אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

This research is centered around the application of machine learning approaches to domains in which training labels are prohibitively expensive to obtain or training data in general are impossible to procure.

Initially, the study focused on investigating weak supervision in medical applications, in which data labelling is extremely expensive; specifically showcasing how a restricted number of high-quality labels can significantly improve algorithm performance in diagnosing chest diseases from X-ray images. Subsequently, the research shifted its emphasis towards tackling intricate problems in physics, such as quantum chemistry and quantum optics, which inherently encounter difficulties in database creation.

The challenges in physics pertain to integrating physical constraints into machine learning models to confine the model's search space, thereby mitigating the need for extensive experiments or simulations. An initial study addresses a problem in quantum chemistry that revolves around determining the ground state energy of molecules. We consider an ab-initio quantum chemistry method, which involves incorporating the model with all pertinent physical constraints of the problem and enabling it to converge towards the wave function that represents the electron distribution around the molecule.

A second research endeavor in physics centers on employing advanced computational learning tools to address inverse design problems in quantum optics to obtain the desired high dimensional bi-photon entanglement. The objective is to qualitatively represent the physical model in a differentiable manner, particularly focusing on the non-linear interaction of light and matter. Importantly, this is achieved without relying on any empirical or theoretical data that contains the design of the optical system and the resulting entangled photons. We further validate our approach against experimental results.