Ross Altman

Ross Altman is a physicist with diverse interests and skills in data analysis and problem-solving, drawing from over 15 years of programming experience in languages like C++, Python, and JavaScript.

His background includes a strong foundation in engineering, mathematics, and computation, along with practical knowledge in big data architecture, machine learning, and high performance computing.

Ross is known for his ability to identify new applications, ask probing questions, and tackle problems through interdisciplinary approaches.

He is a self-motivated individual with a continuous learning mindset, often engaged in various pet projects and initiatives.

With proven leadership, communication, and teamwork abilities, Ross has successfully seen projects through from inception to completion.

His academic contributions include writing papers (with an Erdős number of 4) and ongoing work in curating a vast database of string theory vacuum spaces.

Currently, Ross is involved in developing machine learning techniques for string theory as a key member of the String Data group at Northeastern University and beyond.

Apart from his professional endeavors, Ross Altman is an enthusiast of zen philosophy, surrealist art, history, psychology, and natural sciences, which significantly influence his work and interests.

Ross holds a Master's degree in Engineering, Applied Physics from Cornell University, a Ph.D. in Theoretical Physics from Northeastern University, and a Bachelor's degree in Applied and Engineering Physics, also from Cornell University.

In his professional journey, Ross has contributed to various organizations, working as a Data Scientist at Inari, a Fellow at Insight Data Science, and holding research and teaching positions at Northeastern University, Rensselaer Polytechnic Institute, and Cornell University, among others.