Greek mathematician Euclid, known as the father of geometry, revolutionized the understanding of shapes over 2,000 years ago. Today, MIT professor Justin Solomon applies modern geometric techniques to diverse problems, from machine-learning model testing to medical imaging and generative AI. He fosters diversity in geometric research and aims to improve unsupervised machine learning models.
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The Power of Geometric Techniques in AI and Data Science
Unlocking Insights with Modern Geometric Techniques
Justin Solomon, an associate professor at MIT, is leveraging modern geometric techniques to solve complex problems in AI and data science. By comparing datasets using geometric tools, valuable insight can be gained into the performance of machine-learning models.
Applications of Geometric Techniques
Solomon’s team works on aligning 3D organ scans in medical imaging and enabling autonomous vehicles to identify pedestrians using spatial data. They also conduct high-dimensional statistical research using geometric tools to construct better generative AI models for tasks such as creating new images.
Practical Applications
Geometric algorithms developed for computer animation are directly relevant to today’s generative AI and probability tasks. Solomon’s work shows the practical impact of geometric techniques in addressing real-world challenges.
Driving Change and Inclusivity in Research
Summer Geometry Initiative
Solomon launched a paid research program for undergraduates, aimed at making geometric research accessible to underrepresented students. The program has led to a more diverse field of researchers and has impacted the composition of incoming PhD classes at multiple institutions.
Diverse Perspectives in AI and Data Science
Solomon emphasizes the need for a more diverse field of researchers to tackle the growing list of problems in machine learning and statistics using geometric techniques. This diversity brings new ideas and perspectives to the field.
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