This article appeared in the Spring 2014 issue of Current Exchange Magazine.

I think it had a tremendous impact. There weren’t many kids in the neighborhood and my sister was four years older, so I was required to have resources that were self-supportive. My mom made me a butterfly net and I would go off in the fields and catch butterflies. She would give me all her old strainers and bowls from the kitchen and I would go out in the swamps and catch tadpoles and crayfish, and my parents would buy me very rudimentary science books. I remember once reading about an experiment that had been done on accelerated metamorphosis using thyroid hormone so I would beg my father for thyroid hormone and he would get some from Argonne National Lab (where he worked), and I would do experiments on the animals that I had accumulated in the neighborhood.

You’re digging up the haphazard aspects of my life! I thought I would be a biology major; I had been appointed a James scholar at the University of Illinois which was for a certain percentage of students who had done well in high school, and I’m probably the only person who’s ever turned down a James Scholarship. It wasn’t monetary, but it was an honorary title and allowed you to take accelerated courses. And my father said, “Why don’t you wait and see how you do.” I don’t think my father really thought I was going to do very well in school. And so I turned it down.

When I looked at the biology programs they had biology either for honors students or for teachers, and I thought, “Well I don’t want to be a teacher.”

When I looked at the chemistry program you could just be a chemistry major, so I just selected to be a chemistry major. And so before I ever got to do biology I realized that I really liked chemistry, math, and physics. I never got around to doing biology during the time that I was an undergraduate.

My aunt was definitely supportive. She wanted to go to medical school but was turned down. Later on, she worked as a technician at Argonne National Labs. And she was very strongly feminist, always supportive of women. When the National Organization for Women formed, she and her husband would march in the streets. My sister and I were the beneficiaries of her strong feminist attitudes.

It really wasn’t until I was on the faculty at the University of Chicago. My aunt would come into Chicago regularly, and I remember one time we were out to dinner and I had been at the University maybe two or three years and she asked me if I had ever felt discriminated against in going through my career. And I said, “No, not at all.” And she said, “Oh that’s strange, your sister has.”

And it never occurred to me. I did know that my sister had not gotten into the University of Michigan graduate school because they had told her that they didn’t like to accept women they thought would get pregnant and quit school, and so she ended up going to MIT.

There were aspects that I was cognizant of that were blatant, but then the more I started to think about it I thought about it the more I started to realize that my advisor from graduate school didn’t think women belonged in science, and the last advice he gave me before leaving is that if I got married and there were two jobs available in different cities that he hoped that I’d go to the one that my husband was going to.

I think it was because I always felt that I needed to work harder. I just took them as pieces of advice that I didn’t have any reason to question or think weren’t rooted in genuine advice for me. I just thought this was the assessment of my advisor and that I should work harder.

When I was at MIT as a post doc I began to really love what I was doing. Howard Green, my post doc advisor, wanted me to look at protein assembly, and I thought I really want to isolate messenger RNAs and look at gene expression using other approaches. So he let me do that without paying any attention to me, and that was very helpful. For the first time, nobody was telling me what to do, and it was do or die.

It really wasn’t until that point that I realized I had to be resourceful. Nothing was available; there were no biotech companies at the time so you had to do everything on your own, but the resources were available. For me working with the cell biologists and deciding that I wanted to do more molecular experiments, when all of these great labs were around me, taught me the value of interacting with a lot of other people. It was really that MIT experience that gave me that breadth of ability to be resourceful and interact with other people that I think has carried me along the way.

We had really gotten to the point where we had identified critical residues that were necessary for the filament assembly process. We were doing filament assembly in test tubes and getting defects by putting mutant genes into cultured cells and looking at how that affected in a dominant negative fashion the overall cytoskeletal architecture. We really got to the point where these were the major proteins that were expressed by skin.

The globin genes were the major genes that were expressed by erythrocytes and at that point, it was known that sickle cell anemia and thalassemia were due to mutations in globin. So that was the point where it seemed to me that there ought to be patients walking around with keratin mutations and we had these mutations that disrupted filament assembly but looking in a petri dish at a disrupted cytoskeletal architecture didn’t give us a clue as to what human disease we should be looking at.

Irwin Freedberg, probably the most famous dermatologist at the time, (the chair of the NYU Dermatology) later told me that he had a list of fifty potential human skin disorders that were likely disorders of keratin and Epidermolysis bullosa simplex wasn’t on the list. So it wasn’t obvious what human disease to look for. And again, naively talking to my students and post docs, I thought that if there are patients walking around with these diseases, and the mutations are acting in a dominant negative fashion, if we put these genes into mice using transgenic technology we would maybe end up having mice tell us what we should be looking for.

It was probably naïve at the time, I was not a member of HHMI when I made the commitment to do transgenics. I was hoping that maybe 10 mice would be about all I needed, and I had no clue how expensive mouse work was.

It would have very quickly been one, but I was appointed to HHMI in 1988, and at that point I had the resources needed to support my pie-in -the-sky ideas. Hughes is more or less the same kind of challenge that I really enjoy, “You’re successful, here’s the money, now go do something that is different from what you would probably do, different from what other people are doing and come come back and see us in five years.” So you’re constantly being challenged, and I’ve always enjoyed that kind of challenge.

I encourage people to think broadly about the approaches to take to solve a problem. You have a question in science and you want to come up with the best way of getting there. Not, “These are the skills I know, now what questions can I ask?”

Safety is a natural instinct, I think. The natural instinct is “here are the skills I know how to do, and I’m really good at those skills, so I’ll just keep on doing them.” And it’s much less comfortable to take the strategy of saying “these are the skills I know how to do, but I’d really like to do this; I’d really like to know the answer to this question.”

For me it took a long time to develop the ability to think through what it is that I wanted to do because I was going in so many different directions and somehow managed to work out. In retrospect, that was a gift because I had gone through really quite dramatic changes in my approach and in my career. When I realized “I can do this”, that gave me the confidence to say, “I don’t know if I can do the next step, but I know I want to try.”

I think one has to somewhat develop that fearlessness. Everything has been a gift; to be paid to work in a laboratory and be surrounded by students and post docs that are smart and to be able to ask these questions – it’s such a gift. Maybe one day the funding will run out, or I’ll have to do something else, but if I have to go paint or draw or do something else, I’ll figure that out when I have to.

I guess I’ve always felt that way; I never really thought about a job at the University of Chicago. I thought of finding a small school with a few smart people and some students, and then this would be great. Maybe it’s that short-term sort of sense… I’ve always been comfortable with that. I guess I’m still comfortable with the notion that if I have to do something different someday, I have plenty of things I want to do with my life.

I pick the science, but if I had to tomorrow do something else, I could do it. I tell people not to worry so much about funding, not to worry so much about “What if it’s not going to work out?” That’s what I tell people in my group, worry about what you’re going to do if you do what you like doing. Just let it play itself out.

I think it is helpful just so that there has been this misperception that if a woman is going to go into science and really take a route of really doing the top state-of- the-art science that she can’t have children, it’s just too much for her to do.

A close friend of mine, Susan Lindquist, is an example that shows this conception is completely false. I make a point of saying that I believe just the opposite. It’s important to stand back from your science from time to time, and it’s also important to decide personally, “What’s going to make me happy? What’s going to make me excited about coming in to the lab everyday to do my experiments?”

And sometimes the answer to that is just not to put blinders on. Some people can completely burn out by doing that or be very unhappy and that’s going to affect how well they do their experiments and how well they think. For me, it’s been traveling, opera, ballet, whatever; but it’s always the need to do something.

If I hadn’t have spent so much time rationalizing when the right time to have children was and then crafting a life for ten years without children and then one day realizing that my life was completely incompatible with having children.

My husband and I spent about a month talking about well now is a good time to have kids, and we just looked at each other after that and said there’s no way we wanted to do that. I thought at the time that maybe I’d regret it when I’m older, but so far I’ve never had any regrets.

Positional cloning… It’s hard to say whether we would take the approach we had taken. It’s a lot simpler now but it doesn’t inform you about physiological relevance, whereas the approach we took allowed us to understand the biology of what was going on, we just didn’t know which genetic disease we should be applying that to. But as soon as we did, we understood the whole biology of the system. Even if you say, “Well it’s easy to do positional cloning”, what’s the protein doing? And that’s the challenge that I find exciting.

You have to say what drives you. For me being trained as a biochemist I wanted to know how things work, how does this happen? I love the icing on the cake with the relationship to genetic disease, but I guess I’m a hardcore basic scientist, and I always will be.

In 1998, you obtained your M.D. and by 2013, you had held editing positions at The Scientist, Scientific American and Reuters Health. What made you turn to journalism after medical school?

I had been active in journalism since college – high school, really – and medical school, so when I found myself running home every night after being on call to report and write columns and other assignments, I decided leaving medicine after my internship year would be a good way to see if journalism would work for me in the long term. That was 1999. Fourteen years later, so far, so good.

In 2010, you started Embargo Watch to comment on embargoes in scientific journals. What led you to start Embargo Watch?

I had been thinking about embargoes for several years, ever since writing about a silly sanction the World Health Organization had imposed on the New York Times. I read Vincent Kiernan’s excellent Embargoed Science, and realized that embargoes were helping journals and other scientific institutions control the flow of information. So after following these issues for a few years, I was inspired by my ScienceOnline counterparts to start a blog on the subject. I realized that blogs were perfect vehicles for obsessions such as the one I have about embargoes.

Later that year, you co-founded Retraction Watch, a blog to discuss retracted papers in science. What was your goal with Retraction Watch?

Adam Marcus – he and I co-founded Retraction Watch – would like to see more transparency in science, and particularly when science goes wrong somehow. Retractions are a great window into that. We also found retractions to be a great source of stories. The response to the blog, however – we are on target to beat five million pageviews in 2013, and are mentioned in major media almost every week – has surpassed anything we could have imagined. The conversations people are having about scientific integrity suggest we’ve really hit a nerve.

Have you noticed any trends in the number of papers retracted? Has it gone up in number?

The number of retractions per year grew ten-fold from 2001 to 2010, as has been reported by Nature, while the number of papers published per year has only grown 44%. So clearly, retractions are on the rise.

Is this because there are more flawed papers being published or is it because journals are more proactive about retractions?

This remains the $64,000 question. In a recent paper, Daniele Fanelli argues that it’s mostly because journals have developed policies for retractions, and are looking for fraud. But others – notably Ferric Fang and colleagues – have shown that more retractions are due to misconduct. So it may be both.

You recently joined MedPage Today as Global Editorial Director. What is your role at MedPage and how is the focus different from other publications?

MedPage Today’s core audience is health care providers. We welcome all readers, but we’re geared toward doctors, nurses, physician assistants, and the like. My role is to oversee the editorial staff and strategy. Building on our strengths as the world’s leading provider of clinical news, we’re going to broaden our focus to include more health care provider voices and drive even more engagement with our readers.

In a TEDMED talk you gave in 2012, you spoke about the US being over-medicalized. Could you summarize your thoughts about this issue?

Thanks in no small part to our fee-for-service health care system, the vast majority of incentives drive providers to order more and more tests and procedures, and write more prescriptions. It’s not enough to just treat the real diseases that affect people. Normal variations become diseases that require treatment, which only leads to other conditions that result from those treatments. We’re all part of the problem, though. Americans are not very good at “Don’t just do something, stand there.” The amount of money sloshing around medicine, as others have put it, means that drug companies and hospitals feel the need to constantly create new markets to please investors.

From your profile and your involvement in many projects, it seems you’re highly entrepreneurial. Is that the case? If so, how does an entrepreneurial spirit fit into science and journalism?

I do think it’s important for science journalists to take advantage of what technology now offers in terms of reporting and reaching audiences. What Adam and I are doing with Retraction Watch, what MedPage Today is beginning to do – none of that would have been possible a decade ago, perhaps not even more recently. It’s a really exciting – if unsettling – time in journalism, and I’m enthusiastic about all the new models that are bursting with entrepreneurial spirit.

It’s customary in my lab to check the top science journals weekly to see what articles have been posted to the advanced online publication sections. It isn’t uncommon for me to arrive at lab in the morning and be greeted with,

“Have you read so-and-so’s new paper?”

“No. When was it published?”

“Today.”

Point being, scientists are eager to access new scientific data as soon as it’s made available. The latest findings motivate new research questions and fill in gaps in knowledge, helping scientists to design experiments and interpret the results of work in progress. While advance online publication makes articles available as soon as the peer review process is complete, that process can still take many months. Imagine accessing an early version of a manuscript at the same time, or even before, it’s been received by journal editors.

That’s the idea behind the new biology preprint server from Cold Spring Harbor Laboratory, bioRxiv.

bioRxiv allows research scientists to upload and read (for free) manuscripts prior to peer review and publication, thus disseminating results freely and rapidly. Once a manuscript is posted to bioRxiv, it receives a citable web address and is discoverable in web searches. Scientists can then comment on the merits and weaknesses of the manuscript on bioRxiv, potentially influencing how the final version appears. This model is based in part on Cornell University’s arXiv service, which has been a mainstay of the math and physics research communities for the past two decades.

I talked to Dr. John Inglis, Executive Director and Publisher of CSHL Press about his plans for bioRxiv.

The first conversation about the role of a preprint server in biology took place between me and Paul Ginsparg (the founder of arXiv) about 10 years ago. My conclusion at that stage was that it would not work in biology as it was at that time. More recently, we’ve been tracking the increase in amount of biology that’s been posted to arXiv.

You can see a dramatic increase in the volume of material posted to the quantitative biology section, which is the only section they have that addresses biology. ArXiv was aware of this trend but had no problem with our attempting to build a complementary site that had features and functions that were more familiar to people in mainstream biology. In fact, Paul Ginsparg became the first member of our advisory board. While we have no formal association with arXiv we are very similar in terms of our goals and aspirations, and hope to be talking to them as we get further along.

A manuscript can go through a very lengthy process of review at a given journal and then the answer can be “no thanks.” In the meantime, a draft of that manuscript can be on the bioRxiv server for the benefit of the broader community who now have access to the information it contains. It has to be taken skeptically because it has not been peer-reviewed, but its contents may be valuable and should be critically evaluated. Eventually one hopes that the manuscript will be adapted for the purposes of a journal, and it will find its way into the formal literature. We keep saying at every opportunity that bioRxiv is not a journal, with the quality assurance and imprimatur that journals traditionally provide. It’s a tool for the rapid distribution of research results and feedback on work in progress.

Through its conferences, courses, and publications, Cold Spring Harbor Laboratory has provided a service of communication to the scientific community for many decades, going back to 1933 and the first Symposium in Quantitative Biology. We see bioRxiv as an extension of that: a new tool for professional communication amongst scientists, one that operates in the digital world and involves critical evaluation, skepticism, attack and defense – all the sorts of interactions that are on display at meetings here on the campus.

We have two levels of scrutiny. One of them being a purely clerical one to ensure that what has been submitted is not obscene or spam. Then we have a growing community of bioRxiv Affiliates, working scientists who have agreed to just look at submitted manuscripts without evaluating their quality and identify them as science rather than non-science or pseudoscience. That’s doable at this early stage when the number of submitted manuscripts is still manageable.

We have two ways of tagging the material that’s deposited – one is to put it in a subject category, for example, “genomics”. The other one is more interesting – we’re asking authors to flag as “new results”, “confirmatory results”, and “contradictory results” to give the reader a little bit of extra insight into what the significance of the manuscript is. That’s another part of the service we consider this server can provide. It’s difficult to get confirmatory and contradictory results published formally in journals, so this is a way of sharing that information.

In the beginning it’s being supported by the Laboratory. You’ll see when you look at the site that it’s pretty basic; it’s not sophisticated, not slick. We haven’t spent a fortune on graphic design. It’s essentially a bunch of manuscripts formatted as PDFs, with supplementary data where appropriate but no bells, no whistles. You take it as you find it. I have held back from doing anything about outside financial support until we get up and running. Soon I’ll start a round of conversations with organizations that might help us with the running costs.

We’re not aligning ourselves with any movement at all. What we’re doing is responding to growing interest and desire within the scientific community for different forms of communication that are more open, more immediate, and in many ways more unfiltered than the conventional journal system is. As the publisher of two of the top three genetics journals, CSHL Press is very invested in the value that journals bring to the scientific process and we’re very proud of the peer review that takes place at our journals.

There is a tendency at the moment to dismiss peer review as obstructive or harmful in some way, but I’m not willing to take that position. Is it perfect? No, but I see a lot of people putting an enormous amount of effort into that process, and I think that’s very valuable.

I hope bioRxiv will become part of the ecosystem of scientific communication and be judged and valued for what it is, not for what it isn’t. I think journals will continue to have an important role in the filtration and validation processes they have always had. I don’t see that bioRxiv will necessarily undermine that role, but I do think it can assist in a variety of other ways with the rapid dissemination of results, communication of work in progress, and facilitation of community feedback, and doing all of that in an open way. That’s a little different than the concept of open access that is changing so much of how journals operate.

We want to communicate with the scientific community in its widest sense. What’s going to capture attention obviously requires scale, and scale on bioRxiv requires a culture shift within biomedical science. What would upset me most is that there is a knee-jerk negative reaction to bioRxiv that doesn’t allow it the chance to reach its full potential. Obviously we want this to be a success and will work hard to make it so, but ultimately it’s the research community that will determine that.

At this stage we don’t know all the ways that bioRxiv will function. It’s a new idea for most people in biology, and I think it’s going to be fascinating to see how the community deals with it.

bioRxiv launched on November 11, 2013 and is currently accepting submissions. You can learn more at biorxiv.org, and see a list of academic journals by preprint policy curated by bioRxiv advisory board member Dr. Leonid Krugylak.

I’ve always been interested in humor and physics. I’d planned on an academic career right from the start, so I never considered comedy as anything but fun, but over time I found myself doing more and more.

Sticking up satirical safety posters around the lab, writing comedy academic papers, spending far too long on article-length e-mails to friends in other parts of the world.

Humor makes everything better, especially when you’re working.

I first went to college to do computer science, but changed to physics, figuring that I could always buy a computer for my home, but a high-energy laser system was less likely to be in my personal budget. Physics became my entire life plan. I never doubted that I’d be an academic, a professor poking reality and then telling people about. Over time that morphed into a focus on lecturing as I learned how much I enjoyed interacting with people and telling stories. That evolved its final form when I became a humor writer instead.

Existing! Physics is everything. It’s the fundamental truth of existence, and our most powerful tool for making the most of doing so. It’s the satisfaction of solving an abstract puzzle coupled with the achievement of doing things that don’t just change but actually are the world. And since it obeys rules and equations, it’s really quite simple compared to things like people and emotions. But we’re all expected to work with those every day.

That’s using some work I did back in university. The lab has an interesting tweak on laser processing. Longer pulses can ablate material but cause thermal effects in the material left behind, melting and burning the surrounding area, while ultrafast pulses can vaporise the target material so neatly the surrounding material doesn’t even notice it’s gone. By using thousands of ultrafast pulses so that they’re spread over that longer timescale, you can neatly remove the target material while causing controlled thermal treatment of the borders left behind. The idea is that would be useful for annealing materials (I was working on fused silica), or cauterization in surgery. I haven’t been involved in that for a while though.

I got my first article into Cracked six years ago. Being contacted by Cracked changed my life. Until then the idea of being paid for writing simply hadn’t occurred to me. They’re brilliant about giving people a chance to get paid for words – anyone interested should check out http://www.cracked.com/write-for-cracked/. I’ve been writing for CBS Man Cave for a year and a half.

Of course! You have to write every day, and if you can invent some way to write more often than that, do that too. And tell me how. As a freelancer the real challenge is remembering to do something useful other than writing every day as well.

Yes, as many as will have me. Right now my other clients include GameGavel.com, Outfunny.com, and a bunch of others, including copywriting firms. You, person reading, do you want to hire a writer? Contact me at [email protected].

Always. I also rely on it when I’m mixing drinks or walking down the street. Scientific training improves your brain. It isn’t a set of equations, it’s a system for interacting with the world. It helps you solve problems, develop thoughts and avoid mistaking desire or laziness for facts and reasons.

As much as possible in the time available.

There’s a great tip by Teller (of Penn &), he says “Make the secret a lot more trouble than the trick seems worth.” He’s talking about magic, but it applies to everything. Don’t measure out your effort in terms of how much you’re getting paid, or how long the piece should be. Make as much effort as you possibly can. This piece is a piece of you, sent out into the world to stand alone, and it won’t be able to say “This one was rushed” or “They weren’t paying much”. Or if it does say those things to a reader, that’s even worse.

There are a lot of pieces where you can tell that someone reached the desired word count and thought “done”. In my favorite columns, my first draft was triple or quadruple the final word count, which is brilliant, because I can brutally compress and combine and cut and cut and cut until what’s left is the best. Then, of course, you cut some more.

Michaelangelo talked about cutting away the bits of marble that weren’t statue. Writers have to do that too, but they have to squeeze out the two ton chunk of marble in the first place. As you’d imagine, that can be painful. So painful many people think that’s the work. But that’s just the start.

If your article contains everything you know about a subject, it’s immediately obvious. And not in a good way.

They’re not separable in that way. People like to think of brains as little Dungeons & Dragons characters, with different skills and abilities they can level up independently, but it’s a single super-connected network. Everything you put in gets processed and blended and affects everything that it comes out. How good you are at anything subtly influences how good you are at everything else. That’s why scientific training is so powerful: it permanently upgrades the your brain, and everything you do with it from then on.

On November 13, Dr. Incandela will give a talk at the Simons Foundation in New York, where he will discuss the discovery of the Higgs Boson, as well as what is to come. Register now; seats are limited!

Prior to his talk, we asked him a few questions to help us better understand the basics about this elusive particle.

What is special about the Higgs boson is that it is a fundamental particle with no spin (spin 0) and it is the first such particle ever discovered. Fundamental spin 0 particles are peculiar in many ways, as they can provide a force field that has no direction in space. Such a force field may be at the root of the inflationary eras of our universe. A field based on spin 0 particles could have a constant value everywhere in the universe without affecting the isotropy of space-time.

Simulated signature of a Higgs Boson as a result of a collision between two protons. © 1997 CERN

I do not use this term, nor do any of the physicists I know at the LHC so I can only speculate as to why some people have called it this. It may be for the following reasons. Higgs particles are the basis of a special force field that permeates the universe. This force field has a constant strength everywhere and it interacts with most of the fundamental particles and engenders them with mass.

In the Standard Model of particle physics, all massive fundamental particles get their mass this way. (This is not the only source of mass. A proton or neutron gets most of its mass from the strong binding energy of the quarks.) Without the Higgs mechanism the electron would be massless, and so atoms could not be formed. There would be no stars, planets, and no people.

What they did was purely theoretical. In fact it was not yet quite connecting to a specific description of nature. At that time it was already known that the two nuclear forces (known simply as the weak and strong forces) appeared to be short-distance forces, unlike electromagnetism and gravity. Short-distance forces could not easily be explained in quantum field theory.

Many people tried to solve this problem. Brout, Englert and Higgs were among the first to come up with a solution. Namely, that of a spin 0 field with a constant value in space that actually can limit the distance over which a force can act (which is equivalent to making the force carrying particles have mass). Steven Weinberg and others used this idea a few years later to successfully describe the weak nuclear force.

Dr. Joe Incandela © 2012 CERN

When we say we see ‘an excess of 5 sigma’ we mean that we see an excess of candidate Higgs events produced in high energy collisions of protons that is well above what we would expect for background processes.

We carefully estimate the number of events expected from backgrounds and the uncertainty on that number. This uncertainty in the background equals 1 sigma (equivalently 1 standard deviation). We require the excess we see for any mass value to be at least 5 times this number above the best estimate of the background. In statistics this would mean that the excess has less than about a 3 in 10 million chance of being a statistical fluctuation of the background. This is a very high standard, but it is in reality very difficult to do these experiments and it is possible to make mistakes and be fooled in a number of ways.

Well theoretically there can be many different kinds of bosons left to be found. Indications were pretty good that this was a Higgs boson (e.g. because of the rates at which we saw it decay to pairs of photons or pairs of Z bosons) but we were not sure if it was the simple type of boson predicted in the Standard Model or one of several that might exist in more complete theories of nature. It could also have been a more exotic spin 2 boson for instance or a boson that is a composite of other particles.

It would be difficult to rule out these other possibilities with absolute certainty, but we have done many studies since the 4th of July announcement, with much more data, and more sophisticated analysis methods and so far everything is consistent with a spin 0 Higgs boson, like the one in the Standard Model. More work is needed however.

As I noted above, this is the first fundamental spin 0 particle ever discovered and as such, gives physicists confidence to develop theories based on spin 0 force fields that would explain the inflationary periods in which the universe expanded rapidly.

In addition, within the Standard Model of Particle physics, the newly measured mass of this particle, together with information we have on the top quark mass, allow us to assess the stability of the universe and potentially understand how long it will last before undergoing a major transition to something very different. There are in fact hundreds of ideas coming out. The discovery papers we submitted at the end of July 2012 already have more than 1800 citations giving some idea of how much interest this has generated in the field.

In my talk on November 13 at the Simons Foundation I will review the basics of particle physics and the reason for the Higgs boson to have been predicted and then I will cover all the hard work it took to make the discovery. I will then present the newer results, talk about what we will do in the near future and even further down the road to understand this new particle better.

Finally I will briefly talk about the big open questions (beyond the Higgs boson) that we hope to start to answer in coming years. This includes for instance trying to understand what is dark matter, which is more abundant than ordinary matter by about a factor of 5.

A video from PhD Comics to explain the Higgs Boson

Dr. Fortier is also an established researcher and active advocate of research education for youth. During her last visit to the 2013 Women in Science, Engineering and Medicine Symposium, we had a chance to speak with her about women in engineering and the future of science.

What does NSERC offer students?

For the undergraduate program, we want to give students a chance early on to experience what research might look like – either research in a university or in an industrial laboratory. One of the challenges we have in this country is that we do not have a very high number of students at the graduate level in the Natural Sciences and Engineering compared to other countries. Canada lags way behind in graduate programs, so one of the intents of the program is to give people a chance to see what research would look like and whether they would be interested in pursuing research in the future.

What advice would you give students undertaking the NSERC award?

First of all, I’d say to be really engaged as a member of the team, because that’s what a lot of the research programs are about – being part of the group of people who are pursuing some areas of knowledge together. Take advantage of being part of this team: talk with the members, see how their experience is, and get advice from them. Any guidance they can give you is very important. Sometimes people think of a researcher as a person isolated in his/her little corner, but that is not what it is like in almost all of the research areas. Most people are part of teams, working with others.

Secondly, it is also important to always give your best. You want your team members to give their best, and you have to make the same commitment yourself. The third thing is to persevere – whenever you do something that is exploratory, you’re looking for something new, some knowledge that nobody has in the world yet, and that’s fantastic, but it’s not an easy path. And along this path, you’ll have wonderful discoveries that will be exciting but you’ll also have some disappointment. You think about a problem and make a hypothesis, so you devise an experiment to test it. Sometimes the experiment doesn’t yield the data that you hoped for and you think ‘ah, this is not good’. It is quite disappointing, but very successful researchers are those who are able to face this situation and regain their motivation fast. They then start thinking about it again. That is part of pursuing new knowledge. You’ll not always be right. I remember reading Francis Crick, who wrote a book about his life in research. It’s called What Mad Pursuit. Now, Francis Crick is a Nobel laureate for the discovery of the DNA structure. He attributed his success in part to his fertile mind. He always had new ideas and he says that some of them were in fact not really good. But it is that ability to be…

…constantly thinking?

Yes. I think that’s an important but tough lesson to learn as you pursue research. We rarely talk about this, but even negative results are important in science, because they do contribute to learning. When an hypothesis doesn’t turn out to be correct, it can add substantially to your knowledge.

I noticed that you will be giving the closing remarks at ‘Women in Science, Engineering and Medicine Symposium.’ What do you think about the female role in these fields?

There are certainly more women in the fields of sciences and engineering than 50 years ago. We made tremendous progress so far, but I think there still are not enough women looking at science and engineering as areas where their efforts and hard work can turn into a career. So we need to continue to work on that.

Who is the female scientist you look up to in particular?

I think you don’t notice them sometimes but it’s only afterwards that you realize that some people were your role models. The one very important to me was the Teaching Assistant in my first course in crystallography. She was a young graduate student. I looked at her, and I thought ‘I could be that’. It was really inspiring for me as an undergrad to have this woman as my TA.

That’s something I’ve never heard before, because people often mention their professors.

At that time, professors were too far from me, and she was closer in age. After that I had a professor who was my thesis supervisor, a woman who was one of the giants in crystallography. In fact, crystallography had early on extremely accomplished women. I remember when Dorothy Hodgkin came to visit our lab- and this is a Nobel Prize winner. Imagine that she comes to your lab and sits by your chair to ask you what you are doing. There were many inspiring women whom I had the opportunity to meet. I say today, I look a lot at women that are part of our world, women that we support with NSERC.

They are role models in different ways. I look at what they are doing – their incredible contributions, their energy, their ideas, their creativity, their ability to be great project managers – they are role models in that they inspire me about my role at NSERC, because part of my role at NSERC is to make sure that these people are successful. It’s an inspiration for me to see these extremely successful and accomplished women. I often think, “I have the privilege of having a great responsibility”, which is to support them.

What does the future hold for scientists? Are there particularly promising areas?

There is so much more to discover and to learn, but also some important challenges that we need to face as citizens of the same planet. Today, there is hardly any area that is not holding a lot of promise, because when you look at how you will either pursue a great opportunity or a challenge, you realize that you need people with different expertise. Something that is important to realize is the incredible work that’s being done by engineers, particularly in advancing the technology to lead discoveries in our worlds.

Similarly, I told you that I’m a crystallographer; a few years ago, a Nobel Prize in chemistry was awarded for the discovery of the structure of the ribosome. What made the discovery of the structure of the ribosome at a very high resolution level possible was all the technology that had been developed in detection, in synchrotron, radiation, in computing, and so on. It’s always fantastic for me to see this incredible symbiosis between technology and discovery.

CAREER PATH

When you started your Ph.D., did you know you would go to industry upon graduating?

No. In fact, I did a Ph.D. because I wanted to figure out whether I wanted to pursue a career in academia or industry. By the time I started my research work in the mid 1970’s, silicon integrated circuits (ICs) really took off and I was immediately attracted by that direction. By the time I was writing my Ph.D. thesis, I knew that I would probably pursue a career in industry.

My thesis was a very theoretical analysis of semiconductor device behavior and I wanted to know how to do IC design. One of the job offers I received was to do IC design at Bell Northern Research, so I went back from California to Ottawa, where I designed one of the components of the first digital switch machine.

What did you do after Bell-Northern?

Intel started a research center in Portland, Oregon and they recruited a lot of the research scientists from Bell-Northern Research, so I decided to join Intel where I got to work on their commercial products. I was there for about 6 years.

But in 1983 – 1984, Taiwan started to become a hub for the semiconductor industry and they encouraged people with that background to go back to Taiwan and start semiconductor companies. So I had a decision to make: do I stay at Intel doing advanced product design, or do I take a big risk and join an early-stage startup and be unsure of what the future has in store for me a year down the road?

I went for the startup, which was called Quasel, as Chief Engineer. We wanted to build dynamic memory (DRAM), but I was very naïve at the time and didn’t realize that there are cycles to any industry: By the time we could produce our components, DRAM chips were selling cheaper than we could produce ours, so the company didn’t succeed. This was in 1986.

After that, I gathered a small team and started a small company that moved away from commodity components such as DRAM chips (because it’s a difficult market) and instead, focused on programmable chips.

How did you go from that to becoming an investor?

Almost a year into that startup, one of the investors of Quasel, Acer CEO Stan Shih, offered to merge my company with Acer, where I ran the Acer R&D Labs. That’s when I started my corporate executive career. About six years later, I came back to Silicon Valley as the President and CEO of Acer America, where I also got exposure to the field of investment because I felt that it was a rising trend.

In 1997, we setup Acer Technology Ventures (ATV), a venture fund that invested in very early stage startups. That’s how I evolved from a corporate executive to an investor. And I’ve been an investor ever since.

What do you think made you successful?

I was willing to take risks because I wasn’t afraid of failure. I told myself “If I fail, I will have learned something”.

STARTUPS & VCs

How does venture capital work from an investor’s point of view?

If I raise $100M from other investors, usually that money has to be invested in 5 to 7 years; you can’t sit on the money. So you have to use up the money during that period.How successful you are depends on how good is the return you get. And the successful VCs start to get more money so their size gets bigger and bigger. Some large Silicon Valley funds are now half a billion dollars; that’s a huge amount of money.

That’s a good thing, right?

It depends. Each VC has so-called venture partners and a large VC may have 10 or 20 partners that look for companies to invest. But if you have $500M overall to invest, each person needs to invest $25M in a short period. So what happens is that you tend to not invest in companies that require small amounts of money, which is bad news for early-stage companies.

Since the VCs now generally look for very large deals, that has changed the original nature of the VC industry. Today, those early-stage startups are now picked up by angel investors or seed stage/early-stage funds. Those early-stage companies will also typically get more one-on-one mentorship. But the way people get funding is constantly evolving. Outside of VCs, there’s also crowdfunding websites such as Kickstarter that are good places to start.

What makes you invest in a startup?

Three things: idea, market and team. Beyond the idea, I consider whether it covers a reasonably large market. The other aspect I consider is the team. Are they passionate about what they do? Experience is not as critical for young entrepreneurs but most importantly, do they have the ability to learn quickly and build a solid team around them?

Do VCs fund new ideas or better approaches to the same idea?

Some investors prefer investing in companies that do things better, faster or cheaper, while others prefer companies who do things that have never been done before. I prefer the latter.

Do you prefer when the founder stays in the company once investment is made?

It really depends. There are two kinds of founders: those who are very technical and only want to see their idea come to fruition, after which they want to return to do more fundamental research. In that case, we can find someone who’s capable of managing the company. Then there are founders who want to expand their knowledge on how to build up their business. When you start, ideally you’ll have a founder who has a technical background and who will eventually become a capable CEO. That’s the ideal case.

But often, a company will be started by 2-3 individuals and among them, maybe one will be more technical and the other will be more business oriented. That person usually ends up running the company.

LOOKING BACK…

Have you ever invested in a company and later found out you made a big mistake?

Oh yes! Typically, VCs invest in a number of portfolio companies. Our business model is that, out of 10 companies, there will be a spectrum of success: a couple will be very successful, 2 or 3 will be good and the rest will fail.

And even the very successful ones are very rarely overnight successes. Companies often find themselves redefining their business model along the way, and a good VC always tries to help companies figure out a way to go around those challenges

Some undergraduate students are nearing graduation and aren’t sure whether they should go on to grad school or industry. Do you have any advice for them?

The opportunities of today are much broader than 30-40 years ago and I don’t think there is one definite path. I think what students need to do is to find out where their passions lie.

For example, if you’re interested in doing research and experimentation, there are two paths. One is to continue on the academic path by going to graduate school, while the other is to conduct research in industrial labs. Today, the latter opportunity is less of an option; back in my day, there were many research institutions like Bell Labs, RCA Labs and Xerox PARC who were dedicated to tackling research questions. But today, this research is mostly attached to large corporations and has become more targeted to commercial applications, although a few of them still do advanced research like Intel and Google.

But once you get into that industry environment, I think it’s difficult to go back to academia. On the other hand, you can do a postdoctoral fellowship and maybe even become an assistant professor, and you probably still have the chance to go back to industry.

How do you remember your time at McGill?

I always felt like the time I spent at McGill was one of the best moments of my life. I really enjoyed the city, the school, the people I met and the professors I worked with.

Later on in your life, you will find that what matters is not only what you learn in class and in exams, but also the people you meet and the culture you’re exposed to, because each of you come from diverse background and have different ways of thinking

Tell us about your background before college.

I have a very odd background. I grew up in a very poor rural area, where education wasn’t really something that was promoted and then went into the military. Through that, I got into college and when I started, it was a very intellectual sort of exercise, trying to discover how smart I was and how far I could push myself. So the combination of CS and pure math was a very natural place to go to push myself.

You started out by studying Math and CS. What got you into biology?

My undergraduate degree wasn’t so challenging so I decided to do my graduate studies in pure math, which I view as one of the most conceptually difficult areas of study. I was going through that but I always had an applied bent, and in pure math, it’s doing math for math’s sake: it’s not encouraged to figure out whether what you work on would satisfy another area of study. So out of curiosity, I wanted to figure out how everything we’re doing fits together, why we’re here and so on.

At UCLA, once I got the Ph.D. candidacy in pure math, I made the jump to a bio/math dual Ph.D. program that had the right level of rigor. I didn’t want to be a mathematical biologist of the type that were very good mathematicians but didn’t have a very deep understanding of the problems in biology and how to design your own experiments. I wanted to grasp that intuition; I wanted to think like a biologist.

Are you a biologist who does computation or a mathematician doing biology?

I view myself as a biologist who is heavily computational. Mathematicians that I’ve worked with in the past would not respect at all what I’m doing now as being real math. That always stings me a little bit, because biology has classically not been a very quantitative science so the kind of math that we do (e.g. Bayesian network reconstruction) is difficult but it’s not what a mathematician would view as the hardest thing!

Did you go to industry immediately after your Ph.D.?

Yes. What I saw while finishing up my Ph.D. was this revolution around technologies like the gene chips (used to recognize DNA from samples being tested) and microarrays (used to measure gene expression levels). Many companies said they’d start generating massive scales of data, house them in big databases and mine them, and that was unheard of in biology.

So I was very interested in those technologies and looked around for how I could get access to them. Roche Biosciences was among the first to sign all these big deals with the companies who were making the technology, and they appreciated the fact that you needed someone much more mathematical to look at the data. So I joined Roche. It was perfect timing.

What attracted you to Roche?

They had access to technologies that none of the universities had because of the outrageous costs at the time. What also drove me to Roche were the big resources and the excitement of having to carry out the right experiments to show proof-of-concept.

Because I was one of the first to apply statistical analysis for gene chips, I gained a certain degree of fame doing that, which caught the attention of the heads of Roche. But all of a sudden I was spending 50% of my time in meetings and fighting for why doing this is important instead of actually doing the science.

What did you do next?

I started talking to Rosetta, a startup that focused on building the technologies behind gene chips. After a year and a half at Roche, I went to Rosetta because they were more focused on the science. Also, since it was a startup, there was no bureaucracy or politics.

About a year and half later, Rosetta was bought by Merck. They loved what I was doing and they invested very heavily in that arm for 5-6 years. We did lots of good science and published a lot of papers. By the time I left Merck, we were responsible for about half of all the new drug discovery programs, so we were also delivering on the business side.

Why did you leave Merck?

Merck also got limiting because, as we were learning more about building these Bayesian networks, we wanted to go to the next level. We told them what we thought the next step should be but the price tag was about a billion dollars. That was too expensive for any one company to fund, so they were thinking more along the lines of turning this area into a pre-competitive space, where companies would be able to share all the data between each other.

After a lot of discussion, the co-Founder of Rosetta and I left Merck to found SAGE Bionetworks, a non-for-profit research center in Seattle. We focused on open-access biology: how to facilitate sharing of big data, how to build models and validate them, and enabling others to interact with those models.

What did you do once at SAGE?

Now that SAGE was set in motion, I joined Pacific Biosciences (PacBio). The idea was that I would setup a new institute in the Bay Area that would focus more on data generation and model building. PacBio knew that going in, and they liked the idea of me spending 75% of my time doing research outside the company because it would cost too much to have that big of a research effort going on internally. And for 25% of the time, I would be the Chief Scientific Officer at PacBio.

Although we got offers from UCSF and Stanford to set up the institute there, we needed ~$100M to really make a go at that project and we were having trouble finding enough money to make that project more than just my lab and myself. As I expanded the search for money, I locked onto Mount Sinai because we found donors inclined to give the $100M to do this effort.

What do you like best about Mount Sinai?

Compared to Stanford and UCSF, Mount Sinai had a reduced bureaucracy. Here, there’s a CEO who runs the hospital and the medical school. It’s a command-and-control architecture that I’m used to from the business side, where it’s easier to see things get done than one where every decision needs a committee. And it is smack down in the middle of a medical center, which will allow us to impact decision making directly in the clinic. That was very attractive. Moving to the East Coast is not something I thought I would ever do but all the pieces fell together!

Do you have advice about choosing between academia and industry?

I’ve always had a foot in academia and another in industry. Before joining Mount Sinai, however, the heavier foot was always in industry. This is the first time my heavy foot is in academia. I’ve seen both worlds for a long time and I think what academia offers is the ability to be your own CEO, grow out your own program and even though there are funding issues, you have much greater flexibility than in a company. You can make the kind of partnerships you need to leverage what is happening in industry and I view that as a more favorite path.

But I will say that the industry path offers, especially to young investigators, clarity of purpose and focus. Going to a startup is an experience like no other. Unlike academia, where you’re able to float and your timelines aren’t so critical, in a biotech startup, you’re living six months to six months. You have money and you see the cliff of when the money will end and if you don’t meet milestones, you’re going off that cliff and you’ll have to fire half the people in the company. That drives you to form bonds and work as a team to accomplish things far bigger than you could ever do. What you learn from that is invaluable.

The other advantage that I learned at Merck is: If what you’re working on is in the critical path of a company, the scale of resources you can get to carry out your vision is an order or two of magnitude greater than what you can ever get funded to do in academia, especially if it’s something new and risky.

What are the privacy issues with DNA sequencing becoming more popular?

We always want to protect data that can personally identify us but there’s another component of that which is the expectation of privacy. For example, your social security number can identify who you are and you should have an expectation of privacy around that. On the other hand, there are things like your face, which can be used to identify you but you have no reasonable expectation of privacy around your face.

DNA used to be in the camp of the social security number: of course you want to keep your DNA protected because it defines who you are. What’s changing now, however, is that the technology is becoming so amazing that, in 10 years, sequencing your genome will become as easy as taking a photograph? When that happens, there will still be the personal identifiable issue but the expectation of privacy will go away, because how can you have expectation of privacy around something that is as easy as taking a photograph. That is the transition we’re in. Of course you can take steps to protect the information but there are limits to what kind of privacy you can expect. Educating the population and legislators about that is critically important. The next step is to make laws that prevent discrimination based on that data.

What’s the biggest change you’d like to see in biology?

If biology wants to go to the next level in achieving an understanding of all the complex things we see, it needs to become much more quantitative and information-driven. Biology has to become a more physics-like discipline.

If biologists don’t do that, they’ll become irrelevant when Google, Amazon and other computer science powerhouses come in and do it before them. They won’t wait for the biologists to give them permission to analyze that data so if biologists aren’t there to work with them, they’ll be supplanted. I don’t think that’s extreme when you consider competitions where solving a biological problem gives you a $20,000 or $50,000 prize. If you look at who’s on the top of the leaderboard, none of them are biologists. Last time I checked, the top of the leaderboard was an accountant from Australia who knows nothing about biology.

Scientists often speak of “sequencing the human genome”. Is there really a single genome we can use as reference?

You used the key word ‘reference’: the Human Genome Project (HGP) was said to have sequenced the human genome. Really, the more accurate phrase that should’ve been used was: we created a reference sequence of the human genome. You shouldn’t think of the product of the HGP as the sequence of a human being, because that’s not actually true. In fact, what the HGP produced was a sequence of all human chromosomes—roughly 3 billion letters in total. But any given human being has 6 billion letters: 3 billion from mom, 3 billion from dad.

So we have to distinguish a reference sequence, which is the hypothetical representation of the sequence of each human chromosome, from a personal genome sequence, which is the full representation of the two copies of each of your chromosomes.

Is the reference genome very different from our genomes?

In some ways, it is. A reference sequence only differs from the genome you got from your parents by about 1 in a 1000 bases, which means that the reference sequence represents what you’ll find in the human species at about 99.9%. On the one hand, that’s incredibly similar. But on the other hand, the richness of what we want to learn is in that 0.1%; that’s what we’re most interested in if we think about health and disease. Those differences are called genetic variants and they can confer risk for disease or give protective characteristics.

So generating the human genome sequence is the HGP’s attempt at providing a framework—a reference or starting point—for being able to understand sequence differences and correlate those to health, disease, drug response and so forth.

So we can’t simply compare the reference to someone’s genome and look for differences?

That’s right. You don’t want to ask the question “does my genome differ from the reference?” That’s too simple of a question. The question you want to ask is “given those variants at this particular place in the genome, have they ever been seen before?” If so, how often have they been seen?

We now have databases that not only list all the variants that exist but also tell us the frequency with which we see them. So just because you differ from the reference sequence doesn’t mean anything.

Is all this research happening because it’s becoming cheaper to sequence?

A very important aspect is indeed the cost of sequencing that is dropping precipitously. The first human genome sequence cost us about 3 billion dollars—best $3 billion ever spent. Now the cost of sequencing your entire genome is on the order of $10,000, so we’ve gone from a billion to $10,000 in about 8 years. That’s pretty good. But we’re motivated to do this primarily because we know we have to: We can’t just have 1 human genome sequence, we need a whole lot more.

Can we go down to $100?

We proposed $1,000 in 2003 and we thought we were crazy. We would love it to be cheaper and cheaper but the truth of the matter is that we shouldn’t lose sight of where we are now. The $1,000 is very cheap compared to the cost of understanding what it actually means.

I don’t think that much about the cost of genome sequencing because I think we will eventually coast to the $1,000, or even $100, genome. That’s not where the burden is. Right now, the grand challenge is understanding that sequence: if I handed you your genome sequence—the perfect complete 6 billion letters—you would have to invest a lot of money to understand it. As of now, we don’t yet know how to interpret all that data, so the challenge now really lies not in data generation but data analysis.

How different are two genomes?

You and I roughly differ by 3 to 5 million single nucleotides, and the great majority of those are completely innocent—they have no phenotypic consequences. A small subset of those do, but we have very little knowledge about how to sift through them. We can make the list of variants but we’re not yet at the point where we can identify which ones we need to focus on and what their effect on human health is. We now need to take these catalogues of variants and start attributing biological and clinical relevance to them—that’s the next decade. Maybe people like you will help us figure it out.

Have we spotted some variants that are responsible for, say, cancer?

Sure, but we’re still at the very tip of the iceberg. Cancer is a great example because there’s a lot of action: It’s very clear that we need to sequence (and are now sequencing) cancer genomes and cataloging things that come in, and here’s why:

You can take 100 tumor samples and analyze them under the microscope: They’ll all look the same. But when you sequence their genomes, you might see that 50 of them tend to have a certain set of variants (i.e. a signature) and maybe the remaining 50 will have another signature.

We might even correlate a signature with groups of people that respond poorly to therapy. It would be great if we knew this upfront because it means we wouldn’t have to put poor reponders through chemotherapy. Instead, we’d look for more appropriate treatments

In your talk, you mentioned that optical communication is becoming more practical for shorter distances. What has permitted this reduction?

In the past, optical fibers were ultrafine and very delicate strands glass that required careful installation by highly trained personnel and tight mechanical tolerances. The cost of such installations could only be justified for transoceanic telecommunication or similar long-haul communication.

Advances in optics have recently allowed thicker and more bendable optical fibers to carry data at rates of 10+Gb/s. That’s fast enough to transmit an entire blue-ray disc in under 30 seconds. Using a thicker fiber relaxes tolerances everywhere in the system, making fiber optic installation easier, cheaper, and more robust.

Why is it difficult to do optical communication at very small distances?

The challenge is to make optical links economical and practical for use over very small distances. Currently, inexpensive optics are capable of communicating at data rates up to around 14 Gb/s, with research progressing towards commercial systems at 28 Gb/s. However, the optoelectronics at either end of the links (i.e. components responsible for converting the data to & from electrical signals) are very similar to those used 20 years ago when optical communication was reserved for long-haul links. The cost of these optoelectronic components are limiting the application of optical communication in areas that are cost-sensitive.

One area of research in your lab is CMOS photodetectors. Why is using CMOS an improvement?

CMOS is clearly the technology of our age. It has given us an ability to mass produce high-performance transistors at such low cost that it has transformed the world.CMOS has not only advanced computer chips. Digital image sensors were, for a long time, manufactured using CCD technology. But what really made image sensors ubiquitous was the discovery that by embedding a small circuit alongside each pixel of the sensor, CMOS image sensors can have a quality comparable to CCD sensors. Today, CMOS image sensors and digital cameras are everywhere, and creative uses for them continue to emerge.

Similarly, CMOS photodetectors with GHz bandwidth will enable a whole new set of applications for optical communication with far-reaching impact on our modern information age. Not only will they make optical communication less expensive, but more importantly they will enable optical links to be mass produced and integrated seamlessly into computing, memory, and wireless technologies using nanoscale CMOS manufacturing technologies.

In your talk, you mentioned that it is difficult to put photodetectors on CMOS. Why is that?

CMOS technology has been refined over decades to facilitate the fabrication of very high performance transistors. Unfortunately, the requirements of high performance transistors conflict with the requirements of high performance photodetectors: Tiny transistors require very thin interfaces between n-type and p-type silicon, whereas photodectors perform better when these interfaces, called depletion regions, are thick enough to absorb all photons incident on the detector.

When photodetectors are made using narrow depletion regions, many photons penetrate right through the depletion region resulting in a slow persistent current that can obscure the received data.

What has been done to circumvent these problems?

Several labs are trying to develop new manufacturing technologies that will permit the manufacture of both high performance transistors AND photodetectors. Unfortunately, those approaches imply increased cost. Our approach is improve the performance of CMOS photodetectors by making clever use of the high performance transistors already available in today’s CMOS at no additional cost. This is analogous to the advances that permitted CMOS technology to revolutionize image sensors.

What’s the difference with light travelling on a chip and when it does in optical fiber?

Light is significantly attenuated as it propagates through electronic chips, even after only a few millimeters. Light can travel along optical fiber for kilometers with little or no appreciable attenuation.

What other projects do you work on?

Another project my lab (the Integrated Systems Laboratory) is currently working on is to improve the energy-efficiency of distributed supercomputing environments by targeting the interconnections within them. The total energy per year consumed by compute servers is 220 TWh, roughly 10% of which is attributable to I/O.

Hence, even research that improves I/O energy efficiency by only 1% in these installations yields a savings equivalent to the average electricity consumption of 20,000 homes. Our research promises improvements far exceeding 1%