If you're a student or early-career professional seeking to develop expertise in AI strategy, solutions architecture, or AI engineering, I occasionally mentor individuals on projects that align with these areas. My mentoring focuses on bridging the gap between technical implementation and business strategy - helping you understand not just how to build AI systems, but how to architect solutions that deliver real business value.

Whether we collaborate on a technical blog post, a proof-of-concept implementation, or a strategic analysis, I expect to produce tangible results that demonstrate both technical excellence and business acumen.

If you would like to schedule an interview, please send me an email.

Current mentees

None - feel free to apply!

Previous mentees

Riley Wong was a Computer Science major at the University of Pennsylvania. Over Fall 2017, we collaborated with a CHOP Emergency Room physician to develop an "early warning system" for detecting ER patients that have a high probability of being moved to the ICU. This project focused heavily on using machine learning as the prediction system.

Daniel Angell was a Computer Science major at Drexel University. Through Fall 2017, we collaborated on the development of the award-winning Automated Machine Learning framework called TPOT, which automates the process of designing and optimizing machine learning pipelines.

Nichole Rigby was a Biology Masters student at Temple University. Over Fall 2016, we gathered several datasets from Kaggle and evaluated TPOT on over a dozen real-world data science problems to compare how state-of-the-art AutoML algorithms compare to real-world data scientists.

Sahil Shah was a Computer Science major at the University of Pennsylvania. Over Fall 2016, we continued the development of an award-winning Automated Machine Learning framework called TPOT, which automates the process of designing and optimizing machine learning pipelines.

Tuan Nguyen was pursuing a double major in Mathematics and Computer Science at Swarthmore College. Over Summer 2016, we developed a Python version of the Multifactor Dimensionality Reduction (MDR) feature construction algorithm and integrated it into our Automated Machine Learning framework, TPOT.

Rolando Garcia was a Computer Science major at Arizona State University. Over Summer 2016, we developed a Python tool called DELFT that automates the process of designing artificial neural network architectures for deep learning.

Akshay Varik was a Mechanical Engineering and Applied Mechanics Masters student at the University of Pennsylvania. We collaborated on a massive benchmark of the scikit-learn machine learning library in Python, and aided the scikit-learn developers in choosing more reasonable default parameters for their model implementations.

Zairah Mustahsan was an Embedded Systems Masters student at the University of Pennsylvania. We collaborated on a massive benchmark of the scikit-learn machine learning library in Python, and aided the scikit-learn developers in choosing more reasonable default parameters for their model implementations.

Patrick Haley was a Computer Science undergraduate student from the University of Texas at Austin. In 2015, we published our third publication resulting from our research on the many-eyes theory and its effect on the evolution of animal grouping behavior.

Robert Bato was a Computational Mathematics undergraduate student at Michigan State University. Over Fall '14 and Spring '15, we collaborated on several data analysis projects (e.g., [1] [2] [3]) while Rob refined his data science skills in Python.

Erik Miller-Galow was a Mathematics undergraduate student at Michigan State University. Over Fall '14 and Spring '15, we developed a new method for applying evolutionary computation to MNIST hand-written digit classification.

Zoë Beckett was an Economics undergraduate student at Oberlin College. In Summer '14, she visited our lab to work with Tracey Jabbour on an applied Markov Brain project. In this project, we evolved Markov Brains to learn and detect what factors have the largest impact on consumer decision making when it comes to purchasing American-made cars.

Tracey Jabbour was a Mathematics undergraduate student at Michigan State University. In Summer '14, she joined the lab to work with Zoë Beckett on an applied Markov Brain project. In this project, we evolved Markov Brains to learn and detect what factors have the largest impact on consumer decision making when it comes to purchasing American-made cars.

Michael Bauer was a high school student from Okemos High. In Summer '13, we followed up on a previously published Avida study looking at the long-term stability of evolved ecosystems. In this follow-up study, we used Avida to produce a high-resolution view of how rapidly evolving digital ecosystems change over time.