Mukesh Ghimire, Phd Email & Phone Number

Oklahoma City, Oklahoma, United States

Developed predictive models for time series forecasting using machine learning algorithms to optimize customer workflows, resulting in a 20% increase in operational efficiency for clients in the medical sector.Optimized machine learning models for resource efficiency via feature engineering and hyperparameter tuning, resulting in a 30% reduction in processing time and improved scalability for clients with growing data volumes.Conducted exploratory data analysis on diverse datasets, identifying key patterns and trends to inform strategic decision‑making for clients.Prepared and delivered client presentations, explaining technical concepts in a clear and accessible way, fostering client confidence in data‑driven software solutions.Stayed abreast of emerging technologies, incorporating new tools and techniques into projects, resulting in more innovative machine learning solutions.

New York, United States

Employed Monte Carlo simulations to derive informed prior distributions, enhancing model precision.Analyze simulation outputs' posterior distributions to optimize signal-to-background ratio for noise reduction.Leveraged simulation data to implement experimental structural enhancements, boosting signal yield and mitigating background noise.Utilized CAD software to design new circuit components, enhancing signal triggers and data acquisition protocols.Mentored undergraduates, imparting technical knowledge in particle physics and data analysis.

Geneva, Switzerland

Designed, programmed, and integrated custom electronics with FPGAs, enhancing data acquisition capabilities.Leveraged theory-based and data-based approaches to model signal data, scaling theoretical predictions to experimental data while accounting for instrument uncertainties.Employed convolution techniques, functional fits, and error analysis to compare detector test data with models.Optimized algorithms and software by implementing compact data storage, streamlining data flow, and reducing free parameters.Maintained data records and source documentation on GitHub, adhering to best documentation practices.

New York, New York, United States

Developed simulations and analysis techniques using statistical models such as Von Neumann methods, Poisson and Gaussian distributions, linear and nonlinear regressions.Enhanced physical models using data-driven methodologies, generating probability distribution functions from the latest data and fitting analytical functions via nonlinear interpolation.Evaluated experimental results' statistical significance using a customized frequentist approach employing Poisson likelihood functions.Curated and synthesized data and literature from Arxiv, Physical Review, and other scientific journals.Led a collaborative project involving 30 members from 9 universities/organizations, showcasing effective teamwork and leadership.

New York, New York, United States

Taught classes of 10‑30 undergraduate/graduate students in various physics topics. Designed classwork to best engage students and help with aspects of the courses with which they struggled most. Graded homework and exams with extensive feedback in order to ensure students learn from their mistakes. Developed the capability to think on the spot and answer difficult questions in front of an audience.Courses Taught:Undergraduate Quantum Mechanics IIGraduate DynamicsUndergraduate General RelativityUndergraduate General Physics I

Doctor Of Philosophy (Ph.D.), Physics

Bachelor Of Science - Bs (Summa Cum Laude), Mathematics And Physics, Majors: 4.00, Cumulative: 3.89

Activities and Societies: Phi Beta Kappa, Sigma Pi Sigma