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Boaz Lerner
Associate Professor
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Boaz Lerner is an Associate Professor at the Department of Industrial Engineering & Management (IEM) of Ben-Gurion University (BGU), Israel. He is the head of the M.Sc. program in Data Science at IEM and in the steering committee of the BGU Data Science Research Center. His main areas of research lie in machine learning with applications to 'real-world' problems. Current research focuses on:
A short pitch (in Hebrew)
· (14 September, 2020) Ben-Gurion University launches Oazis, its academic accelerator
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Artificial Intelligence in Health Care: Engaging ALS Patients
(2020-2021)
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Identification of risk factors for Parkinson’s disease using
machine-learning analysis of longitudinal multidimensional clinical data
(2020-2023)
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Early diagnosis, risk factor identification, and better treatment of
inflammatory bowel disease using machine-learning analysis of longitudinal
multidimensional clinical data (2020-2021)
· Phenomics – Precision Agriculture, Israel Innovation Authority (2018-2020)
· A System for Computerized Analysis, Stratification, Prediction, and Monitoring of ALS Disease Progression, Israel Innovation Authority (2018-2019)
· Robotics in Rehabilitation, ABC Robotics Initiative (2018-2019)
· 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
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Hila Avisar (Ph.D.) – Risk factor
identification for Parkinson’s disease from longitudinal clinical data
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Stas Khoroshevsky (Ph.D.)
– Image interpretation using transfer learning by a deep network (with
Bar-Hillel)
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Amir Dolev (M.Sc.) – Missing
data imputation in healthcare
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Matan Gilboa (M.Sc.) – Machine learning in diabetes
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Moraz Finegold (M.Sc.) – Concept drift in health
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Roy Wolf (M.Sc.) – Risk factor identification and
early diagnosis of a disease
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Maya Tzemach (M.Sc.) – Machine learning in
inflammatory bowel disease
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Gilad Erez (M.Sc.) – Concept drift detection for
disease development analysis
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Chagit Friss (M.Sc.) – Analysis of the utilization of
antibiotics in IBD
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Astar Mordechai (M.Sc.) – Early diagnosis and risk
factor identification by concept drift detection
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Roy Plaut (M.Sc.), TBD
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Gil Biton (M.Sc.), TBD
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Tom Damari (M.Sc.), TBD
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Iddo Salton (2004) (M.Sc.)
Classification of imbalanced data using neural networks
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Yaniv Gurwicz (2004) (M.Sc.)
Classification using Bayesian multinets
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Ra'anan Yehezkel (2004) (M.Sc.)
Bayesian network structure learning using recursive autonomy
identification
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Roy Malka (2005) (M.Sc.)
Bayesian network classifiers
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Boaz Vigdor (2005) (M.Sc.)
Pattern recognition using a probability-driven fuzzy ARTMAP classifier
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Lior Konis (2006) (M.Sc.)
Radar target classification using dynamic Bayesian networks
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Lev Koushnir (2007) (M.Sc.)
A unified methodology for image analysis and classification
of dot and non-dot-like fluorescence in situ hybridization signals
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David Bechor (2007) (M.Sc.)
Studying and comparing initial starting points of the
K-means clustering algorithm
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Yair Meidan (2008) (M.Sc.)
Identifying and quantifying factors affecting waiting time
and its prediction in manufacturing fabs using machine learning (with G.
Rabinowitz)
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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
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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
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Dan Halbersberg (2013)
(M.Sc.)
Scoring a structure in learning a Bayesian network
classifier
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Asaf Cohen (2013) (M.Sc.)
Exploiting interactions in learning a Bayesian network
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Noam Nelke (2014) (M.Sc.)
Trend-based accuracy estimation for machine learning
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Nuaman Asbeh (2014) (Ph.D.)
Learning latent variable models by pairwise cluster
comparison
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Alon Amedi (2015) (M.Sc.)
A greedy branching approach for Bayesian network structure learning
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Liran Nahum (2015) (M.Sc.)
Concept-drift detection in Bayesian networks by
parameter sequential monitoring
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Faina Khoroshevsky (2016) (M.SC.)
Mobility-pattern discovery and next-place prediction
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Jonathan Gordon (2016) (M.Sc.)
A machine-learning analysis of ALS
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Noa
Ben-David (2017) (M.Sc.)
Bayesian network structure learning using edge probabilities
and integer linear programming
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Eyal
Ben-Zion (2017) (Ph.D.)
Analysis
and prediction of human mobility patterns by learning dynamic models
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Oded Zinman (2018) (M.Sc.)
Identification of social function land use in urban
areas
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Aviv Nahon (2018) (M.Sc.)
Algorithms
for learning temporal models for understanding ALS
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Alon Shpigler (2018) (M.Sc.)
A generative model for regularization and analysis of
deep neural networks activity (with Bar Hillel)
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Hila Avisar (2019) (M.Sc.)
Identifying
relations between lipidome and the diagnosis of Parkinson’s disease and
its severity using machine learning
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Shir Kashi (2019) (M.Sc.)
A machine-learning model for automatic detection of
movement compensations in stroke patients (with Levy-Tzedek and Rokach)
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Yael Konforti (2020) (M.Sc.)
Probabilistic
interpretation and visualization of deep neural network (with Bar Hillel)
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Dan Halbersberg (2021) (Ph.D.)
Learning
temporal latent variable models
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Ben Hadad (2021) (M.Sc.)
Data-driven analysis of
neurodegenerative diseases using machine-learning algorithms
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Shon Mendelson (2021) (M.Sc.)
Concept
drift in machine learning – Detection and
relearning
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Yaniv Malowany (2021) (M.Sc.)
Algorithm for learning latent variable models
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Dor Simoni (M.Sc.) (2022)
Risk
factor and biomarker identification
for ALS using statistical methods and machine learning
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Hanan Mann (M.Sc.) (2022)
Deep
learning in digital healthcare
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Yoav Reisner (M.Sc.) (2022)
Early
diagnosis and risk factor identification by concept drift detection with
application to inflammatory bowel disease and amyotrophic lateral sclerosis
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Ofir Kedem (M.Sc.) (2022)
Amyotrophic
lateral sclerosis prediction and risk factor identification using machine
learning and a questionnaire
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Shoam Shabat (M.Sc.) (2022)
Algorithms
for learning latent variable models
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Ori Ben-Yehuda (M.Sc.) (2022)
Early
diagnosis of pulmonary embolism using machine learning
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Yotam Baron (M.Sc.) (2022)
Ensemble-based
concept drift detection using Bayesian networks
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Jerusalem
Post (19 February, 2019)
· On the front page of BioWorld MedTech, 28 February, 2019, including an interview
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Health
Periodical (3 October, 2019)
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Machine learning & data mining
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Introduction to probability
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Selected topics in machine learning
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Machine learning
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Introduction of the Data Science track
at IEM (in Hebrew) AND “Why IEM
at BGU is the best place to do a master degree in Data Science in Israel?”
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Program
description (in Hebrew)
Resources
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How to
Publish a Scientific Paper (Harvard Catalyst)
This page is
maintained by Boaz Lerner (boaz@bgu.ac.il)