Q+A Interview

What are you passionate about?

I truly love learning new things and finding innovative solutions that will solve problems. The process of tearing apart a problem and tackling each challenge to meet what the customer truly needs is thrilling. Sometimes what they need is not what they initially claim they want, but by working closely with them, you both can relieve the true pain point.

What do you like most about MANTECH?

I have been amazed by the camaraderie and support that I’ve seen from everyone within MANTECH. There hasn’t been a single person who hasn’t gone above and beyond their duties and responsibilities to help me. I even had a MANTECH Vice President, who is nowhere near my organizational role, help me with simple data entry into MANTECH systems that I wasn’t familiar with yet.

What is or has been the most unusual or interesting job you’ve ever had?

To pay for college, I worked as an engineering assistant in the construction industry for companies with tuition reimbursement policies, including one that helped design and build the DC Metro tunnels.

What advice do you have for those looking to start a career in Data and AI?

For most people, I would recommend finding a domain they love and applying data and AI to solving a problem in that domain. You’ll have more of an impact and gain greater satisfaction from the results.

What’s the coolest thing you’re working on now?

We’ve been working on applying systems and mission engineering to risk disaggregation and online learning for mission-informed decision intelligence, including NASA’s deep space habitats.

What do you wish people understood about Data and AI?

I’ve seen a lot of companies, people, and students in many domains attempt to blindly dump data into AI models, which is not a good approach. They can gain some initial results which seem promising, but those results will ultimately fail when applied to the field. You need to know if you even have the right data with a “signal” for the model to learn from, ensure that the model will not be confused by noise in data (like correlations), and avoid removing outliers from the dataset when you should be building a model that explains them, otherwise the model will not be resilient in the real world.

How do you balance career and home life?

I’ve learned over the years, including through a near-death experience caused by stress, that the best way to balance career and home is clear and concise communication with everyone. Be open and communicative about your workload, limitations, and availability rather than being scared to speak up and be considered a bad team member. Any good boss or teammate will not consider that as a negative, but a very valuable positive, because it puts less risk on the team successfully meeting deliverables.

What’s your favorite place in the world?

My favorite place in the world is the Appalachian rainforest. I enjoy the cool mountain breezes, the soft rain, the sound of leaves in the wind, birds singing in the forest, and water cascading down the valleys – so tranquil.