Prof. Borenstein’s work focuses on computational study of the human microbiome, spearheading research in microbiome computational systems biology. World-wide research initiatives and multi-omic studies now provide detailed, multi-faceted, and large-scale data about the microbiome, calling for the development of new methods for integrating and analyzing such complex data and for obtaining insights into the role of the microbiome in health and disease. Prof. Borenstein’s lab has extensive experience in developing such methods, using techniques inspired by metabolic modeling, data science, machine learning, and network theory. Over the past years, Prof Borentein’s lab has introduced novel frameworks for processing, annotating, and normalizing metagenomic data, for model-based integration of multi-omic microbiome data (and specifically for characterizing, modeling, and predicting the link between microbiome and metabolome profiles), for inferring species interactions, and for modeling and designing microbiome activity. Current research further aims to understand the link between the microbiome and diet, focusing specifically on dietary fibers. Combined, this research aims to provide a systems-based understanding of the microbiome, and ultimately to inform microbiome-based diagnosis and therapy. 

Prof. Borenstein is a Full Professor at the Faculty of Medicine and at the Blavatnik School of Computer Science at Tel Aviv University. He is also an external professor at the Santa Fe Institute, an affiliate professor at the University of Washington, and the new head of TAU’s Edmond J. Safra Center for Bioinformatics. Prof. Borenstein received his BSc in physics and computer science and his PhD in computer science from Tel-Aviv University, and held a joint postdoctoral position at Stanford University and the Santa Fe Institute. In 2010, he joined the Department of Genome Sciences at the University of Washington as a faculty member, and in 2018, moved to Tel Aviv University with a joint appointment in Medicine and in Computer Science. Prof. Borenstein is the recipient of various prestigious awards including the Alfred P. Sloan Fellowship and the NIH New Innovator Award.

Research in my lab resides on the interface between developing novel methodologies for non-invasive MRI, and applying them to study tissues structure and function in biology and medicine. One main focuses of my lab is quantitative MRI (qMRI), and microscopic MRI (MRI). Using these tools we improve the sensitivity of subtle pathological processes, as well as study microscopic features of tissues, and specifically characterization of the myeloarchitecture of the central nervous system, in order to gain more insight into the pathophysiology of demyelinating diseases such as multiple sclerosis.

I have special interest in integrating Technology and Bio technology in the filed of obstetrics and gynaecology. Expanding research and using advanced AI tools to improve women’s health particularly through involving technology enhancement. I am committed to lifelong and professional development, aiming to contribute significantly to the field of maternal and fetal medicine in the future.

Understanding how genetic variation affects our risk for developing diseases is a major challenge of current human genetic research. Our lab develops and applies novel bioinformatics methods for deciphering links between each individual own genome and his/her predisposition to common diseases such as cancer, diabetes, heart diseases etc. 

I'm a neurobiologist with expertise in human brain imaging and animal models of brain disease. I manage translational research in neuro-oncology, with projects ranging from building multi-dimensional patient databases, devising machine learning models combining radiotherapy and MRI to predict response to treatment, and incorporating non-genetic molecular analysis of brain tumors into clinical decision making. I collaborate with medical doctors, radiotherapy experts and basic research scientists in order to provide solutions to outstanding clinical questions in the treatment of patients with primary and metastatic brain tumors.

From gene to drug development, sex-specific precision medicine for brain disease. Professor Gozes discovered activity-dependent neuroprotective protein (ADNP), essential for brain development and function and further discovered the ADNP fragment, drug candidate davunetide, now intended for sex specific drug development.

Our research focuses on developing artificial intelligence tools and methods for applications in health and well-being. In our research we develop new algorithms and models and study their pros and cons, both theoretical and empirical ones, by collaborating with leading medical institutions

On top heading the research unit, computerization of the division and innovation projects by comprehensive R&D infrastructure providing dynamic operational solutions to allow tailored research, medical and clinical abilities. Specialized in the development of investigational drugs and clinical trials in Oxford University. Managing the Belong colon and rectal cancer group. With vast experience and networking to increase crucial information availability and shorter professional response time, all in order to help and fight against cancer more efficiently while improving our quality of life.

The main area of research is the application of machine learning technologies, especially deep learning, in dental medicine, maxillofacial radiology and pathology and related fields. A particular focus is on improving the quality of various medical images and automatic pathology detection.

With a passion for healthcare and innovation, she is working to integrate technological innovation into her clinical and managerial domains. As innovation leader for Dana children's hospital, her work focuses on remodeling healthcare to better meet new challenges in an era of disruptive digital technologies, by collaborating with technological partners and innovation leaders such as the Israel Innovation Institute.

She is the leader of several innovative projects, including a joint project with the Technion in machine-learning based medical technologies research, funded by a prestigious grant from the Israel Innovation Authority, and serves as a medical advisor to MedTech companies. Dr. Lavie holds a master's degree in public administration from Harvard Kennedy School.

Joachim Meyer studies decision processes involving humans (including physicians and nurses) and intelligent systems, conducting analytical work, simulations, and empirical studies in the laboratory and the field. Research dealt with nurses' responses to alarms and other information in intensive care, physicians' use of information from decision support, dispatchers' use of algorithmic recommendations for allocating emergency vehicles, and other health-care-related topics.

We study brain activity during sleep, how it supports      cognition and restorative functions, and how it affects sensation and      consciousness. We also develop sleep technologies and AI-based analysis of      sleep EEG data to improve early diagnosis and offer novel treatments in      neuropsychiatric disorders

In my lab, we study infectious diseases using computational tools such as machine learning, causal inference, and mathematical modeling. We apply these tools to medical data to estimate the effects of treatment with different antibiotics on resistance, predict bacterial infections in hospitalized patients, assess the spread of mosquito-borne diseases under changing climate conditions, and more.

Erez's research is dedicated to developing Artificial Intelligence (AI) models for understanding, predicting, and influencing human behavior using real-world data from sources like smartphones and wearable devices. Over the past years, his focus has particularly shifted towards the intersection of AI and healthcare.

Dr. Mor Saban, PhD, is a senior faculty member in the Department of Nursing at the School of Health Professions, Faculty of Medicine. She leads the DataMED Lab, a prominent research facility specializing in data science applications in healthcare. Her work focuses on evaluating the effectiveness of various technologies, developing optimization models for enhanced patient care using methods such as GenAI (LLMs), NLP, and ML, and analyzing clinical decision-making processes.

Analysis of genomic and clinical data. Development of methodologies, algorithms and software for understanding disease. Use of machine-learning for prediction, inference and interpretation tasks based on electronic medical records. Main disease topics: cancer, kidney disease, infection, covid-19, neurological diseases.

In the contemporary landscape marked by simultaneous approvals of new drugs for breast cancer by the FDA, the complexity of determining optimal treatment selection and sequencing for enhanced response rates and prolonged survival has become increasingly pronounced. Our research delves into a personalized approach to cancer care for individuals with breast cancer, leveraging ex-vivo organ cultures alongside extensive analyses of large datasets and clinical databases. The objective is to discern molecular regulators and biomarkers that play pivotal roles in the mechanisms of resistance within breast cancer. By doing so, we aim to optimize drug selection strategies for patients diagnosed with breast cancer, ultimately advancing the precision and effectiveness of treatment protocols.

I study incentive design from a computational perspective. I'm interested in how algorithms can help create better incentives in complex systems like healthcare, for example, through pay-for-performance models.

Cancer Immunology and immunotherapy,

 brain immunology

We combine digital health measures, such as Ecological Momentary Assessment, with behavioural and neuroimaging testing to better understand the mechanisms of mental health symptoms in health and in patient populations. To analyse the data, we use advanced statistical methods, including Bayesian methods and machine learning.

Dan Zeltzer is an applied health economist studying the impacts of new modes of healthcare delivery, such as telemedicine, digital tools, and artificial intelligence. His work examines how these technologies influence care utilization, cost, and quality and explores their broader effects on healthcare markets.

The study in the Laboratory of Bioinspired Materials and Nanotechnology is focused on mimicking self-assembly processes that occur in nature, including biomineralization processes and the organization of short peptides and amino acids into ordered nanostructures. The group designs and synthesizes 3D hydrogel scaffolds for tissue engineering and develops new materials for 3D printing to form personalized scaffolds. In addition, we develop controlled drug-release vehicles and antimicrobial moieties.

My research subject is Visual Computing: everything from Computer Graphics to Computer Vision, through Geometry Processing, animation, and Computational Fabrication (3D printing). Within this vast area, most of my lab's work is on controlling generative models for images, 3D motion, 3D shapes, and even some medical imaging and music, and is proud to release some of the world's currently leading models and techniques.

The Ben-David lab applies genomic and functional approaches to studying a fundamental trait of cancer, called aneuploidy  a change in the number of chromosomes in cancer cells. Ultimately, his research aspires to expand the understanding of the genetic basis of cancer, and to open new avenues for personalized cancer treatments.

Real-world clinical data analytics for epidemiological research and innovation in primary care settings, including pharmacoepidemiology, outcome research, and preventive medicine.

Dr. Ilana Dubovi’s research is focused on learning sciences and educational technology across healthcare and medical disciplines. Drawing on her background as a registered nurse and a Ph.D. in technology in education, she empirically studies how to advance healthcare professional education, as well as patient education. With this goal in mind, she develops adaptive and intelligent educational technologies to facilitate meaningful and engaging learning. Dr. Dubovi has a particular interest in developing novel learning analytics techniques that utilize psycho-physiological signals to capture and understand learning processes.

Clinical and translational research in oncology. Interested in collaborations in the field of deep learning, digital pathology and personalized medicine.

Jason Friedman studies questions related to human motor control and motor learning. In particular, he is interested in how we can accelerate motor learning and rehabilitation using different strategies, including advanced computational and machine-learning techniques. He studies these questions in typically developed adults and people with different motor disorders

Personalized medicine for rheumatic disease patients. Treatment options include various types of medications: conventional synthetic disease-modifying anti-rheumatic drugs, biological therapies and targeted synthetic small molecules. However, no single therapeutic mode is affective for all patients, and there are no predictive biomarkers for most drugs. The identification of such biomarkers would greatly assist in differentiating between individuals who are likely to benefit from a specific therapy from those who are not. Our ongoing project is designed to apply ex-vivo testing methods for response to a variety of anti-rheumatic drugs.

New approaches for resolution of synovial inflammation. The current clinical treatment of synovial inflammation relies upon intra-articular administration of glucocorticoid (GCs) injections directly to the joints. GCs may be associated with side effects, such as cartilage loss. Novel alternative approaches to manage inflammatory arthritis remain an unmet need of our lab. We investigate novel therapeutic approaches to restore the inflammatory process in the synovium.

Personalized medicine for rheumatic disease patients. Treatment options include various types of medications: conventional synthetic disease-modifying anti-rheumatic drugs, biological therapies and targeted synthetic small molecules. However, no single therapeutic mode is affective for all patients, and there are no predictive biomarkers for most drugs. The identification of such biomarkers would greatly assist in differentiating between individuals who are likely to benefit from a specific therapy from those who are not. Our ongoing project is designed to apply ex-vivo testing methods for response to a variety of anti-rheumatic drugs.

2) New approaches for resolution of synovial inflammation. The current clinical treatment of synovial inflammation relies upon intra-articular administration of glucocorticoid (GCs) injections directly to the joints. GCs may be associated with side effects, such as cartilage loss. Novel alternative approaches to manage inflammatory arthritis remain an unmet need of our lab. We investigate novel therapeutic approaches to restore the inflammatory process in the synovium.

Prof. Jeffrey M. Hausdorff, Ph.D., and the research team that he   directs study gait, balance, motor control, and brain function, with a   special focus on gait variability, motor-cognitive interactions, and fall   risk in older adults and patients with neurological diseases such as   Parkinson's disease and multiple sclerosis. Much of their work involves the   application of signal processing and machine learning approaches to extract   clinically relevant information from wearable devices. Prof. Hausdorff has served   on the board of directors of the International Society of Postural and Gait   Research, as a member of its Strategic Planning Committee, and as a member of   the Movement Disorders Society Task Force on Technology and currently serves   on the board of directors of the International Society for the Measurement of   Physical Behaviour, is a member of the Gait Advisory Committee for the   Michael J. Fox Foundation, is a member of an international task force that is   developing guidelines for the prevention and management of falls in older   adults, and is a member of the International Advisory Committee on Research   Institute for Intelligent Wearable Systems.

Prof. Yael Hanein's research primarily centers on developing innovative technologies, with a particular emphasis on neural interfaces and bioelectronics. Her work aims to advance the fields of neuroengineering and biomedicine, ultimately improving our understanding and treatment of neurological disorders.

Development of novel digital approaches to hematology diagnostics. These include image analysis of peripheral blood and bone marrow specimens, digitalization of routine laboratory processes, data mining from hematological specimens and their processing. 2. Big data analysis of laboratory tests in the fields of benign and malignant hematology and blood coagulation disorders. 3. Dissection of signal transduction pathways in malignant B-cells, and mechanisms-based development and improvement of immune therapeutics.

Medical Image Processing, Medical Image Understanding, Medical Imaging

My research mainly focuses on computational multiphysics models and mainly on biomechanics, where interaction is inherent and experimentation is limited. The current research topics include a numerical investigation of mitral valves, failing hearts, the mechanics of blood circulation in the placenta, and spinal cord injuries. The goal of my research is to understand the mechanisms of cardiovascular disease and provide clinically relevant insights that will help improve the available treatments.

Daniel's research focuses on developing and implementing statistical methods in widespread domains, with emphasis on methods for causal inference and survival analysis in biomedical settings from real-life and observational data. Additionally, he maintains ongoing collaborations with clinicians, epidemiologists, economists, and computer scientists in academia and health organizations with their goals often being reaching conclusions about causal effects from rich datasets.

I am a vascular surgeon, specializing in open and endovascular interventions to various arteries in the body (carotid, aorta, femoral etc.).  I am doing clinical research in the field of vascular surgery, as well as basic science research in arterial and venous diseases.

My main clinical and research fields are vertigo, tinnitus, hearing loss and tinnitus. I am looking at the effect of cardiovascular risk factors on hearing loss, the effect of tinnitus on cognition and mental function, as well as other research prospects such as the effect of systemic diseases on the vestibular system.

Our lab explores the molecular mechanisms underlying brain disease and develops MRI-based methods for early diagnosis and therapy optimization, with potential applications in cancer, stroke, and neurodegenerative disorders. We design and implement machine-learning-based strategies for quantitative molecular parameters imaging.

Tal's research is focused on various fields of molecular evolution and bioinformatics and combines methods from machine-learning, statistical inference, phylogenomics and microbial pathogenesis.

Director of oncology day-care, Chief researcher at the Cancer Research Unit, Specialist in medical oncology in general and lung cancer in particular.

Studying the evolution and genomics of viruses.

Mahmood's research interests span various areas related to computer security and privacy, with a particular focus on trustworthy machine learning. Among others, he explores human-centered defenses to enable privacy-preserving sharing of medical data, paving the way for advances in research and health tech.

Analysis of genomic and clinical data. Development of methodologies, algorithms and software for understanding disease. Use of machine-learning for prediction, inference and interpretation tasks based on electronic medical records. Main disease topics: cancer, kidney disease, infection, covid-19, neurological diseases.

My group focuses on modelling biomedical data using networks and applying graph algorithmic and machine learning techniques to mine these data and extract patterns of biological or medical significance.

Tom's lab studies the neural basis of behavior change and is interested in the process of value construction in value-based decision making. Research tools in the lab include structural and functional MRI, eye tracking, wearables, computational models and virtual reality. Since October 2021,Tom is the director of a Minerva center for the study of human intelligence in immersive, augmented and mixed realities and a co-director and founder of TAU XR.Tom's lab is committed to the principles of open and new science.

We focus on exploring the connection between brain structure, function and human behavior, using magnetic resonance imaging (MRI). We investigate what underlies behavioral and functional differences between individuals, such as changes in functional and structural connectivity and differences in the brains microstructure. In addition, we study learning-related brain plasticity by developing behavioral tasks that induce functional and structural brain modifications. For our work, we use advanced mathematical and statistical analysis methods.

Personalization, Data Mining, Machine Learning and Recommender Systems with emphasis on context inference with deep learning techniques and its usage in recommender systems.

My research focuses on computer vision and deep learning with a keen interest in medical imaging, microscopy, and genetics.

Patient-specific CT scans are being pulled from the hospital's PACS and bones of interest are being segmented by deep-learning (DL) algorithms. These are virtually loaded by physiological loads and their response is simulated by finite element analysis (FEA). Another DL algorithm analyses the relevant FEA results, presenting to the orthopedic surgeon the risk of fracture and assists in patient-specific fixation and implant selection.We validate our simulations by in-vitro experiments on fresh frozen human bones, and by clinical retrospective and prospective studies. Our algorithms were implemented and are being used at Tel Aviv Sourasky MC to monitor the femoral risk of fracture in patients with tumors in their femurs. We also completed a clinical study that demonstrates the high sensitivity and specificity of our algorithms for predicting osteopenic/osteoporotic risk of femoral fractures in the elderly population.

My research is a translational in nature in a   struggle to broaden our understanding of primary and metastatic brain tumors   with the hope of improving patient's treatment. By collaborations with   biology, engineering, imaging and physics experts in and outside Israel we   developed new MR biomarkers (TRAM's) and try to organize multiple levels of   clinical data in order to find new insights regarding this complicated group   of patients.