This is the website of the CS Theory group at Tufts University. Our research area covers various topics in theoretical computer science, including algorithms, computational complexity, cryptography, quantum computations, computational geometry.
We are currently recruiting PhD students to start in September 2024 (application deadline is December 15). Further information can be found here.
Megumi Ando
Theoretical foundations of anonymous communications
Lenore Cowen
Data science, graph algorithms, approximate routing, classification and clustering for high-dimensional data, coloring and its generalizations, computational molecular biology
Saeed Mehraban
Quantum information and computation, quantum complexity theory, quantum pseudo-randomness
Vladimir Podolskii
Computational complexity, ontology-mediated queries, min-plus geometry
Diane Souvaine
Computational geometry, design and analysis of algorithms, computational complexity
Prosenjit Bose
Вiscrete and computational geometry, algorithms, data structures, graph theory
Peter Love
Quantum algorithms, quantum simulation
Kasso Okoudjou
Applied and computational harmonic analysis, cpectral analysis of Laplacian-based operators on graphs and fractals, quantum walks on graphs and fractals
Samantha Petti
Computational biology, probability and statistical inference, design and analysis of algorithms
Csaba D. Tóth
Discrete and computational geometry, combinatorial algorithms
Anselm Blumer
Machine learning, information theory, data compression, string algorithms, computational biology
Michael Joseph
Arsalan Motamedi
Christopher Ratigan
Mingqian Chen
Dale Jacobs
In Spring 2024 Theory Meetings are on Thursdays at 1:30-2:30PM. Please contact Vladimir Podolskii to join mailing list.
Spring 2024 time: TBA. Please contact Saeed Mehraban to join mailing list.
Spring 2024 time: TBA. Please contact Diane Souvaine to join mailing list.
Physics & Computation Reading Group
Daniel Mitropolsky (Columbia), The Simplest Neural Models, and a Hypothesis for Language in the Brain
Location: JCC 170
Abstract. How do neurons, in their collective action, beget cognition, as well as intelligence and reasoning? As Richard Axel recently put it, we do not have a logic for the transformation of neural activity into thought and action; discerning this logic as the most important future direction of neuroscience. I will present a mathematical neural model of brain computation called NEMO, whose key ingredients are spiking neurons, random synapses and weights, local inhibition, and Hebbian plasticity (no backpropagation). Concepts are represented by interconnected co-firing assemblies of neurons that emerge organically from the dynamical system of its equations. We show that it is possible to carry out complex operations on these concept representations, such as copying, merging, completion from small subsets, and sequence memorization. I will present how to use NEMO to implement an efficient parser of a small but non-trivial subset of English (leading to a surprising new characterization of context-free languages), and a more recent model of the language organ in the baby brain that learns the meaning of words, and basic syntax, from whole sentences with grounded input. We will also touch on lower bounds in the model, and the idea of a fine-grained complexity theory of the brain.