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A computer engineer explores the limits of geometric principles.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Faculty, Machine learning, MIT Schwarzman College of Computing, Profile, School of Engineering, UncategorizedApril 14, 202438Views0Likes0Comments
More than 2000 years after Greek mathematician Euclid revolutionized the understanding of shapes, MIT associate professor Justin Solomon uses modern geometric techniques to resolve complex problems that seemingly have little to do with shapes. Adopting these techniques to compare two datasets for machine learning model performance, Solomon argues that geometric tools can reveal whether the…

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Bridging the gap between design and production for optical instruments

Artificial Intelligence, Biological engineering, Computer science and technology, Electronics, Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT.nano, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedApril 14, 202437Views0Likes0Comments
Photolithography is a crucial technique in the production of computer chips and optical devices, but it is susceptible to micro discrepancies which can result in the final devices not performing as designed. MIT and the Chinese University of Hong Kong researchers have helped resolve this issue, using machine learning to create a digital simulator that…

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The computational model encapsulates the hard-to-detect transition states in chemical reactions.

Artificial Intelligence, Chemical engineering, Chemistry, Machine learning, National Science Foundation (NSF), Research, School of Engineering, School of Science, UncategorizedApril 14, 202434Views0Likes0Comments
During a chemical reaction, molecules gain energy until they reach a transition state. This is a point from which the reaction must proceed. However, this state is brief and almost impossible to observe experimentally. Traditionally, the structures of these transition states have been calculated with methods rooted in quantum chemistry. This process is extremely time-consuming. The…

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A computer scientist advances the limits of geometry.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Faculty, Machine learning, MIT Schwarzman College of Computing, Profile, School of Engineering, UncategorizedApril 14, 202436Views0Likes0Comments
Over two thousand years ago, Greek mathematician Euclid revolutionized the world with his groundbreaking work in geometry. Today, MIT Associate Professor Justin Solomon is using contemporary geometric techniques to solve intricate problems, which often don't appear to be related to shapes, albeit heavily correlate with data arrangement in a high-dimensional space. Solomon, who is also a…

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Bridging the gap between the design and manufacturing of optical devices.

Artificial Intelligence, Biological engineering, Computer science and technology, Electronics, Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT.nano, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedApril 14, 202436Views0Likes0Comments
Photolithography is a manufacturing process that uses light to precisely etch features onto surfaces, such as producing computer chips and optical devices. However, small imprecisions in the process can sometimes result in devices not being produced to specifications. To close this gap, researchers from MIT and the Chinese University of Hong Kong are employing machine…

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The computational model accurately represents the hard-to-detect transitional phases of chemical reactions.

Artificial Intelligence, Chemical engineering, Chemistry, Machine learning, National Science Foundation (NSF), Research, School of Engineering, School of Science, UncategorizedApril 14, 202435Views0Likes0Comments
An MIT research team has developed an approach that quickly calculates the structure of transition states fundamental in chemical reactions - the fleeting and typically unobservable point that determines whether a reaction proceeds. This new machine learning-based model could assist in developing new reactions and catalysts for creating materials like fuels or drugs, and might…

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A computer technologist is advancing the limits of geometry.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Faculty, Machine learning, MIT Schwarzman College of Computing, Profile, School of Engineering, UncategorizedApril 13, 202438Views0Likes0Comments
Justin Solomon, an associate professor in the MIT Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), employs modern geometric techniques to solve intricate problems often unrelated to shapes. Using these geometric methods, data sets can be compared and the high-dimensional space in which the…

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A computer scientist stretches the limits of geometry.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Faculty, Machine learning, MIT Schwarzman College of Computing, Profile, School of Engineering, UncategorizedApril 13, 202434Views0Likes0Comments
The Greek mathematician Euclid is renowned for laying the groundwork of geometry more than 2,000 years ago. In present times, Justin Solomon, an Associate Professor at MIT's Department of Electrical Engineering and Computer Science, is deriving inspiration from Euclid's fundamental theories and using modern geometric techniques to solve complex problems. Remarkably, these issues frequently bear…

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Bridging the gap between the design and production stages for optical devices.

Artificial Intelligence, Biological engineering, Computer science and technology, Electronics, Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT.nano, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedApril 13, 202438Views0Likes0Comments
Researchers from MIT and the Chinese University of Hong Kong have leveraged machine learning to construct a digital simulator to enhance the precision of photolithography and bridge the gap between design and manufacturing. Photolithography, a crucial manufacturing process in computer chip production and optical device fabrication, suffers from slight deviations that can lead to shortcomings…

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Narrowing the distance between design and production for optical instruments.

Artificial Intelligence, Biological engineering, Computer science and technology, Electronics, Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT.nano, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedApril 13, 202436Views0Likes0Comments
Photolithography, a technique used to etch precise features onto surfaces for the creation of computer chips and optical devices, is often inaccurately executed due to tiny deviations during manufacturing. In an attempt to bridge this gap between design and production, a team of researchers from MIT and the Chinese University of Hong Kong have developed…

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The computational model grasps the hard-to-capture transition phases of chemical reactions.

Artificial Intelligence, Chemical engineering, Chemistry, Machine learning, National Science Foundation (NSF), Research, School of Engineering, School of Science, UncategorizedApril 13, 202430Views0Likes0Comments
During a chemical reaction, molecules gain energy until they reach what is known as the transition state — a point at which the reaction must proceed. This state is extremely short-lived and nearly impossible to observe experimentally. Its structures can be calculated using quantum chemistry techniques, but these methods are very time-consuming. Recently, a team of…

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A computer scientist is expanding the limits of geometry.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Faculty, Machine learning, MIT Schwarzman College of Computing, Profile, School of Engineering, UncategorizedApril 13, 202436Views0Likes0Comments
Justin Solomon, an associate professor at the Massachusetts Institute of Technology (MIT) Department of Electrical Engineering and Computer Science, is applying modern geometric techniques to solve complex problems in machine learning, data science, and computer graphics. He leads the Geometric Data Processing Group, half of which works on optimizing two- and three-dimensional geometric data in…

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