למידה חישובית
סמינר בלמידה חישובית
למידה חישובית
ראיה ממוחשבת ורובוטיקה
למידה עמוקה
מערכות מרובות סוכנים
סמינר מערכות תבוניות ערים חכמות

2023, Afeka-Ariel Fund, Toward Breakthrough in the Field of Renewable Energy: AI-Based Decision Support System for Harnessing the Potential of Halide Perovskites, Principal Investigator, A joint project with Dr. Lena Yadgarov

2022, Mafat, Robust Deep Learning Model using Curriculum Learning, Principal Investigator

2021-2022, Mafat, Accuracy vs. Computation Cost in Distributed Deep Neural Networks Principal Investigator

2020, Mafat, Detecting structural faults in helicopters using artificial intelligence, Co-Researcher, A joint project with Makienko I., Gildish E. and Dr. Grebstein M.

2014-2017, Mafat, “Consumer brain computer interface devices for continuous authentication” Principal Investigator, A joint project with Prof. Anat Ratnovsky

2012-2013, Ben-Gurion University, Homeland Security Research Institute, “Web Text Mining for Monitoring Social Resilience”, Principal Investigator, A joint project with Prof. Yuval Cohen

2012-2013, Mekorot Water Technologies Entrepreneurship Center,“Outlier detection for water quality monitoring”, Principal Investigator with Dr. Dina Goren-Bar and Dr. Neta Rabin

2010-2011, Israel Chief Scientist ‘NeGeV” project, “Utility theory applied to social recommender systems”, Researcher, A Joint project with Dr. Dina Goren-Bar

Halevy, B. Y., Aperstein, Y., & Di Castro, D. (2023). Offline Skill Graph (OSG): A Framework for Learning and Planning using Offline Reinforcement Learning Skills. arXiv preprint arXiv:2306.13630.

Rokach, L., Aperstein, Y., & Akselrod-Ballin, A. End to End Active Learning Framework for Chest-Abdominal Ct Scans Segmentation. Available at SSRN 4502421.

Gildish, E., Grebshtein, M., Aperstein, Y., & Makienko, I. (2023). Vibration-Based Estimation of Gearbox Operating Conditions: Machine Learning Approach. In 2023 International Conference on Control, Automation and Diagnosis (ICCAD)(pp. 1-6). IEEE.

Lahiany, A., & Aperstein, Y. (2022). PTEENet: Post-Trained Early-Exit Neural Networks Augmentation for Inference Cost Optimization. IEEE Access, 10, 69680-69687.

Gildish, E., Grebshtein, M., Aperstein, Y., Kushnirski, A., & Makienko, I. (2022, June). Helicopter bolt loosening monitoring using vibrations and machine learning. In PHM Society European Conference (Vol. 7, No. 1, pp. 146-155).

Shavit, D., Davidovits, M., Kushnirsky, A., & Aperstein, Y. (2022). Temporal causality-based feature selection for fault prediction in rotorcraft flight controls. IFAC-PapersOnLine, 55(2), 235-239.

Aperstein, Y., Cohen, L., Bendavid, I., Cohen, J., Grozovsky, E., Rotem, T., & Singer, P. (2019). Improved ICU mortality prediction based on SOFA scores and gastrointestinal parameters. PloS one, 14(9), e0222599.

Bloch, E., Rotem, T., Cohen, J., Singer, P., & Aperstein, Y. (2019). Machine learning models for analysis of vital signs dynamics: a case for sepsis onset prediction. Journal of healthcare engineering, 2019.

Cohen Y., Aperstein Y. (2015) Service Oriented Acquisition Models for Serving Products with Short Expiration Period”, Series: Lecture Notes in Business Information Processing, Vol. 201, Springer. Proceedings, Exploring Services Science, 6th International Conference, IESS.

Aperstein, Y., Maymon, Y., Cohen, Y., & Singer, G. (2013). Nationality and risk attitude: Testing differences and similarities of investors' behavior in selected financial markets. Global Finance Journal, 24(2), 114-118.

Aperstein, Y., & Kannai, Y. (2006). Demand properties of concavifiable preferences. Journal of Mathematical Economics, 43(1), 36-55.

Aperstein, Y., & Holzman, R. (2003). The core and the bargaining set in glove-market games. International journal of game theory, 32, 189-204.

Apartsin, A., Aperstein, Y., & Gurvich, V. (1998). A circular graph—counterexample to the Duchet kernel conjecture. Discrete Mathematics, 178(1-3), 229-231.