The past two years demonstrated how important data analyses are for policy making directly affecting public health. Following the development and distribution of vaccines, trustworthy and carefully considered analyses have shown to be paramount. Nevertheless, the extent to which causal conclusions can be drawn has been central to the public debate. One key challenge is the ever-changing environment: different variants, vaccine coverage and the pandemic state. These issues characterize a more general target: learning causal effects for infectious diseases.  Within this field, a major challenge is evaluating the impact of prescribing different antibiotics on future antibiotic resistance, yet another global threat. A recent study estimated the number of worldwide deaths attributed to antibiotic resistance in 2019 to be 5 million.  
In this talk, I will present these challenges and discuss our approach towards overcoming them in several projects concerning antibiotic effects on resistance and vaccine effects.
 

Dr. Daniel Nevo is an Assistant Professor in the Department of Statistics and Operations Research at Tel Aviv University since 2018. Before that he was a postdoctoral fellow at Harvard Departments of Biostatistics and Epidemiology (2016-2018). Daniel Received his PhD in Statistics from the Hebrew University of Jerusalem (2016). Daniel’s research focuses on causal inference in widespread domains, and specifically on developing and implementing causal inference methods for real-life problems. Daniel has been collaborating with clinicians, epidemiologists, economists, and computer scientists in academia and health organizations to reach conclusions about causal effects from rich datasets.

Our blood carries multiple molecules that can represent an individual's condition in health and disease. Using advanced sequencing technologies, we monitor tiny amounts of DNA in blood in order to observe the early stages of cancer and examine embryo development. Our deep learning algorithms applied on billions of DNA molecules lead to the classification of cancer types, early detection of colorectal cancer, and identification of single-point mutations during the first trimester of embryonic development. Our work will assist clinical teams in assessing the patient’s medical illness and to devise precision therapy based on deep evaluation of an individual’s molecular status.

Professor Noam Shomron is passionate about using basic science to advance better healthcare. Professor Shomron heads the Functional Genomic Team at the Faculty of Medicine at Tel Aviv University, after training at MIT. He leads a multidisciplinary team of scientists that develops computational methods for parsing big-data in the bio-medical field using Artificial Intelligence. Shomron applied for more than 30 patents, published more than 200 peer reviewed publications on multiple genomic fields including medicine, agriculture, and business. Shomron is also the Editor of ‘Deep Sequencing Data Analysis’ book (Springer, Edition I 2013, and II 2021); Director of ‘Rare-Genomics’ Israel (NPO); Academic Director of ‘ScienceAbroad’ (NPO); Co-founder and Chief Scientific Officer of Variantyx which provides clinical interpretation of whole genome sequences; Co-founder and Chief Scientific Officer of GotSho which writes software for genetic and Biotech labs; Co-founder and Chief Scientific Officer of IdentifAI which establishes noninvasive prenatal diagnostics at a single nucleotide resolution from a first trimester blood test.

The human genome consists of three billion characters of A,T,G, and C bases, and two copies that we inherit from each parent establish the instructions for our biochemistry. Analyzing genome sequence data is important to resolving cases of genetic disease, which affects roughly 6% of individuals, and understanding broad predisposition to common diseases. In this talk, we will discuss deep learning approaches to analyze genomic data. This includes DeepVariant, a widely-used, open-source method for variant detection using Convolutional Neural Networks, DeepConsensus, an open-source method for sequence error correction using Transformers, as well as DeepNull and ML-based phenotyping which combine multi-layer perceptrons and Convolutional Neural Networks with traditional statistical approaches to discover new genetic associations. The talk will focus on how the deep learning approaches were adapted to the biological data, and will briefly touch on results of external application of the methods.

Andrew leads product development for the genomics team in Google Health. The genomics team develops methods that improve real-world applications in clinical genomics, population-level sequencing, drug discovery, and the combination of genomic and clinical data. Prior to Google, Andrew was Chief Scientific Officer at DNAnexus, where he supported many of the first large-scale genomics projects, such as the CHARGE Consortium, Regeneron-Gesinger and Regeneron-UKBiobank cohorts, the 3000 Rice Genomes Project, PrecisionFDA, and the St. Jude Pediatric Cancer Cloud. Andrew holds a PhD in Molecular Biology from Stanford University and a Bachelor’s degree in Physics from the University of Virginia.

The rapid development of artificial intelligence tools has led to unprecedented impact on almost any field of life, and specifically on image-to-image translation tasks. However, these techniques typically require large amounts of data which may not be available in fields such as medical imaging. A promising alternative is the mixture of signal models with powerful denoisers, providing a general solution for imaging tasks which can operate in low-data regimes. Two frameworks which follow this approach are regularization-by-denoising (RED) and plug-and-play priors (PnP) that have shown state-of-the-art performance in various imaging problems. However, the stability of common RED and PnP methods, which is crucial in medical applications, cannot be completely guaranteed. Here we introduce potential-driven neural networks where the image-to-image translation maps are built as gradients of neural networks. Specifically, we train ponterial-driven denoisers and employ them to solve model-based imaging problems via a convergent gradient-descent scheme. The resultant technique provides comparable performance to RED and PnP methods with provable stability.   

Regev is a research scientist at Verily Research (formerly Google Life Sciences), currently studying artificial intelligence techniques for important biomedical problems, including applications in endoscopy, minimally invasive surgery, microscopy and drug design. His research interests include computer vision, signal processing and optimization, focusing on interpretable and foundational designs of deep networks for medical applications. Regev holds a Ph.D. in Electrical and Computer Engineering from the Technion - Israel Institute of Technology.

11:15 - 11:25

 Moderator: Prof. Ron Shamir, Sackler Professor of Bioinformatics in the Blavatnik School of Computer Science, Tel Aviv University

11:25 - 11:45 Learning to triage patients at scale

 Dr. Kira Radinsky, Chairperson and CTO, Diagnostic Robotics

AI algorithms for personalized medicine necessitate large DNA databases of patient information.  Here, I will show that genetic data is intrinsically and easily identifiable using simple internet searches in consumer genetic websites. These websites offer powerful parametric models that predict genealogical relationships using the genetic data and allow finding distant relatives of the target, which can eventually lead to target identification. By inspecting genetic data of 1.28 million individuals tested with consumer genomics, we investigated the power of this technique. We project that about 60% of the searches for individuals of European descent will result in a third-cousin or closer match, which theoretically allows their identification using demographic identifiers. Moreover, the technique could implicate nearly any U.S. individual of European descent in the near future. We demonstrate that the technique can also identify research participants of a public sequencing project. On the basis of these results, we propose a potential mitigation strategy and policy implications for human subject research.

Dr. Yaniv Erlich is the CEO of Eleven Therapeutics and the CSO of MyHeritage.com. Prior to these positions he was an Associate Professor of Computer Science and Computational Biology at Columbia University and a Principal Investigator at the Whitehead Institute, MIT. Dr. Erlich received his bachelor’s degree from Tel-Aviv University, Israel (2006) and a PhD from the Watson School of Biological Sciences at Cold Spring Harbor Laboratory (2010). Dr. Erlich’s research interest is computational human genetics. Dr. Erlich is a TEDMED speaker (2018), the recipient of DARPA’s Young Faculty Award (2017), the Burroughs Welcome Career Award (2013), Harold M. Weintraub award (2010), the IEEE/ACM-CS HPC award (2008), and he was selected as one of 2010 Tomorrow’s PIs team of Genome Technology.

Deep neural networks provide unprecedented performance gains in many real-world problems in signal and image processing. Despite these gains, the future development and practical deployment of deep networks are hindered by their black-box nature, i.e., a lack of interpretability and the need for very large training sets. On the other hand, signal processing has traditionally relied on classical statistical modeling techniques that utilize mathematical formulations representing the underlying physics, prior information and additional domain knowledge. Simple classical models are useful but sensitive to inaccuracies and may lead to poor performance when real systems display complex or dynamic behavior. Here we introduce various approaches to model based learning which merge parametric models with optimization tools leading to efficient, interpretable networks from reasonably sized training sets. We will consider examples of such model-based deep networks to image deblurring, image separation, super resolution in ultrasound and microscopy, and finally we will see how model-based methods can also be used for efficient diagnosis of COVID19 using X-ray and ultrasound.

Yonina C. Eldar is a Professor in the Department of Math and Computer Science at the Weizmann Institute of Science, Rehovot, Israel, where she heads the center for Biomedical Engineering and Signal Processing. She is also a Visiting Professor at MIT and at the Broad Institute and an Adjunct Professor at Duke University, and was a Visiting Professor at Stanford University.

She is a member of the Israel Academy of Sciences and Humanities, an IEEE Fellow

and a EURASIP Fellow. She has received many awards for excellence in

research and teaching, including the IEEE Signal Processing Society Technical

Achievement Award, the IEEE/AESS Fred Nathanson Memorial Radar Award,

the IEEE Kiyo Tomiyasu Award, the Michael Bruno Memorial Award from

the Rothschild Foundation, the Weizmann Prize for Exact Sciences, and the

Wolf Foundation Krill Prize for Excellence in Scientific Research. She is the

Editor in Chief of Foundations and Trends in Signal Processing, and serves

the IEEE on several technical and award committees. She heads the Committee for Promoting Gender Fairness in Higher Education Institutions in Israel.

Given the current population of Covid mutations, what is the probability that we encounter a new variant that has not been seen before? This fundamental question is known as the missing mass problem. In this work we introduce a new scheme for missing mass estimation. Our proposed framework provides novel risk bounds and improves upon currently known methods. Importantly, it is easy to apply and does not require additional modeling assumptions. This makes it a favorable choice for many practical applications.

Dr. Amichai Painsky is an Assistant Professor at the Industrial Engineering Department of Tel Aviv University. Amichai received his B.Sc. in Electrical Engineering from Tel Aviv University (2007), his Masters in Electrical Engineering from Princeton University (2009) and his Ph.D. in Statistics from Tel Aviv University (2016). Following his graduation, Amichai joined Massachusetts Institute of Technology (MIT) as a postdoctoral fellow (2019). Amichai’s research focuses on statistical inference and learning, and their connection to information theory.