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The AI 4 Good Lab
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Our goal
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Research Themes of The AI 4 Good Lab
AI and Medicine
AI revolutionizes healthcare, allowing for a better understanding, diagnosis and treatment of numerous diseases and an improved evaluation of prognosis. We are working on leveraging AI capabilities and medical data in order to produce better AI-powered clinical decision support tools for clinicians and stakeholders focusing on improving mental health and other medical interventions.
AI-Powered Security
Artificial intelligence is changing the game in security setting, analyzing massive quantities of risk data, leveraging advanced game-theoretical models and optimization techniques to speed response times and better deter potential offenders. We are focusing on making our roads safer through intelligent traffic enforcement allocations including drones and smart cameras thus augmenting the capabilities of under-resourced security operations in the field
Machine learning and People
It has become quite common these days to hear people refer to modern machine learning systems as “black boxes”. We work on making ML-based systems more transparent, self-explanatory and beneficial for people. These efforts include providing explanations for users in recommender systems environments (e.g., dating sites), learning from people in order to improve the ML-prediction and its ecological validity and many more.
Scientometrics
The evaluation of research and its various components is essential in the scholarly world in order to assess the value that research brings to light. In order to promote high-quality research, it is important to develop firm understanding of the factors which take part in the scholarly realm and develop appropriate policies for the key players (authors, institutes, journals, etc.). We use AI and ML technologies to model, study and investigate this complex eco-system in order to shed light on “the science of science”.
Human-Agent Interaction
In recent years we have seen an ever increasing number of human interacting intelligent agents. These agents are and are expected to be all around us — from autonomous vehicles on the roads, intelligent healthcare support systems on our physicians’ computers and on our smartphones to robots at our homes and intelligent tools in the workplace. However, designing intelligent agents that are capable of interacting proficiently with people still remains one of the major research challenges for upcoming years. We work on designing efficient interaction methods and techniques, especially in order to make autonomous agents more beneficial for the society.
Current Students
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Shir Aviv, TBD , PhD Candidate
Nitzan Haimovitch, Behavioral Chatbots, MA Candidate
Ofir Rocach, TBD , MA Candidate
Former Students
Moshe Cohen, Human Aspects in Speeding up Reinforcement Learning, MSc, 2018
Zvi Lapp, Optimizing Medical Imaging Exposure in Radiologist Worklist, MSc, 2019
Akiva Kleinerman, The Effects of Transparency in Reciprocal Recommender Systems, MSc (with distinction), 2018
Albert Ahronian, Learning Algorithms for Understanding Intentions in Autonomous Cars, MSc, 2019