Ioannis E. Livieris received his B.Sc., M.Sc. and Ph.D. degrees in Mathematics from the University of Patras, Greece in 2006, 2008 and 2012 respectively. His research interests include numerical optimization, neural networks and its application in bioinformatics. He is a member of the ESDLab since 2008. See his personal web page.
Degrees
 2012: Ph.D. from Department of Mathematics, University of Patras.
 2008: M.Sc. in "Computational Mathematics & Informatics", Department of Mathematics, University of Patras, Greece.
 2006: Bachelor Degree in Mathematics (speciality in Computational Mathematics & Informatics), Department of Mathematics, University of Patras, Greece.
Dissertations
 Ph.D. Thesis: Nonlinear Conjugate Gradient Methods for Optimization and Neural Network Training. Supervisor: Professor P. Pintelas.
 M.Sc. Thesis: Performance Evaluation of Algorithms for Neural Network Training and Applications. Supevisor: Professor P. Pintelas.
 B.Sc. Thesis: Constraint Propagation Problems. Bachelor Thesis Supevisor: Associate Professor T.N. Grapsa.
Courses
Email :
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Phone : 2610 997833
Fax : 2610 997313

A novel validation framework to enhance deep learning models in timeseries forecasting 



An advanced deep learning model for shortterm forecasting U.S. natural gas price and movement 



Investigating the problem of cryptocurrency price prediction  A deep learning approach 



A CNNLSTM model for gold price time series forecastings 



On ensemble techniques of weightconstrained neural networks 



An advanced active set LBFGS algorithm for training constrained neural networks 



Fuzzy Information Diffusion in Twitter by Considering User's Influence 



Weightconstrained neural networks in forecasting tourist volumes: a case study 



Forecasting stock price index movement using a constrained deep neural network training algorithm 



An improved weightconstrained neural network training algorithm 



An adaptive nonmonotone active set weight constrained neural network training algorithm 



Forecasting economyrelated data utilizing constrained recurrent neural networks 



Employing constrained neural networks for forecasting new product's sales increase 



An efficient preprocessing tool for supervised sentiment analysis on Twitter data 



Predicting secondary structure for human proteins based on ChouFasman method 



A weighted voting ensemble SSL algorithm for the detection of lung abnormalities from Xrays 



Gender recognition by voice using an improved selflabeled algorithm 



A semisupervised selftrained twolevel algorithm for forecasting students' graduation time 



Predicting anxiety disorders and suicide tendency using machine learning: a review 



Improving the classification efficiency of an ANN utilizing a new training methodology 




