Ennis M
- Research Program Mentor
PhD candidate at Northwestern University
Expertise
Physics, Mathematics, AI, Quantum computing, Astrophysics
Bio
I'm a Ph.D. researcher in Applied Physics at Northwestern University, where I work at the intersection of machine learning and quantum computing, teaching algorithms to understand and correct the noise that makes quantum hardware so frustratingly imperfect. What I love most about this field is that it sits right where deep physics meets cutting-edge AI: one day I'm reasoning about how qubits lose information at near-absolute-zero temperatures, and the next I'm designing neural networks that learn to predict and outsmart those errors. I've mentored 30+ students through their own research journeys, and I've guided 20+ of them all the way to a publication, and honestly, watching a student go from "I don't know if I can do this" to a published author is the best part of my week. Outside the lab, I'm endlessly curious about how things work and how to communicate big ideas simply, whether that's through a good science explainer, a long hike where I can think without a screen in front of me, or building little coding projects just to see if I can. I believe the best research mentorship feels less like a lecture and more like exploring an unknown together—so if you're excited about quantum, physics, machine learning, or just figuring out a hard problem from scratch, I'd love to work with you.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.
Machine Learning for Quantum Computing: Teaching AI to Tame Quantum Noise
Quantum computers promise to revolutionize fields like finance, drug discovery, and cryptography—but today's quantum hardware is incredibly noisy, and that noise corrupts results before useful computation can finish. In this project, the student will use machine learning to predict and correct these errors, working with the same kind of problem that real quantum researchers tackle today.
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.