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Structure Department of Information Engineering
Telephone 0498277946
Qualification Professore ordinario
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Curriculum Vitae
<b>Education and Employment</b>

Fabio Vandin received the Ph.D. (2010) in Information Engineering from the University of Padova. From 2010 to 2013 he has been a researcher at the Department of Computer Science and at the Center for Computational Molecular Biology of Brown University (USA), with various titles (Postdoctoral Researcher, Assistant Professor of Research). From January 2014 to June 2015 he has been an Assistant Professor at the Department of Mathematics and Computer Science of the University of Southern Denmark. Since June 2015, he is an Associate Professor at the Department of Information Engineering of the University of Padova. From January 2016 to May 2016 he has been a Research Fellow at the Simons Institute for the Theory of Computing at the University of California, Berkeley (USA).

<b>Awards and Honors</b>
In 2013, he has been awarded the Best Paper Award at the 17th International Conference on Computational Molecular Biology for his work on an efficient and practical approximation algorithm to properly assess the significance of patterns associated with survival time in large cancer datasets.In 2011, he has been awarded the ``Sergio Gambi’’ Best Ph.D. thesis award from the University of Padova for his thesis ``Mining of Significant Patterns: Theory and Practice”.

<b>Scientific Boards</b>
Fabio Vandin is a member of the editorial board of the Journal of Graph Algorithms and Application since 2016. He is also a member of the editorial board of BMC Bioinformatics, since 2016, and of Systems Medicine, since 2017. Since 2017, he has been a member of the Scientific Advisory Board of the Bertinoro International Center for Informatics (BiCi).

<b>Keynote Lectures</b>

Fabio Vandin has been invited to give a plenary talk at the Algorithmic for Biology session of the Computability in Europe international conference (Finland, 2017) and a plenary invited talk at the Complex Networks international workshop (Italy, 2016), both on algorithms to uncover significant structures in networks. He has been a Distinguished Speaker at the Edmond J. Safra Center of Tel-Aviv University (Israel). He is regularly invited to give talks at international research institutions, including University of California Berkeley (USA), ETH (Switzerland), KTH (Sweden), Simon Fraser University (Canada), IFOM-IEO (Milan, Italy), NTU (Singapore), NUS (Singapore), University of California Davis (USA), IT University of Copenhagen (Denmark), Pennsylvania State University (USA), NII Shonan Meeting Center (Japan), University of Minnesota (USA), Worcester Polytechnic Institute (USA), Institute for Pure and Applied Mathematics of University of California at Los Angeles (USA). He has also given different seminars within PhD schools at various institutions, including the University of Pisa (Italy) and IFOM-IEO (Milan, Italy).

Lecturer's Curriculum (PDF): 7566CF3223DF7F5262200CD219E045C0.pdf

Research areas
His research interests are in efficient and rigorous algorithms for the extraction of useful information from large amounts of data, and he has an established expertise on algorithmic and statistical methods for data mining and machine learning. He has applied his methods mostly to biomedical applications within computational biology, but his work has found application in a variety of areas, including social network analysis and wireless networks. Within computation biology, he has developed the first method to reliably identify significantly mutated subnetworks from large amounts of cancer mutation data. He has also developed efficient algorithms for the extraction of statistically significant and meaningful combinatorial patterns of mutations in cancer, and other algorithms for cancer genomics. The algorithms he has developed have been used to extract novel biological insights from large amounts of data generated by many large cancer sequencing studies.

His research results have been published in more than 50 papers appearing in international journals and conference proceedings. He has co-authored papers in leading computer science venues (e.g., J. ACM, PODS, VLDB) as well as bioinformatic venues (e.g., Nature Genetics, Genome Research). He has contributed several algorithms for the analysis of networks of mutations in cancer, with contributions in high impact biological journals (e.g., Cell, Nature, NEJM).

<b>Research Funding</b>

During the years, Fabio Vandin has been an investigator and co-investigator of several projects on the development of efficient algorithms for large datasets, funded by the National Science Foundation (NSF), while in the USA, and by the University of Padova while in Italy.

<b>Pubblicazioni Principali degli Ultimi 10 Anni</b>

Adam Kirsch, Michael Mitzenmacher, Andrea Pietracaprina, Geppino Pucci, Eli Upfal, Fabio Vandin (2012). An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets. JOURNAL OF THE ASSOCIATION FOR COMPUTING MACHINERY, vol. 59, p. 1-22, ISSN: 0004-5411, doi: 10.1145/2220357.2220359

Matteo Ceccarello, Carlo Fantozzi, Andrea Pietracaprina, Geppino Pucci, Fabio Vandin (2017). Clustering Uncertain Graphs. PROCEEDINGS OF THE VLDB ENDOWMENT, ISSN: 2150-8097, doi: 10.1145/3164135.3164143

Riondato Matteo, Vandin Fabio (2014). Finding the True Frequent Itemsets. In: SIAM International Conference on Data Mining 2014, SDM 2014. vol. 1, p. 497-505, Society for Industrial and Applied Mathematics Publications, ISBN: 9781510811515, usa, 2014, doi: 10.1137/1.9781611973440.57

PIETRACAPRINA ANDREA, RIONDATO M, UPFAL E, VANDIN F (2010). Mining top-K frequent itemsets through progressive sampling. DATA MINING AND KNOWLEDGE DISCOVERY, vol. 21, p. 310-326, ISSN: 1384-5810, doi: 10.1007/s10618-010-0185-7

Anagnostopoulos Aris, Kumar Ravi, Mahdian Mohammad, Upfal Eli, Vandin Fabio (2012). Algorithms on evolving graphs. In: ITCS 2012 - Innovations in Theoretical Computer Science Conference. p. 149-160, ISBN: 9781450311151, Cambridge, MA, usa, 2012, doi: 10.1145/2090236.2090249.

VANDIN, FABIO, Upfal, Eli, Raphael, Benjamin J. (2012). De novo discovery of mutated driver pathways in cancer. GENOME RESEARCH, vol. 22, p. 375-385, ISSN: 1088-9051, doi: 10.1101/gr.120477.111

Leiserson Mark D.M.*, Vandin Fabio*, Wu Hsin-Ta, Dobson Jason R., Eldridge Jonathan V., Thomas Jacob L., Papoutsaki Alexandra, Kim Younhun, Niu Beifang, Mclellan Michael, Lawrence Michael S., Gonzalez-Perez Abel, Tamborero David, Cheng Yuwei, Ryslik Gregory A., Lopez-Bigas Nuria, Getz Gad, Ding Li, Raphael Benjamin J. (2015). Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. NATURE GENETICS, vol. 47, p. 106-114, ISSN: 1061-4036, doi: 10.1038/ng.3168 - Articolo in rivista

VANDIN, FABIO, Upfal, Eli, Raphael, Benjamin J. (2011). Algorithms for detecting significantly mutated pathways in cancer. JOURNAL OF COMPUTATIONAL BIOLOGY, vol. 18, p. 507-522, ISSN: 1066-5277, doi: 10.1089/cmb.2010.0265

Vandin Fabio, Papoutsaki Alexandra, Raphael Benjamin J., Upfal Eli (2015). Accurate Computation of Survival Statistics in Genome-Wide Studies. PLOS COMPUTATIONAL BIOLOGY, vol. 11, e1004071, ISSN: 1553-734X, doi: 10.1371/journal.pcbi.1004071

Raphael Benjamin J., Dobson Jason R., Oesper Layla, Vandin Fabio (2014). Identifying driver mutations in sequenced cancer genomes: Computational approaches to enable precision medicine. GENOME MEDICINE, vol. 6, 5, ISSN: 1756-994X, doi: 10.1186/gm524

VANDIN, FABIO, Upfal, Eli, Raphael, Benjamin J. (2012). Algorithms and genome sequencing: Identifying driver pathways in cancer. COMPUTER, vol. 45, p. 39-46, ISSN: 0018-9162, doi: 10.1109/MC.2012.71

VANDIN, FABIO, Upfal, Eli, Raphael, Benjamin J. (2012). Finding driver pathways in cancer: Models and algorithms. ALGORITHMS FOR MOLECULAR BIOLOGY, vol. 7, 23, ISSN: 1748-7188, doi: 10.1186/1748-7188-7-23

List of taught course units in A.Y. 2019/20
Degree course code (?) Degree course track Course unit code Course unit name Credits Year Period Lang. Teacher in charge
IN0507 COMMON IN04111234 9 2nd Year (2019/20) First
IN0521 COMMON INP9087775 6 1st Year (2019/20) First