סמינר בלמידה חישובית
למידה חישובית
ראיה ממוחשבת ורובוטיקה
למידה עמוקה
מערכות מרובות סוכנים
סמינר מערכות תבוניות ערים חכמות
עיבוד שפה טבעית
מידול וחיזוי סדרות עיתיות
רכב אוטונומי
יישומי בינה מלאכותית  לזיהוי אנומליות
מערכות תבוניות ברפואה
תורת המשחקים ובינה מבוזרת
בינה מלאכותית
שיטות אופטימיזציה

2024, Ministry of Innovation, Science & Technology, Retrospective Analysis of the Correlation Between Glycated Hemoglobin Levels, GLP-1 Agonist Treatment, and Incidence of Common Brain Disorders , Principal Investigator with Dr. Zeev Nitzan

2024, Mafat, A system for Generative AI-assisted retrieval and prediction of energetic material properties, Principal Investigator

2024, Mafat, LLM-based multiagent systems for analysis of competing hypotheses, Principal Investigator

2023, Mafat, Incremental PTEENet for Inference Cost Optimization, Principal Investigator

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

Rokach, L., Aperstein, Y., & Akselrod-Ballin, A. (2024). Deep active learning framework for chest-abdominal CT scans segmentation. Expert Systems with Applications, 125522

Berman, J., Pinhasov, B., Tshuva, M., Aperstein, Y (2024). syN-BEATS for robust pollutant forecasting in data-limited context. Environmental Monitoring and Assessment 196, 1002.

Pinhasov, B., Lapid, R., Ohayon, R., Sipper, M., & Aperstein, Y. (2024). XAI-Based Detection of Adversarial Attacks on Deepfake Detectors. TMLR.

Berman, J., Aperstein, Y., & Yosipof, A. (2024, September). Elucidation of Molecular Substructures from Nuclear Magnetic Resonance Spectra Using Gradient Boosting. In International Conference on Artificial Neural Networks (pp. 31-42). Cham: Springer Nature Switzerland.

Cohen, Y., & Aperstein, Y. (2024). Generative Shopfloor Layout Design: Challenges and Proposed Modelling Approach. IFAC-PapersOnLine, 58(19), 748-753.

Yalov-Handzel, S, Cohen,I and Aperstein, Y. (2024). Comparative Analysis of CNN Architectures and Loss Functions on Age Estimation of Archaeological Artifacts. Journal of Computer Applications in Archaeology, 7(1): 185-194.

Gildish, E., Grebshtein, M., Aperstein, Y., & Makienko, I. (2023). Vibration Signal Decomposition using Dilated CNN. Annual Conference of the PHM Society, 15(1). 

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.

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.