Steven Lopez is looking for a molecule that can create a new material to take carbon dioxide out of the atmosphere. The task poses many challenges, but foremost among them is: Where to begin?

“Imagine all the grains of sand on earth and under the oceans. Now multiply that by a million. That’s how many possible molecules there are,” says Lopez, assistant professor of chemistry and chemical biology at Northeastern. Put another way, there are about 10,000,000,000,000,000,000,000,000 potential molecular combinations to choose from, far more than any one computer can analyze.

“Right now, we can have about one billion molecules in the database,” he continues. “That’s enough to at least scratch the surface.”

Lopez will conduct this research as one of four principal investigators in the newly launched Institute for Data-Driven Dynamical Design. The goal of the institute, which is funded by the National Science Foundation, is to use machine learning to sift through information and discover new sustainable materials.

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What is your major, and when are you graduating?  

I’m pursuing a Master of Science in Applied Mathematics, with a specialization in data science. I will be graduating in spring 2022! 

Why did you decide to enroll at Northeastern University and pursue an advanced degree in mathematics?  

Northeastern University’s experiential learning program was the primary reason I decided to enroll as a graduate student. The experiential orientation of the program has not only allowed me to acquire a clear understanding of abstract graduate-level mathematics but, most importantly, has provided me with the opportunity to concurrently apply my learnings and continue fulfilling my potential as an aspiring data scientist through real-world industry experience. 

What is your experience in the program like so far?  

My experience as a graduate mathematics student has exceeded all of my expectations. The faculty I have had a chance to interact with all seem like they genuinely care about your personal and professional success. As a result, pursuing an advanced degree here has been one of the best decisions I’ve made.  

 Did a COS faculty or staff member help you excel in this program?  

All of my professors have had a hand in helping me excel at Northeastern! I would also like to give a special thanks to Patty Corrigan for her help during my co-op search last spring. 

 What is your favorite course in your program? Why?   

The most interesting course in my program was MA 7243 machine learning and statistical theory with Professor Nathaniel Bade because it provided a practical end-to-end overview of the machine learning development pipeline, from problem inception and data acquisition to model training and validation. I could not recommend this course enough to students interested in learning machine learning hands-on. 

 Tell us about your co-op experiences.  

I’m currently working as a Data Analytics Co-op, on the Global Safety and Support Tools team, at Amazon Robotics! My team’s goal is to proactively identify opportunities for improvement within the Amazon Robotics solution by developing software tools that support and further automate operations across our global Fulfillment and Transportation Centers network. I work closely with software engineers, product managers, system engineers, and data engineers to develop, validate, and deploy operational data models and metrics. Since the start of my co-op, I’ve had the opportunity to take full ownership of the development and deployment of an operational performance anomaly detection model to support the solutions that facilitate Robin (robot arm) issue ticketing and resolution for automated sortation centers. I have definitely learned a lot of new practical skills that I would not have been able to learn in a classroom, but I also find myself applying what I have learned as a graduate mathematics student on a daily basis. This co-op has served as the perfect complement to my graduate studies by helping deepen my understanding of complex subjects such as mathematical modeling, numerical analysis, machine learning, and probability through first-hand experience.    

 Do you have any advice for currently enrolled students pursuing co-ops?  

The process of pursuing co-ops can be stressful, but it’s important to remember that you’re not alone, so make sure you reach out to your co-op advisor and professors as early as possible so that they can help guide your search and provide the feedback you need to be the best version of yourself. Also, quality over quantity of applications is the right approach for guaranteeing a successful co-op search. Finally, research the companies you are interested in working for and carefully tailor your resume and cover letter to reflect a company’s respective needs.   

How have your classes enhanced your co-op experiences?  

They enhance each other! My classes help me build a solid foundational understanding of the mathematics behind virtually all data science methodologies necessary for modeling complex system processes and prescribing sound data solutions. My co-op experience helps me frame the application mindset required to truly understand the theoretical concepts discussed in class. 

What are your post-graduation plans?  

After graduating, I’m planning to pursue a full-time data science role. 

Do you have any advice for graduate students looking for work experience in similar fields?   

Yes! It’s important to note that you do not necessarily need to major in applied mathematics or computer science to become a data scientist. That being said, you should have a solid understanding of mathematics and use R or Python for data manipulation and analysis. For graduate students interested in data science who don’t already have direct industry experience, it’s crucial to build up your professional portfolio. The best way to achieve this is to complete a set of personal data science-related projects in problem spaces that interest you. Visit sites like Kaggle to explore data-sets that interest you and take inspiration from the projects others have already completed in the problem-space of your interest. The most natural and powerful form of learning is through hands-on experience, learning by doing, so just dive in! 

I’m a Masters of Science in Applied Mathematics student and will complete my final course in December 2021.  

Why did you decide to enroll at Northeastern University and pursue an advanced degree in mathematics? 

As a robotics software engineer with a computer science background, I felt limited in writing complex algorithms to solve today’s problems without a firmer grasp of applied mathematics. I served in the Marine Corps for five years, so I had forgotten a lot of my fundamentals of calculus and probability. However, this program was really attractive because it is incredibly supportive of veterans! So I worked to relearn the basics before starting to build on that and grow within the program.  

What is your experience in the program like? 

I  appreciate how easy it’s been to apply the topics I’ve been learning in classes to domains I would actually work with on the job. For example, I know I’m interested in the robotics space. Even though many of my classmates are in fields like finance and medicine, I’ve still made many of my projects relevant to my career aspirations. Some of these experiences include: creating a model for a lidar, using quadrature to estimate target positions over time, and sensor-based deep learning. In addition, as most of my time at Northeastern has been during the pandemic, online learning has made it easier for me to achieve more as I’ve had to handle more of the childcare during COVID.  

Did a COS faculty or staff member help you excel in this program? 

Yes! The faculty has been outstanding, and I would have to give a big thanks to four of them.  

Before starting, I met with Professor Bade, who assured me that things would still be possible even though I would be having a baby my first semester. In addition, he shared helpful insights like needing to take the non-math courses over the summer as math classes wouldn’t be available then.  

Professor Wang was very helpful and willing to guide me through an independent study course exploring Neural Style Transfer and GANs, which led to creating a children’s book I’ll be publishing on Amazon! 

Professor Brorson had incredible lessons and was very generous with helping make sure I understood how to complete homework and my mini-projects.  

Professor Lippner was very patient in helping me learn the concepts of proofs every office hour, as I had no background on proofs before his graph theory class.  

What is your favorite course in your program? Why? 

I enjoyed my mathematical methods and modeling class. It helped highlight the power mathematics has in answering real-world questions by analyzing the problem through math. I also enjoyed my final project, creating a model of a small lidar, using different environmental sensors to see how changes in the environment affected the sensor’s ability to sense accurately.    

How will your MS in applied mathematics enhance your professional career? 

I will return to work with so many more tools for creating mathematical algorithms, and the algorithms I create will be better. Also, I no longer feel intimidated when I read professional papers, as so much more of what I’m reading makes sense, and I’ll now be able to do it in my day-to-day work.  

What are your post-graduation plans? 

I am still exploring opportunities for when I graduate. I hope to work in a field where I can write interesting algorithms to process sensor data or help a robot make decisions.  

Do you have any advice for graduate students looking for work experience in similar fields? 

First, get your hands dirty with projects. The more you try things, the more you learn, and the more experience you’ll bring to a job. Second, explore networking opportunities! Boston Image and Vision meetup host many exciting events where you can go and meet others in the same field, enjoy interesting topics, and form connections. 

Take a minute to contemplate a sentence: Individually, each letter of each word doesn’t hold much significance. But put them together into words, organize those words into sentences, and then these symbols convey meaning.

You may never have seen the specific string of words in a given sentence before, but because you understand the abstract rules of language, your mind is able to figure out what the sentence is communicating. However, if the letters are shuffled around into gibberish that do not adhere to rules, you may not glean any sort of meaning from them.

The capacity for language is thought to be a key attribute that sets humans apart from other species on our planet. And humans seem to be pretty good at learning language from an early age. But scientists have debated why infants are so adept at that kind of learning.

That question has long been confounding because babies could be learning language by hearing their parents and others talk even before being born—and before scientists can study their behavior and comprehension. Are infants’ brains specially tuned to language, or is it simply speech that attracts their attention?

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