BoazLernerPic2010.JPG

Boaz Lerner

Associate Professor

Dept. of Industrial Engineering & Management
Ben-Gurion University
Beer-Sheva 84105
Israel

+972 8 6479 375 (Office)
+972 8 6472 958 (Fax)

boaz@bgu.ac.il

Boaz Lerner is an Associate Professor at the Department of Industrial Engineering & Management (IEM) of Ben-Gurion University, Israel. He is the head of the M.Sc. program in Data Science at IEM. His main areas of research lie in machine learning, statistical pattern recognition, and data mining approaches to data analysis with applications to 'real-world' problems. Current research focuses on:

Brief Biography

Publications

Software

Professional Activities

 Machine Learning and Data Mining Lab

Projects

Currently funded:

·         Robotics in Rehabilitation, ABC Robotics Initiative

·         A System for Computerized Analysis, Stratification, Prediction, and Monitoring of ALS Disease Progression, Israel Innovation Authority (A talk given in NeuroSense Therapeutics Conference, Jan 21, 2018)

·         Phenomics – Precision Agriculture, Israel Innovation Authority

Past Projects:

·         Metro 450 – Sampling optimization for X-ray metrology, funded by the Chief Scientist of the Ministry of Economy and Industry

·         Learning and mining patterns using stochastic models, funded by the Ministry of Science and Technology

·         Identification of factors that account for young drivers’ crash involvement and involvement prediction using machine learning, funded by the National Authority of Road Safety

·         Identification of factors that account for young drivers’ crash involvement, involvement prediction, and evaluation of the impact of Or Yarok kit on the involvement using machine learning, funded by Ran Naor Foundation for the Advancement of Road Safety Research

·         Others in: medical diagnosis (Parkinson, Alzheimer); genetic abnormality and pre-natal diagnosis (chromosome, FISH); radar target classification; prediction of road accident severity; invoice reading; detonator detection; ferromagnetic target detection; cycle-time predication; reticle inspection; failure early detection; foreign object debris (FOD) detection; electronic warfare

Students of my Machine Learning & Data Mining Lab

·         Oded Zinman (M.Sc.) – Identification of social function land use in urban areas

·         Dan Halbersberg (Ph.D.) – Learning temporal latent variable models

·         Aviv Nahon (M.Sc.) – Algorithms for learning temporal models for understanding ALS

·         Alon Shpigler (M.Sc.) – A generative model for regularization and analysis of deep neural networks activity (with Bar Hillel)

·         Yael Konforti (M.Sc.) – Probabilistic interpretation and visualization of deep neural network (with Bar Hillel)

·         Shir Kashi (M.Sc.) – Robotics in rehabilitation (with Levy-Tzedek and Rokach)

·         Hila Avisar (M.Sc.) – Parkinson’s disease early diagnosis

·         Ben Hadad (M.Sc.) – Analysis of disease spreading patterns for ALS progression prediction using an assumption-free machine learning

·         Shon Mendelson (M.Sc.) – Concept drift in machine learning – Detection and relearning

·         Yaniv Malowany (M.Sc.) – Towards free-assumption learning latent variable models

Past students

·         Iddo Salton (2004) (M.Sc.)

Classification of imbalanced data using neural networks

·         Yaniv Gurwicz (2004) (M.Sc.)

Classification using Bayesian multinets

·         Ra'anan Yehezkel (2004) (M.Sc.)

Bayesian network structure learning using recursive autonomy identification

·         Roy Malka (2005) (M.Sc.)

Bayesian network classifiers

·         Boaz Vigdor (2005) (M.Sc.)

Pattern recognition using a probability-driven fuzzy ARTMAP classifier

·         Lior Konis (2006) (M.Sc.)

Radar target classification using dynamic Bayesian networks

·         Lev Koushnir (2007) (M.Sc.)

A unified methodology for image analysis and classification of dot and non-dot-like fluorescence in situ hybridization signals

·         David Bechor (2007) (M.Sc.)

Studying and comparing initial starting points of the K-means clustering algorithm

·         Yair Meidan (2008) (M.Sc.)

Identifying and quantifying factors affecting waiting time and its prediction in manufacturing fabs using machine learning (with G. Rabinowitz)

·         Tal Alumot (2009) (M.Sc.)

On sensitivity to parameters of methods of Bayesian network structure learning, parameter estimation and combination of learning methods

·         Roy Kelner (2010) (M.Sc.)

Learning Bayesian network classifiers by risk minimization

·         Tali Alterman (2011) (M.Sc.)

Machine learning for explanation and prediction of accident severity: Learning strategies in imbalanced problems

·         Noam Cohen (2011) (M.Sc.)

On the impact of missing data on machine learning algorithms and sensitivity reduction to missing data by dynamic allocation of neighbors (with A. Even)

·         Naama Simchon (2011) (M.Sc.)

Selective constraint-based structure learning for Bayesian networks

·         Elad Ben Akoune (2011) (M.Sc.)

A robust value difference metric for feature-oriented imputation (with A. Even)

·         Rafi Bojmel (2011) (M.Sc.)

Automatic threshold selection for Bayesian network structure learning algorithms

·         Maydan Wienreb (2011) (M.Sc.)

Analysis and Prediction of FAB's Work in Process Using Machine Learning: Trading Between Accuracy and Information in Classification Problem (with G. Rabinwitz)

·         Michal Caspi (2011) (M.Sc)

Adaptive thresholding in learning the structure of a Bayesian network

·         Hanna Belyavin (2012) (M.Sc.)

Learning the structure of a Bayesian network using multiple test corrections

·         Idit Bernstein (2012) (M.Sc.)

Improving NBC using structure extension by ensemble selection and instance cloning

·         Dan Halbersberg (2013) (M.Sc.)

Scoring a structure in learning a Bayesian network classifier

·         Asaf Cohen (2013) (M.Sc.)

Exploiting interactions in learning a Bayesian network

·         Noam Nelke (2014) (M.Sc.)

Trend-based accuracy estimation for machine learning

·         Nuaman Asbeh (2014) (Ph.D.)

Learning latent variable models by pairwise cluster comparison

·         Alon Amedi (2015) (M.Sc.)

A greedy branching approach for Bayesian network structure learning

·         Liran Nahum (2015) (M.Sc.)

Concept-drift detection in Bayesian networks by parameter sequential monitoring

·         Faina Khoroshevsky (2016) (M.SC.)

Mobility-pattern discovery and next-place prediction

·         Jonathan Gordon (2016) (M.Sc.)

A machine-learning analysis of ALS

·         Noa Ben-David (2017) (M.Sc.)

Bayesian network structure learning using edge probabilities and integer linear programming

·         Eyal Ben-Zion (2017) (Ph.D.)

Analysis and prediction of human mobility patterns by learning dynamic models

Teaching

·         Machine learning & data mining

·         Introduction to probability

·         Selected topics in machine learning

·         Machine learning

Master (M.Sc.) in Industrial Engineering & Management with Specialization in Data Science

·         Program description (in Hebrew)

 

This page is maintained by Boaz Lerner (boaz@bgu.ac.il)