Alumna Delia Mocanu is a double husky and 2014 PhD recipient in Physics. During her time at Northeastern, she developed a passion for network science, working on data projects with incredible scale. Now at Facebook, she finds herself working on one of the largest data systems in history— News Feed.

She participated in a written engagement with the Northeastern COS on going industry, why epidemiology works better in the dark, and the most important skill to succeed in data science.

Growing up, did you find that you were interested in physics? Or did that interest develop later in life?

I grew up in Romania and I did not like Physics much at the time because it was too formulaic. In a weird twist of events, as a freshman in college here in the US, I actually switched from Chemical Engineering to Physics/Math double major.. 

At some point I realized that Physics made a lot of sense for me and I just enjoyed pure science more than engineering. I liked the rush of solving problems from scratch.

Why Northeastern?

I jumped around a bit until I landed on what I wanted to do. I was originally curious about Astroparticle Physics and I wanted to know everything about how the universe worked. During my first year at NEU, it became clear that I was seeking something more fast-paced. [The irony is not lost on me, this was very antithetical to why I switched from Engineering to Physics four years earlier.]

At Northeastern, seeing the kind of work Barabasi and Vespignani were doing, I immediately recognized that this interdisciplinary field (Network Science/Complex Systems) was more aligned to my existing interests and personal values. 

Is there anything that stands out about your graduate/Phd experience?

My advisor really emphasized the idea of ownership, and I liked that we were held to very high standards. Looking back at it now, I always felt like what I was putting my time in truly mattered. Prof. Vespignani was very good at instilling energy to the group. 

Did your experience working in Professor Vespignani’s MOBS Lab and other research facilities shape your career decisions?

Absolutely. What I cherish about this PhD experience is that we felt very plugged in; we had well funded projects that were designed to solve real problems, in real time. 

We loved doing something that mattered. I was simultaneously learning something and solving a problem involving millions of people. Little did I know I was going to reach billions later.

You’re currently working at Facebook as a Data Scientist. What does your current role entail?

I work in News Feed, and I’ve been here since I started at Facebook. What makes this role especially stimulating is building solutions that work at scale. I still very much rely on the thought processes and models of the world that I adopted during my PhD. Right now, I couldn’t imagine a better place to apply these. 

However, my favorite part of my job is actually identifying opportunities. When I find something worth investing in, I put all my energy into making it a reality. That last part is the most rewarding and it is really more about general problem solving than it is about any specific math/engineering skill. 

Have you spoken to friends or colleagues about Professor Vespignani’s COVID-19 models and what they are trying to accomplish? Has it been a source of pride, frustration, or a little of both?

To my friends mostly, yes. I touched epidemiology models a bit during grad school. At the time I remember thinking ‘I hope this software makes a difference someday,’ but I never thought we’d see something like this. Healthy skepticism is good, but I’m quite surprised to see the amount of pushback against these predictions at large, so I would say this has caused a bit of frustration. 

Frankly, epidemiology is best when you don’t know that it exists and when the predictions don’t come true. Otherwise, it is not too dissimilar from weather forecasting; every modeling exercise comes with error bars, but one can tell the difference between a major hurricane and a light summer rain. However, in epidemiology you ‘can’ actually turn the would-be hurricane into a light summer rain. Taking action invalidates the original prediction, that’s really the goal. Whereas if your predictions come true, you have failed; that’s the curse of this field. 

Computational epidemiology has advanced so much in the past two decades, that it’s quite challenging to establish a common language even with other highly technical folks.

What do you find most rewarding about data science?

It’s the most rewarding job you can possibly imagine. It’s always changing and you are constantly learning new things or building new tools so that you can iterate faster. 

It’s not just about the act of doing the analysis, but more about where the data fits. A lot of data science is problem solving, which is what I liked about Physics in the first place. You don’t have a solution and no one has ever solved this problem before. There are no instructions and every single day feels like a journey. That dynamic aspect  is very important to me.

What was the most important thing you learned at Northeastern?

Professor Vespignani wanted nothing short of perfection. He would sometimes ask you to iterate on the same chart a dozen times before it felt right. It’s about communicating this data in the best way possible. If I have to repeat the same steps several times before I get it right, then I do it, and I think it’s worth it. As a result, I do notice when others take shortcuts.

I can’t stress this enough: the analysis is not an end in itself.

Is there advice you would give to students who are interested in this field or the type of work you’re doing now?

Don’t be afraid of change, your interests will continue to evolve over time. Look at your PhD program as a time in your life to discover what you like doing and work with your advisor through that process, as they should guide you in making the most out of your career. 

Your goal in academia is to publish papers and advance knowledge, while you may not necessarily implement them right away, and that’s ok. If you choose the industry path, your focus will be on the application itself. The optimal mathematical solution may need a 20-fold simplification so that you can enable the rest of the team to be part of it. 

Anything else you’d like to add?

I do want to acknowledge the fact that Northeastern did an incredible job bringing professors from other universities and building great research programs, and not just in network science.

It’s incredible. I do think that I was very lucky to be part of Northeastern because that sort of environment so focused on research is very, very important. I really loved it.

Big news! Northeastern researchers have identified 40 new potential drugs that could treat COVID-19. Albert-László Barabási, Robert Gray Dodge Professor of Network Science and University Distinguished Professor of physics, believes the best drug candidates will probably be those that don’t target the proteins that SARS-CoV-2 initially attacks but work within the same subcellular neighborhood.

This story was originally published on News@Northeastern on April 2, 2020. To read more, click here!

Faster electronics, better communication devices, more efficient ways to store data are just some of the outcomes that the researchers can think of – if magnetite’s puzzle of hidden powers could be figured out. Eventually, it lead to new ways to manipulate materials and improving electronics by harnessing the behavior of their electrons. Physics Professor Gregory Fiete and Postdoctoral Researcher Martin Rodriguez-Vega are working towards this.

This article was originally published on News@Northeastern on March 17, 2020. To read more, click here!

Humans have been studying electric charge for thousands of years, and the results have shaped modern civilization. Our daily lives depend on electric lighting, smartphones, cars, and computers, in ways that the first individuals to take note of a static shock or a bolt of lightning could never have imagined.

Now, physicists at Northeastern have discovered a new way to manipulate electric charge. And the changes to the future of our technology could be monumental.

“When such phenomena are discovered, imagination is the limit,” says Swastik Kar, an associate professor of physics. “It could change the way we can detect and communicate signals. It could change the way we can sense things and the storage of information, and possibilities that we may not have even thought of yet.”

The ability to move, manipulate, and store electrons is key to the vast majority of modern technology, whether we’re trying to harvest energy from the sun or play Plants vs. Zombies on our phone. In a paper published in Nanoscale, the researchers described a way to make electrons do something entirely new: Distribute themselves evenly into a stationary, crystalline pattern.

“I’m tempted to say it’s almost like a new phase of matter,” Kar says. “Because it’s just purely electronic.”

University Distinguished Professor of physics Arun Bansil (left) and associate professor of physics Swastik Kar accidentally discovered a new way to manipulate electronic charge. Photo by Matthew Modoono/Northeastern University

The phenomenon appeared while the researchers were running experiments with crystalline materials that are only a few atoms thick, known as 2D materials. These materials are made up of a repeating pattern of atoms, like an endless checkerboard, and are so thin that the electrons in them can only move in two dimensions.

Stacking these ultra-thin materials can create unusual effects as the layers interact at a quantum level.

Kar and his colleagues were examining two such 2D materials, bismuth selenide and a transition metal dichalcogenide, layered on top of each other like sheets of paper. That’s when things started to get weird.

Electrons should repel one another—they’re negatively charged, and move away from other negatively charged things. But that’s not what the electrons in these layers were doing. They were forming a stationary pattern.

“At certain angles, these materials seem to form a way to share their electrons that ends up forming this geometrically periodic third lattice,” Kar says. “A perfectly repeatable array of pure electronic puddles that resides between the two layers.”

At first, Kar assumed the result was a mistake. The crystalline structures of 2D materials are too small to observe directly, so physicists use special microscopes that fire beams of electrons instead of light. As the electrons pass through the material, they interfere with each other and create a pattern. The specific pattern (and a bunch of math) can be used to recreate the shape of the 2D material.

When the resulting pattern revealed a third layer that couldn’t be coming from either of the other two, Kar thought something had gone wrong in the creation of the material or in the measurement process. Similar phenomena have been observed before, but only at extremely low temperatures. Kar’s observations were at room temperature.

“Have you ever walked into a meadow and seen an apple tree with mangoes hanging from it?” Kar asks. “Of course we thought something was wrong. This couldn’t be happening.”

But after repeated testing and experiments led by doctoral student Zachariah Hennighausen, their results remained the same. There was a new lattice-style pattern of charged spots appearing between the 2D materials. And that pattern changed with the orientation of the two sandwiching layers.

As Kar and his team had been working on the experimental investigation, Arun Bansil, a university distinguished professor of physics at Northeastern, and doctoral student Chistopher Lane were examining the theoretical possibilities, to understand how this could be happening.

Electrons in a material are always bouncing around, Bansil explains, as they are pulled on by the positively charged nuclei of atoms and repelled by other negatively charged electrons. But in this case, something about the way these charges are laid out is pooling electrons in a specific pattern.

“They produce these regions where there are, if you like, ditches of some kind in the potential landscape, which are enough to force these electrons to create these puddles of charge,” Bansil says. “The only reason electrons will form into puddles is because there’s a potential hole there.”

These ditches, so to speak, are created by a combination of quantum mechanical and physical factors, Bansil says.

When two repeating patterns or grids are offset, they combine to create a new pattern (you can replicate this at home by overlapping the teeth of two flat combs). Each 2D material has a repeating structure, and the researchers demonstrated that the pattern created when those materials are stacked determines where electrons will end up.

“That is where it becomes quantum mechanically favorable for the puddles to reside,” Kar says. “It’s almost guiding those electron puddles to remain there and nowhere else. It is fascinating.”

While the understanding of this phenomenon is still in its infancy, it has the potential to impact the future of electronics, sensing and detection systems, and information processing.

“The excitement at this point is in being able to potentially demonstrate something that people have never thought could exist at room temperature before,” Kar says. “And now, the sky’s the limit in terms of how we can harness it.”

This story was originally published on News@Northeastern on February 26, 2020