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lyyoness's Projects

als-exploratory-data-analysis icon als-exploratory-data-analysis

In this project I present a concise overview of past attempts at brain-computer interface (BCI) communication with ALS patients and some of the hypotheses about why they have been unsuccessful to date. My project is an exploratory data analysis of 86 hours of current EEG data collected from a CLIS (completely locked-in state) ALS patient. The analysis is aimed at extracting cognitive activity in different frequency bands and observing their change over time. The goal of this is to identify cognitive measures that are stable enough to facilitate the identification of "high interest/high alertness" periods during which BCI communication with the patient is more likely to succeed. The preliminary results of the data analysis are presented and a follow-up experiment (currently in progress) is described.

cs110-solving-problems-with-algorithms icon cs110-solving-problems-with-algorithms

Hashing, searching, sorting, tree algorithms, dynamic programming, greedy algorithms, divide and conquer, random number generation, and randomized algorithms are examples of algorithms students learn to exploit to solve problems ranging from logistics and route optimization to DNA sequencing.

cs111-mathematical-and-computational-models icon cs111-mathematical-and-computational-models

This course covers at a high level the core concepts central to modern mathematical and computational research: set theory, combinatorics, probability, graph theory, and differential equations, as well as modeling methods such as Monte Carlo techniques, and agent-based modeling. Topics are chosen from diverse fields, including food webs, industrial optimization, and voting.

cs112-externalities-of-inequality icon cs112-externalities-of-inequality

Analysing effects of income levels on income redistribution preferences through replication and extension of Externalities of Inequality (https://www.politics.ox.ac.uk/materials/publications/14128/externalitiesofinequality.pdf )

cs112-information-based-decisions icon cs112-information-based-decisions

Learned how to extract meaning from data using modern approaches such as Bayesian Inference. Applied the tools of decision science to solve a wide range of problems, by applying statistical inference and formal models of decision making to design practical solutions. Students frame and quantify a range of scenarios to address real problems in the life sciences, energy and technology industries.

cs146-modern-computational-statistics icon cs146-modern-computational-statistics

Covers the theory that underlies computationally intensive statistical methods. Topics include bootstrapping and resampling, Markov chain Monte Carlo methods, density estimation, curve fitting, multivariate analysis, and nonparametric methods.

cs152-harnessing-artificial-intelligence-algorithms icon cs152-harnessing-artificial-intelligence-algorithms

Applies methods and algorithms from Artificial Intelligence (AI) — such as propositional logic, logic programming, predicate calculus, and computational reasoning — to a diverse range of applications from robot navigation to restaurant selection with expert systems.

cs156-machine-learning icon cs156-machine-learning

Learning to apply core machine learning techniques — such as classification, perceptron, neural networks, support vector machines, hidden Markov models, and nonparametric models of clustering — as well as fundamental concepts such as feature selection, cross-validation and over-fitting. Programming machine learning algorithms to make sense of a wide range of data, such as genetic data, data used to perform customer segmentation or data used to predict the outcome of elections.

cs166-modeling-simulation-and-decision-making icon cs166-modeling-simulation-and-decision-making

Learning how to apply advanced decision techniques such as real options, Monte Carlo simulation, network concepts from graph theory, probability theory and statistical physics to analyze and predict the behavior of social, economic and transportation networks. Examples include project portfolio management, pharmaceutical drug development, oil and gas investment decisions as well as philanthropic portfolio decisions requiring high-stake tradeoffs in highly uncertain environments.

cs50-51---formal-analysis icon cs50-51---formal-analysis

Formal Analyses focuses on rigorous critical thinking. Students learn formal reasoning and logic, mathematical and statistical models, algorithms and introductory machine learning, and key elements of decision theory. A key component of the course is learning to identify the biases and other pitfalls that may color the formulation and interpretation of arguments, models, and decisions.

pymmh3 icon pymmh3

a pure python MurmurHash3 implementation.

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