For as long as she can remember, Merilys Huhn has loved solving problems. She has always been deeply intrigued by how people work and act together—her background in law and sociology can attest to that. Today, Merilys applies this experience in ethics as an Associate Data Scientist at Pandata. We sat down with Merilys to learn more about what makes her tick.
I was born and raised in Cleveland, Ohio. When I was a kid, I wanted to be an engineer. I loved problem-solving. In the years since then, I have lived and worked in areas all over the U.S., from California to Massachusetts to Virginia to Washington, D.C. (to name a few).
I have always been driven by the pursuit of understanding. I headed to The George Washington University to study mathematics, then on to Washington and Lee University School of Law to earn a Doctor of Law degree. After earning my JD and working in social services for a few years, I decided to pursue a PhD in sociology at Stanford University, where I am currently using a mix of machine learning solutions and qualitative analysis to understand social interaction.
I enjoy math. I like that it is concrete and certain. I like being able to work on a problem and eventually say, “This is true, and this is why.” I also care about ethical equalities in our systems. At law school, I worked with capital defenders on capital murder cases. After law school, I worked for a time in social services for Veterans Affairs. I am always asking, “How can we better address a particular issue? How can we find proof for why things should be done a certain way?” My background in law and sociology powers my work today in designing ethical AI solutions through a human-centered approach.
I have discovered that everyone on our team has an interesting story. My legal background helps me when thinking about AI in an ethical way. It helps me think about the right mental framework to use when approaching AI. And it also makes me seek to produce trusted results.
The main difference for me is precedents. In law, cases depend to a large degree on precedents. In other words, court decisions are considered authority for deciding subsequent cases involving identical or similar facts, or similar legal issues. But in data science, we are always challenging what has happened before. We are always trying to disprove things, change things. We spend our days not looking at the past, but at the future, through constant improvement.
My technical focus is on deep learning and pipeline architecture, using machine learning solutions and qualitative analysis to understand social interaction. But I am also developing subject matter expertise in Trusted AI. In addition, I’m involved in networking with people inside and outside our field, evangelizing about what we bring to the table as a company.
What are some trends you think will be important to data science and AI moving forward?
I am anticipating the day when we have many more ways to automate exploratory data analysis. We will still need someone to do the work, but there will be more tools to make my job simpler, even though the challenges are more complex.
I also believe that Trusted AI is going to be vital in the future. Trusted AI means setting up your pipeline to ensure that, at every stage, you’re able to audit, evaluate and understand what you’ve been doing and what your results are.
What advice would you give to someone entering the data science field?
Apply for the job. Take the chance.
Merilys’s journey is just one of the many unique backgrounds that make up the Pandata team of expert data scientists. If your organization feels ready to work with the Pandata team to design and implement the right AI solution, schedule an AI Exploration Session with our team.
In this session, we can discuss your current needs, and dive deeper into ways your organization can implement human-centered AI and machine learning solutions to accelerate your business goals.
Contributor – Nicole Ponstingle McCaffrey is the COO, AI Translator at Pandata.