Who is Aaron Dharna? - Data Scientist and Researcher

Full NameAaron Dharna
Current PositionPh.D. student in Computer Science at The University of British Columbia
EducationPh.D. in Computer Science, Master's in Data Analytics, B.S. in Mathematics and Computer Science
LanguagesNot specified
Key SkillsMachine Learning, Research, Software Engineering, Data Analytics
Notable ProjectsCo-generation of game levels and game-playing agents, Training a smartcab to Drive, Creating Customer Segments
Publications"Can Machines 'Learn' Halide Perovskite Crystal Formation without Accurate Physicochemical Features?"
CertificationsMachine Learning Engineer NanoDegree from Udacity


Aaron Dharna's academic journey began at Fordham University, located in New York City, where he earned a Bachelor of Science (B.S.) in Mathematics and Computer Science from January 2012 to January 2016. Fordham University is known for its strong liberal arts education and Jesuit traditions. Following his undergraduate studies, Aaron pursued a Master's degree in Data Analytics at Fordham University Graduate School of Arts and Sciences from January 2018 to January 2020, furthering his expertise in the field. He then attended the New Jersey Institute of Technology from August 2020 to May 2022, although the specific degree pursued is not mentioned. Currently, Aaron is a Ph.D. student in Computer Science at The University of British Columbia (UBC) since August 2022. UBC is a global center for research and teaching, consistently ranked among the top 20 public universities in the world.

Professional Experience

Aaron Dharna's professional experience is rich and varied, with a strong focus on machine learning and research. He began his career as a Substitute Teacher at the School District of Clayton in Clayton, MO, from October 2016 to August 2017, where he was responsible for maintaining classroom order and ensuring adherence to the school's policies.

He then transitioned into the tech industry as a Software Engineering Intern at nTopology Inc., a New York-based company specializing in computational modeling software, from October 2017 to March 2018. Here, Aaron worked on developing data-driven design and functional modeling tools.

Following this, Aaron served as a Machine Learning Engineer Intern at Instrumental Inc., a company in the San Francisco Bay Area that uses machine learning for manufacturing optimization, from June 2018 to August 2018. His contributions included improving anomaly detection in image processing and integrating new algorithms into the company's machine learning stack.

Aaron's role as a Mathematics Tutor at Fordham University from August 2014 to May 2016 allowed him to foster students' mathematical skills and thinking, preparing them for independent problem-solving.

His research experience includes a stint as a Robotics Research Assistant at Fordham University from January 2016 to May 2016, where he worked on developing sensory fusion software for robotics applications.

As a Computational Research Intern at Fordham University from May 2019 to October 2019, Aaron contributed to the DARPA-SD2E - ESCALATE project, integrating machine learning models to aid materials discovery.

Aaron's most significant research contributions were made during his time as a Machine Learning Researcher at New York University from August 2019 to June 2022. He worked on generating new paired learning environments and agents, combining evolutionary algorithms, neural networks, optimization, and reinforcement learning. His work was supervised by Dr. Julian Togelius and resulted in a peer-reviewed paper.

Lastly, Aaron was a Research Scientist at Geometric Data Analytics, Inc. for a brief period from June 2022 to August 2022, focusing on multi-agent reinforcement learning research.

Projects and Publications

Aaron Dharna has been involved in several impactful projects, such as training a smartcab to drive using reinforcement learning, creating customer segments through unsupervised analysis, and predicting victory in online team-based zero-sum games using machine learning techniques. His work on image classification and building Python Bayesian Networks showcases his proficiency in applying complex algorithms to practical problems.

His publication "Can Machines 'Learn' Halide Perovskite Crystal Formation without Accurate Physicochemical Features?" was published by ACS in January 2020, contributing to the field of materials science and machine learning.

Certifications and Courses

Aaron has completed a range of courses, including Artificial Intelligence, Complex Analysis, Data Mining, and more, which have equipped him with a deep understanding of various mathematical and computer science concepts. He is also a certified Machine Learning Engineer, having earned a NanoDegree from Udacity, which attests to his skills in building and deploying machine learning models.

Aaron Dharna's professional journey is marked by a continuous pursuit of knowledge and innovation in the fields of machine learning and data science. His contributions to research and his dedication to education reflect a commitment to advancing the intersection of computer science and mathematics.