Thomas E. Abraham

What I do

Data Science

Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured and unstructured data. Data science is a concept to unify statistics, data analysis, informatics, and their related methods in order to understand and analyze actual phenomena with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. Services include but are not limited to:

Data Strategy
Exploratory Data Analysis
Model Deployment
Decision Intelligence
Model Building and Evaluation
Statistical Methods
Data Mining
Data Visualization
Linear Optimization
Data Cleaning

Machine Learning and Artificial Intelligence

Machine learning programs can perform tasks without being explicitly programmed to do so. It involves computers learning from data provided so that they carry out certain tasks. As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what was then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis. Services include but are not limited to:

Applied Machine Learning
Recommendation Systems
Constraint Satisfaction Problems
Supervised Learning
Sequential Data Models
Reinforcement Learning
Regression
Association Analysis
Logical Agents
Bayesian Methods
Applied Artificial Intelligence
Natural Language Processing
Classification Algorithms
Intelligent Agents and Uniformed Search
Deep Learning
Unsupervised Learning
Heuristic Search
Robotics
Clustering Methods
Adversarial Search and Games

Education

Before moving into data science, I served as Director of Development for a non-profit supporting social and educational justice in India. I have since taught across the fields and courses listed below. I have tutored undergraduate students from the University of Toronto, Simon Fraser University, Purdue University, and the Universities of Illinois, Michigan, North Carolina, and Southern California; taught graduate and post-graduate students from the Massachusetts Institute of Technology, California Institute of Technology, Columbia University, Hong Kong University of Science and Technology, and the Indian Institutes of Technology; and worked collaboratively with professionals from J.P. Morgan Chase and the Walmart Corporation.

Advanced Excel
Formal Languages and Finite Automata
Data Science
Advanced SQL
Parallel Processing
Data Visualization
Intro-Advanced Python
Linear Algebra
Dashboard Design
Intro-Advanced R
Multivariate Calculus
Advanced Operations Research
Intro-Advanced Algorithms
Scientific Computing
Machine Learning
Graph Theory
Data Analytics
Image Processing
Computability Theory
Linear Optimization
Neural Networks-Deep Learning
Logic and Reasoning
Physics
Computer Vision
Discrete Mathematics
Philosophy
Intro - Artificial Intelligence
Probability and Statistics

About Me

Who I am

data scientist | applied statistics & machine learning

I am a data scientist who prides himself on the principles of perpetual education, humanism, and futurism. As a passionate student of both hard and soft sciences, I believe in an interdisciplinary and interpersonal approach to problem-solving.

My work spans classification, regression, time-series forecasting, natural language processing, and computer vision, using Python, R, PyTorch, TensorFlow, and scikit-learn. I completed Columbia University's postgraduate program in Machine Learning and Artificial Intelligence and am currently finishing a Master of Liberal Arts in Mathematics for Teaching at Harvard Extension School.

Much of what I know about explaining quantitative work came from teaching it. I have taught applied machine learning, statistics, and data engineering to graduate students and working professionals, and AP Statistics to high school students, for whom I designed and deployed a full-stack learning platform with more than thirty interactive statistical simulations. Teaching statistics to people who have to actually use it is the best training I have had in making results legible to the people who need to act on them.

I am looking for roles in data science and quantitative analysis where the work involves both building the model and explaining what it means.

Hello, I am Thomas Abraham

What I do

Data Science

Machine Learning and Artificial Intelligence

Education

What I did

Who I am