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Clark H

- Research Program Mentor

PhD at University of Wisconsin - Madison

Expertise

machine learning, artificial intelligence, microcontrollers, engineering, electric vehicles, electric energy systems

Bio

I am an Engineer - Educator - Design Thinker - Innovator - Husband - Father - Brother – Son; I have done research and product development for 11 years for General Motors, Westinghouse, and Visteon, creating fuel cell cars and megawatt power conversion systems. For the past 13 years, I have taught engineering at the university level, mentoring hundreds of students on projects that range from collision avoidance systems for electric vehicles to biomedical devices that help stroke patients recover faster. Today, I am an active researcher and consultant, using machine learning to create autonomous mobile robots. I have a Phd in electrical engineering from the University of Wisconsin-Madison and I am a MIT certified Master Trainer in Educational Mobile Computing. One of the joys in my life is helping students explore and develop new ideas by helping them learn how to think like a researcher when formulating a problem, think like a Designer when understanding the context of their user and think like an engineer when creating technological and social solutions that will impact the world. Outside of work, I enjoy hiking and kayaking and have completed 44 of 46 Adirondack high peaks. I can't wait to get back to the mountains.

Project ideas

Project ideas are meant to help inspire student thinking about their own project. Students are in the driver seat of their research and are free to use any or none of the ideas shared by their mentors.

Will it go viral?

What does it take for a video or social media post to go viral? Can artificial intelligence predict what posts are more likely to go viral and which are likely to never be seen? What are the limits of AI today and what would be required to train an AI to recognize the 'next big thing?'

How small is your window on the world?

Each of us uses our news feed, social media, and favorite media outlets to learn about and understand our daily world. But nearly everything we see or hear on digital media is chosen for us by hidden algorithms, limiting what we see to a small window on the world. Is there a way to figure out just how small is that window on the world. Is there a way we can make it bigger? Who controls the algorithms that choose what you see and what is the motivation for algorithm to make those choices?

Automatic AI song recommendations based on facial recognition

Is it possible to build a AI system that recommends songs based on your emotional-state as determined from AI face recognition? Can the best face recognition systems detect microexpressions? Can you tell if a person likes or dislikes a song based on facial microexpressions? What are the AI underpinnings that would make this possible?

Coding skills

C, MatLab, Python.

Languages I know

elementary Spanish

Teaching experience

I have taught engineering at the university level for 13 years, mentoring hundreds of students on projects that range from collision avoidance systems for electric vehicles to biomedical devices that help stroke patients recover faster. As an MIT certified Master Trainer in Educational Mobile Computing I have trained teachers in NYC on mobile app development and have mentored high schools students in FIRST Robotics.

Credentials

Work experience

Rochester Institute of Technology (2008 - Current)
Associate Professor
AppliedLogix (2020 - Current)
Consultant

Education

State University of New York at Buffalo
BS Bachelor of Science (1989)
Electrical Engineering
University of Wisconsin - Madison
PhD Doctor of Philosophy (1997)
Electrical Engineering

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