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ZIMIN INSTITUTE

DATA-DRIVEN ACCELERATION THAT IMPACTS HEALTHCARE

The Zimin Institute for AI Solutions in Healthcare was launched June 2022 as a joint-initiative between the Zimin Foundation and the Technion – Israel Institute of Technology through its Tech.AI.Bio-Med arm

Our mission is to better healthcare through development and application of machine learning and artificial intelligence technologies. We see room for such solutions at all levels of the healthcare and life sciences sectors from development of precision medicine diagnostics and AI-based technologies that enhance therapeutic discovery, to altering clinical routine and patient management in hospitals, HMOs and at home treatment.

We strongly believe that transformative impact occurs when innovation rubs against the real-world. These interactions ensure that the innovative solution is fine tuned by real-world problems, and is developed with foresight on how the innovation can best make an impact that will truly transform the real-world. As such, the Zimin Institute seeks to support needs based innovation development in academia and guide its towards commercialization.

Zimin
ZIMINFOUNDATION.ORG
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EXECUTIVE COMMITTEE

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    Shai Shen-Orr

    ProfessorTechnion Faculty of Medicine, Co-Director, Tech.AI

    Shai is an Associate Professor at the Technion’s Faculty of Medicine, working in the fields of Computational Biology, Systems Immunology & Precision Medicine. He has been developing machine learning to study the drivers of immune variation, particularly in the context of aging, and to further Immune-based Precision Medicine.

    Shai directs Tech.AI.BioMed and heads the Zimin Institute for AI solutions for healthcare. He is the founder and Chief Scientist of CytoReason, a PharmaAI company developing a machine learning model of human disease aimed at closing the data-insight gap in drug development.

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Meet our researchers

EXECUTIVE OVERSIGHT BOARD

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    Shaul Markovitch

    ProfessorA faculty member in the Computer Science department

    Artificial Intelligence, Machine Learning, Natural Language Semantics, Anytime Learning, Active and Selective Learning, Information retrieval, Multi-agent Systems, Adversary search, Opponent Modeling, Resource-bounded reasoning, Cost-sensitive Learning.

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    Amir Yehudayoff

    Professor

    Contributing the theory of machine learning through connections to information theory, and computational complexity theory.

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    Aviv Tamar

    Associate Professor

    My research focuses on AI and machine learning, with an emphasis on robotics applications. My long term goal is to bring robots into human-centered domains such as homes and hospitals. Towards this goal, some fundamental questions need to be solved, such as how can machines learn models of their environments that are useful for performing tasks, and how to learn behavior from interaction in an interpretable and safe manner. Most of my work falls under the framework of reinforcement learning, and its connections to representation learning and planning.

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    Arie Admon

    Professor Emeritus

    Proteomics, Cancer Vaccines, Antigen Processing and Presentation, Big data of proteomics and peptidomics, Bioinformatics of proteomics and mass spectrometry.

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    Anat Rafaeli

    Professor

    Human behavior in the context of organizational service interactions. Big data and automation tools are used to objectively analyze emotion and behavior of participants in online conversations.

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    Alon Grinberg Dana

    Assistant Professor

    Closed-loop software and hardware platforms driving chemical discovery through automated hypothesis generation, refinement, validation, and revision, resulting in predictive chemical kinetic models.

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SCIENTIFIC ADVISORY BOARD

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    Nadav Merlis

    Assistant Professor

    Theory of sequential decision-making under uncertainty, including reinforcement learning, multi-armed bandit problems, learning-augmented algorithms, and more.

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    Orit Hazzan

    Professor

    Researches GenAI’s impact on computer science education, STEM education policy, and education in times of crisis, bridging academia, industry, and society through curriculum design and agile projects.

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    Anat Levin

    Professor

    Computational imaging.

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    Miriam Zacksenhouse

    Professor

    Control policies that facilitates learning and sim2real transfer with applications to robot assembly and legged locomotion; Invasive and non-invasive Brain Machine Interfaces (BMIs) and error related processing;

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Call for Proposals

We are pleased to launch the 1st round of the Zimin Institute for AI Solutions in Healthcare. The scope of this call will center around medical informatics and AI-based diagnostics.
Grants shall be up to 100K for one year.

Submission deadline is Nov. 7th.

Full proposal information

AWARDED PROJECTS

Harnessing AI to predict omics signature out of histological data to improve biopsy-based diagnostics and prognosis

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    Yonatan Savir

Cancer diagnostics of biopsies measured by spectral imaging

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    Yuval Garini

AI-boosted affordable MRI for the masses

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    Efrat Shimron

A novel AI framework for cancer phenotyping and treatment planning

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    Ron Kimmel

    Computer vision, graphics, Geometric machine learning and big data, computational medicine and biometry, applied metric and differential geometries.

AI-based optical rapid detection of biomarkers

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    Yoav Shechtman

    Computational imaging, fluorescence microscopy, cellular imaging, 3D imaging, super-resolution microscopy, wavefront shaping.

Personalized recommendation system for treating hospitalized heart failure patients with acute kidney injury

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    Uri Shalit

    Machine learning; causal inference; machine learning for healthcare; deep learning.

Detection of Brain Tumors from Eye Images Using Deep Learning “Vision AI”

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    Joachim Behar

    Medical machine learning, unstructured data analysis.

AI-Enabled Lens- and Label-Free Non-Invasive Decoding of Cancer Organoids and Proteo-Genomic Development Stages

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    Hossam Haick

    Nano-array devices for screening, diagnosis and monitoring of disease, nanomaterial-based chemical (flexible) sensors, electronic skin, breath analysis, volatile biomarkers, and cell-to-cell communication.

AI-Driven Analysis of Heartbeat Interval Dynamics for Atrial Fibrillation Screening in Sinus Rhythm

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    Yael Yaniv

    Automatic diseases classification, Cell Biophysics, Heart rate variability analysis, Mobile health devices, Prediction and detection of atrial and ventricular fibrillation, Sinoatrial node cell activity.

MBSS-T1: AI-Powered Motion Correction for Cardiac T1 Mapping in Free-Breathing MRI

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    Moti Freiman

    AI methods for computational MRI, quantitative imaging, WSI pathology analysis, and clinical NLP for medical decision support.

Harnessing AI to predict omics signature out of histological data to improve biopsy-based diagnostics and prognosis

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    Shaul Markovitch

    Artificial Intelligence, Machine Learning, Natural Language Semantics, Anytime Learning, Active and Selective Learning, Information retrieval, Multi-agent Systems, Adversary search, Opponent Modeling, Resource-bounded reasoning, Cost-sensitive Learning.

AI-based optical DNA mapping for fast pathogen

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    Yoav Shechtman

    Computational imaging, fluorescence microscopy, cellular imaging, 3D imaging, super-resolution microscopy, wavefront shaping.

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