The sequence alignment problem is one of the most fundamental issues in bioinformatics and indeed a plethora of methods has been devised to tackle it. In practice, these methods are applied with the same parameters (i.e., default configuration) on every given input, implicitly assuming a single evolutionary model. This is clearly an oversimplification of biological reality.
Here we introduce AlignNLP, a state-of-the-art methodology for aligning sequences using a deep neural network. AlignNLP accounts for the possible variability of the evolutionary process among different lineages by using an ensemble of transformers. Each transformer is trained on millions of samples from different evolutionary models, leading to a superb alignment accuracy, overperforming commonly used methods, such as MAFFT and Prank. 

Edo Dotan is a graduate student at Tel Aviv University, Israel. In his work, he harnesses recent advances in the machine-learning field in order to revolutionize techniques in computational biology. For example, in a recent study, supervised by Profs. Tal Pupko and Adi Stern (Tel-Aviv University) and Dr. Yonatan Belinkov (Technion), Edo applied natural language processing (NLP) algorithm to one of the most fundamental tasks in bioinformatics, namely, to align sequences, i.e., tracing the homology relationships between genomic regions that descendent with modification from a common ancestor. He also applies machine-learning tools to study Covid-19 viral genome evolution. Edo holds a B.Sc. in computer science and has gained hands-on experience in the industry at Corephotonics (acquired by Samsung) before joining academia.

Pulmonary embolism (PE) is a common life-threatening condition with a challenging diagnosis, as patients often present with nonspecific symptoms. Prompt and accurate detection of PE and specifically an assessment of its severity are critical for managing patient treatment. We introduce diverse multimodal fusion models that are capable of utilizing weakly-labeled multi-modal data, combining both volumetric pixel data and clinical patient data for automatic risk stratification of PE. The best performing multimodality model achieves an AUC of 0.96 for assessing PE severity, with a sensitivity of 90% and specificity of 94%. To the best of our knowledge, this is the first study that attempted to automatically assess PE severity.

EE PhD student at Tel Aviv University, advised by Prof. Hayit Greenspan. Prior to that, I received my Bachelor's degree in EE at Tel Aviv University. My current research interests include deep learning and computer vision with applications to medical imaging, especially, fusing diverse data modalities such as imaging, free text, and structured tabular data for medical prognosis models.

13:15 - 13:25

 Reuven Shnaps, Ph.D., Chief Analytics Officer, Earnix

13:25 - 13:45 The Moral Status of a Humanoid Robot

 Mois Navon, Lecturer, Ben Gurion University; PhD student, Bar Ilan University

The aim of this contribution is a "regulation and governance" reading of the new EU initiative on regulating Artificial Intelligence. Thus its focus is not on the "first order" fundamental rights perspective but rather the "second order" mechanisms that are necessary for their effective application on the ground. 

Amit is in charge of developing and deploying the Directorate's domestic and international legal policy, and manages the INCD legal department. This includes legal counsel for policy, regulation and operations. Amit has been active in law, technology and policy issues within government since 2002, including Copyright law, Data Protection and Privacy, Electronic signatures, E-government, and Cybersecurity, and has represented the Israeli government in the Israeli Knesset, and in the international sphere. In 2019 Amit represented Israel in the drafting of the OECD Recommendations on AI. Before joining the INCD in 2014 to set up its legal department, Amit was Head of the Legal Department in the Israeli Law Information and Technology Authority in the Ministry of Justice (ILITA), now renamed The Privacy Protection Authority, Israel's Data Protection Authority. In this context he was involved in international data protection issues, including the EU Adequacy finding of Israel's Data Protection regime and in the OECD. Since 2013, Amit teaches a graduate course on Law and Information Technology in the Center for Law and Technology in the Haifa University Faculty of Law, and since 2021 also in the TAU Graduate program on Cyber, Politics and Government.

14:15 - 14:30 Imaging COVID

 Hayit Greenspan, Professor of Biomedical Engineering in the Faculty of Engineering, Tel-Aviv University

Various efforts for early detection and prompt isolation of the COVID-19 pandemic are pivotal to breaking transmission chains and containing outbreaks. In this talk, I will briefly discuss our four projects that utilize data of a large-scale for early detection of 1) hotspots and regional outbreaks using mobility data from cellphone devices, 2) early signs of infections using wearable devices, 3) positive test outcomes using historical electronic medical records, and 4) deteriorations of hospitalized patients using rich medical data.  Then, I will elaborate on one of the projects and its potential applicability for policy determination.

Dr. Yamin (Ph.D.) is a faculty member at the Department of Industrial Engineering at Tel Aviv University, and a former faculty member in the Center of Infectious Disease Modeling and Analysis, in the school of public health at Yale University. Dr. Yamin received four research prizes awards and five teaching prizes awards. Dr. Yamin’s studies contributed to impact health policy against influenza in Israel, offered novel strategies to eliminate Ebola that were implemented in Liberia. He received a European Research Council (ERC) grant to pursue research on the early detection of infectious diseases using smartphones. He recently advised the Ministry of Health and the Ministry of Finance to identify hotspots and apply effective strategies against the COVID-19 Pandemic in Israel.

COVID19 pandemic challenged society and organizations to make a trade-off between individual health safety on one hand, and socio-economic progress on the other. In a way, we're all grappling to return to a new normal. But, what would that new normal be? How and when can we return to that new normal without compromising our health safety? Uncertainties related to new variants, vaccines and their efficacies, loss of immunity, and socio-economic factors make such questions difficult to answer. We developed a novel configurable digital twin that can be contextualized for a city or an organization to predict possible disruptions of business-as-usual activities due to the COVID pandemic. It also helps to explore various what-if scenarios for defining strategies towards a safer return to new normalcy. Our approach combines multiple types of system modelling & simulation techniques and adopts established data science concepts. We demonstrated the efficacy of our approach in the context of an Indian city and an organization. In this talk, I will present our approach, early experiences, and learnings.  

Souvik Barat is a principal scientist at Tata Consultancy Services Research, India. He has 20+ years of experience in industrial research and his research interests include Digital Twin technology, Modelling and simulation of complex systems, Reinforcement Learning, Model- Driven Engineering, Software Product Lines, and Business Process Management. 

At present, he is actively involved in developing digital twins for complex business and societal systems. His work on digital twin for business systems led to the Best Innovation Award 2019 in TCS and contributed towards Gold Stevie Award 2021 under AI/ML category.  His effort in modeling a city to evaluate the efficacy of non-pharmaceutical interventions for controlling COVID-19 infections is extensively used by city-based health care organizations and municipality corporation.    
      
Earlier, he was a lead architect of a model-driven toolset that has been used for delivering large IT systems for over a decade and led a research initiative to develop a platform for product line architecture. 

Souvik has several patents to his credit and has authored several book chapters, journals and conference papers. He holds Ph.D. from Middlesex University, London, and a master’s degree from the Indian Institute of Technology (IIT), Madras. 
 

Artificial intelligence techniques such as machine and deep learning have large potential to better exploit the rich information that can be derived from health and healthcare data to promote public and individual health. It can change our healthcare system from a reactive to a proactive one (towards prevention), and by learning from previous patients we can personalize treatment, leading to "precision medicine". 

As the quality of many AI techniques crucially depends on the data on which it is trained, an important prerequisite for the successful implementation of AI in healthcare is access to high quality data. This is complex in the health domain, where there is fragmentation of expertise, data and resources. Therefore, in the Dutch health and life sciences domain different stakeholders have established a joint vision towards a Dutch health data infrastructure, and launched the Health RI initiative (www.health-ri.org). Health RI pursues the integration of highly diverse collections of longitudinal health and biomedical data, e.g. generated at different hospitals and research centers, to empower researchers and organizations to develop better personalized medicine and health solutions. The covid pandemic has shown the importance of such an approach, and in this presentation the steps towards a national Covid data portal will be presented.
 

Wiro Niessen is professor in Biomedical Image Analysis and Machine Learning at Erasmus MC and Delft University of Technology. His interest is in the development, and validation of quantitative biomedical image analysis methods, and linking imaging and genetic data for improved disease diagnosis and prognosis, using machine learning. He supervised 56 PhD students in these fields. He is fellow and was president of the MICCAI Society, and is CTO of Health-RI, which aims to develop a national health data infrastructure for research and innovation. In 2015 he received the Simon Stevin award, the largest prize in the Netherlands in Applied Sciences. In 2017 he was elected to the Royal Netherlands Academy of Arts and Sciences. In 2012 he founded Quantib, an AI company in medical imaging, where he is now scientific lead.