Tomer Koren

Krill Prize 2023
Tel-Aviv University

Tomer Koren

 

Affiliation at the time of the award:

Tel Aviv University

The Sackler Faculty of Exact Sciences,

Blavatnik School of Computer Science

 

Award citation:

“for unique contributions in the field of computer learning, understanding algorithms and their improvement”.

 

Dr. Tomer Koren’s research deals with theory and algorithms for mathematical optimization and their properties and uses in computer learning and reinforcement learning. Optimization methods are currently at the heart of the work in the field of Artificial Intelligence – AI, and are used to train almost any learning-based system in a wide variety of fields and applications.

Despite this, there is still much that is still unknown in regards to the generalization properties of these algorithms, and especially their surprising ability to provide insights and predictions about information details that they have never seen before. One of Dr. Koren’s main research goals is to strengthen the theoretical foundations behind the breakthrough successes of computer learning in recent years. This reinforcement is essential to better understand why the learning methods we use today succeed above and beyond expectations (that is, beyond what classical theory promises), and how they can be improved and optimized.

Yotam Drier

Krill Prize 2023
The Hebrew University

Yotam Drier

 

Affiliation at the time of the award:

The Hebrew University of Jerusalem

Faculty of Medicine, Department of Immunology and Cancer Research

 

Award citation:

“for original contributions in the field of cancer research and their combination with the development of new algorithms for data analysis, the development of new experimental methods and the prediction of relevant changes”.

 

Cells tightly regulate the levels of each gene, and dysregulation can lead to diseases such as cancer. Dysregulation can be caused by genetic alterations, epigenetic alterations (chemical modifications on the DNA), or changes in chromosomal folding. Our chromosomes are extremely long linear DNA molecules, folded neatly into the cell nucleus, and this structure is important for proper gene regulation. While the role of genetic alterations in genes in disease is well understood, much less is known about epigenetic and structural alterations

In Dr. Dreyer’s laboratory, they aim to fill this knowledge gap by studying these alterations in multiple cancer types and a few genetic diseases. They combine experimental techniques to systematically characterize epigenomes and chromosomal folding, computational algorithms to integrate these data and predict events that drive disease, and experimental validation of these predictions.

Shay Moran

Krill Prize 2023
Technion

Shay Tamar

 

Affiliation at the time of the award:

Technion

Faculty of Mathematics

 

Award citation:

“for unique contributions in machine learning research and generalization theory”.

 

Machine learning, better known to most of us as Artificial Intelligence – AI is applied in a wide variety of fields – starting with engineering challenges such as autonomous components and ending with social political fields that include sensitive personal data such as the management and accessibility of information on social networks such as Facebook or Twitter.

Dr. Moran’s research focuses on one of the most important branches of machine learning, which is called generalization theory and aims to quantitatively understand how machine learning generalizes from the individual to the general. This branch has made a significant contribution to the revolutionary technological breakthroughs that the field has experienced in recent years.

The latest breakthroughs in generalization theory demonstrate phenomenas that cannot be explained using previous techniques and sometimes even contradict classical principles in learning and statistics. The latest breakthroughs in generalization theory demonstrate phenomena that cannot be explained using previous techniques and sometimes even contradict classical principles in learning and statistics. One of the main reasons for this is that the classical generalization theory is based on definitions that focus on the worst case, and is therefore too pessimistic. This means that in practical machine learning problems the input usually does not fit the worst case, and experiments show that it is often possible to successfully learn based on training on far fewer examples than the number required by the predictions of the classical theory. Dr. Moran’s research aims to develop generalization theories that complement classical theory and enable more accurately model modern learning tasks, including tasks involving sensitive data.

Aviv Tamar

Krill Prize 2023
Technion

Aviv Tamar

 

Affiliation at the time of the award:

Technion

Faculty of Electrical and Computer Engineering named after Viterbi

 

Award citation:

“for the development of algorithms for automatic robotic learning using reinforcements”.

 

A robot that washes the dishes in the sink for you, folds the laundry and takes the dog out for a walk, this is not a dream – in a laboratory for robotic learning, Dr. Aviv Tamar investigates how machines can learn to perform tasks automatically by “reinforcement learning”. His research might have a great impact on our lives in the coming decade.

Dr. Tamar’s research deals with teaching robots how to learn and act in human natural environments. That is, how robots can learn new tasks quickly and using prior knowledge, and how can they learn about the physical world through playing with their surroundings.

To achieve these goals, Dr. Tamar’s research group develops fundamental learning algorithms that uses a technique called reinforcement learning. In the laboratory, these algorithms are applied to a variety of robotic tasks, including motion planning, object manipulation, and assembly.
One of Dr. Tamar’s main contributions is in the development of algorithms that allow robots to explore their environment automatically, making their learning in new tasks simpler and faster. Through research collaborations, Dr. Tamar’s lab team is working to discover how to use reinforcement learning to solve Real-world problems outside of robotics, including improving Internet performance and strengthening blockchain security.

Leeat Keren

Krill Prize 2023
Weizmann

Leeat Keren

 

Affiliation at the time of the award:

Weizmann Institute of science

Faculty of Biology, Department of Molecular Biology of the Cell

 

Award citation:

“for the development of innovative technologies for molecular imaging and for revealing the defense mechanisms of cancer tumor cells against an immune response, with the aim of developing innovative treatments for cancer”.

 

The development of cancer is a complex process that depends on the interrelationships between the individual cells of the tumor, the cells around it and the immune system – which can act both to promote and to suppress the invasion and development of the tumor. All participants are thought to be important in tumor biology, yet their interactions and relative contributions are largely unknown.

In Dr. Keren’s laboratory, they study the way in which different cells of the tumor and the immune system communicate as a system to collectively define the development of cancer and results as a response to treatment. For this purpose, Dr. Keren’s laboratory develops innovative technologies to perform complex imaging, which provides unparalleled molecular observation both on the tumor and on immune cells. In the laboratory, samples are taken from patients to identify mechanisms that help the tumor cells to successfully escape the immune response, and also develop innovative treatments in order to direct the power of the immune system against cancer.

Jeffery W. Kelly

Wolf Prize Laureate in Chemistry 2023

Jeffery W. Kelly

 

Affiliation at the time of the award:

Scripps Research Institute, USA

 

Award citation:

“for developing a clinical strategy to ameliorate pathological protein aggregation”.

 

Prize share:

Jeffery W. Kelly

Chuan He

Hiroaki Suga

 

“for pioneering discoveries that illuminate the functions and pathological dysfunctions of RNA and proteins and for creating strategies to harness the capabilities of these biopolymers in new ways to ameliorate human diseases”.

 

Prof. Jeffery W. Kelly is the Lita Annenberg Hazen Professor of Chemistry at The Scripps Research Institute. Kelly received his BS in chemistry from the State University of New York at Fredonia, his Ph.D. in organic chemistry from the University of North Carolina at Chapel Hill (1986), and performed postdoctoral research in bio-organic chemistry at Rockefeller University (1989).

Most protein molecules must fold into defined three-dimensional structures to acquire their functional activity. However, some proteins can adopt several folding states, and their biologically active state may be only marginally stable. Misfolded proteins can form toxic aggregates, such as soluble oligomers and fibrillar amyloid deposits, which may lead to neurodegeneration in Alzheimer’s disease and many other pathologies. All cells contain an extensive protein homeostasis network of protein folding devices, such as molecular chaperones and other factors that prevent or regulate protein aggregation. These defense networks tend to decline during aging, facilitating the manifestation of aggregate deposition diseases.

Prof. Kelly’s research focuses on understanding protein folding, misfolding, and aggregation and using chemical and biological approaches to develop novel therapeutic strategies to combat diseases caused by protein misfolding and aggregation. He contributed significantly to the fight against neurodegenerative diseases by discovering the mechanism of protein aggregation in amyloid diseases that affect the heart and nervous system. He showed the mechanism by which a protein, transthyretin, unravels and agglomerates into clusters that kill cells, tissues, and ultimately patients and developed a molecular approach to stabilize this protein.
Kelly successfully synthesized the first regulatory-agency-approved drug, “tafamidis vyndaqel”. This pioneering drug, marketed worldwide, significantly slows the progression of Familial Amyloid Polyneuropathy, a neurodegenerative disease, and Familial and Sporadic TTR Cardiomyopathy disease, which causes heart failure.

Jeffery W. Kelly is awarded the Wolf prize for developing a new and clinically impactful strategy to ameliorate disease caused by pathological protein aggregation. His seminal contributions revealed fundamental features of protein homeostasis (proteostasis) at the molecular level, including the interplay among protein folding, misfolding, and aggregation. Dysregulation of proteostasis is associated with a spectrum of human diseases. Kelly’s laboratory used these fundamental insights to develop the drug “tafamidis”, which halts or slows disease progression in patients suffering from transthyretin amyloidosis. This approach may be applicable to other proteostasis-based disorders.