Ila Fiete, PI
Ila Fiete is a Professor in the Department of Brain and Cognitive Sciences and an Associate Member of the McGovern Institute at MIT. She obtained her undergraduate degrees in Physics and Mathematics at the University of Michigan and her M.A. and Ph.D. in Physics at Harvard, under the guidance of Sebastian Seung at MIT. Her postdoctoral work was at the Kavli Institute for Theoretical Physics at Santa Barbara, and at Caltech, where she was a Broad Fellow. She was subsequently on the faculty of the University of Texas at Austin in the Center for Learning and Memory. Ila Fiete is an HHMI Faculty Scholar. She has been a CIFAR Senior Fellow, a McKnight Scholar, an ONR Young Investigator, an Alfred P. Sloan Foundation Fellow and a Searle Scholar.
I am a postdoctoral researcher originally from Cologne, Germany. The core of my research is a combination of mathematics, neuroscience, and computer science. My current focus lies on spatial representations in the brain, and their role in more general cognitive computations and intelligence. More concretely I am interested in how self localization and planning is performed in the hippocampal formation and how this can inform us about computations in the neocortex. My background however lies in pure mathematics in the field of geometric and differential topology – contact and symplectic topology for what it’s worth. Believe me, it sounds worse than it actually is 🙂
Before I joined the Fiete group in April 2019, I worked at Numenta, a private research lab with a focus on cortical theory. Before that I was a postdoc at the Institute of Science and Technology (IST) Austria and the University of Cologne
I am a postdoctoral researcher at the Fiete Lab, whose primary interests are dynamics and network science problems in the context of neuroscience. I received my PhD from the physics department at the University of Maryland under the supervision of Prof. Edward Ott and Prof. Michelle Girvan, where I focused on the role of network structure on the dynamics of neuronal networks and other complex systems. Before coming to the US, I completed my BS in physics from the Indian Institute of Technology, Kanpur in India.
Apart from research, I also enjoy playing board games, participating in quizzes and trivia nights, taking part in adventure sports, fencing, swimming, cooking, and gardening.
I’m a postdoctoral research fellow in the Fiete lab since 2020. Before that, I obtained the M.Sc. degree in Applied Sciences (Mathematical Engineering) in 2016 and the Ph.D. degree in Engineering and Technology in 2020, both from UCLouvain, Belgium. My Ph.D. advisors were Prof. Michel Verleysen and Prof. André Mouraux. I have worked on the design of experimental paradigms and signal filtering algorithms to probe thermal perception and associated brain responses in humans. My core interests currently lie in understanding how neuronal circuits process nociceptive stimuli and lead to pain perception. I aim to study how artificial neural networks can produce outcomes matching behavioral and neurophysiological measurements from subjects who are experiencing pain.
Besides research, I enjoy running, cycling, playing tennis, hiking, and sports in general. 🙂
I am a graduate student in the Department of Physics at MIT, originally from Mumbai, India. I studied physics and math in my undergrad and worked on several problems in biophysics and quantitative biology broadly, ranging from the hydrodynamics of active media to optimal decision making in C. elegans. After coming to MIT, following rotations in the Physics of Living Systems group, I got interested in theoretical neuroscience and joined the Fiete lab in 2019.
I am interested in exploring the computational and theoretical principles underlying cognition and intelligence in the human brain. My previous work includes building neural models of context dependent decision making in the prefrontal cortex and spiking neuron models of bayesian inference (based on online learning of priors from life experience). I am currently exploring the coding principles in the hippocampal circuits implicated in spatial navigation, and their role in cognitive computations like structure learning and relational reasoning.
Before joining the Fiete lab, I was at the Center for Theoretical Neuroscience, University of Waterloo (UW) where I completed my MASc with Dr. Chris Eliasmith, studying Systems Design Engineering and Theoretical Neuroscience. Before that, I studied Electrical Engineering during my Undergrad at UW.
I’m a Master’s student in Harvard’s School of Engineering and Applied Sciences IACS program. I joined the lab from Uber, where I worked as a data scientist under Andrea Pasqua on anomaly detection and time series forecasting and placed third in the company-wide multi-week machine learning hackathon.
I am interested in a broad range of questions within theoretical neuroscience. One long term goal is motivated by the apparent discrepancy that while the recent deep learning renaissance has recapitulated diverse behavioral and neural experimental findings and produced cognitive-like capabilities that we know no other artificial mechanism for producing, deep artificial networks fail outside extremely limited domains, display critical deviations from their biological cousins and remain poorly understood black boxes that rarely yield predictions to direct experimental work. My goal is to develop a better scientific theory of deep neural networks to resolve these discrepancies and improve the usefulness of artificial networks in bridging cognitive and systems neuroscience.
I’m a graduate student in the EECS department at MIT. I studied as an undergrad in EECS at MIT and did research focusing on quantifying and enhancing adversarial robustness of artificial neural networks as well as investigating biologically plausible learning algorithms for neural networks. I am broadly interested in the intersection of machine learning and neuroscience, particularly with the question of making artificial neural networks more aligned with biological neural networks. I hope progress in these directions will allow artificial neural networks to be applied to more general and challenging tasks.
Leenoy Meshulam, Swartz Fellow, UWashington
Manyi Yim, Postdoc and Instructor, SMU
Tzuhsuan Ma, Postdoc, HHMI/Janelia
Rishidev Chaudhuri, Assistant Professor, UC Davis
Ingmar Kanitscheider, Research Scientist, OpenAI
Christopher Roth, Graduate student, UT Austin
Berk Gercek (undergrad), Graduate student, University of Geneva
Biraj Pandey (undergrad), Graduate student, University of Washington
Maxwell Gray (undergrad), Graduate student, University of Washington
Abhranil Das, Graduate student, UT Austin
Birgit Kriener, Research scientist, University of Oslo, Norway
Kijung Yoon, Assistant Professor, Hanyang University, Korea
John Widloski, Postdoctoral researcher, UC Berkeley
Michael Buice, Senior scientist, Allen Institute
Yongseok Yoo, Assistant Professor, Incheon National University, Korea
Abhinav Singh, Data scientist, Peak, UK
Daniel Robles Llana, Postdoc, University of Geneva
Ila Varma (from Caltech group), Assistant Professor, UC San Diego
Prashant Joshi (from Caltech group), Head, Artificial Intelligence and Machine Learning group, Fractal Analytics, India
Ni Ji (from Caltech group), Postdoc, MIT
Peter Welinder (from Caltech group), Senior Scientist, OpenAI