Building the future of AI-powered legal techLinkSquares was named one of the 10 top most innovative companies in AI by FastCompany in 2022: https://www.fastcompany.com/90724347/most-innovative-companies-artificial-intelligence-2022

• Building and deploying cutting-edge machine learning models to extract the most useful information from legal agreements

Volunteer Technical Advisor to three post-doctoral fellows transitioning from academia to data science, providing industry guidance on project development and interview skills.

• Developed tree-based machine learning model in Python using LightGBM to forecast delays in Boston’s MBTA rapid rail system• Provided predictions for delays using the MBTA performance API, Dark Sky weather API, and special events, allowing for delay forecasts multiple days in advance, aiding riders in route planning• Deployed a Flask-based web app on AWS for delay forecasts on the MBTA Green Line

• Performed pioneering large-scale numerical simulations in Fortran of the interaction of electromagnetic fields and fluids created in the collision of high-energy nuclei, co-led the analysis and interpretation of simulation results, resulting in quantitative predictions of charged particle production in particle collision experiments• Uncovered interdisciplinary connections on the nature of condensate formation and plasma instabilities in matter under extreme conditions, verified analytical arguments with explicit numerical simulations using original C++ code• Generated, processed, and analyzed hundreds of gigabytes of simulation data using Python packages NumPy and SciPy and employing Bayesian statistics; made visualization using Matplotlib• Co-Principle Investigator of computational grants receiving a combined 12M computing hours per annum on US Department of Energy supercomputers• Mentored a Ph.D. student writing C++ code to numerically solve large sets of coupled non-linear partial differential equations which describe particle correlations generated by subatomic interactions• Postdoctoral Research Associate in the QCD Theory group under Prof. Tuomas Lappi. • Concurrent with position of Adjoint scientist at the Helsinki Institute of Physics.• Resigned before end of term to pursue machine learning and data science

Concurrent with Research Associate position at University of Jyväskylä.

• Built data-guided models for correlations between particles produced in high-energy nuclear collisions of protons and nuclei, providing insights on the internal dynamics of nuclei and providing predictions for experimental searches• Co-lead author of 10,000+ line highly-parallelized C++ code to perform simulations on a TOP500 super- computer of subatomic particles interacting in extreme conditions similar to the early universe, resulting in concrete evidence for the viability of detecting theoretically predicted exotic subatomic effects in planned and ongoing particle physics experiments in the US and Europe• Lead a four member team in an Intel KNL hackathon event held at Brookhaven National Lab, achieving a performance improvement by a factor of four by restructuring OpenMP and MPI parallelization schemes• Physics PhD program at Stony Brook University (SUNY)• Concurrent with Graduate Research Assistant position at Brookhaven National Lab• Advised by Profs. Dmitri Kharzeev and Raju Venugopalan

• Gave overview lectures for an introductory physics labs, oversaw three sections of 20 student labs.• Conducted oral exams for each lab, graded lab reports, proctored and graded exams• Staffed a help desk and held office hours• Teaching P123/124 (Physics for the Life Sciences- Mechanics/Electricity and Magnetism Lab) and P134 (Physics for Scientists and Engineers- Electricity and Magnetism Lab).

Concurrent with graduate Research Assistant position at Stony Brook University

• Visiting PhD student in the group of Prof. Dr. Juergen Berges at the Institute for Theoretical Physics, developed international collaborations on studies of real-time classical-statistical lattice gauge theory

• Visiting PhD student in the group of Prof. Dr. Juergen Berges at the Institute for Theoretical Physics, developed international collaborations on studies of real-time classical-statistical lattice gauge theory• Gave a group seminar

• Analyzed data from MILC lattice gauge theory calculations for hadron spectroscopy.• Supervised by Prof. Steven Gottlieb.

• Graded homework and quizzes. • Held office hours and exam reviews. • Gave lectures when the professor was out of town.

• Tutoring student athletes for various courses in physics and mathematics.

• Summer project to analyze a new class of lattice QCD correlation functions for the proton in order for us to better calculate the ground and excited state masses. • Supervised by Prof. Kostas Orginos• This research was presented at the 2012 Division of Nuclear Physics Meeting in Santa Monica, CA as part of the Conference Experience for Undergraduate program. • This work was supported by a $5,000 grant from the NSF.