tracks
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Information Management Engineering
Ongoing developments in information technologies are enabling the creation of information systems in a variety of fields, with an ever-increasing scale and sophistication. At the same time, users’ demands from information systems are also growing. Information system engineers are required to develop applications and products whose complexity and intricacy are constantly increasing. These systems utilize the latest technologies such as communication and distributed systems, command and control using artificial intelligence, data organization and retrieval, organizational resource management systems, e-commerce systems, integrated hardware and software systems and decision support systems. Continue Reading Information Management Engineering
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Track in Cognitive Science
The cognitive science track enables students to combine the degree program in Data Science and Engineering with a specialization in the cognitive sciences. The track program is unique in the landscape of undergraduate cognitive science programs in Israel due to its computational orientation and its emphasis on the links between cognitive science and artificial intelligence (AI). The track further offers an opportunity to conduct a research project under the supervision of a faculty member. Continue Reading Track in Cognitive Science
seminars
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Robust and Actionable ML via Causality
Abstract: Artificial Intelligence is increasingly deployed in high-stakes fields such as healthcare, where two critical challenges emerge: models must generalize to real-world variations and their predictions must be actionable for decision-makers. The generalization challenge arises when AI is applied to data that differs from its training set, a phenomenon known as distribution shift. The actionability challenge… Continue Reading Robust and Actionable ML via Causality
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Robust and Actionable ML via Causality
Abstract: Artificial Intelligence is increasingly deployed in high-stakes fields such as healthcare, where two critical challenges emerge: models must generalize to real-world variations and their predictions must be actionable for decision-makers. The generalization challenge arises when AI is applied to data that differs from its training set, a phenomenon known as distribution shift. The actionability challenge… Continue Reading Robust and Actionable ML via Causality
