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 (co-advised by Daniel Fisher). 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.
Apart from my science, I love spending time with family and am passionate about gardening and restoring native vegetation, as well as cooking and food history.
MaryRuth Miller enjoys dual careers- one here at MIT as an administrative assistant to both Ila Fiete (since 2021) and Nancy Kanwisher (since 2018), and a second as a classical soprano. Locally, she regularly performs with the Handel and Haydn Society, the Henry Purcell Society of Boston, and Emmanuel Music. She recently made her debut with New York City’s acclaimed Clarion Society and the GRAMMY® award winning Oregon Bach Festival Chorus. In the 2022-2023 season, MaryRuth will make her debut with the Santa Fe Desert Chorale and BWV- Cleveland’s Bach Choir.
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 am interested in the mechanisms by which the brain flexibly plans and controls movement at different timescales in order to efficiently shape structured behaviors. To investigate these questions, my research leverages theoretical and modeling approaches to probe how the architecture of motor and premotor brain circuits shapes neural dynamics to support function. In addition, I test and revise models through analysis of neural recordings.
Before joining Ila Fiete’s lab at MIT, I was a postdoctoral researcher at Columbia University working with Sean Escola and Larry Abbott. My previous step was a Ph.D. under the guidance of Wulfram Gerstner and Angelo Arleo at Ecole Polytechnique Federale de Lausanne and Pierre and Marie Curie University.
Apart from science, I enjoy singing in my bathroom, distance running, and generally spending time outdoors.
I’m a postdoctoral researcher in the Fiete lab, exploring ideas about the geometry of neural dynamics and computation by using differential geometry to study neural function. I’ve got my PhD at the Sainsbury Wellcome Centre in London on work combining the experimental study and computational modelling of instinctive behaviors in rodents. Before that I obtained an undergraduate degree in biotechnologies at the university of Milano, Italy and a Masters in experimental neuroscience in Edinburgh, Scotland.
I am an ICoN postdoctoral fellow at MIT, studying the brain from the perspective of dynamical systems theory and machine learning. I earned my PhD from the Department of Brain and Cognitive Sciences at MIT, where I was supervised by Professors Earl K. Miller and Jean-Jacques Slotine. Before that, I studied physics and math as an undergraduate at Rutgers University-New Brunswick. When I’m not doing science, I’m usually making music, playing with my dog, reading, breathing, sleeping, etc.
I am interested in studying the role of network dynamics in implementing cortical computations. In the visual domain, my research has focused on understanding the basis of visual “routines” through recurrent neural network models of reentrant processes and, along the way, solutions to the technical challenges that prevent using recurrent models at scale. I am particularly enthusiastic about problems at the interface of perception and cognition, and I build tools to study these at scale in the context of naturalistic tasks. I got my Ph.D. at Brown University under Dr. Thomas Serre’s guidance and my Bachelor’s in Computer Science and Biophysics from National University of Singapore, where I was a Turing Fellow.
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 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.
I am a graduate student in the EECS department at MIT. Before that, I completed my MSc degree in ECE advised by Professor Bohyung Han and BS degree in CSE at Seoul National University, mainly focusing on computer vision. My research journey is to reduce the gap between natural and artificial intelligence by discovering what makes humans start to learn or explore such as curiosity and how humans can learn from a small amount of data or coarse-grained data while machines cannot. To do so, I am broadly interested in the intersection of machine learning, neuroscience, and psychology.
Ling Liang Dong
I am a graduate student in the Brain and Cognitive Sciences department at MIT. I am broadly interested in the intersection between the computational and neurosciences, that is: how can mathematical and computational methods be used to better understand the mind, and how can insights about the mind in turn be used to inform algorithmic and architectural solutions to modern computational challenges? My current focus lies on the cognitive map and how the brain performs abstraction for efficient knowledge representation and flexible inferences.
Previously, I worked on investigating strategies utilizing the unique social roles occupied by robots to elicit social facilitation and improve team dynamics in human-robot teams at the Yale Social Robotics Lab, and on public opinion forecasting as well as analyzing patterns of production and consumption in online news at Microsoft Research New York. More generally, I am interested in the applications of data science for social good, particularly in the fields of education and healthcare.
Adam Joseph Eisen
I’m a graduate student in the Brain and Cognitive Sciences department at MIT, co-advised by Earl Miller & Ila Fiete. I’m from Toronto, Canada, and did my undergraduate degree in Applied Mathematics & Computer Engineering at Queen’s University. Currently I’m interested in communication between brain areas, and how this mechanism might underlie cognition and consciousness. To answer this question, I’m using nonlinear dynamics tools to analyze experimental data and performing computational modelling. Prior to joining BCS, I built computer vision algorithms at the solar aerial inspection company Heliolytics and worked on developing machine learning tools to predict disease outcomes from genetics at The Hospital for Sick Children. In my free time, I spend time playing and writing music, getting outside, and cooking.
I am a graduate student in the Department of Physics at MIT. Previously I studied physics and psychology at Wesleyan University (CT), where I mostly worked on computational soft matter research. I have long been fascinated by the human brain and cognition, and hoped to investigate the relevant mechanisms using mathematical and computational methods. Therefore, I decided to do research in theoretical neuroscience by joining the Fiete Lab in Fall 2021.
I am a graduate student in the Brain and Cognitive Sciences department at MIT. I am interested in how neural dynamics implement computation within the brain in the context of perception and control. Before joining the Fiete Lab, I studied computer engineering as an undergraduate at the University of Illinois at Urbana Champaign and subsequently worked as a Software Engineer for several years at Google and Deepmind. I’ve worked on various projects ranging from systems engineering for network ASICs to RL for Datacenter cooling. I became interested in neuroscience while studying algorithms for disentangling representations. In my free time, I enjoy running along the Charles and playing tennis.
Andrew Abraham Kirjner
Abhiram (Abhi) Iyer
Computer Science PhD Student, Stanford University
Rylan earned his Master’s at Harvard in Computational Science and Engineering, working with Ila on reverse-engineering recurrent neural networks (NeurIPS 2020) and testing hypotheses in mouse neuropixel recordings, developing a theory for distributional reinforcement learning, and efficient inference streaming algorithms for Bayesian nonparametric models (UAI 2021, ICML 2022 (Under Review)). Rylan continues to work under Ila’s mentorship on ongoing research projects.
Graduate student, UCL
I finished my Master’s and undergrad at MIT as a double major in Computer Science and Neuroscience, and will be starting my PhD at the Gatsby Computational Neuroscience Unit in London. While my research endeavors in the past have been pretty varied (ranging from computational fluid dynamics to attentional modulation for few-shot learning), I plan to pursue the direction of more human-like agents that can actively transfer knowledge between different tasks and domains. In my free time, I love playing strategy board games, watching action movies, and listening to epic fantasy audiobooks.
BCS Fellow in Computation (independent postdoc)
I’m Postdoctoral Fellow in Brain and Cognitive Sciences, through the BCS Fellows in Computation program. My research is on the intersection between natural and artificial intelligence. My current research focuses on how learning algorithms in the artificial domain can be brought closer to the natural domain. This includes learning in online environments and leveraging learning signals only available in natural domains such as temporal consistency. I received my Ph.D. from UC Berkeley at the Redwood Center for Theoretical Neuroscience under the supervision of Bruno Olshausen. Outside of research, my interests focus on all topics related to food; where to acquire it, how to cook it, and most importantly consuming it. My current focus is on Asian cuisine and natural sources of umami.
Mingye (Christina) Wang 2021-present
Patrick Udeh 2021-present
Opalina Vetrichelvan 2022
Gabrielle Kaili-May Liu 2022
Keith Murray 2022
Erdong Guo 2021
Anka Hu 2021
Joy Ma 2021
Anna Rasmussen 2021
Hangdong (Harry) Wang 2021
William Xu 2021
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 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
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