Ingeniería de la Computación (ICIC), which is a National Research Institute of CONICET.

Since 1996 Ignacio Ponzoni has participated in several research projects as a researcher, co-head or head of different research groups. During this period he has produced about one hundred research reports in the form of journal articles, conference and workshop papers.

Ignacio Ponzoni has been actively involved with different technical events serving as chair, steering committee member, program committee member or reviewer. He has been president of the Argentinean Association on Computational Biology and Bioinformatics (A2B2C) (www.a2b2c.org) from May 2013 to May 2015, and is also a member of the International Society for Computational Biology (ISCB). Currently, his research interests focus on evolutionary computing and machine learning methods applied to systems biology and molecular informatics.

Main Research Interests:

  • Machine learning algorithms for predicting physicochemical properties for drug design
  • Metaheuristic techniques for inferring biological networks in systems biology
  • Evolutionary computing applied to gene expression analysis