The Wolf Prize laureates

// order posts by year $posts_by_year;

Jeffery W. Kelly

Wolf Prize Laureate in Chemistry 2023

Sir Richard Julian Long

 

Award citation:

“for redefining the possibilities of art-making and transforming the parameters of visual art”.

 

Prize share:

Sir Richard Julian Long

Fujiko Nakaya

 

Sir Richard Julian Long is an English sculptor and one of the best-known British land artists. He lives and works in Bristol, the city in which he was born. Long studied at the West of England College of Art (1962-1965) and continued his studies at the St. Martin’s School of Art and Design, London (1966-1968). Considered one of the most influential artists, Richard Long’s works have extended the possibilities of sculpture beyond traditional materials and methods. Long’s works engage with the landscape, investigating nature and his experience within nature. His work is typically displayed with materials or through documentary photographs of his performances and experiences.

When Richard Long was 18, he walked on the downs near his native Bristol. He began rolling a snowball through the snow, and when it became too big to push further, he took out his camera – then, instead of snapshotting the giant snowball, he photographed the dark meandering track it had left in the snow. This image, one of his earliest works of land art, was named “Snowball Track”. He was then a student at the West of England College of Art in Bristol, but he was dismissed from the course because his work was considered too provocative and perhaps ahead of its time.

Walking is central to Long’s work as a way of perceiving and recording landscape; early in his career, he established the precedent that art could be a journey and that a sculpture could be deconstructed over the distance of a journey. Walking as a medium has enabled him to articulate ideas about time and space. He seeks freedom of movement and expression and a balance with the natural world through a physical and personal engagement with the land, working with nature to reflect its impermanence and the changing processes of time. His beguilingly simple works commonly take the form of geometric shapes-circles, lines, ellipses, and spirals and use raw materials,
such as stones and driftwood, found along the way. These works are often simple interventions, marks of passage, and leave little or no trace, and are documented through photographs or text works that record his ideas, observations, and experiences.

Richard Long is awarded the Wolf Prize for being a pioneer of conceptual art centered on personal interaction with the natural world. In 1967, his work A Line Made by Walking introduced a contemporary reimagining of human experience in nature as a subject for art. Over the course of nearly six decades, his solitary walks throughout the world have generated a complex body of work comprising sculptures, photographs, drawings, and texts. The materials for these artworks, echoing the walks themselves, are nature-based: rocks and stones, logs and twigs, mud and soil. The tools of time-marking and map-making, place-naming and record-keeping all figure together to create works that commingle factual observation and artistic invention. Long’s deep engagement with the natural environment as process, subject, material, and vocabulary has established him as a key figure of his generation and one whose work resonates powerfully with present-day concerns.

Hiroaki Suga

Wolf Prize Laureate in Chemistry 2023

Sir Richard Julian Long

 

Award citation:

“for redefining the possibilities of art-making and transforming the parameters of visual art”.

 

Prize share:

Sir Richard Julian Long

Fujiko Nakaya

 

Sir Richard Julian Long is an English sculptor and one of the best-known British land artists. He lives and works in Bristol, the city in which he was born. Long studied at the West of England College of Art (1962-1965) and continued his studies at the St. Martin’s School of Art and Design, London (1966-1968). Considered one of the most influential artists, Richard Long’s works have extended the possibilities of sculpture beyond traditional materials and methods. Long’s works engage with the landscape, investigating nature and his experience within nature. His work is typically displayed with materials or through documentary photographs of his performances and experiences.

When Richard Long was 18, he walked on the downs near his native Bristol. He began rolling a snowball through the snow, and when it became too big to push further, he took out his camera – then, instead of snapshotting the giant snowball, he photographed the dark meandering track it had left in the snow. This image, one of his earliest works of land art, was named “Snowball Track”. He was then a student at the West of England College of Art in Bristol, but he was dismissed from the course because his work was considered too provocative and perhaps ahead of its time.

Walking is central to Long’s work as a way of perceiving and recording landscape; early in his career, he established the precedent that art could be a journey and that a sculpture could be deconstructed over the distance of a journey. Walking as a medium has enabled him to articulate ideas about time and space. He seeks freedom of movement and expression and a balance with the natural world through a physical and personal engagement with the land, working with nature to reflect its impermanence and the changing processes of time. His beguilingly simple works commonly take the form of geometric shapes-circles, lines, ellipses, and spirals and use raw materials,
such as stones and driftwood, found along the way. These works are often simple interventions, marks of passage, and leave little or no trace, and are documented through photographs or text works that record his ideas, observations, and experiences.

Richard Long is awarded the Wolf Prize for being a pioneer of conceptual art centered on personal interaction with the natural world. In 1967, his work A Line Made by Walking introduced a contemporary reimagining of human experience in nature as a subject for art. Over the course of nearly six decades, his solitary walks throughout the world have generated a complex body of work comprising sculptures, photographs, drawings, and texts. The materials for these artworks, echoing the walks themselves, are nature-based: rocks and stones, logs and twigs, mud and soil. The tools of time-marking and map-making, place-naming and record-keeping all figure together to create works that commingle factual observation and artistic invention. Long’s deep engagement with the natural environment as process, subject, material, and vocabulary has established him as a key figure of his generation and one whose work resonates powerfully with present-day concerns.

Ingrid Daubechies

Wolf Prize Laureate in Mathematics 2023

Sir Richard Julian Long

 

Award citation:

“for redefining the possibilities of art-making and transforming the parameters of visual art”.

 

Prize share:

Sir Richard Julian Long

Fujiko Nakaya

 

Sir Richard Julian Long is an English sculptor and one of the best-known British land artists. He lives and works in Bristol, the city in which he was born. Long studied at the West of England College of Art (1962-1965) and continued his studies at the St. Martin’s School of Art and Design, London (1966-1968). Considered one of the most influential artists, Richard Long’s works have extended the possibilities of sculpture beyond traditional materials and methods. Long’s works engage with the landscape, investigating nature and his experience within nature. His work is typically displayed with materials or through documentary photographs of his performances and experiences.

When Richard Long was 18, he walked on the downs near his native Bristol. He began rolling a snowball through the snow, and when it became too big to push further, he took out his camera – then, instead of snapshotting the giant snowball, he photographed the dark meandering track it had left in the snow. This image, one of his earliest works of land art, was named “Snowball Track”. He was then a student at the West of England College of Art in Bristol, but he was dismissed from the course because his work was considered too provocative and perhaps ahead of its time.

Walking is central to Long’s work as a way of perceiving and recording landscape; early in his career, he established the precedent that art could be a journey and that a sculpture could be deconstructed over the distance of a journey. Walking as a medium has enabled him to articulate ideas about time and space. He seeks freedom of movement and expression and a balance with the natural world through a physical and personal engagement with the land, working with nature to reflect its impermanence and the changing processes of time. His beguilingly simple works commonly take the form of geometric shapes-circles, lines, ellipses, and spirals and use raw materials,
such as stones and driftwood, found along the way. These works are often simple interventions, marks of passage, and leave little or no trace, and are documented through photographs or text works that record his ideas, observations, and experiences.

Richard Long is awarded the Wolf Prize for being a pioneer of conceptual art centered on personal interaction with the natural world. In 1967, his work A Line Made by Walking introduced a contemporary reimagining of human experience in nature as a subject for art. Over the course of nearly six decades, his solitary walks throughout the world have generated a complex body of work comprising sculptures, photographs, drawings, and texts. The materials for these artworks, echoing the walks themselves, are nature-based: rocks and stones, logs and twigs, mud and soil. The tools of time-marking and map-making, place-naming and record-keeping all figure together to create works that commingle factual observation and artistic invention. Long’s deep engagement with the natural environment as process, subject, material, and vocabulary has established him as a key figure of his generation and one whose work resonates powerfully with present-day concerns.

Chuan He

Wolf Prize Laureate in Chemistry 2023

Sir Richard Julian Long

 

Award citation:

“for redefining the possibilities of art-making and transforming the parameters of visual art”.

 

Prize share:

Sir Richard Julian Long

Fujiko Nakaya

 

Sir Richard Julian Long is an English sculptor and one of the best-known British land artists. He lives and works in Bristol, the city in which he was born. Long studied at the West of England College of Art (1962-1965) and continued his studies at the St. Martin’s School of Art and Design, London (1966-1968). Considered one of the most influential artists, Richard Long’s works have extended the possibilities of sculpture beyond traditional materials and methods. Long’s works engage with the landscape, investigating nature and his experience within nature. His work is typically displayed with materials or through documentary photographs of his performances and experiences.

When Richard Long was 18, he walked on the downs near his native Bristol. He began rolling a snowball through the snow, and when it became too big to push further, he took out his camera – then, instead of snapshotting the giant snowball, he photographed the dark meandering track it had left in the snow. This image, one of his earliest works of land art, was named “Snowball Track”. He was then a student at the West of England College of Art in Bristol, but he was dismissed from the course because his work was considered too provocative and perhaps ahead of its time.

Walking is central to Long’s work as a way of perceiving and recording landscape; early in his career, he established the precedent that art could be a journey and that a sculpture could be deconstructed over the distance of a journey. Walking as a medium has enabled him to articulate ideas about time and space. He seeks freedom of movement and expression and a balance with the natural world through a physical and personal engagement with the land, working with nature to reflect its impermanence and the changing processes of time. His beguilingly simple works commonly take the form of geometric shapes-circles, lines, ellipses, and spirals and use raw materials,
such as stones and driftwood, found along the way. These works are often simple interventions, marks of passage, and leave little or no trace, and are documented through photographs or text works that record his ideas, observations, and experiences.

Richard Long is awarded the Wolf Prize for being a pioneer of conceptual art centered on personal interaction with the natural world. In 1967, his work A Line Made by Walking introduced a contemporary reimagining of human experience in nature as a subject for art. Over the course of nearly six decades, his solitary walks throughout the world have generated a complex body of work comprising sculptures, photographs, drawings, and texts. The materials for these artworks, echoing the walks themselves, are nature-based: rocks and stones, logs and twigs, mud and soil. The tools of time-marking and map-making, place-naming and record-keeping all figure together to create works that commingle factual observation and artistic invention. Long’s deep engagement with the natural environment as process, subject, material, and vocabulary has established him as a key figure of his generation and one whose work resonates powerfully with present-day concerns.

Daniel Joshua Drucker

Wolf Prize Laureate in Medicine 2023

Sir Richard Julian Long

 

Award citation:

“for redefining the possibilities of art-making and transforming the parameters of visual art”.

 

Prize share:

Sir Richard Julian Long

Fujiko Nakaya

 

Sir Richard Julian Long is an English sculptor and one of the best-known British land artists. He lives and works in Bristol, the city in which he was born. Long studied at the West of England College of Art (1962-1965) and continued his studies at the St. Martin’s School of Art and Design, London (1966-1968). Considered one of the most influential artists, Richard Long’s works have extended the possibilities of sculpture beyond traditional materials and methods. Long’s works engage with the landscape, investigating nature and his experience within nature. His work is typically displayed with materials or through documentary photographs of his performances and experiences.

When Richard Long was 18, he walked on the downs near his native Bristol. He began rolling a snowball through the snow, and when it became too big to push further, he took out his camera – then, instead of snapshotting the giant snowball, he photographed the dark meandering track it had left in the snow. This image, one of his earliest works of land art, was named “Snowball Track”. He was then a student at the West of England College of Art in Bristol, but he was dismissed from the course because his work was considered too provocative and perhaps ahead of its time.

Walking is central to Long’s work as a way of perceiving and recording landscape; early in his career, he established the precedent that art could be a journey and that a sculpture could be deconstructed over the distance of a journey. Walking as a medium has enabled him to articulate ideas about time and space. He seeks freedom of movement and expression and a balance with the natural world through a physical and personal engagement with the land, working with nature to reflect its impermanence and the changing processes of time. His beguilingly simple works commonly take the form of geometric shapes-circles, lines, ellipses, and spirals and use raw materials,
such as stones and driftwood, found along the way. These works are often simple interventions, marks of passage, and leave little or no trace, and are documented through photographs or text works that record his ideas, observations, and experiences.

Richard Long is awarded the Wolf Prize for being a pioneer of conceptual art centered on personal interaction with the natural world. In 1967, his work A Line Made by Walking introduced a contemporary reimagining of human experience in nature as a subject for art. Over the course of nearly six decades, his solitary walks throughout the world have generated a complex body of work comprising sculptures, photographs, drawings, and texts. The materials for these artworks, echoing the walks themselves, are nature-based: rocks and stones, logs and twigs, mud and soil. The tools of time-marking and map-making, place-naming and record-keeping all figure together to create works that commingle factual observation and artistic invention. Long’s deep engagement with the natural environment as process, subject, material, and vocabulary has established him as a key figure of his generation and one whose work resonates powerfully with present-day concerns.

Fujiko Nakaya

Wolf Prize Laureate in Arts 2023

Sir Richard Julian Long

 

Award citation:

“for redefining the possibilities of art-making and transforming the parameters of visual art”.

 

Prize share:

Sir Richard Julian Long

Fujiko Nakaya

 

Sir Richard Julian Long is an English sculptor and one of the best-known British land artists. He lives and works in Bristol, the city in which he was born. Long studied at the West of England College of Art (1962-1965) and continued his studies at the St. Martin’s School of Art and Design, London (1966-1968). Considered one of the most influential artists, Richard Long’s works have extended the possibilities of sculpture beyond traditional materials and methods. Long’s works engage with the landscape, investigating nature and his experience within nature. His work is typically displayed with materials or through documentary photographs of his performances and experiences.

When Richard Long was 18, he walked on the downs near his native Bristol. He began rolling a snowball through the snow, and when it became too big to push further, he took out his camera – then, instead of snapshotting the giant snowball, he photographed the dark meandering track it had left in the snow. This image, one of his earliest works of land art, was named “Snowball Track”. He was then a student at the West of England College of Art in Bristol, but he was dismissed from the course because his work was considered too provocative and perhaps ahead of its time.

Walking is central to Long’s work as a way of perceiving and recording landscape; early in his career, he established the precedent that art could be a journey and that a sculpture could be deconstructed over the distance of a journey. Walking as a medium has enabled him to articulate ideas about time and space. He seeks freedom of movement and expression and a balance with the natural world through a physical and personal engagement with the land, working with nature to reflect its impermanence and the changing processes of time. His beguilingly simple works commonly take the form of geometric shapes-circles, lines, ellipses, and spirals and use raw materials,
such as stones and driftwood, found along the way. These works are often simple interventions, marks of passage, and leave little or no trace, and are documented through photographs or text works that record his ideas, observations, and experiences.

Richard Long is awarded the Wolf Prize for being a pioneer of conceptual art centered on personal interaction with the natural world. In 1967, his work A Line Made by Walking introduced a contemporary reimagining of human experience in nature as a subject for art. Over the course of nearly six decades, his solitary walks throughout the world have generated a complex body of work comprising sculptures, photographs, drawings, and texts. The materials for these artworks, echoing the walks themselves, are nature-based: rocks and stones, logs and twigs, mud and soil. The tools of time-marking and map-making, place-naming and record-keeping all figure together to create works that commingle factual observation and artistic invention. Long’s deep engagement with the natural environment as process, subject, material, and vocabulary has established him as a key figure of his generation and one whose work resonates powerfully with present-day concerns.

Martinus Th. van Genuchten

Wolf Prize Laureate in Agriculture 2023

Sir Richard Julian Long

 

Award citation:

“for redefining the possibilities of art-making and transforming the parameters of visual art”.

 

Prize share:

Sir Richard Julian Long

Fujiko Nakaya

 

Sir Richard Julian Long is an English sculptor and one of the best-known British land artists. He lives and works in Bristol, the city in which he was born. Long studied at the West of England College of Art (1962-1965) and continued his studies at the St. Martin’s School of Art and Design, London (1966-1968). Considered one of the most influential artists, Richard Long’s works have extended the possibilities of sculpture beyond traditional materials and methods. Long’s works engage with the landscape, investigating nature and his experience within nature. His work is typically displayed with materials or through documentary photographs of his performances and experiences.

When Richard Long was 18, he walked on the downs near his native Bristol. He began rolling a snowball through the snow, and when it became too big to push further, he took out his camera – then, instead of snapshotting the giant snowball, he photographed the dark meandering track it had left in the snow. This image, one of his earliest works of land art, was named “Snowball Track”. He was then a student at the West of England College of Art in Bristol, but he was dismissed from the course because his work was considered too provocative and perhaps ahead of its time.

Walking is central to Long’s work as a way of perceiving and recording landscape; early in his career, he established the precedent that art could be a journey and that a sculpture could be deconstructed over the distance of a journey. Walking as a medium has enabled him to articulate ideas about time and space. He seeks freedom of movement and expression and a balance with the natural world through a physical and personal engagement with the land, working with nature to reflect its impermanence and the changing processes of time. His beguilingly simple works commonly take the form of geometric shapes-circles, lines, ellipses, and spirals and use raw materials,
such as stones and driftwood, found along the way. These works are often simple interventions, marks of passage, and leave little or no trace, and are documented through photographs or text works that record his ideas, observations, and experiences.

Richard Long is awarded the Wolf Prize for being a pioneer of conceptual art centered on personal interaction with the natural world. In 1967, his work A Line Made by Walking introduced a contemporary reimagining of human experience in nature as a subject for art. Over the course of nearly six decades, his solitary walks throughout the world have generated a complex body of work comprising sculptures, photographs, drawings, and texts. The materials for these artworks, echoing the walks themselves, are nature-based: rocks and stones, logs and twigs, mud and soil. The tools of time-marking and map-making, place-naming and record-keeping all figure together to create works that commingle factual observation and artistic invention. Long’s deep engagement with the natural environment as process, subject, material, and vocabulary has established him as a key figure of his generation and one whose work resonates powerfully with present-day concerns.

Richard Long

Wolf Prize Laureate in Arts 2023

Sir Richard Julian Long

 

Award citation:

“for redefining the possibilities of art-making and transforming the parameters of visual art”.

 

Prize share:

Sir Richard Julian Long

Fujiko Nakaya

 

Sir Richard Julian Long is an English sculptor and one of the best-known British land artists. He lives and works in Bristol, the city in which he was born. Long studied at the West of England College of Art (1962-1965) and continued his studies at the St. Martin’s School of Art and Design, London (1966-1968). Considered one of the most influential artists, Richard Long’s works have extended the possibilities of sculpture beyond traditional materials and methods. Long’s works engage with the landscape, investigating nature and his experience within nature. His work is typically displayed with materials or through documentary photographs of his performances and experiences.

When Richard Long was 18, he walked on the downs near his native Bristol. He began rolling a snowball through the snow, and when it became too big to push further, he took out his camera – then, instead of snapshotting the giant snowball, he photographed the dark meandering track it had left in the snow. This image, one of his earliest works of land art, was named “Snowball Track”. He was then a student at the West of England College of Art in Bristol, but he was dismissed from the course because his work was considered too provocative and perhaps ahead of its time.

Walking is central to Long’s work as a way of perceiving and recording landscape; early in his career, he established the precedent that art could be a journey and that a sculpture could be deconstructed over the distance of a journey. Walking as a medium has enabled him to articulate ideas about time and space. He seeks freedom of movement and expression and a balance with the natural world through a physical and personal engagement with the land, working with nature to reflect its impermanence and the changing processes of time. His beguilingly simple works commonly take the form of geometric shapes-circles, lines, ellipses, and spirals and use raw materials,
such as stones and driftwood, found along the way. These works are often simple interventions, marks of passage, and leave little or no trace, and are documented through photographs or text works that record his ideas, observations, and experiences.

Richard Long is awarded the Wolf Prize for being a pioneer of conceptual art centered on personal interaction with the natural world. In 1967, his work A Line Made by Walking introduced a contemporary reimagining of human experience in nature as a subject for art. Over the course of nearly six decades, his solitary walks throughout the world have generated a complex body of work comprising sculptures, photographs, drawings, and texts. The materials for these artworks, echoing the walks themselves, are nature-based: rocks and stones, logs and twigs, mud and soil. The tools of time-marking and map-making, place-naming and record-keeping all figure together to create works that commingle factual observation and artistic invention. Long’s deep engagement with the natural environment as process, subject, material, and vocabulary has established him as a key figure of his generation and one whose work resonates powerfully with present-day concerns.

Prizes and scholarships laureates

// order posts by year $posts_by_year;

Nir Shlezinger

Krill Prize 2024
Ben-Gurion University

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.

Chaya Keller

Krill Prize 2024
Ariel University

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.

Raya Sorkin

Krill Prize 2024
Tel-Aviv University

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.

Hila Peleg

Krill Prize 2024
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.

Itamar Harel

Krill Prize 2024
The Hebrew University

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.

Yaniv Romano

Krill Prize 2024
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.

Renana Gershoni-Poranne

Krill Prize 2024
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.

Neta Shlezinger

Krill Prize 2024
The Hebrew University

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.

Mor Nitzan

Krill Prize 2024
The Hebrew University

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.

Yoav Livneh

Krill Prize 2024
Weizmann Institute

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.

Yuval Hart

Krill Prize 2023
The Hebrew University

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.

Tomer Koren

Krill Prize 2023
Tel-Aviv University

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.

Inbal Talgam-Cohen

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.

Nitzan Gonen

Krill Prize 2023
Bar-Ilan University

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.

Viviane Slon

Krill Prize 2023
Tel-Aviv University

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.

Yotam Drier

Krill Prize 2023
The Hebrew University

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.

Ido Goldstein

Krill Prize 2023
The Hebrew University

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.

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.

gallery