From ScienceMag:

I was in the fourth year of my Ph.D. in tumor immunology when I gave a talk at a major international conference. I had rehearsed every slide, every transition, determined to present my results as a coherent scientific story. But near the end I paused and said something I had not practiced. “This research is personal; I’m not only a researcher, but also a survivor of childhood leukemia.” The words surprised me as soon as they left my mouth. I felt I had crossed an invisible professional line I had spent years trying not to approach.

I was diagnosed with acute lymphoblastic leukemia when I was 3 years old. My earliest memories are not of classrooms or playgrounds, but of hospital rooms and seemingly constant fatigue caused by the chemotherapy drugs.

With treatment, I eventually went into remission. But as I grew older and learned the biology of leukemia, one idea unsettled me: The immune system designed to protect me had failed. Cells had multiplied without restraint. Signals meant to maintain order had broken down. Biology became personal for me. I became obsessed with the questions of what cancer is and what survival means biologically. Pursuing science didn’t feel like a career choice; it felt like picking up an unfinished story.

Yet when I entered graduate school, I did not tell anyone about my history—not my lab mates, not even my adviser. I thought professionalism meant keeping my personal life separate from my scientific one. But that separation required constant vigilance. When conversations turned to hospital appointments, childhood, illness, or what had brought us to cancer research, I learned to redirect gently or stay quiet. I answered honestly, but never fully. I worried disclosure might affect how I was seen. Would colleagues doubt my stamina? Would mentors hesitate to invest in me? Would I always be “the survivor” instead of simply a scientist? Would people think I was leveraging sympathy to earn a place in science?

I left India to pursue research abroad, first in the United States, then Israel, and eventually the United Kingdom. In the lab, I felt capable. Outside it, I often felt uncertain. There were evenings alone in my apartment when the distance from home felt vast. In those moments, I sometimes thought about the child I once was, lying in a hospital bed, exhausted, dependent on treatments developed by researchers I would never meet, who had chosen to dedicate their life to understanding diseases like mine. Slowly, I began to realize I was becoming that researcher myself. That thought didn’t make the path any easier, but gave it meaning.

I spoke out at that conference because of a realization that had been slowly coalescing for years: I could no longer keep my personal history and my profession in separate compartments. I did not expect my revelation to alter anything beyond that room. But in the weeks that followed, I began to see that many of my fears had been unfounded. Colleagues did not question my professionalism; they understood my urgency, and our conversations deepened. A student confided that she had her own medical history she rarely mentioned. Later, after I’d become more accustomed to sharing my story, a young patient told me hearing my story made a scientific career feel imaginable.

The shift was internal as well. Previously, a negative result in the lab could send me spiraling into self-doubt. Now, the setbacks are still frustrating, but they no longer feel existential. I remind myself I’ve already survived something far less predictable than an assay that did not work. Flawed experiments have become part of the process, not a measure of my worth.

That day at the podium the words arrived before I fully understood why. Only later did I recognize that it wasn’t my past that had weighed on me, but rather the effort of keeping it separate. Being a survivor doesn’t make me a better scientist, but it shapes how I think about my science. It gives context to long hours and the slow pace of discovery. My personal story has become part of my identity as a scientist, not as a credential, but as a reminder of why the questions matter, and why I chose to ask them.

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From ScienceMag:

The other day, a new research idea struck me. The conceptual path was clear, but the execution would require real effort—synthesizing the literature, writing code, training models, performing statistical analysis. Just a few years ago, the next step would have been a no-brainer. I would recruit a graduate student into my lab and allow them to run with the project, providing guidance along the way. This time, an uncomfortable thought crept into my head: Should I just give these tasks to artificial intelligence (AI) rather than take a chance on a student?

I thought about the skills I had when I started graduate school more than a decade ago, and how much mentoring it took to get me where I am today. I had zero research experience when I emailed faculty to say I was interested in computer science Ph.D. programs. I did my basic due diligence, reading up on what they worked on. But sitting in their offices, listening to them talk about robotics, algorithms, and natural language processing, I had little to no clue what these concepts really meant.

One professor saw past my ignorance and agreed to take me on. I was incredibly grateful for the opportunity, but the first few months were a harsh reality check. I worked tirelessly—reading, summarizing, drafting ideas, and trying to make sense of it all. Yet, whenever I would present my work to my adviser, she would look at the nonsense I had presented, give me feedback, and send me back to start from scratch.

I thought about quitting. I felt I was constantly disappointing her. But she didn’t give up on me. Perhaps she believed in my potential, perhaps she saw I was doing the best I could, or perhaps she simply believed in the process of cultivating a scholar. It took a year or so of immense patience before I finally produced something we could build on. From there, I slowly transformed from a clueless novice into a junior colleague.

Years later, when I became a professor, I watched my own students struggle to make progress, just as I once had. My calendar filled up with meetings where my main job was to untangle their confusion. Eventually, though, the investment paid off, and I experienced the deep satisfaction of watching them transform into capable junior collaborators.

Now, AI has introduced a new option. It is certainly no extraordinary intellectual partner. But it can competently perform a lot of the work I need immediately; AI requires no ramp-ups, no meetings, and absolutely no emotional support. It is forcing a quiet, uncomfortable shift in my mindset.

The issue is not whether my students are valuable. In the long run, they are invaluable. The issue is that their value emerges slowly, whereas AI delivers immediate returns. I feel somewhat embarrassed to admit how tempting this is. In our culture, preferring an algorithm to a trainee feels like a betrayal of the academic mission.

Yet I see these calculations shaping the labs around me. Close colleagues are quietly refraining from taking on as many students as they used to. When they do take students, they are noticeably pickier.

My immediate instinct is to expect any student I recruit in this new environment to contribute at a much higher level from the outset. But to meet those elevated expectations, a student would likely rely heavily on the same AI tools I could turn to on my own. In the process, they may bypass the valuable experience of struggling through early tasks and learning from their mistakes. Students, I worry, could simply become an intermediary between the raw idea and the AI’s output.

For faculty, meanwhile, the pressure to produce remains relentless and the scientific pace is unforgiving, making a productive and frictionless AI even more tempting. The real danger I see is not that AI will entirely replace graduate students in the foreseeable future. It is that the default assumption that taking on students is simply part of any professor’s academic journey will quietly erode. In some cases, the most pragmatic solution could be to use an AI.

I’m not sure where that will leave students who start with no research experience. Personally, I am seriously tempted not to take a chance on a novice for my new project—which means today, I probably wouldn’t recruit my younger self.

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From ScienceMag:

The girl in the lab coat was extracting DNA from a piece of lettuce. She held the pipette like it was something sacred—like it might break if she breathed too hard. Beside her, a boy adjusted his goggles, avoiding eye contact. He didn’t ask a single question. Not because he didn’t have any, but because somewhere along the way, someone taught him to stay quiet. Outside those walls, their parents were at work under the Arizona sun, harvesting the same crop. They pulled lettuce from the earth to feed the country. Their kids pulled out its genetic material to understand it. The overlap was intentional. In this 1-week summer camp, we aimed to show students that there is a path from the agricultural work their communities have done for generations to STEM.

The program was personal for me because I, too, grew up in an agricultural town, the son of immigrant farmworkers. Schools were underfunded, the guidance counselor overworked, and expectations modest. College wasn’t the assumed path—it was the exception. I know what it’s like to sit in classrooms that prepare you for labor, not leadership, and to feel the quiet sorting that tells some students they belong in universities and others they don’t.

I graduated high school with a 1.9 GPA, so community college was my only option—and even then, I struggled. My first year was marked by a string of withdrawals and failing grades, culminating in a 0.0 GPA. But slowly, class by class, I found my footing. A few instructors encouraged me to stay with it, and eventually I was able to transfer. A decade and a half later, I had earned a master’s degree at Johns Hopkins University and a doctorate from Harvard University—outcomes the student I once was could never have imagined.

Today, I am a tenure-track faculty member at Arizona State University, a role that still feels improbable given my beginnings. Shortly after I started, state education officials approached my academic unit with an idea: to launch a STEM program for students from migratory farmworker families, a group that is underrepresented in science despite descending from generations of agricultural knowledge holders.

I know what programs like this can make possible. I am a product of federally supported training programs that intervened at critical stages in my own education. When I was an undergraduate student, for instance, a Department of Education program for students from disadvantaged backgrounds helped nudge me toward doctoral study when that path still felt distant. I have long believed that genius is evenly distributed across society, and that it just needs room to surface through exposure to science.

So I accepted the state’s challenge, and with colleagues developed a program that enrolled 50 to 80 high school students each year for four summers. Students lived in dorms, ate in dining halls, and rotated through immersive, hands-on labs led by faculty. Designed to replicate the university experience, the weeklong program aimed to make science tangible and accessible. Evaluation across cohorts showed consistent gains, including increased interest in STEM careers as well as meaningful rises in college aspirations. On paper, the program worked.

My favorite outcomes, however, were ones not captured by numbers. For many students, this was their first time away from home. They arrived shy and guarded, unsure how to introduce themselves or how to relate to the academic world. As the days progressed and they stepped into university labs and saw people who looked like them, they began confidently asking questions and talking openly about wanting to become doctors and researchers. By the end, they were reluctant to leave the community they had built in just 1 week.

The experience stayed with me because I was once the student with a 1.9 GPA, unsure of my place, waiting for someone to see potential that the data could not show. I could identify with the girl who once hesitated with the pipette and now steadies it with confidence. I see myself in the boy who had not asked a single question but now leans forward, curious and engaged.

I wish the system did a better job of looking beyond traditional academic metrics when assessing potential. Because watching these students, I am reminded how transformative that moment can be when someone finally sees in you what you could not yet see in yourself.

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From ScienceMag:

I had known my contract was ending. I had just completed the final interview for a position abroad and was already preparing for the move. But when a message arrived saying, “Your university email account will be deactivated in 30 days,” I felt strangely unmoored. For early-career researchers like me, the global academic landscape can feel daunting. Permanent positions are scarce, competition is intense, and many of us move from one temporary position to another, often across countries and continents, trying to build a scientific identity. Losing my institutional email address felt like losing a small but vital piece of the scientist I had become.

My academic journey began with a Ph.D. in my home country of China, followed by a postdoctoral fellowship in Saudi Arabia, and then a series of positions in Australia that were either tied to a grant or temporary. Each move brought expanded research directions, wider collaborations, greater responsibility, and deeper engagement with students, but none came with long-term security. At times, the path forward felt exciting; at others, deeply tenuous. Constant relocation was hard for my family as well, requiring us to adapt to new cities and communities while I tried to maintain momentum in my work.

When I arrived at my most recent position in 2021, I was eager to prove myself. I was appointed to a contract faculty role, responsible for leading a small research group while establishing an independent research program. My days were filled with troubleshooting experiments, writing manuscripts, drafting grant proposals, and learning to mentor my first students. I began to form collaborations across time zones, and my institutional email became the channel through which these relationships took root. Through that address, I submitted manuscripts, coordinated projects, reviewed papers, and answered late-night questions from students testing out their first ambitious ideas. Messages also arrived from prospective Ph.D. students, some of whom would later join my group. My scientific life gathered there, thread by thread.

As my contract neared its end late last year, I focused on preparing for the transition and helping my students and researchers find new positions within the university. Still, the deactivation notice felt like a door closing, faster than I was prepared for.

I scrambled to reroute everything to my personal and former professional addresses until I gained access to a new one. Inevitably, things slipped. I nearly missed invitations to contribute to special issues, and several manuscript review requests went unnoticed until I logged into the submission portals by chance. A former mentee’s request for a reference letter was delayed. Some collaborators’ messages never reached me. One former student who urgently needed help with her manuscript eventually tracked me down through social media. Each disruption reminded me how much of academic life relies on simply being reachable. Writing from a generic address felt different, as though my professional standing had been diminished. Fairly or not, an institutional email address signals belonging—to a department, a university, a scientific community.

Universities often speak of lifelong learning and long-term impact. Moving through different institutions has shown me how meaningful it can be when those values extend even to the smallest details. Years ago, after finishing my first postdoctoral fellowship, I expected my email access to disappear the moment my university ID card stopped working. Instead, it remained active for years. Every so often, former colleagues sent holiday greetings or shared good news. Readers wrote with questions about publications still tied to that email. Those messages reminded me that even though my contract had ended, my place in that community had not. By allowing researchers to keep their institutional email address for at least 6 months after their position ends, universities could better support those of us navigating the uncertain early stages of an academic career.

I am now preparing to settle into a tenure-track position in China. For the moment, I rely on my personal and former institutional addresses for academic tasks while waiting to form new connections through my new professional email. As researchers, we are always building—and rebuilding—a sense of belonging.

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From ScienceMag:

As a chemistry lecturer, I was never quite sure how much course material my students were truly absorbing, so I decided to embark on an experiment. At the end of one lecture, I gave each student an index card to write on. Then, I listed on the board the key topics I had covered and asked the class to copy them onto the cards and write green, yellow, or red next to each topic. Green meant “I understand this well,” yellow told me “I am somewhat unsure,” and red indicated a more dire problem: “I am confused or lost.” I wasn’t sure how the approach would work. I waited eagerly to collect and examine the cards, hoping for a comprehensive, real-time picture of how much my students actually understood.

Years earlier, when I first started to teach as a graduate teaching assistant, I simply gave my lectures and did not question whether my methods were effective. If no one raised a hand, I took it as a sign that the material was clear. But over time, I began to realize I shouldn’t make that assumption. After exams, I would discover concepts students had misunderstood, and I would wonder how I could have intervened earlier. Because students had to master some concepts before moving onto other topics, not catching problems early meant misconceptions compounded over time.

As a scientist, I was trained to seek evidence, test hypotheses, and adjust based on data. However, in the classroom, I was teaching without any feedback. It felt like speaking into the void, without an opportunity to make adjustments.

I needed a way to gauge students’ understanding before it was too late. I could have given regular quizzes to assess where they were in their learning. But what I really wanted to test was my own teaching methods, and I was looking for a low-pressure way to invite feedback. So, soon after I became a chemistry instructor, I started to experiment with the index cards. After each lecture, I would collect them and identify the topics with the most yellow and red comments.

The distribution of colors—what I refer to as “traffic signals”—was eye opening. Concepts I thought were straightforward showed an unexpected mix of yellow and red. For the first time, I had a snapshot of comprehension across the class, and it quickly transformed how I taught. I began to see lectures not as performance, but as an experiment that allowed me to test, adjust, and learn alongside my students.

Importantly, the yellow and red signals told me I needed to slow down. After one lecture that included a concept called freezing point depression, it became clear that many students were struggling to understand the idea. Some had green responses, but most were in the yellow and red. So I began the next class with a targeted review and a real-world example: why salt lowers the freezing point of water on roads. I also made a point of telling students then and during other lectures that if they still had questions about the yellow and red topics we reviewed, they were welcome to stop by during my office hours for additional help.

When the COVID-19 pandemic hit and teaching moved online, I could not use physical cards, so I developed a digital version. At the end of each lecture, I displayed the key topics on my shared screen and asked the students to send me a chat message with their level of understanding for each topic. For example, a student might send a chat message that read “green, green, red, yellow, green.”

As I continued to use the cards, I found they didn’t just help me. They also helped the students reflect on what they had been taught, becoming more aware of gaps in their understanding. “I didn’t realize how much I understood until I had to choose a color,” one told me. Others admitted they felt relieved when I shared the distribution of responses and they saw they weren’t alone in not fully understanding a concept.

Each batch of students has been unique. So, I am continuing to use the cards, tinkering with my material and teaching approach as I go. Although I developed this strategy for chemistry lectures, it could be used anywhere feedback matters: lab meetings, professional workshops, even research seminars. The broader lesson is that science communication, like science, thrives on evidence and awareness.

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