Principal Investigator (Assistant Professor)

Ankit B. Patel

Ankit is broadly interested in the intersection between machine learning and computational neuroscience. He works with neuroscientists to build a bridge between artificial and real neuronal networks, using theories and experiments with artificial nets to understand and make testable predictions about real brain circuits. Ankit returned to academia after spending 6 years in industry, building real-time inference systems trained on large-scale data for ballistic missile defense (MIT Lincoln Laboratory),and high-frequency trading. He received his graduate and undergraduate degrees in Computer Science and Applied Mathematics from Harvard University and completed my postdoctoral training at Rice University with Richard G. Baraniuk.

Postdoctoral Researchers

Ryan Pyle

I am currently a Postdoctoral researcher working with Dr. Patel. My research is focused on understanding the implicit bias of neural networks, and using new insights to build better tools and visualizers to aid in understanding. I am also interested understanding recurrent networks and reservoir computing for modeling physical systems.

Justin Sahs

I am currently a Postdoctoral researcher working with Dr. Patel.

Yashwanth Lagisetty

I am currently a Postdoctoral researcher working with Dr. Patel. I am also an MD/PhD student at UTHealth with an interest in neuropsychiatry and cognative diseases. My background is physics and math and my current research focus is on applying the theories of implicit bias to gene regulatory networks in biological systems to uncover and understands the ways in which evolution works.

Ph.D. Students

I’m a third year CS PhD student. My research focuses on building and improving machine learning / deep learning models for science problems in an explainable way. The fields of subjects involve quantum many-body physics, computational chemical sensing, medicine, etc. I also worked on understanding GANs’ behavior in the function space in the rich and kernel regimes. I’m interested in adversarial learning schemes in general. M.S. Data Science, University of Pennsylvania. B.E. Industrial Engineering, Tsinghua University.

I am a PhD student in Computer Science at Rice University. I research in the Morfeus lab at MD Anderson under Dr. Kristy Brock* and Dr. Ankit Patel. I am primarily interested in using deep learning in computer vision applications, particularly applications in the medical field.

Dat Tran

I’m a PhD student at Baylor College of Medicine.

Undergraduate Students

Alexa Thomases

My name is Alexa Thomases and I am a senior undergraduate at Rice University. I’m studying Electrical and Computer Engineering with a minor in Data Science. My primary area of interest is the intersection of machine learning (particularly NLP and reinforcement learning) with healthcare. I hope to use my technical knowledge from course study to leverage machine learning techniques to improve healthcare outcomes globally.


I’m a Computational Biology Ph.D. focusing on the role of recurrent computation in visual perception by using neurally-inspired deep learning models. In addition, I have an interest in canonical correlation analysis, representational dissimilarity analysis, and other methods to understand the computational differences between two trained neural networks. Moreover, I am part of the NINAI (Neuroscience-Inspired Networks for Artificial Intelligence) team, whose goal is to conduct brain research for machine learning. Next: Postdoc Researcher, Yale University.

I am currently a Ph.D. student in the ECE department at Rice University. My research interests lie in interpretability and explainability of deep learning, and generative models such as generative adversarial networks. Next: Research Scientist, NVIDIA.

Wanjia (Robin) Liu

My name is Robin and I graduated with an MS in Computer Science. My research in Patel Lab focused on learning retinomorphic event-driven representations, inspired by biological retina on a functional level, for deep learning video tasks such as action recognition and reinforcement learning. Next: Google.

Hi! I’m Ameesh, and I’m a junior undergrad at Rice studying Computer Science. I’ve most recently worked on the LSTM Probing project in the lab, where I helped theorize the underlying structure of how an LSTM learns written languages. Next: EECS PhD Student, University of California, Berkeley.

Andrew Dumit

While working in Dr. Patel’s lab, I worked on translating the locust LGMD neuron model into an RNN framework in TensorFlow. I graduated Rice in 2017 with a BA in Statistics and now work at Buoy Health, a health tech startup, on the machine learning side of understanding and improving on our ability to figure out what is causing a patient’s symptoms. Next: Buoy Health.

I’m an undergraduate intern currently researching with Professor Aaron Courville on Visual Reasoning. I’m in the final year of my Bachelor’s in Computer Science at Rice University. In the past, I’ve contributed towards state-of-the-art semi-supervised deep learning models, worked to improve indoor location accuracy algorithms for Google Maps, and built fraud detection models for Uber. My current research interests lie in Deep Learning, Reinforcement Learning, and Reasoning. Check out my website for more info about me if you’re interested! Next: Research Scientist, Anthropic.

Raymond Cano

Raymond graduated from Rice in 2017 with a B.A. in Computer Science. He spent the first 8 months researching here working to build a software package around ODE-based Recurrent Networks. His technical passions include Vision, NLP, Codebase Sustainability, and avoiding the mouse in any way possible. he currently works as a Backend Engineer at Plaid Technologies and is planning to attend Graduate School starting in 2019. Outside of technology, Raymond enjoys basketball among other sports and makes music. Next: Plaid.

Rhonald Lua

My name is Rhonald. At the Patel Lab, I used methods in deep learning to estimate parameters of spiking neuron models and differential equations from data. I also worked on a project to train neural networks to classify actions occurring in videos. In the process, I studied and clarified how backpropagation works and how to take insights and inspiration from human and animal vision to distill the essentials of motion for action recognition in videos. Currently: I work as a tour guide at Space Center Houston/ Johnson Space Center visitor center, and intern at Lazarus3D.