Recent graduate from the University of California San Diego majoring in Cognitive Science Specializing in Machine Learning and Neural Computation BS. Actively looking for an internship or full time position relating to data science and machine learning roles.
Excellent leadership, organizational, project management skills and group
Excellent written and oral communication skills for both public speaking and interpersonal communication.
Strong ability to communicate using visual presentations, powerpoint and data visualization.
Experience with data science and analytic skills, machine learning algorithms and libraries, A.I. algorithms, genetic algorithms, particle swarm optimization algorithms, gradient descent algorithms.
Experience with programming Languages, Java, Matlab, and Python using Jupyter Notebooks, and packages and libraries including NumPy, Sklearn, Matplotlib, Seaborn, Pandas etc.
Creative problems solving and project design, using exploratory analysis for data modeling and creating unique analysis pipelines designed individually for the project.
Experience with brain computer interfaces, EEG-based BCIs, pattern recognition neural signal processing, convolutions, filtering, fourier (spectral) analysis.
Fundamental understanding of neuroscience, psychology, development of cognition and cognitive models. Theories of situated, distributed, enactive, and embodied cognition
work experience volunteered projects
SAN DIEGO DATA LIBRARY, LA JOLLA, CA
Jun 2021- Current
Currently working as project manager for a project involving overlaying data components over SEZ mapping with Eric Busboom, owner and proprietor of Civic Knowledge and San Diego Data Library. We use geo-spatial mapping and regression to make inferences of economic growth as it correlates to nighttime light intensity. Nighttime lights should serve as a proxy for economic activity to study the effects of special economic zones. This project is ongoing through the summer.
COGNITIVE SCIENCE DEPARTMENT, LA JOLLA, CA
Jun 2021- Current
Under the supervision of Professor Virginia De Sa the Associate Director of the Halicioglu Data Institute I have been assisting the cognitive science department at UCSD, in debugging and testing the brainwave sensing headset. This includes checking the hardware and running diagnostic tests to check the headsets are working properly. Currently creating a written tutorial and video presentation tutorial on how to use the headsets for next year students.
DRAGONCHAIN - DEN.SOCIAL, BELLEVUE, WA
Project Director/ Developer
Feb 2021- Current
In collaboration with other members of the Cognoscere Student organization, we actively work with Dragonchain and their social media site Den to perform sentiment and data analysis on their social media platform. Utilizing Python packages such as NLTK, we run sentiment analysis on the various layers within Den. By performing data analysis, we identify trends in the data, such as post popularity by sentiment score, and compare the layers in the social media platform.
COGNOSCERE STUDENT ORGANIZATION, LA JOLLA, CA
President Dec 2020 - Sept 2021
I am the President and Founder of Cognoscere, a pre-professional student organization created at UCSD. The organization created an online community of students seeking to network and collaborate on projects related to data science, machine learning and computer science. I successfully expanded the organization from 8 members to 42 members by recruiting new members, outreaching students from across multiple departments. As president, I successfully cultivated working relationships with companies for my organization to network and begin projects with. During my time within the organization I personally acquired projects opportunities for our members to work on, including Dragonchain within their social media site Den.Social, assistance debugging and writing a tutorial for Neurosky Headsets within the Cognitive Science Department at UCSD, and most recently with Eric Busboom owner and proprietor of Civic Knowledge and San Diego Data Library.
UCSD - SEPTEMBER 2018-JUNE 2021
BS Cognitive Science Specialty in Machine Learning and Neural Computation - GPA: (3.50/4.0)
This area of specialization approaches modeling cognition and building cognitive systems using computational and mathematical approaches, theoretical neuroscience, as well as software engineering and data science. Included advanced courses in neural networks, artificial intelligence, supervised and unsupervised machine learning algorithms, brain computer interfaces, vector calculus, probability, and linear algebra.
MIRACOSTA - SEPTEMBER 2014-JUNE 2018
AA Liberal Arts Mathematics and Science GPA: (3.56/4.0)
Completed degree in mathematics and science including coursework specifically for transferring as an engineering student at UCSD. Completed all necessary prerequisites and was originally accepted as an engineering student. Included physics series, mathematics in calculus, discrete mathematics and linear algebra, and complete series of chemistry.
Skills & Accomplishments
COGS 9. INTRODUCTION TO DATA SCIENCE
Concepts of data and its role in science will be introduced, as well as the ideas behind data-mining, text-mining, machine learning, and graph theory, and how scientists and companies are leveraging those methods to uncover new insights into human cognition.
COGS 18. INTRODUCTION TO PYTHON
This class will teach fundamental Python programming skills and practices, including the “Zen of Python.” Students will focus on scientific computing and learn to write functions and tests, as well as how to debug code using the Jupyter Notebook programming environment.
COGS 100. CYBORGS NOW AND IN THE FUTURE
Covers the theories of situated, distributed, enactive, and embodied cognition. Explains how cyborgs are a natural consequence of our current understanding of embodied minds embedded in culturally shaped niches; how mental systems can be distributed over other people and things.
COGS 108. DATA SCIENCE IN PRACTICE
Data science is multidisciplinary, covering computer science, statistics, cognitive science and psychology, data visualization, artificial intelligence and machine learning, among others. This course teaches critical skills needed to pursue a data science career using hands-on programming and experimental challenges.
COGS 109. MODELING AND DATA ANALYSIS
Exposure to the basic computational methods useful throughout cognitive science. Computing basic statistics, modeling learning individuals, evolving populations, communicating agents, and corpus-based linguistics will be considered
COGS 118B. INTRODUCTION TO MACHINE LEARNING
A rigorous introduction to machine learning, topics include maximum likelihood estimation, Bayesian parameter estimation, clustering, principal component analysis, and some application areas
COGS 118C : NEURAL SIGNAL PROCESSING
This course will cover theoretical foundations and practical applications of signal processing to neural data. Topics include EEG/field potential methods (filtering, Fourier (spectral) analysis, coherence) and spike train analysis (reverse correlation, spike sorting, multielectrode recordings). Some applications to neural imaging (optical microscopy, fMRI) data will also be discussed.
COGS 138. NEURAL DATA SCIENCE
Project-based course in which students will use computational notebooks to perform exploratory data analyses and to test hypotheses in large neuroscience datasets, including the differences between unique neuron types, leveraging text mining of the neuroscience literature, and human neuroimaging analyses.
COGS 189. BRAIN COMPUTER INTERFACES
This course will discuss signal processing, pattern recognition algorithms, and human-computer interaction issues in EEG-based brain-computer interfaces. Other types of brain-computer interfaces will also be discussed.
COGS 160 (B00): GENETIC ALGORITHMS
This course will teach in detail some of the key biology inspired A.I. algorithms. It will cover genetic algorithms (GA), particle swarm optimization algorithms (PSO), and other optimization algorithms like simulated annealing and gradient descent.