Q&A: Navigating academia and industry in India
Arjun R shares advice on choosing the career path for you
What do you do? Do you enjoy it? Why?
I am a doctoral candidate at the National Institute of Technology Karnataka. My dissertation focuses on intelligent systems for predictive modelling in financial applications. Aside from my PhD research topic, I enjoy working on problems that have the potential to make an impact through different aspects of research such as social, business and technology. Through my PhD programme, I have moulded research skills to work on funded projects or serve as independent consultant.
What were your early career ambitions?
After not performing well at school, my parents suggested that I take up a course at an ITI (Industrial Training Institute). This meant that I would be a skilled technician in an organisation after graduating. However, I decided to study for a Diploma in Electronics and Communication Engineering, which is technically a higher degree, as I did not want to limit my career prospects. The initial semesters were tough, but later I picked up with the help of learning in peer student groups.
How did you make the decision between pursuing a career in academia or industry?
Once I completed my diploma, thoughts of applying for jobs surfaced. However, at that time in India, a new scheme was introduced whereby diploma holders could apply and join the second year of an undergraduate programme. I stayed at the same institute where I had studied for my diploma and was awarded a BTech in Information Technology in 2007. Around this time, the economic recession was prevailing, the dot-com bubble had mostly subsided and the industry in India was changing rapidly. I felt that my place was not in industry, as I did not believe I had the excellent coding skills it required. I took up my first job as a lecturer on a contractual basis at Cochin University Engineering College at Kuttanad, which is a state-funded public university. After my first stint as a lecturer, I personally felt that academia provided a better comfort zone and space for professional growth.
What were some of the challenges on your journey?
Most of colleges where I worked already started to regulate for more qualified (PG/PhD) teaching staff. I was interviewed and received a job offer from Amrita University in Quilon in early 2008. But in that year, a breakthrough occurred while I was trying to qualify for GATE (Graduate Aptitude for Engineering), a national exam that provide chances to pursue Master’s and PhD programmes. More recently, this exam has been a criterion for selection for some positions public sector companies. I received a strong grade and rank, which I don’t think I would have got without the exposure and subject knowledge I’d acquired during my undergraduate training.
What did you do next?
After unsuccessful applications to graduate schools for Master’s programmes, I worked for a short period as technical staff at an institute for a government funded project on digitisation of a library with nationwide reach. Later in 2009, I applied for an MTech (Master of Technology) programme at a state public university and was selected for a teaching assistance scholarship. As part of the dissertation project work, I applied for an internship at ISRO (Indian Space Research Organization). Even without support from the university or a scholarship from ISRO, I worked on a two-semester project on the development of a software tool prototype for space research applications, which resulted in a related IEEE publication.
How does being based in India affect the way you work?
There have not been many drastic changes in India, from academic point of view, in recent years. There are constant checks and performance reviews either in government posts or private institutions. To an extent, although private institutions offer higher salaries, they also demand a higher workload as part of accreditations that may actually work positively in long run.
What advice would you have for others trying to work in a similar sort of environment?
There can be a sense of lethargy and inertia certain positions. The best policy is to keep searching for grants for funded projects, extend your professional skills, such as research reviewing and talking at conference and workshops. Undertake student support programmes like mentoring and community initiatives for spreading knowledge.
What do you love about living and working in India?
In my case, the government funded my research and hence I feel a sense of moral duty to give back to my nation. India has potential for growth both scientifically and economically; at least historically that has been evident.
What’s your top career tip to younger colleagues?
Stay focused and keep your eyes open for higher education and research opportunities. Reach out to your seniors, teachers and peers for advice.
What else would you say to others trying to build a scientific career in India?
From my experience, joining the best-ranked institute does not necessarily mean you will receive top training or skills, unless you have a true passion for your research. Smart work and motivation can instil students with the confidence to perform well and be recognised in academia. Make use of generous government scholarships as well as privately funded schemes.
India debates a nationwide tenure system
Academic staff disagree on the merits, and the downsides, of scrapping a common year-long probation scheme. Academic researchers and administrators in India are debating the benefits of adopting a tenure-track system similar to that of the United States in Indian research institutes and universities. A few, including the Indian Institute of Science in Bengaluru, are already using the system, whereas others, such as the Indian Institutes of Technology, have a probationary-period process. Under that scheme, the performance of new faculty members is assessed after one year by a review committee, often comprised of department heads and institutional administrators. Some scientists are calling for the nationwide adoption of a five-year tenure-track review structure. After around five years, research faculty members are reviewed on the basis of their publications and funding received. Teaching ability and service to the institution usually have a supporting role. If the candidate is granted tenure, they receive a permanent appointment. If they are not, the appointment is terminated. Under the probationary system in India, research faculty members who receive a positive assessment at the end of their first year are given permanent positions as assistant professors. After another five years, they can apply to become associate professors — a position with higher rank and pay. If they are unsuccessful, however, their appointments are not terminated. Faculty members can stay at their institutions as assistant professors until they retire. Those who endorse the tenure-track system say that the probationary system allows low-performing researchers to remain in their posts. “How do we ensure that quick appointments to a very well paid, highly privileged and permanent position does not encourage complacency?” asks ecologist Vishwesha Guttal, who was awarded tenure in 2016 at the Indian Institute of Science, five years after he was hired. The issue of tenure-track versus one-year probation has sparked discussion and debate among academic scientists in India, partly in response to an interview published in June in the newspaper Hindustan Times with Jayant Udgaonkar, director of the Indian Institute of Science Education and Research in Pune. In the interview, he advocated adopting the tenure-track system nationally. His comments followed the release of a draft policy in May by the Ministry of Human Resource Development, which oversees higher education in India, recommending a gradual national adoption of the tenure-track system. The ministry could not be reached for comment. Udgaonkar, a biochemist, says that it is difficult to properly assess a researcher’s progress in a single year. He thinks that the tenure-track system provides scientific accountability and allows a candidate who has been given strong support and regular feedback to receive a comprehensive assessment at the end of five or sometimes seven years. But many do not agree. Theoretical physicist Arvind, director at the Indian Institute of Science Education and Research campus in Mohali, says that a five-year tenure track will increase job insecurity and put pressure on new faculty members to pursue only short-term research goals during that period. “Academia requires stability,” he says, adding that there is a paucity of fallback options for candidates who don’t make the cut. India has few second- or third-tier research institutions where a scientist whose bid for tenure is rejected elsewhere can seek another appointment, and few commensurate industry positions. Institutional support, easy access to equipment and resources, and timely disbursal of government grant funds have long been sore points in Indian academia; they have also been a talking point in discussions about adopting a tenure system. Gagandeep Kang, an academic gastroenterologist and executive director of the Translational Health Science and Technology Institute in Faridabad, says that institutions and government need to improve access to funding and resources to level the playing field for researchers who are up for tenure and allow for a more-rigorous review process. Ramaswamy Subramanian, a structural biologist and director of the Bindley Bioscience Center at Purdue University in West Lafayette, Indiana, says that if tenure is adopted, the process will need to be uniformly objective and fair. Subramanian, who has held tenured positions in Sweden and the United States, points out that tenure-review committee members in India, usually senior scientists and administrators, are likely to lack personal experience of the process. A nationwide system is unlikely to be adopted soon, predicts Arvind. “Each institution is autonomous,” he says. “There may, at best, be suggestions that the governing boards of individual institutions can then consider.” If you have a career story that you'd like to share, then please complete this form, or send your outline by email.
The lives of female scientists in India are being chronicled online
More than 100 researchers describe their work and the struggles they face, including gender bias and achieving a positive work–life balance. Two science journalists in India continue to build on The Life of Science, a multimedia website that they designed and launched in 2016 to highlight the research and lives of more than 100 women in the country. The site, founded and run by Nandita Jayaraj and Aashima Dogra, aims to chronicle the scientists’ experiences in the lab and field. Jayaraj and Dogra, who work full-time on the site, compile feature stories, blogposts, podcasts, video and picture features about the women, whose work spans the fields of science, technology, engineering and mathematics (STEM). The journalists met in 2014 in Bangalore, while working on a now-defunct children’s science magazine. When this shut down in 2015, they decided to explore their mutual interest in science communication. Dogra had already planned to travel the country on a brief busman’s holiday, and visited the Indian Agricultural Research Institute in Kalimpong to talk to women who worked there. Meanwhile, Jayaraj was interviewing geophysicist Kusala Rajendran at the Indian Institute of Science in Bangalore and biophysicist Aruna Dhathathreyan at the Central Leather Research Institute in Chennai. When the two journalists conferred about the information they had gathered, they decided to create a website to publicize the stories. “We were curious about the science under way in laboratories in our back yard,” says Jayaraj about the site’s early days. “We also wanted to break the stereotype of the scientist as an old male person.” As the two began writing full-time, they crowdfunded for their work on the Indian platform BitGiving. Jayaraj and Dogra have since launched a second campaign to fund their work on the site, which includes compiling some of the content into two books. Each scientist’s story offers a glimpse into her world — from the physical environment in which she lives and works, to the nature of her research and how she reached her present position. “I particularly like how the narratives let us see the woman behind the science and scientific journey,” says Vidita Vaidya, a neuroscientist at the Tata Institute of Fundamental Research in Mumbai, who is featured on the site. The site showcases India’s diverse research landscape. Some of the scientists work with state-of-the-art equipment such as dilution refrigerators, confocal microscopes and high-performance computing clusters; others make the most of sparse funds and scant supplies. Yet the stories’ common threads resonate with many others who aspire to, or are navigating, a scientific career: the struggles to balance family life and career, and to counter bias and stereotypes. The interviewees offer ideas for ameliorating some of the struggles, such as establishing campus child-care facilities and promoting female scientists into leadership positions. “Nothing on this scale has ever been done before,” says Vaidya. She hopes that the site can help bring together those who are profiled, as well as other women who work in STEM in India. Jayaraj and Dogra continue to find more women to profile. Viewer numbers and other metrics are not available, but the developers intend to continue the site in perpetuity. Indian online news sites including The Wire and Firstpost have syndicated some of the articles. Those profiled are delighted at the chance to connect with readers. Number theorist Kaneenika Sinha at the Indian Institute of Science Education and Research in Pune has received e-mails from parents seeking suggestions for training their mathematically talented child, junior scientists who plan to repatriate and want ‘insider’ information, and students with questions about her work. Jayaraj and Dogra are experimenting with different formats, including photo stories, cartoons and podcasts. “We see The Life of Science not really as an entity or ‘our’ project,” the two say, “but what it stands for — and that is the voices of women in science.”
Is Science In Trouble?
A Conversation With Colin Camerer About the Replication Crisis News Writer: Emily Velasco Credit: Caltech If there's a central tenet that unites all of the sciences, it's probably that scientists should approach discovery without bias and with a healthy dose of skepticism. The idea is that the best way to reach the truth is to allow the facts to lead where they will, even if it's not where you intended to go. But that can be easier said than done. Humans have unconscious biases that are hard to shake, and most people don't like to be wrong. In the past several years, scientists have discovered troubling evidence that those biases may be affecting the integrity of the research process in many fields. The evidence also suggests that even when scientists operate with the best intentions, serious errors are more common than expected because even subtle differences in the way an experimental procedure is conducted can throw off the findings. When biases and errors leak into research, other scientists attempting the same experiment may find that they can't replicate the findings of the original researcher. This has given the broader issue its name: the replication crisis. Colin Camerer Colin Camerer, Caltech's Robert Kirby Professor of Behavioral Economics and the T&C Chen Center for Social and Decision Neuroscience Leadership Chair, executive officer for the Social Sciences and director of the T&C Chen Center for Social and Decision Neuroscience, has been at the forefront of research into the replication crisis. He has penned a number of studies on the topic and is an ardent advocate for reform. We talked with Camerer about how bad the problem is and what can be done to correct it; and the "open science" movement, which encourages the sharing of data, information, and materials among researchers.What exactly is the replication crisis? What instigated all of this is the discovery that many findings—originally in medicine but later in areas of psychology, in economics, and probably in every field—just don't replicate or reproduce as well as we would hope. By reproduce, I mean taking data someone collected for a study and doing the same analysis just to see if you get the same results. People can get substantial differences, for example, if they use newer statistics than were available to the original researchers. The earliest studies into reproducibility also found that sometimes it's hard to even get people to share their data in a timely and clear way. There was a norm that data sharing is sort of a bonus, but isn't absolutely a necessary part of the job of being a scientist.How big of a problem is this? I would say it's big enough to be very concerning. I'll give an example from social psychology, which has been one of the most problematic areas. In social psychology, there's an idea called priming, which means if I make you think about one thing subconsciously, those thoughts may activate related associations and change your behavior in some surprising way. Many studies on priming were done by John Bargh, who is a well-known psychologist at Yale. Bargh and his colleagues got young people to think about being old and then had them sit at a table and do a test. But the test was just a filler, because the researchers weren't interested in the results of the test. They were interested in how thinking about being old affected the behavior of the young people. When the young people were done with the filler test, the research team timed how long it took them to get up from the table and walk to an elevator. They found that the people who were primed to think about being old walked slower than the control group that had not received that priming. They were trying to get a dramatic result showing that mental associations about old people affect physical behavior. The problem was that when others tried to replicate the study, the original findings didn't replicate very well. In one replication, something even worse happened. Some of the assistants in that experiment were told the priming would make the young subjects walk slower, and others were told the priming would make them walk more quickly—this is what we call a reactance or boomerang effect. And what the assistants were told to expect influenced their measurements of how fast the subjects walked, even though they were timing with stopwatches. The assistants' stopwatch measures were biased compared to an automated timer. I mention this example because it's the kind of study we think of as too cute to be true. When the failure to replicate came out, there was a big uproar about how much skill an experimenter needs to do a proper replication.You recently explored this issue in a pair of papers. What did you find? In our first paper, we looked at experimental economics, which is something that was pioneered here at Caltech. We took 18 papers from multiple institutions that were published in two of the leading economics journals. These are the papers you would hope would replicate the best. What we found was that 14 out of 18 replicated fairly well, but four of them didn't. It's important to note that in two of those four cases, we made slight deviations in how the experiment was done. That's a reminder that small changes can make a big difference in replication. For example, if you're studying political psychology and partisanship and you replicate a paper from 2010, the results today might be very different because the political climate has changed. It's not that the authors of the original paper made a mistake, it's that the phenomenon in their study changed. In our second paper, we looked at social science papers published between 2010 and 2015 in Science and Nature, which are the flagship general science journals. We were interested in them because these were highly cited papers and were seen as very influential. We picked out the ones that wouldn't be overly laborious to replicate, and we ended up with 21 papers. What we found was that only about 60 percent replicated, and the ones that didn't replicate tended to focus on things like priming, which I mentioned before. Priming has turned out to be the least replicable phenomenon. It's a shame because the underlying concept—that thinking about one thing elevates associations to related things—is undoubtedly true.How does something like that happen? One cause of findings not replicating is what we call "p-hacking." P-value is a measure of the statistical likelihood that your hypothesis is true. If the p-value is low, an effect is highly unlikely to be a fluke due to chance. In social science and medicine, for example, you are usually testing whether changing the conditions of the experiment changes behavior. You really want to get a low p-value because it means that the condition you changed did have an effect. P-hacking is when you keep trying different analyses with your data until you get the p-value to be low. A good example of p-hacking is deleting data points that don't fit your hypothesis—outliers—from your data set. There are statistical methods to deal with outliers, but sometimes people expect to see a correlation and don't find much of one, for example. So then they think of a plausible reason to discard a few outlier points, because by doing that they can get the correlation to be bigger. That practice can be abused, but at the same time, there sometimes are outliers that should be discarded. For example, if subjects blink too much when you are trying to measure visual perception, it is reasonable to edit out the blinks or not use some subjects. Another explanation is that sometimes scientists are simply helped along by luck. When someone else tries to replicate that original experiment but doesn't get the same good luck, they won't get the same results.In the sciences, you're supposed be impartial and say, "Here's my hypothesis, and I'm going to prove it right or wrong." So, why do people tweak the results to get an answer they want? At the top of the pyramid is outright fraud and, happily, that's pretty rare. Typically, if you do a postmortem or a confessional in the case of fraud, you find a scientist who feels tremendous pressure. Sometimes it's personal—"I just wanted to be respected"—and sometimes it's grant money or being too ashamed to come clean. In the fraudulent cases, scientists get away with a small amount of deception, and they get very dug in because they're really betting their careers on it. The finding they faked might be what gets them invited to conferences and gets them lots of funding. Then it's too embarrassing to stop and confess what they've been doing all along.There are also faulty scientific practices less egregious than outright fraud, right? Sure. It is the scientist who thinks, "I know I'm right, and even though these data didn't prove it, I'm sure I could run a lot more experiments and prove it. So I'm just going to help the process along by creating the best version of the data." It's like cosmetic surgery for data. And again, there are incentives driving this. Often in Big Science and Big Medicine, you're supporting a lot of people on your grant. If something really goes wrong with your big theory or your pathbreaking method, those people get laid off and their careers are harmed. Another force that contributes to weak replicability is that, in science, we rely to a very large extent on norms of honor and the idea that people care about the process and want to get to the truth. There's a tremendous amount of trust involved. If I get a paper to review from a leading journal, I'm not necessarily thinking like a police detective about whether it's fabricated. A lot of the frauds were only uncovered because there was a pattern across many different papers. One paper was too good to be true, and the next one was too good to be true, and so on. Nobody's good enough to get 10 too-good-to-be-trues in a row. So, often, it's kind of a fluke. Somebody slips or a person notices and then asks for the data and digs a little further.What best practices should scientists follow to avoid falling into these traps? There are many things we can do—I call it the reproducibility upgrade. One is preregistration, which means before you collect your data, you publicly explain and post online exactly what data you're going to collect, why you picked your sample size, and exactly what analysis you are going to run. Then if you do very different analysis and get a good result, people can question why you departed from what you preregistered and whether the unplanned analyses were p-hacked. The more general rubric is called open science, in which you act like basically everything you do should be available to other people except for certain things like patient privacy. That includes original data, code, instructions, and experimental materials like video recordings—everything. Meta-analysis is another method I think we're going to see more and more of. That's where you combine the results of studies that are all trying to measure the same general effect. You can use that information to find evidence of things like publication bias, which is a sort of groupthink. For example, there's strong experimental evidence that giving people smaller plates causes them to eat less. So maybe you're studying small and large plates, and you don't find any effect on portion size. You might think to yourself, "I probably made a mistake. I'm not going to try to publish that." Or you might say, "Wow! That's really interesting. I didn't get a small-plate effect. I'm going to send it to a journal." And the editors or referees say, "You probably made a mistake. We're not going to publish it." Those are publication biases. They can be caused by scientists withholding results or by journals not publishing them because they get an unconventional result. If a group of scientists comes to believe something is true and the contrary evidence gets ignored or swept under the rug, that means a lot of people are trying to come to some collective conclusion about something that's not true. The big damage is that it's a colossal waste of time, and it can harm public perceptions of how solid science is in general.Are people receptive to the changes you suggest? I would say 90 percent of people have been very supportive. One piece of very good news is the Open Science Framework has been supported by the Laura and John Arnold Foundation, which is a big private foundation, and by other donors. The private foundations are in a unique position to spend a lot of money on things like this. Our first grant to do replications in experimental economics came when I met the program officer from the Alfred P. Sloan Foundation. I told him we were piloting a big project replicating economics experiments. He got excited, and it was figuratively like he took a bag of cash out of his briefcase right there. My collaborators in Sweden and Austria later got a particularly big grant for $1.5 million to work on replication. Now that there's some momentum, funding agencies have been reasonably generous, which is great. Another thing that's been interesting is that while journals are not keen on publishing a replication of one paper, they really like what we've done, which is a batch of replications. A few months into working on the first replication paper in experimental economics funded by Sloan, I got an email from an editor at Science who said, "I heard you're working on this replication thing. Have you thought about where to publish it?" That's a wink-wink, coy way of saying "Please send it to us" without any promise being made. They did eventually publish it.What challenges do you see going forward? I think the main challenge is determining where the responsibility lies. Until about 2000, the conventional wisdom was, "Nobody will pay for your replication and nobody will publish your replication. And if it doesn't come out right, you'll just make an enemy. Don't bother to replicate." Students were often told not to do replication because it would be bad for their careers. I think that's false, but it is true that nobody is going to win a big prize for replicating somebody else's work. The best career path in science comes from showing that you can do something original, important, and creative. Replication is exactly the opposite. It is important for somebody to do it, but it's not creative. It's something that most scientists want someone else to do. What is needed are institutions to generate steady, ongoing replications, rather than relying on scientists who are trying to be creative and make breakthroughs to do it. It could be a few centers that are just dedicated to replicating. They could pick every fifth paper published in a given journal, replicate it, and post their results online. It would be like auditing, or a kind of Consumer Reports for science. I think some institutions like that will emerge. Or perhaps granting agencies, like the National Institutes of Health or the National Science Foundation, should be responsible for building in safeguards. They could have an audit process that sets aside grant money to do a replication and check your work. For me this is like a hobby. Now I hope that some other group of careful people who are very passionate and smart will take up the baton and start to do replications very routinely.