University of Haifa Data Science Research Center

The University of Haifa’s Data Science Research Center is focused on strengthening methodological data science research projects via multidisciplinary research across university departments and with industry and the public sector. Few words on data science (from meeting with the School of Public Health)

The data revolution and the increase in information and communication technologies elevate public interest and make everyone in academia, industry, non-profit organizations and the public sector a developer and/or user of data science (DS) methods and tools.
The University of Haifa (UoH) has an ideal combination of research disciplines in both core and applications of DS.
The proposed DS research center of the UoH (DS-RC@UoH) will integrate the core areas, such as machine learning, deep learning, big data, distributed algorithms, statistical inference and methodology, big data management, etc., and applications that use these methodologies, such as computer vision, natural language processing, computational biology, informatics, astronomy, etc.

The primary objective of the university-wide overarching center is to:

  •  Develop a mechanism to advance methodological DS research in an integrative/harmonized manner across all the faculties, schools, departments and DS-related centers;
  •  Increase the usage of DS methods and tools across all departments and disciplines;
  • Boost collaboration with industry and the public sector in order to address real-life problems to better society.
  •  Enhance the transformation of the University of Haifa to the digital Era, digital sciences, digital humanities, and digital social sciences. We believe that the world and the academia as part of it are going through major transformations, and in the near future all researchers will need to comprehend DS technologies to stay in the leading edge of research.

To address its mission, the center will increase opportunities for collaboration among core DS scientists, DS scientists who are developing methods for specific application domains, and researchers in non-DS domains. The latter are users of DS tools who can define new needs and challenges that may spark innovative DS methodologies and step up the DS research in their domains (e.g. humanities, social sciences, etc.). Second, DS members who are established in working with and attaining data from the industry and the public sectors will develop strategies and activities to facilitate collaboration of additional researchers with these sectors.

Support and guidance on research projects involving data science applications

Hosting researcher from abroad - TASHPAHA

Funding a data science article presentation at *A conference or open access publishing on Q1 journals - TASHPAHA

המועצה להשכלה גבוהה : מוקדי מחקר פורץ דרך (Moonshot) בתחום הבינה המלאכותית

IDSAI Call for proposals for running expert workshops

Due 31.1.2025

IDSAI Call for proposals for inter-disciplinary, inter-center research projects

Due 14.2.2025

Blogs

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When the past meets the future: Determining Origins of Ancient Metal Objects using a Machine Learning Approach

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Using Less communication to approximate mathematical properties over large, noncentralized data

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Is it diversity or just a mess? When RNA splicing does not go according to plan

Shaked Shanas, Martin Mikl, Judith Somekh
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Can Facial Landmarks Guide us to Understanding Animal Emotions? 

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Looking Inside the Black Box of Meditation Using Artificial Intelligence

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A new Analysis method for the basketball “Hot-Hand” fallacy

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A new Analysis method for the basketball “Hot-Hand” fallacy

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Can cognitive biases during pregnancy predict future development of postpartum depression and PTSD?

Vanessa Cywiak, Hadas Okon-Singer, Nur Givon-Benjio, Ido Solt, Eyal Fruchter, Hagit Hel-Or
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2022 Artificial Intelligence Computer Vision Health Post-Doc Psychology Research Blog

It Feels Good to Feel Bad: Between-and Within-Session Changes in Emotions During Psychological Treatment for Depression

Hadar Fisher, Philip T. Reiss, Dovrat Atias, Simon Shamay-Tsoory, Sigal Zilcha-Mano
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2021 Biodiversity Computer Vision Deep Learning Image Processing Machine Learning Neural Networks Research Blog Seed

3D Imaging for Coral Reef Ecology

Matan Yuval, Naama Pearl, Amit Peleg, Dan Tchernov, Tali Treibitz, Avi Bar Massada
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Can we automatically determine the proficiency of Hebrew second language learners

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2022 Computer Science Computer Vision Data Mining Marine Science Marine Technologies Research Blog Seed

An Efficient Drifters Deployment Strategy to Evaluate Water Current Velocity Fields

Murad Tukan, Eli Biton, Roee Diamant
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Say Hello to the New Digital DR DOLITTLE: AI that Knows How to Read Cats’ Emotions

Marcelo Feighelstein, Anna Zamansky, Ilan Shimshoni
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Can a Computerized Task Treat Depression

Gal Rabinovich, Reut Shani, Tomer Sidi, Hadas Okon-Singer
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2022 Deep Learning Health Machine Learning Neural Networks PhD Research Blog

Can you smell pain: A  Multi-modal signature for chronic pain

Michal Weiss, Hadeel Salameh, Elias Mansour, Hossam Haick, Pavel Goldstein
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If the Shoe Fits… – Wait, What if It Doesn’t

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