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McClain Thiel

I'm currently building WEALTHAWK, working as a data scientist at nference and working on my MS in computer science at Georgia Tech.

From San Diego, then temporary resident of the Bay and now currently in Cambridge, MA.

Contact Info

  • E-mailMcClain.Thiel@berkeley.edu
  • Phone 949-280-3248

This website is basically a running resume but I'm working on adding some cool stuff if I ever find the time. I'm interested in machine learning (especially creative ways to squeeze more training out of limited data) computer vision, bioinformatics and the intersection of medicine and tech. Outside of work I workout, play basketball, try to travel and futilely attempting to pick up other hobbies like guitar, drawing and foreign languages.

Work Experience

  • nference


    Data Scientist

    We make some amazing tools that bring the full power of AI to medical and bio research industries. I develop some algorithms and do some consulting.




    I founded this company during my senior year of college when my dad, who works in wealth management, was complaining about how inefficient the industry is when sourcing new leads. We developed this platform to revolutionize the way financial advisors find clients. Our software uses ever vigilant sypders and the latest in AI and graph theory to find clients your competitors dont even know about for a fraction of the price.

  • Seidr


    I took a semester off from school during my senior year mostly due to covid and started this little consulting company. I do some stray ML and developing work.

    Past Clients

    Aila Health is an early stage precision healthcare start up focused on using machine learning to improve the treatment of chronic conditions, primarily autoimmune disorders. I am currently working on building several ML systems including building knowledge systems to support physicians.

    Arricor is a PNW based machine learning consulting company. I am building AI products using advanced NLP for products not yet made public.

  • CureMetrix

    Fall 2020

    Data Science Intern

    My primary goal is to improve the accuracy of medical computer vision systems with advanced data augmentation. I run experiments involving applying state of the art papers to Curemetrix products and data. I also do mare tradition data science work including quantifying the efficacy of our products and comparing it to the efficacy of radiologists. I consolidate this information into reports for FDA approval.

  • Hulu

    Summer 2020

    Software Engineering Intern

    Developed a web application security lab in conjunction with the cyber security and cloud engineering teams. Used lab to programmatically deploy vulnerable web applications to GKE and then attack the applications while monitoring behavior. Later deployed RASP protections over the web applications and measured how much protection each RASP provided. Recommended RASP vendor to senior leadership.

  • Climate.Ai

    January 2020 - Current

    Lead Consultant

    My team at ML@B consulted with climate.ai to prototype a machine learning system which uses geospatial satellite data to predict and analyze crop production in underdeveloped regions. Our prototype has since been pushed into production.

  • UC Berkeley Division of Data Science

    August 2019 - December 2019

    Principal Investigator

    I led a team of 5 undergraduates in a research project through the Data Science Discovery program at UC Berkeley. Our goal was to use DCGANs (deep convolutional generative adversarial networks) to enhance and augment undersized data sets for medical applications. The end goal is to improve accuracy of object detection systems by producing more data from a limited sample size. Our novel approach increased accuracy on the test set by an average of 12.4%.

  • ZMicro

    Summer 2019

    Machine Learning Engineer

    I built a prototype object detection and localization system to be run in real time for medical applications using YOLOv3 and Mask-RCNN. The project was to help medical professionals find polyps in colonoscopy footage while performing the operation. Achieved frame accuracy of >98% and can theoretically run upto 36 frames per second when optimized.

  • UC Berkeley IST

    October 2018 - May 2019

    Network engineering Intern

    Assisted UC network engineers with maintenance upkeep and expansion of the campus wide network infrastructure.

  • La Jolla Institute of Allergy and Immunology

    Summer 2016


    Worked in Dr. Shresta's lab during the summer of 2016 studying flaviviruses such as Zika and dengue fever during the Zika outbreak in Brazil. Duties included data collection and management.


  • Georgia Tech

    M.S. Computer Science


    Specialization in machine learning (of course)

  • UC Berkeley

    B.A. Data Science

    Graduated May 2021

    Minor: BioEngineering, Certificate in Entrepreneurship & Technology via SCET, Design Certificate from Jacob's institute, member and officer at Machine Learning @ Berkeley

  • London School of Economics

    Certificate in Machine Learning

    Summer 2019

    Highest grade in program

  • Miracosta College

    A.A. Liberal arts with a focus on math and science

    Spring 2018

    Completed with honors in Spring of 2018.



    Summer 2020

    On July 6 2020, the Trump administration announced a policy that said that all international students not enrolled in in-person classes for the coming semester must leave the country. I found this policy obnoxious and so with the help of some friends I made a website. This site allows international and domestic students to connect to ensure that everyone can get the classes they need. The site was up and running within 60 hours of its inception and got over 5000 hits within 12 hours of its launch.


    Summer 2020

    In response to nationwide protests in June 2020, I created Anonymize as an anti-surveillance resource for those who might face retribution for being seen at political events or rallies. I had heard multiple reports of people being harassed or fired from their job because they were in the background of a picture that didn't align with their employers political beliefs and that was later published on a media outlet. My app solves this problem by using facial recognition and images processing to find faces and blur them automatically. This site is still under development.

    Robust Computer Vision

    May 2020

    A common problem in the world of computer vision is building systems that perform as well as humans in conditions that are adversarial to the was machines see. My team and I developed a novel data augmentation and training procedure to improve accuracy of computer vision systems on natural adversarial examples. Report

    Monocular Distancing

    May 2020

    A known problem in computer vision and image processing is trying to gague distance without any objects of known size in the frame. I used a machine learning based approach to make highly accurate guesses about the distance between a persons eyes based on their demographics and then used prospective geometry to estimate their distance. My approach was accurate within 4% up to 20 ft with my laptop's webcam. github Demo

    Blind NLP in Arabic

    Summer 2019

    Natual Language Processing is very well studied in English, but I wanted to see if the same techniques would work in other languages with substantial in syntax and structure. I used traditional approches including Niave bayes, RNN, and clustering to classify and cluster 2000 tweets written in MSA (modern standard Arabic) and Jordanian Arabic. Github

    Beating Vegas

    Spring 2019

    Group project for Data Science in Practice. We build a machine learning pipeline that would scrape data about NBA teams and Vegas betting odds and would apply a conglomoration of algorithms to the data in order to recommend a bet for any given day. The project achived a theoretically small but profitable margin.

    Polyp Vision

    Summer 2019

    I build a real-time object detection and localization to help doctors spot life threatening polyps in colonoscopy footage and other medical procedures. Approach was fine -tuning the YOLOv3 algorithm with hand labeled samples of polyps. System achieved an accuracy of >98% while running at about 5 fps on my laptop. Github

Certificates and Awards

  • Dean's List

    Cloud Engineer with Google Cloud - In progress

    GIAC Security Essentials

    National Merit Scholar (Honorable mention)

    National AP Scholar