Kishau Rogers
42 posts
Kishau Rogers is an award-winning technology entrepreneur specializing in wrangling complexity using computer science, systems thinking, creativity, and common sense. She is the Founder & CEO of Time Study, Inc., a high-growth startup offering solutions for using machine learning, advanced natural language processing, and data science to automatically tell a story of how enterprise employees spend their time and its impact on the areas that matter. As the Founder and CEO of Time Study, Kishau is one of the first Black women in Virginia to raise millions in venture capital to scale her tech startup. She is also the owner of Websmith Studio and the editor of the bigThinking project, a resource for promoting the principles of systems thinking.
Kishau’s work is featured in Forbes, The Wall Street Journal, Entrepreneur, and Black Enterprise. In addition, she is a recipient of many awards, including the VCU Distinguished Alumni (in Computer Science), NAWBO Wells Fargo STEM award, the Lyn McDermid Community Impact Award, and the MBL Entrepreneur of the Year Award.
In her commitment to using technology as a tool for social good, she also serves as an advisor to organizations and initiatives like AI for Afrika, Think Of Us, WAAW Foundation, Level Up Ventures, Virginia Commonwealth University Department of Computer Science, the first U.S. White House Hackathon for Foster Care and SheHacks Africa, a software engineering intensive providing training to women & girls across Africa.
She holds a Computer Science degree and has over twenty-five years of experience in the technology industry and more than 15 years of entrepreneurial leadership.
How to Be A Systems Thinker feat. Mary Bateson
Insights on ignorance and becoming a systems thinker featuring Mary Catherine Bateson.
It’s Complicated: On Imagination, Marvin Minsky & Shawn Carter
Computers remain dependent on humans to be able to imagine & architect what the computer should & can do. And perhaps we’re not building machines to be able to do anything beyond specialized intelligence because we don’t yet have reverence for the power of imagination and all that is required to discover and execute better ideas.
Creating Feedback Loops for Big Ideas
When executing a big idea, the need to protect it and the unpredictable chemistry of engagement leaves little room for the right people to provide deeper insights earlier in the process. The reality is that we don't know anything until we create space for enough (of the right) people to connect and engage with our ideas.
Artificial Intelligence & the Future of Work : Superheroes Needed
Here are nine key roles that are necessary for the future of work and for people that are interested in using artificial intelligence (AI) to solve the world’s biggest problems. Inspired by superheroes (real & imaginary).
Skills Needed to Solve Problems with Data : Getting Started
If you're interested in a career as a data analyst or data scientist, here are some fundamental skills to develop ...
The North Star : Make Smart Decisions Faster
As a person that develops technology solutions, your toughest challenges are rarely technical. Often it’s people, complex business rules, and dysfunctional behaviors that result in bloated, over-engineered solutions that ultimately fail. We often skip building a proper business foundation because it’s sexier to fail fast and fail often, but if you’re already under-capitalized, the best thing you can do is lay a proper foundation that allows you to simplify your decisions.
Unstructured data – Opportunities, Challenges, Dangers + Homework
It is estimated that 80% of new data generated is unstructured, largely driven by audio, video and rich media content. Here are a few opportunities and challenges presented with increased availability and use of unstructured data.
Artificial Intelligence : 4 Key Challenges Facing Engineers
The internet is filled with stories of AI that resulted in negative human impact and unintended consequences. If you are developing or plan to develop artificial intelligence solutions, here are some questions for your consideration:
Characteristics of Machine Learning solutions
It is estimated that 87% of data science projects never reach production. One of the pitfalls to developing a production-ready machine learning solution is the ability to define if it's an appropriate tool for solving the problem. Not every problem should be solved with machine learning.