Ennis M
- Research Program Mentor
PhD candidate at Northwestern University
Expertise
Physics and Mathematics.
Bio
Ennis is a Ph.D. student in Applied Physics at Northwestern. He received his undergraduate degrees in Physics and Mathematics from University of Cincinnati in Ohio. Before joining QUEST, he worked on models of interacting axion-like dark energy and dark matter. He then gained some laboratory experience with experiments involving superconducting quantum circuits. Outside of academia, his interests include playing basketball and reading Arabic poetry.Project ideas
Explore the workings of basic quantum algorithms and simulate their behavior on a classical computer.
Quantum algorithms like Grover's search algorithm or the Deutsch-Josza algorithm are fundamental to understanding quantum computing’s potential. This project would involve coding and simulating these algorithms using Python libraries like Qiskit or Cirq. The student could analyze the efficiency of these algorithms by comparing their simulated performance with classical equivalents.
Use machine learning techniques to classify exoplanets based on their potential habitability.
This project would use existing datasets from missions like Kepler and TESS, which contain information about known exoplanets (e.g., orbital radius, star type, size, and temperature). The student can train a machine learning model to classify exoplanets based on their habitability potential. They could use criteria such as the planet's location in the habitable zone and properties like temperature and mass to predict habitability.
Build and train a neural network model to identify gravitational wave signals in noisy data from sources like LIGO.
Gravitational waves are often hidden within large amounts of noise in observational data. The student could use publicly available gravitational wave datasets and apply deep learning techniques, such as convolutional neural networks, to detect the presence of these waves. This project could include data preprocessing, feature extraction, and model training and evaluation.