Sculpture meets science when physicist Albert-László Barabási makes art from network patterns
As a teenager, Albert-László Barabási wanted to be a sculptor. Then he discovered physics and went on to invent a new field of network science.
But the scientist’s interest in art never went away. Rather, it became integral in Barabási’s research uncovering the patterns in the networks that underpin our world. And that medley of art and science that has yielded a new form of art, now on display in an exhibit in Germany, as well as online.
“In art it is very common that you take ideas from science, shapes from science, and concepts from science and put it into the art space,” says Barabási, Robert Gray Dodge professor of network science and a distinguished university professor of physics at Northeastern. “Here it happened inversely. My interest in art and my active engagement with the art community has allowed me to take the forms and the visual representations from the art that I was experiencing and bring it back to scientific visualization.”
Read the full story at [email protected].
Why kids hold the key to herd immunity
To win the battle against the pandemic, kids will be vital.
The fight against COVID-19 has long been focused on adults – particularly older adults. But kids are becoming a more prominent part of the conversation. We likely won’t see an end to the pandemic, experts say, until children can get vaccinated.
On Wednesday, Pfizer and its partner BioNTech announced the results of its coronavirus vaccine trial in adolescents 12 to 16 years old. It’s safe and effective in that age group, the company reported. The Pfizer-BioNTech vaccine currently is approved for emergency use by the FDA for ages 16 and up, and extending use to that younger age group will take a separate approval process. In March, both Pfizer and Moderna announced that they have begun trials of their respective COVID-19 vaccines in children under the age of 12 and as young as six months. Moderna also has a vaccine trial underway in adolescents.
Read the full story at [email protected]
Diseases spread differently, region by region. This Mathematical model shows how.
Considering how many factors contribute to the worldwide spread of airborne infectious diseases, forecasting pandemics can be a daunting task.
In an attempt to reflect that complex reality, Northeastern’s Laboratory for the Modeling of Biological and Sociotechnical Systems (MOBS Lab) has developed a new, data-driven model that factors in patterns of interpersonal behavior down to the state or province level, enabling epidemiologists to get a closer, more specific look at how diseases spread.
“There’s no one model that fits all nations,” says Ana Pastore y Piontti, an associate research scientist in the MOBS Lab and co-author of a paper outlining this new model, which was published in the journal Nature. “We can break down contact patterns into subnational levels where the people are interacting.”
Read the full story at [email protected]
What can we expect from the new mutation of the coronavirus?
The SARS-CoV-2 virus acquires a new mutation in its genetic structure about every two weeks, according to the Centers for Disease Control and Prevention. Most mutations have no effect on how deadly or contagious the virus is. But the variant known as B.1.1.7 could be more contagious—though not necessarily more deadly—than previous strains. “It’s probably in more countries than we know,” Scarpino says of B.1.1.7, which was first identified in the United Kingdom. “That’s why we need the kind of widespread genomic surveillance the U.K. has instead of constantly playing catch-up.” Read the full story at [email protected]
The SARS-CoV-2 virus acquires a new mutation in its genetic structure about every two weeks, according to the Centers for Disease Control and Prevention. Most mutations have no effect on how deadly or contagious the virus is. But the variant known as B.1.1.7 could be more contagious—though not necessarily more deadly—than previous strains.
“It’s probably in more countries than we know,” Scarpino says of B.1.1.7, which was first identified in the United Kingdom. “That’s why we need the kind of widespread genomic surveillance the U.K. has instead of constantly playing catch-up.”
Read the full story at [email protected]
Make a heart-healthy resolution this year
If you’re making resolutions for 2021, why not make one that’s good for your heart?
A new study by researchers from Northeastern University, Harvard University, and Brigham and Women’s Hospital shows that certain foods—including wine, yogurt, carrots, peanuts, breakfast cereal, grapes, and raisins—are associated with a lower risk of developing coronary heart disease.
The researchers also found several foods that were associated with an increased risk of developing heart disease, including processed meat, doughnuts, and white bread.
“Diet-induced diseases are the largest source of death in the U.S.,” says Albert-László Barabási, Robert Gray Dodge Professor of Network Science and university distinguished professor of physics at Northeastern, and one of the researchers in the study.
Read the full story at [email protected]
New Global Partnerships Expand Northeastern Ph.D Programs to Italy and Hong Kong
Northeastern’s doctoral students can forge global networks and pursue experiential learning in an international environment thanks to new agreements with a pair of widely recognized universities in Asia and Europe.
The global experiential doctoral program between Northeastern, the University of Hong Kong, and Sapienza University of Rome is interdisciplinary flexibility, which allows students to pursue doctoral degrees in two different subjects at two different schools, says David Madigan, provost and senior vice president of Northeastern. A doctoral student focusing on physics at Northeastern could also study computer science in Rome, and vice versa.
Read the full story here.
The Sum Total: A Collection of COVID-19 Stories Across COS
When COVID-19 emerged as global threat, it demanded action, and COS heard the call.
Seemingly overnight, a fleet of professors, researchers, technicians, staff, and students mobilized to fight on the front lines of science. Together, and in every discipline of science, they were able to make significant contributions to the collective good, such as: developing epidemic models, serving as advisors to local and national government, studying the virus’ proteins, developing methods for contact tracing, creating the infrastructure for on-campus testing, and more. Even as the pandemic continues, so does their work.
Thanks to [email protected]‘s exceptional team of journalists and photographers, we are now able to present a retrospective of the COS communities efforts.
Here’s a look at the first six months of COVID-19 and how COS fought back.
| March 2, 2020
Research from the Network Science Institute uses mathematical equations to track how “social contagions” spread. This data shows how to follow false news and rumors about COVID-19, and why gossip spreads like a disease itself.
Featuring: Jessica Davis (PhD student), Alessandro Vespignani
Topics: Mathematics, Network Science
| March 6, 2020
The Network Science Institute published a study showing that closing borders and travel bans might slow the spread of COVID-19, but will not stop the spread. Their study used Wuhan travel bans as an example for America.
| March 20, 2020
Thomas Gilbert explains the simple chemistry behind washing your hands with soap and why it’s so effective at killing virus’s and bacteria. Further, why the twenty second rule is important, and how soap can fight the lipid casings of bacteria (that plain water can’t dissolve).
Featuring: Thomas Gilbert
Topics: Chemistry and Chemical Biology
| March 27, 2020
Abhishek Mogili is a Biology co-op student helping prepare hospitals for the incoming onslaught of patients. Acting as an extra set of hands, he helps brace for impact with COVID, a common theme among pre-med co-ops.
Featuring: Abhishek Mogili (Co-op student)
| April 1, 2020
David DeSteno explains how rumors and fear, while useful, can get blown out of proportion. DeSteno goes on to show how this applies to the pandemic, and how to combat our basic instincts.
Featuring: David DeSteno
| May 15, 2020
Using machine learning, coupled with their knowledge of the disease’s amino acids, Mary Jo Ondrechen and Penny Beuning locate the weak points of COVID-19, helping create possible vaccines down the line.
| June 1, 2020
With less volunteers to assist food shelters during the pandemic, the Marine Science Center researchers stepped up, helping to keep meals flowing for those in need.
| June 3, 2020
COVID-19 Misconceptions Are Hard to Fight. Cognitive Psychology Might Help Spot Why People Get the Coronavirus Wrong.
John Coley explains how psychological misconceptions about COVID-19 arise. He goes on to explain how to fight these misconceptions with that same psychology.
Featuring: John Coley
| July 27, 2020
As researchers study SARS-CoV-2 and COVID-19 at breakneck speeds, one key aspect to keep in mind is that the research is happening while everyone watches. “The public is getting front-row seats to the scientific method, probably in a way they never imagined they would’ve experienced,” says Samuel Scarpino, who runs the Emergent Epidemics Lab at Northeastern.
Featuring: Sam Scarpino
Topics: Marine and Environmental Science
| August 5, 2020
Northeastern’s Life Sciences Center is a Cutting Edge Laboratory That Will Process the University’s Coronavirus Tests
The Northeastern Life Science Center receives permission to process the university’s coronavirus tests. This tremendous project is led by Jared Auclair, who runs the Biopharmaceutical Analysis Training Laboratory.
Featuring: Jared Auclair
| August 6, 2020
William Sharp discusses the stresses “mask vs no mask” interactions can cause, and shares how to start the important conversations surrounding them.
Featuring: William Sharp
Flu Season Is Coming and Covid-19 Is Still Here. Can Disease Forecasts Tell Them Apart?
Every year, as winter approaches, the United States gets ready for a potential epidemic. This year, the country is preparing to handle two.
Influenza, or the seasonal flu, kills between 12,000 and 61,000 people in the U.S. annually, according to the Centers for Disease Control and Prevention. The current COVID-19 pandemic has already been responsible for more than 200,000 deaths in the U.S. this year.
Both viruses attack the respiratory system and can have similar symptoms. For researchers trying to track these viruses and predict their spread, untangling them will be a challenge.
“Something like this is completely unprecedented,” says Alessandro Vespignani, Sternberg Family Distinguished University Professor of physics, computer science, and health sciences, and director of Northeastern’s Network Science Institute. “Having a major pandemic and then trying to get insight on the seasonal flu—it’s a completely new game.”
Finding the Needle in the Data Stack: Advice from a Facebook Data Scientist
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.
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.
People in the U.S. Started Moving Around More Before Stay-At-Home Measures Were Lifted
People in the U.S. are outside before they’re supposed to be – wearing masks, meeting outside with small numbers of people, and keeping your distance can help minimize the risks inherent in leaving your house, according to public health officials. If people are traveling slightly further and seeing slightly more people, these safeguards could make a difference. Matteo Chinazzi and Stefan McCabe from the Network Science Institute weigh in on how this can effect the curve during the pandemic.
The Coronavirus Was in the Us in January. We Need to Understand How We Missed It.
COVID-19 was in the United States as early as January, and yet we had no idea. To most people, the virus was a distant worry, if that.
But SARS-CoV-2, the coronavirus that causes COVID-19, was already circulating in major U.S. cities, according to Alessandro Vespignani, Sternberg Family distinguished university professor, who directs Northeastern’s Network Science Institute. And if we want to keep our communities safe going forward, we need to understand how we missed a virus that was right under our noses. “We don’t want to fall into this trap in the future,” Vespignani says.
What researchers are learning now will help us make smart decisions when the number of infections has dropped off and we begin to lift physical-distancing measures.
Herd Immunity Won’t Come Anytime Soon for Covid-19
A vaccine for SARS-CoV-2, the virus that causes COVID-19, is still more than a year away, but some individuals, and governments, are hoping that life can return to normal once enough of us have had the disease.
But estimates that 70-80 percent of the population are going to be infected are way too high, Sam Scarpino says. “It’s going to be somewhere like 5 to 20 percent, and you’re going to have multiple waves of infections because you’re still going to have a large fraction of the population susceptible.” The difference between these numbers, Scarpino said, originates with some of the simplifications that epidemiological modelers make to estimate how a disease will spread.
Network Scientists Identify 40 New Drugs to Test Against Covid-19
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.
‘Social Distancing’ Is Only the First Step Toward Stopping the Covid-19 Pandemic
“We should start thinking how to repurpose industries and places and build labs to do testing. This is what we have to do. There is no other way,” says Alessandro Vespignani, director of the Network Science Institute at Northeastern.
Vespignani believes that wartime efforts will need to be in full effect in order to slow the spread of this virus. This means social distancing for a longer time, in order to slow the disease, and use that time to increase capacity in hospitals, therefore increasing capacity in testing. Vespignani says that the virus will likely resurge, and four weeks of social distancing then going back to normal is not going to cut it.
He says “we should start thinking how to repurpose industries and places and build labs to do testing. This is what we have to do. There is no other way.”