Dr. Yehudit Aperstein

Dr. Yehudit Aperstein

Dr. Yehudit Aperstein is an expert in the fields of Intelligent Systems and Artificial Intelligence. Over the past 15 years, Dr. Aperstein has led dozens of applied research projects within academic frameworks, in collaboration with various organizations, and as a consultant in the industry. Dr. Aperstein specializes in building complex systems that typically integrate a large number of AI-based components that need to operate together under the constraints of the real world. The research led by Dr. Aperstein focuses on solutions for applications in a wide range of fields: predictive maintenance, medicine, aerial image analysis, sensor data analysis, and audio. Dr. Aperstein has received several research grants from different organization.

Dr. Aperstein holds a master's degree in Game Theory from the Technion, a Ph.D. in Mathematical Economics from the Weizmann Institute, and has completed postdoctoral fellowships at Bar-Ilan University and Tel Aviv University. Dr. Aperstein holds a teaching certificate and is a graduate of a two-year program in Economics and Business Management at the Weizmann Institute.

In 2015, Dr. Aperstein established a Master's program in Intelligent Systems at Afeka College and served as the head of the program until 2023.

Artificial Intelligence, Deep Learning, Computer Vision & Robotics, Multi-agent Systems, Autonomous Vehicles, Natural Language Processing, Time Series Analysis, Intelligent Systems in Medicine, Machine Learning, Applications of AI for Anomaly Detection, Data Mining, Seminar in Smart Cities.

Asset Pricing, Financial Mathematics I&II, Fundamentals of Investment, Game Theory, Risk Management, Advanced Seminars in Finance.

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.