Difference between revisions of "Fellow:Howard Brown"
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| − | [[File:HowardBrown.jpg|thumb|none | + | [[File:HowardBrown.jpg|thumb|none]]Howard Brown is an undergraduate student at Hampton University majoring in Computer Science with a minor in Mathematics. His research experience includes optical refrigeration of rare-earth doped solids, solid state laser development and spectroscopy, materials science, high performance computing and artificial intelligence. He spent a decade as a senior editor at a media outlet establishing and maintaining relationships with companies such as Microsoft, Sony, Nintendo and managed a team of writers across multiple entertainment industries. He has experience working at an NGO as well as mentoring high school students in nano science. Upon graduation he plans to pursue a PhD in computer science while creating a discipline-agnostic general form model for solving nonlinear problems across massive data sets. |
| − | + | == Related Links<br/> == | |
| − | + | [[Hampton University|Hampton University]] | |
| − | + | [[Hampton University Strategic Plan|Hampton University Strategic Plan]] | |
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| + | [[Category:Student Contributors|h]] | ||
| + | [[Category:Student Contributors]] | ||
Latest revision as of 17:52, 1 June 2020
Howard Brown is an undergraduate student at Hampton University majoring in Computer Science with a minor in Mathematics. His research experience includes optical refrigeration of rare-earth doped solids, solid state laser development and spectroscopy, materials science, high performance computing and artificial intelligence. He spent a decade as a senior editor at a media outlet establishing and maintaining relationships with companies such as Microsoft, Sony, Nintendo and managed a team of writers across multiple entertainment industries. He has experience working at an NGO as well as mentoring high school students in nano science. Upon graduation he plans to pursue a PhD in computer science while creating a discipline-agnostic general form model for solving nonlinear problems across massive data sets.
