Nolan Essigmann

education

2010-2015 Bachelor of Science in Mathematics Massachusetts Institute of Technology

Focus on experimental mathematics using numerical computation to investigate mathematical objects, and pure mathematics centered on the creation and study of abstractions.

2013 Research Gap Year University of California, Berkeley

Full time Biological Engineering position in the lab of Professor Adam Arkin developing high-throughput metagenomics and interactomics pipelines.

experience

Summer 2015 Micmac Environmental Health Lab The Aroostook Band of Micmacs
Volunteer Lab Technician in Environmental Health

Repaired atomic absorption spectrometer for the Micmac Tribe, allowing them to test tribal waters for toxins in-house; my work saved the lab enough money to hire an additional full time technician.

2013 Laboratory of Professor Adam Arkin University of California, Berkeley
Undergraduate Researcher in Synthetic and Systems Biology

Designed high-throughput protein-protein interactomics pipelines using Python and SQL.

2011-2012 Laboratory of Professor Alice Ting Massachusetts Institute of Technology
Undergraduate Researcher in Chemical Biology

Used genetic engineering tools along with molecular modeling Python libraries to design and rationally construct molecular sensors. Interim lab manager.

technical skills

Mathematics: Highly experienced with complex problem solving.
Programming: Highly experienced with and deeply knowledgable about Python and Julia.; Experienced in the design, analysis, and implementation of algorithms and data structures.
Web Development: Experience using Flask, Django, Angular, D3.js, PostgreSQL, Nginx, and ØMQ.; Experience using AJAX, Websockets, and Server-sent events.; Experience designing and developing RESTful style web services.
Scientific Programming: Highly experienced writing high performance code in experimental mathematics and the sciences.; Experience with parallel and distributed computing in Google Cloud Platform.; Experience writing and utilizing out-of-core, vectorized, and cache-friendly algorithms and data structures in Julia and C++.
Statistics: Experienced in the theory and application of statistics and machine learning, including:
statistical hypothesis testing, classification, regression, model selection, recommender systems, and spectral methods. Significant experience with statistical and numerical linear algebra package ecosystems in Python, Julia, and MATLAB.
Testing and Experimental Design: Highly experienced in experiment/test design in both the sciences and software development.; Experience with Python unittest (along with mock for web services).; Passion for clearly documenting my work using tools like Jupyter Notebooks and Sphinx.