Principal Investigator (Assistant Professor)
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 a postdoctoral training at Rice University with Richard G. Baraniuk.
Tan Minh Nguyen
Tan is currently a Ph.D. student in the Electrical & Computer Engineering Department at Rice University, where he works in the DSP group with Dr. Richard G. Baraniuk and in the Deep Learning group with Dr. Ankit B. Patel. His research is focused on the intersection of Deep Learning, Probabilistic Modeling, and Neuroscience. Tan is part of the NINAI
(Neuroscience-Inspired Networks for Artificial Intelligence) team, whose goal is to conduct brain research for machine learning. Recently, he is also interested in domain adaptation methods for learning from synthetic data and its applications in geophysics. Tan received his BSEE from Rice in May 2014. More about Tan’s research can be found on his website
Josue Ortega Caro
My name is Josue. I am Quantitative and Computational Biology Ph.D. at BCM. My research focuses on the role of canonical computational motifs in visual processing using neural-inspired deep learning models. I have a particular interested in the intersection of Vision and Recurrent neural networks.
Undergraduate Students and Alumni
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.
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.
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.
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!
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.