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    Fred Tanada

    Fred Tanada (He/Him)

    @softbank-capital @draper-fisher-jurvetson-dragonfund • @chestnut-street-ventures @flight-vc-syndicate • @crimson-growth-partners • d.school @stanford.

    Venture Capital Silicon Valley University of Pennsyl...
    fredt1.tumblr.com
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    Experience
    SoftBank Capital
    SoftBank Capital
    Investor
    Chestnut Street Ventures
    Chestnut Street Ventures
    Co-investor 2017 - Present (over 9 years)
    Draper Fisher Jurvetson DragonFund
    Draper Fisher Jurvetson DragonFund
    Finance Manager: Venture Capital
    Flight Ventures
    Flight Ventures
    Venture Capital (Data and Finance Analysis) 2013 - 2017 (about 4 years)
    Crimson Growth Partners
    Crimson Growth Partners
    Venture Partner 2013 - 2014 (about 1 year)
    moolave
    moolave
    Co-founder 2011 - 2012 (almost 2 years)
    U.S. Department of Defense
    U.S. Department of Defense
    Employee
    Tracti.on
    Tracti.on
    Board Member
    moolave
    moolave
    Founder
    Cruise
    Cruise
    Employee
    Oracle
    Oracle
    Employee
    DFJ
    DFJ
    Employee
    Entrepreneurial Advisory Network
    Entrepreneurial Advisory Network
    Advisor
    Founder
    Tracti.on, moolave
    Employee
    Crimson Growth Partners, Tracti.on, U.S. Department of Defense, moolave, General Motors, DFJ, Oracle, Cruise, Flight Ventures, Draper Fisher Jurvetson DragonFund, Chestnut Street Ventures, SoftBank Capital
    Investor
     
    Incubator
     
    Advisor
    Entrepreneurial Advisory Network, Bartermill
    Attorney
     
    Board Member
    Tracti.on
    Mentor
     
    Member
     
    Acquired
     
     
    Projects
    Improving the Neural GPU Architecture for Algorithm Learning (Arxiv Project Summary)
    Improvements to the Neural GPU to reduce training time
    Machine Learning, Data Science, Big Data, Psychometrics, Adaptive Learning · Algorithm learning is a core problem in artificial intelligence with… · More significant implications on automation level that can be achieved by machines. Recently deep learning methods are emerging for synthesizing an algorithm from its input-output examples, the most successful being the Neural GPU, capable of learning multiplication.
    Improving Machine Learning Ability with Fine-Tuning (Arxiv Project Summary)
    Item Response Theory allows for ML measuring ability
    Machine Learning, Data Science, Big Data, Psychometrics, Adaptive Learning · Item Response Theory (IRT) allows for measuring ability of Machine… · More Learning models as compared to a human population. However, it is difficult to create a large dataset to train the ability of deep neural network models (DNNs). We propose fine-tuning as a new training process,
    where a model pre-trained on a large dataset is fine-tuned with a small supplemental training set.
    Self-Driving Car Autonomous Project
    Using Computer Vision, Python, and Machine Learning
    Self-Driving Car Engineer, Python, OpenCV, TensorFlow · Using Computer Vision, Python, and Machine Learning to Detect Lanes and Traffic… · More Signs

    Machine Learning and Computer Vision
    Finnbot
    Chatbot on Stocks, Silicon Valley, and Trends Analysis
    Founder, Html5, Css3, Php5, Java Script, Json, Xml, My Sql · Find stock news and analysis with a bot without leaving Facebook Messenger

    Development
    Facebook Compassion Research
    science of how people relate to each other to social tech
    Developer Advocate · Understanding the science of how 900+ million Facebook users relate with each other in terms of social mitigation, conflict… · More resolution, and compassion as an essential trait in understanding relationships better.

    Partnered with researchers from Columbia, Berkeley, and Yale in the field of communicating emotion and social-emotional learning. Led by Arturo Bejar, Director of Engineering at Facebook.
    Stanford d.school hack.d Project
    D.school founders project
    UX Designer · Concept #1
    d.radio allows you to tune into and listen to d.classes or spaces which are currently broadcasting their audio. You not… · More only listen but are becoming an active participant thru – our secret sauce here

    Concept #2
    d.mix and match allows teachers, students, professional and anyone to build their own ideal learning experience, based on:
    - d.school content
    - external sources: e.g. YouTube, TED Talks, Blogs etc
    and share it with the world.

    The overall problem we tackle is how do we turn passive observers, who just browse the d.website, into active participants, who create, contribute and make the world a better place.
     
    Education
     
    About
    Achievements

    Experience in early stage startups to late stage SaaS companies (>50bn annual revenue) Funds managed from early stage (1.8 billion AUM) to late-stage (100 billion). Worked as operator on self-driving car portfolio companies besides being on the other side of the table. Involved in Typhoon Haiyan relief effort; managed to raise funding to educate out-of-school children. Initiated non-profit agencies with Johns Hopkins for homeless families in Baltimore, outreach meal programs for homeless with UPenn in San Francisco during winter.

    Traded futures and Foreign Exchange for Wall Street. Worked at California State Senate and Defense companies. Stanford d.school hack.d Founding Member.

    Describe the most impressive thing you've done.
    Skills
    Finance Venture Fundraising Due Diligence Market Research Business Strategy
    Locations
    San Francisco Silicon Valley
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