The Krill Prize Winners

Itzhak Tamo

Tel-Aviv University

Coding theory for distributed storage systems and memory devices

The information era we all live in is characterized by an exponential growth in the amount of data generated on a daily basis. As a result, computers, storage systems,
and cloud computing are facing new challenges that did not exist previously in the areas of data management, data protection, data analysis, and more. These challenges
define new problems of fault tolerance, data integrity, systems scalability, energy consumption and thermal dissipation. Therefore, new data management techniques
are in the need to be developed for large data centers (for example those of Facebook, Google and Microsoft), cloud computing, and new memory devices, for the
purpose of complying with the increasing demand for cheap and fast computing power by the end users. Broadly speaking, our main research goal is to face these
new challenges and demands by developing new strategies for fault-tolerance, and improving the current strategies for distributed and non-distribute computer systems. As
a result, these systems will become more resilient to failure, and data loss. Another aspect of our research is developing new data management techniques for memory
devices that better comply with the device’s physical limitations. The expected outcome of this research is an increased reliability of the device, its life expectancy, and
the availability of the stored data. Furthermore, we expect significant savings in energy consumption and electronic waste.

Amit Sever

Tel-Aviv University

The study of Quantum Field Theory, String Theory and
the holographic relation between them

One of the main open questions in our fundamental understanding of the universe is a computational description of strongly coupled phenomenon in Quantum Chromodynamics (QCD). That is the physical theory that best describes the elementary particles in nature. Another fundamental question that has troubled theoretical
physicists for decades is how to unify Quantum Field Theory (QFT), that governs the subatomic world, with General Relativity, that governs phenomena at the scale
of stars, galaxies or the entire universe. Remarkably, it turns out that under certain circumstances these two elemental questions are in fact closely related. That is, a
certain class of QFTs are equivalent to String theory or Gravity in a space-time with one more spatial dimension. My main research interest is to obtain a computational
handle on QCD at finite coupling and a deep understanding of quantum gravity. To do so, I try to solve an interacting gauge theory in four dimensions. I expect that such a
solution would play an analogues role in QFT to the one played by the Hydrogen atom in chemistry. The analog of the Hydrogen is a very special QFT model known as
N=4 SYM. It is simple enough that we can hope to solve it completely while sufficiently rich that we can hope to extract deep lessons about the foundations of QFT from
its solution. Moreover, it is a beautiful example of a gauge theory in four space-time dimensions that belong to the class of theories with a gravity or string theory dual. The
main tools in this program involve the solvability of the two-dimensional string — a surprising and powerful property named integrability. This property is the reason
that we expect to be able to solve the four-dimensional theory exactly.

Meital Landau

Technion

New Structure-activity Paradigms for Amyloids from Pathogenic Microbes

Microbial functional amyloids form protein fibrils that serve as key virulence determinants and participate in aggressive infections. Yet, amyloids are mostly
known for their involvement in fatal neurodegenerative diseases and their structures have been studied mostly in eukaryotes. Our lab pioneered the investigations of bacterial functional amyloids at the atomic level, and published the first structure of an amyloid fibril from bacteria (Tayeb-Fligelman et. al., Science 2017). The structure
revealed a unique cross-α amyloid-like fibril which presented a paradigm shift in amyloid research, where it has been believed that β-rich structures are
central. The novel fibril structure is of PSMα3, a virulent peptide secreted by the pathogenic bacterium Staphylococcus aureus that is toxic to human cells.
Given this and other results we show that the structural and functional repertoire of microbial amyloids is far more diverse than previously anticipated, providing
a rich source of targets for antimicrobial drug discovery.

Charles E. Diesendruck

Technion

New polymers for Modern Chemistry

Our research is on the field of polymer mechanochemistry and the synthesis of new functional polymers. We look at how mechanical force changes the chemistry of a polymer, and how changing the structure of molecules changes the mechanical
properties of plastic materials. In recent years, we have been studying the use of intramolecular covalent cross-links to develop polymers that mimic the
structure of proteins. We’ve made polymers that are “invincible” to force in solution and we are on the pathway to produce materials with high stiffness and
elasticity. Finally, we have also been looking at the chemical decomposition of membranes for alkaline fuel cells. Using fundamental organic chemistry,
significant improvements have been developed in testing new membranes, and we proposed new solutions to a technological problem that could change the energy storage and production landscape.

Yakov Babichenko

Technion

Complexity and Learning of Equilibria

Equilibria are the central solution concepts in game theory. However, these solutions impose strong assumptions on the rationality of the players. Players know the utilities
of all players they are facing, players are able to compute an equilibrium, and finally after computing equilibria players are able to agree on the same equilibrium and
play accordingly. This criticism raises the following natural question:

1. Can players learn to play an equilibrium using some
natural learning rule that does not require the above
strong assumptions?
2. If so, how fast can players learn it?
3. How hard is equilibrium computation?

In my research I am trying to address these question by considering the equilibrium computation task in various computational models; E.g., communication complexity
and query complexity.

Ayelet Erez

Weismann Institute

Deciphering unique metabolic changes in complex diseases for diagnosis and treatment

As a physician- scientist- geneticist, my researchchallenge is to untangle the genetic and metabolic alterations that lead to disease development, for diagnostic and therapeutic applications. To characterize the variables that differentiate healthy cells from the pathological ones, we use advanced genetic and metabolic systems that involve cell models, mice, and patient samples. By stably labeling metabolites, we are able to track the path of a specific metabolite in the healthy cell and compare it to the
corresponding path in the patient’s cell. Because multiple changes occur along disease course, we correlate our findings with samples from patients with inborn errors of metabolism (IEM), to dissect the direct consequences of a specific metabolic
aberration. Revealing a unique metabolic anomaly in patients, enables us to suggest diagnostic and therapeutic options, which identify and target specifically the
metabolic pathway that is essential for the pathological cell, thereby diagnosing, slowing down or even stopping the disease progression.

Adi Salomon

Bar-Ilan University

light matter interaction at the nanoscale

Energy transfer processes between light and molecules and among molecules themselves play important roles in nature, with photosynthesis probably being the
best-known example. In my laboratory at the Bar-Ilan Nanocenter, we study such light-matter interactions at the nanoscale, focusing on organic molecules. To do so,
we use metallic nanostructures to concentrate the light energy. Surface-plasmons, light-driven, collective oscillations of the metal’s free electrons, allow tuning,
enhancing and confining the electromagnetic field to a tiny, sub-wavelength volume. In this context, the overall goal of my wosrk is to modify molecules using plasmonic
modes as a photonic environment, or even as a ‘photonic catalyst’. Having this goal in mind, we have developed during the last four years metallic systems with unique properties. These systems are composed either of well-defined metallic nanostructures, or, more recently, of a large-scale nanoporous metallic network. Both of these systems are different from conventional plasmonic devices, and they are complementary to each other in many aspects. The nanofabricated surfaces we produce are like an artificial
leaf, on which the light energy can be funneled to a desired ‘reaction center’. We use this confined light as a ‘photonic reagent’, which opens new photochemical reaction
channels, or which modifies the potential energy barrier along a given reaction coordinate and thus enhances or inhibits a specific reaction channel. The idea here is to open new routes for photochemical reactions on surfaces
by controlling the electromagnetic-field properties at the metal surface.

Elad Gross

The Hebrew University of Jerusalem

Mechanism-driven regulation of reactivity and selectivity in organocatalysis

The preparation of many materials that are essential for our daily life in a modern society, such as plastics, fuels and fertilizers, heavily relies on the use of catalysts. Although catalysts have been used in the chemical industry for more than a century, there are many details about their structure-reactivity correlations which are not yet clear. One of the questions that I find most intriguing in catalysis research is identifying how the physical and chemical properties of catalysts influence their reactivity. In order to address this goal I use state of the art spectroscopic tools to detect the locations
in which chemical reactions occur on the surface of single particles. Using this approach, we have recently identified that chemical reactivity primarily occurs
on the periphery of metallic particles while lower reactivity was recorded at the center of the particle. These conclusions will enable the development of optimized catalysts based on rational design.

Emmanuel Levy

Weizmann Institute

Protein-protein interactions from atomic to cellular scales

Dr. Levy aims to discover novel functional and evolutionary properties of proteins, which are the main actors of cellular functions. Proteins in a cell are much like people in a company: they move and interact with each other and often form teams or “protein complexes.” During his PhD studies, Dr. Levy studied the structure of protein complexes and discovered common routes for their evolution. During his post-doctoral work, he studied the broader picture of all protein interactions within a cell. He proposed
the concept that many “promiscuous” interactions exist and showed that proteins are under evolutionary pressure to avoid such counter-productive interactions.
Today, Dr. Levy’s group is investigating protein interactions from the atomic to the cellular scales, understanding how even single point mutations can
drive a dramatic re-organization of proteins within the cell, and how proteins can be programmed to assemble into biomaterials.

Anat Milo

Ben-Gurion University of the Negev

Mechanism-driven regulation of reactivity and selectivity in organocatalysis

From energy conversion, through the production of materials, to the synthesis of natural products, pharmaceutics, and fine-chemicals – catalysts have been key to advancing our modern lifestyle. However, most industrial catalytic processes rely on toxic, expensive metals that have to be rigorously removed from the products. Organo¬catalysis offers a greener alternative, but often requires high catalyst loading,
suffers from repro-ducibility issues, and is tailored for a limited range of substrates. To overcome these limitations and unlock organocatalysts’ full potential, it is necessary to uncover the structural origin of their reactivity and selectivity, thereby providing a knowledge-driven approach to their design and optimization. The Milo research group synergistically integrates experimental, computational, and statistical methods to design and construct modular organocatalyst libraries and provide a powerful strategy for discovering and optimizing selective catalytic reactions.