Justice Department calls three NSF diversity programs unconstitutional

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From ScienceMag:

The efforts of the National Science Foundation (NSF) to diversify U.S. science beyond its historical reliance on white men were dealt a blow last week after the Department of Justice (DOJ) ruled that three of its long-running programs were unconstitutional because they discriminate by race or sex and must be ended. At the same time, DOJ said four other NSF programs to strengthen the science, technology, engineering, and math (STEM) workforce pass legal muster and two more could be tweaked to avoid clashing with the ruling (see table, below).

Advocates for the barred NSF programs say the ruling will hurt the ability of the United States to remain competitive in science with other nations. “Anytime we eliminate a program that has been providing opportunities for all students to excel in science, we are limiting the ability of this country to excel,” says mathematician Freeman Hrabowski, president emeritus of the University of Maryland, Baltimore County, whose Meyerhoff Scholars Program has with the help of NSF funding become a national model for increasing the number of Black and Hispanic people in science and medicine. Representative Grace Meng (D–NY), the top Democrat on a congressional spending panel that sets NSF’s budget, vowed to fight what she calls “this very troubling legal opinion.” NSF declined comment.

At the same time, some legal scholars suggest the 12 August opinion by Deputy Assistant Attorney General Josh Craddock may signal that President Donald Trump’s administration is softening its blanket opposition to any diversity, equity, and inclusion program aimed at groups historically underrepresented in science and could be open to race and gender-neutral efforts.

“This is a more sophisticated, nuanced, and, with important exceptions, accurate depiction of federal law than we’ve seen from the Justice Department,” says one lawyer at a firm that advises research institutions battling the slew of Trump executive orders on race and sex/gender who requested anonymity to protect its relationship with the department. “This document articulates an avenue for neutral strategies that is a distinct contrast with a spate of recent findings that assume any neutral strategy is inherently unlawful or simply a mask for racial discrimination.”

As part of its mission to fund academic research, NSF has long been the main federal champion of STEM diversity. Within a few months of Trump retaking office, however, the agency terminated hundreds of grants for projects meant to attract and retain what former NSF Director Sethuraman Panchanathan calls “the missing millions.” Simultaneously, NSF said it would no longer support research projects “that preference some groups at the expense of others, or directly/indirectly exclude individuals or groups.” It also dismantled its Division of Equity for Excellence in STEM, the unit that had been funding many of those efforts.

The DOJ ruling goes one step further. It orders NSF to terminate the Louis Stokes Alliances for Minority Participation (one of the funders of the Meyerhoff scholars), the Alliances for Graduate Education and the Professoriate, and a program serving colleges and universities enrolling a significant number of Hispanic students. It also bars NSF from spending the $104 million appropriated for the programs this year by Congress.

That clawback of funds bolsters Trump’s insistence that his administration can override the wishes of Congress, which specifically authorized the NSF programs and has the constitutional authority to allocate federal dollars. “Congress’s power of the purse is formidable, but it cannot license—much less compel—the Executive Branch to engage in unconstitutional racial discrimination,” Craddock wrote in his 38-page opinion.

NSF’s legal divide

The Department of Justice divided nine National Science Foundation programs aimed at increasing diversity into three pots based on whether it felt they discriminated on the basis of race or sex.

ProgramYear startedCurrent appropriation (millions)
MUST END
Louis Stokes Alliances for Minority Participation1990$50
Alliances for Graduate Education and the Professoriate1998$8
Improving Undergraduate STEM Education: Hispanic Serving Institutions2017$47
NEEDS TWEAKING
Advanced Technological Education1992$75
ADVANCE2001$18
PASSES MUSTER
Graduate Research Fellowship Program1952$285
Tribal Colleges and University Program2001$20
Robert Noyce Teacher Scholarship Program2002$67
Advancing Informal STEM Learning2012$55

NSF has traditionally justified the need for its diversity programs with data showing that Black people, Hispanic people, and women have historically been heavily underrepresented in STEM fields. But Craddock said that approach doesn’t meet the new standard set by the U.S. Supreme Court in a 2023 decision outlawing the use of race in college admissions, which allows for race-based programs only to “remediate specific, identified instances of past discrimination.” He also quotes from a high court ruling this year striking down Louisiana’s use of race in drawing its congressional districts, which declared that “statistical disparities don’t cut it” as a justification for a race-based program.

But some legal analysts argue Craddock has gone too far. “Congress has the constitutional authority to identify systemic racism and other societal inequities and to pass legislation that is intended to address the problem,” says one lawyer, former general counsel at a major research university who also requested anonymity. “There are lawful ways in which to move forward with neutral and inclusive approaches to remove barriers to participation. And I think it is fundamentally wrong [for DOJ] to ignore that fact.”

Craddock’s ruling gives a green light to six other programs that Angela Williams, NSF’s general counsel, in May asked DOJ to evaluate. One was NSF’s prestigious Graduate Research Fellowship Program (GRFP), which includes a 2022 congressional directive “to recruit fellowship applicants from historically underrepresented populations in STEM.”

NSF would be crossing the line if its outreach for the GRFP targeted Black people or women, Craddock told Williams. But there’s an alternative, he explained. “NSF can satisfy this requirement by targeting race-neutral populations historically underrepresented in STEM—such as persons with disabilities, from rural communities, or from low-income background,” Craddock wrote. “The statute does not require NSF to target all historically underrepresented populations.”

An NSF program created in 2001 to increase the number of women in academic science and engineering careers, called ADVANCE, posed a trickier problem. Any approaches aimed exclusively at women would be unconstitutional, Craddock noted in his opinion, but sex-neutral strategies that wind up helping women are OK. “NSF could not fund activities that use sex as an eligibility criterion—such as a female-only Girls Who Code event,” he explained. “But NSF may continue to fund projects that do not give preferential treatment to one sex, even if such projects ultimately tend to benefit women more than men.”

However, institutions receiving NSF funding from the nonbanned programs aren’t off the hook, the lawyers who spoke with Science warn. Craddock’s ruling also prohibits NSF grantees from evaluating the effectiveness of any effort to close racial or gender gaps in science that is deemed unconstitutional.

Although that edict contradicts current case law allowing such research “as long as it does not yield a demonstrable benefit to one student and not another,” says one lawyer, they worry DOJ could eventually use that provision to shut down all NSF programs aimed at improving the diversity of the U.S. scientific workforce.

“So yes, preserving some programs now is a win,” one lawyer says. “But that does not necessarily mean that you can continue doing what you’ve been doing. You need to do it a way that pursues authentic neutral strategies for removing barriers that are not based on a person’s race or sex.”

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NIH is ending a key grant for budding clinical researchers

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From ScienceMag:

Lilia Cervantes was a few years into her career as an internal medicine physician at a Denver hospital when she lost a patient who had become a friend. Hilda was an undocumented Mexican immigrant with kidney disease who did not have health insurance and could only get dialysis weekly at emergency rooms. She died in 2014, leaving behind two young sons. Cervantes felt the injustice of her death. “I decided I needed to learn to do research,” she says.

Cervantes later won an early-career development grant from the National Institutes of Health (NIH) that would pay enough of her salary that she could spend 75% of her time studying barriers to dialysis for Latino people. The grant also funded a small study within this group investigating whether meeting regularly with a community health worker could improve their health by spurring them to get the blood-cleaning procedure more often. The work helped Cervantes—now a professor at the University of Colorado—fall in love with clinical research.

But that grant mechanism will no longer fund trials, she learned at a May meeting of an NIH advisory board she sits on. She’s “devastated,” Cervantes says, noting that she is not speaking for that board or her university. “I’ve lost so much sleep.”

Some NIH staff are also alarmed by the move. “It cuts off a critical training pathway for clinical scientists,” a scarce part of the biomedical research workforce, says Jenna Norton, an NIH program officer who has protested many recent changes at the agency and is speaking in her personal capacity. But others argue the career development awards—NIH makes about 90 of these K grants each year—are not the best use of the agency’s budget, as they only provide enough funding for small, exploratory clinical studies that lack statistical power.

NIH this week announced the ban on clinical trial funding for K award recipients as part of an effort to simplify the grants meant to help early- and midcareer researchers who have mentors become independent investigators. Starting in July 2027, the awards can still be used to gain clinical trial experience in a study led by a mentor, but not to directly fund any clinical trials. The change is intended “to better align K awards with their primary purpose of career development,” the notice says. If a K recipient wants to run a trial, they must find other funding from NIH or sources such as foundations.

The agency is seeking input on “additional strategies” to support budding “scientists who can lead rigorous clinical trials,” the notice adds. Another notice seeks input on two options: a special type of clinical research grant that would be paired with a K award, which would still pay for the person’s salary and mentoring; or a two-stage award broken into a mentored portion, then a research grant. Both would allow new clinical investigators to conduct “studies with greater complexity, rigor, and statistical power,” the NIH press office said in response to a request for comment.

Some NIH observers remain concerned that jettisoning the K clinical trial awards is a mistake. Jeremy Berg, former director of NIH’s basic research institute (and former editor-in-chief of Science), has examined K grants made from 2010 to ’20 to see how many awardees went on to win an R01, the main type of NIH research grant. He found that about 23% later received an R01 for a clinical trial—more than twice the frequency as in the much larger group of nonclinical trial K awardees.

“If I were looking at this [program], I would be pretty close to dancing on the table that this was a huge success,” Berg says.

Berg also worries that marginalized groups and certain research topics may be disproportionately harmed. Almost two-thirds of those receiving the K clinical trial awards were women, and much of the funded research centered on studies on HIV, women, and behavioral research, according to a grant wording analysis conducted by Berg. Those areas “are all things that [President Donald Trump’s] administration is generally not very enthusiastic about,” he says.

Michael Lauer, who stepped down as NIH’s extramural research chief in early 2025, is among those who support the new NIH policy. He says the K clinical trial awards came up 10 years ago when NIH officials took steps to address what they described as too many “small, crappy clinical trials.” They changed standards for NIH trial funding in multiple ways but in the end left those particular K awards alone.

But Lauer’s glad to see them go now. Even small studies with human volunteers “should be directed by experienced people,” he says. In his field of heart disease, Laurer notes that he’s always advised early career researchers to get experience with the large, multiskilled teams that conduct major clinical studies and eventually move up to leading trials.

Cervantes responds that K awards still entail oversight by established investigators, but the type of research on getting proven treatments to patients that she conducted is done by smaller teams within real-world clinics and hospitals—not by the big clinical trial organizations Lauer has in mind. “My concern is that this [eliminating the awards] will lead to further attrition” from this part of the clinical researcher pipeline, she says.

NIH is taking comments on ways to support early-career clinical researchers until 30 September.

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Budget cuts and delays leave hundreds of Argentine postdocs in limbo

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From ScienceMag:

Nearly 400 Argentine researchers have been left jobless and uncertain about their future in science after a delay in the process for securing permanent research positions in the country.

The postdoctoral fellows had applied for a permanent career track with the National Scientific and Technical Research Council (CONICET), Argentina’s main science agency, expecting to hear back by the end of this year. But in April, CONICET announced the results would not be released until August 2027 because of a backlog of applications and cuts in the number of positions available. When the postdocs’ fellowships ended on 31 July, they were left without salaries and scrambling to find work.

The postdocs staged protests around the country on Wednesday, including in downtown Buenos Aires, where they wore Argentina’s national soccer jerseys. “We’re trying to make as much noise as possible,” says limnologist María Luz Padulles of the National University of Moreno. But some are considering moving out of research or seeking opportunities abroad instead. “I’d like leaving the country to be a choice, not an obligation,” says Pablo López, an astronomer at the National University of Córdoba (UNC).

The case is one of the most visible consequences of the budget cuts implemented by President Javier Milei’s government, which have caused the loss of thousands of jobs in the science sector. Public investment in science and technology is at its lowest level in 54 years.

In Argentina, a CONICET postdoc position is a 3-year fellowship that cannot be extended or repeated. For those reaching its end, the agency holds annual calls for permanent positions. But the number of positions on offer has dropped from 850 to 400, and in 2024 the call was suspended entirely, causing a backlog of applications that led CONICET to postpone this year’s decisions.

The affected postdocs have called on CONICET to extend their fellowships until the appointment process is complete. Five of the agency’s eight board members supported doing so, says Jorge Aliaga, a board member representing Argentina’s universities. But CONICET’s government-appointed head, Daniel Salamone, disagreed, Aliaga says: “If he is not convinced, the money is not there.”

In the meantime, many of the scientists are trying to stay within the scientific system as they wait to find out whether their application is successful. Elvira Casagranda, a biologist at the National University of Tucumán, managed to secure a 3-month “half fellowship” to collaborate on a project sustained through international funding. Although family reasons make her reluctant to leave Argentina, she is considering applying for a position in Brazil. “Every day I cry for a while,” Casagranda says.

Padulles was somewhat luckier: Her university took over her salary so her research on water quality in the Reconquista River Basin would not be interrupted. But one of her colleagues, also a postdoctoral fellow, accepted an offer to continue her career in Brazil. “She got tired of the way the scientific system treated her,” Padulles says. “I decided to stay and keep trying.” Others have taken up teaching positions or are considering doing so.

Argentina already has far fewer researchers per capita than the average for Organisation for Economic Co-operation and Development countries, says Diego Hurtado, a senior CONICET researcher at the National University of San Martín. Milei’s policies are making the situation worse, he says. Since he came to power in 2023, more than 6400 jobs have been lost in the sector—including more than 2000 at CONICET—through layoffs, voluntary buyouts, and resignations. “The idea seems to be that the fiscal deficit can be solved by expelling scientists. It’s a bit absurd,” Hurtado says. This year’s government spending on science and technology is projected to fall to about 0.14% of gross domestic product, equivalent to roughly $1.2 billion—the lowest level in decades. “The destruction of Argentine science is being planned,” says Rocío Sánchez, a biologist at UNC and one of the researchers in limbo.

There is still some hope for Sánchez and his fellow postdocs: Congress is expected to vote on extra-budgetary allocations in October and could decide to extend the CONICET fellowships, Aliaga says, though such an outcome is far from certain. An extension would cost about $2 million. Compared with the agency’s $400 million annual budget, that’s a small price to pay, he adds: “From the country’s point of view, these are very valuable people.”

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I had to unplug from AI to rediscover my love of writing

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From ScienceMag:

When I recently opened up my word processor to start to compose a manuscript, I felt like I was learning to write again. I had to find the right words, the right style, the right voice. Every possible phrasing was crowding my mind, and the effort required to select and combine them made the task intimidating. I was frozen, overwhelmed. It had been more than a year since I had put in that kind of effort myself—because I had outsourced my writing to generative artificial intelligence (AI).

I had loved writing since I was a teenager. I grew up at the beginning of the internet, when blogs proliferated, and I spent most of my time after school writing posts on my own blog. My trivial stories about video games found a modest audience, but writing and being read brought me immense joy.

As an adult, I ended up as a radiologist—specializing in images, not words—but I still took pride in my writing style. Composing my first academic paper took dozens of hours, as I meticulously selected each word—then translated it, as I am not a native English speaker. I felt confident about the initial draft, but when I sent it to my supervisor he handed it back full of red notes, asking me to simplify, rephrase, reorganize. I realize now that he was helping me improve—but at the time, I felt frustrated I had not succeeded on my first try, and overwhelmed by the work of rewriting.

In the following months, however, I found a solution. I realized I could run my drafts through ChatGPT and any rough edges would instantly disappear. My writing process became much more efficient and my adviser was ecstatic about my progress. The red notes became rarer, and their content laudatory. Come 2025—the most successful year of my young career—and everyone was impressed by my scientific output. I could write the draft of a manuscript in hours, and reviewers commended the quality of my writing.

Except I did not author most of those texts. I came up with the idea, the experiments, the data, and the references. But the process was devoid of writing, and really, of me. Relying on AI felt like being in a tunnel, seeing nothing but the light of the flawless final submission at the end, oblivious about how I got there. I did not get to experience the pleasures of unraveling a thread of thought, encountering a challenge along the way, overcoming it with a creative twist, rephrasing, rereading, imagining the reader’s reaction. By the end of the year, I had a sense of vertigo: What if I could not write without assistance anymore?

I also began to realize AI was making me a less critical scientist. I would feed the chatbot my results and ask it to write the story I wanted to tell, and it would always oblige, never questioning my ideas. And although the papers it produced seemed good on the surface, once I started to actually read them in detail, I found they often said very little. I realized writing is a process that not only transforms ideas into text, but also allows the writer to continuously question and refine those ideas. The AI never did this.

So when I was invited to contribute an opinion piece for a journal a few months ago, I decided to close my internet browser and open my word processor again. I felt overwhelmed at first. But once I made peace with spending hours instead of minutes to generate a draft, muscle memory kicked in and I found myself having the best time of my academic life in more than a year. Once again, I experienced the bliss of finding the appropriate phrasing or the perfect figure of speech. I felt more engaged with my work, as I could retrace its authorship, from start to finish. And, to my great surprise, writing in my own voice brought an unexpected benefit: I felt proud of my pieces again. I showed them to my family and colleagues, and their reactions felt special. They were experiencing human emotions to human words.

When I showed my next manuscript to my supervisor, the red notes came back. But this time, I took them as a compliment. They were a marker of my own voice—and a reminder of the progress I could still make.

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Visa stress clouds my life as a scientist and parent

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From ScienceMag:

When I picked up my 8-year-old daughter from school on a sunny afternoon in April, I found her excitedly planning out her future. She and her best friends had chosen which high school they wanted to attend and had already decided to join the art club and gymnastics classes. Sometimes they would walk back home together, she said. I smiled and eagerly listened to her excitement. But another thought sat in the back of my mind—one I could not burden her with: I didn’t know whether we would still be in the country by then.

I came to the United Kingdom 5 years ago as a postgraduate, keen to begin my Ph.D. research, yet unsure whether the move was permanent. My wife and I—originally from Pakistan—had been living in Saudi Arabia for years and had felt settled there. I worried about leaving behind a permanent job and close friends to pursue my academic dreams. But within a few months we started to feel that we could make a life here. We made new friends, volunteered with the community, explored beautiful Scotland, and experienced a surprising level of warmth.

My career in the U.K. also proved rewarding. After my Ph.D., I stayed to do a postdoc. I have published papers, mentored students, and worked on exciting projects. And like many other researchers, I initially believed if I excelled in my field and produced good science, stability in life would be certain.

But all along, there has been a constant uncertainty invisible to most of those around me—even my own children. Our future here depends not only on me excelling as a researcher, but also on whether we can afford to remain in the U.K. legally.

Every couple years, my family faces another cycle of visa renewals and the huge associated costs. Over the past 6 years we have spent an eye-watering amount just to continue to live and work in the country, and we are now trying to save nearly £13,000 for the next stage of renewal in 6 months.

U.K. visas are exceptionally expensive compared with many other countries. That’s partly because applicants must pay a health care surcharge up front for the full length of the visa; this alone can rack up thousands of pounds. And although some U.K. universities cover visa costs for the main applicant, this is becoming increasingly rare, and support typically doesn’t extend to children or partners.

For early-career researchers who must also contend with the uncertainty of short-term contracts, this burden can be overwhelming. Financial considerations seep into my everyday life: Before making many ordinary purchases, like buying a bike for my kids, I instinctively think about whether I should instead save that money for future visa costs.

These considerations also dampen my excitement about my research. When friends and co-workers discuss future projects and fellowship applications, they naturally focus on the science and the research questions. So do I. But at the same time, I worry I might not be able to remain to see those plans through. When my supervisor tells me a grant has been successful, my first thought is not about the science, but about whether the project funding covers visas.

The situation is difficult to discuss with family back in Pakistan. Many of them imagine we are living an easy, financially secure life. I feel guilty even thinking about asking for financial help when they believe I should instead be supporting them.

Meanwhile, my children are building lives here without fully understanding the uncertainty their parents quietly carry. Their friends are here. Their schools and sports clubs are here. Their understanding of home is increasingly tied to this country.

When I talk to other international early-career researchers, I’m struck by how many silently face similar worries. Yet these concerns rarely come up in everyday conversations. I wish they were discussed more openly so colleagues and institutional leadership better understood the pressures we are under.

Most days, I continue as normal. I go to the lab, attend group meetings, write papers, and come home to be with my kids. From the outside, I probably look like any other young researcher trying to build a career in academia. But inside, my calculations are constantly running in the background, as I try to ascertain whether we will be able to stay long enough for the future my daughter has already started to imagine.

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Paying attention to awkward silences has made me a better teacher and collaborator

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From ScienceMag:

A few years ago, I left a faculty meeting feeling reassured by how smoothly the conversation had gone. We discussed a project that would bring psychologists like me together with speech pathologists and health researchers to evaluate a rehabilitation program for stroke and other neurological conditions. Around the table, everyone used the same terminology and the discussion moved efficiently from one idea to the next. I took that fluency as evidence that we understood one another. But when the time came to translate our ideas into actual research plans, I was surprised to realize we had been talking past one another. We had mistaken shared jargon for shared understanding.

Early in my career, I’d absorbed the lesson that fluency equals comprehension. In academia, we are trained to value quick answers, polished explanations, and intellectual confidence. As a graduate student, I often felt pressure to fill the silent gap after
the professor had asked a question. Once, a classmate stopped me in the hallway and said, “You always seem to know exactly what to say.” At the time, I took it as a great compliment. I began to equate quick, confident responses with understanding itself.

Years later, that meeting with my colleagues made me question my assumptions. And I noticed the same pattern in a workshop I was teaching for psychology and exercise science students. When I posed theoretical questions, the psychology students responded quickly and confidently. But I saw several exercise science students furrowing their brows and glancing uncertainly at one another. When the discussion shifted to athletes’ real-world performance, the dynamic reversed. Exercise science students spoke fluently about muscle fatigue, progressive overload, and recovery cycles, whereas the psychology students became quieter, shifting in their seats as they tried to follow.

Each group sounded highly competent—until they had to explain their reasoning to the others. When that happened, the room filled with a silence that made me anxious. My instinct was to intervene immediately—to clarify, rephrase, provide structure, rescue the conversation before it became uncomfortable.

Midway through the workshop, I changed my approach. I asked students to explain a core concept from their field in a way anyone could understand. At first, almost no one spoke. Then came tentative attempts. Half-finished explanations. Students began to question each other as they revised their wording. One exercise science student abandoned technical language about recovery cycles and simply explained, “If the body never gets time to adapt, performance eventually drops instead of improving.” The psychology students began to connect the idea to stress, burnout, and cognitive overload. Gradually, the energy in the room changed. The students were no longer looking to me for confirmation. They were negotiating meaning together.

This experience changed how I approached my own collaborations. At a later meeting for the rehab project, I noticed our group repeatedly using the word “engagement” as though we all meant the same thing. I paused the discussion and asked each person to describe the term’s meaning within their field. For one colleague, engagement referred to clinicians’ verbal participation during training sessions. For another, it referred to patients’ adherence to their treatment plan. As an educational psychologist, I thought of engagement largely in terms of what was happening internally—whether people were genuinely motivated and actively processing what they were learning. What initially sounded like agreement was several distinct meanings hidden within a shared term. The conversation slowed once we realized this, but we ultimately designed a better research project because we finally understood where our perspectives diverged.

I teach, collaborate, and listen differently now. In my classes, I have become more comfortable allowing pauses, uncertainty, and unfinished explanations to remain in the room long enough for students to work through them and confront what they really mean. In interdisciplinary collaborations, I spend less time trying to move conversations efficiently toward agreement and more time paying attention to the awkward silences that indicate understanding has begun to break down. When a conversation slows, I no longer see it as a sign of failure. I see it as evidence that we are finally doing the harder work of understanding one another.

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How I use AI in my geology research without losing the science I love

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From ScienceMag:

The maps and colors of my geology work were blurring together in my mind, and it was time for a break. I opened my email and saw the long-awaited result of my application to the 2025 U.S. National Science Foundation (NSF) Graduate Research Fellowship Program: honorable mention. When I checked the awardee list, I noticed an overwhelming trend: computer science, quantum physics, machine learning. Even the few natural science proposals awarded funding seemed more focused on the application of artificial intelligence (AI) than on the actual research question. At first, the tilt toward AI felt like a betrayal, as if the fundamental science was no longer “good enough.” But I have since come to see it as an opportunity.

In my undergraduate studies, I had seen how AI was looming large over our future careers, but it hadn’t made much of an appearance in my earth sciences training. I reveled in studying the tangible things around me, quite unlike the layers of abstractions in AI black boxes. I never envisioned myself using those sophisticated algorithms. When I drafted my fellowship proposal, I followed in the strong tradition of observation, mapping, and statistics.

Then, new federal directives prioritized AI research. Those students who had anticipated the shift were successful. Those of us who worked by the old rules found ourselves left behind.

As I began my Ph.D. not long afterward, my adviser—a computational geophysicist who pushed me to think beyond the tangible into the abstract—and I began to collaborate with an aerospace engineering professor who excelled in the use of machine learning. I began to realize those tools were really an application of the math I already knew. Maybe I, too, could learn to use them.

At first I looked for university courses, but most were locked behind heavy computer science prerequisites. My adviser encouraged me to look beyond the classroom; moving from coursework instruction to experiential learning is key to the Ph.D. journey, after all. I started to design projects to “learn by doing,” but I still needed a guide to get started. That’s when I realized: What better tool to introduce me to AI than AI itself? I began my dive into deep learning by using a large language model to guide me as I installed and trained a machine learning model to tackle an interesting problem in earth sciences.

It was jarring to say the least. Rather than enjoying the beauty of the natural world as I gathered data, I was wading through the harsh computer glare to install deep learning environments. You can walk up to a sinkhole, kick a rock over the edge, and dangle your legs as you enjoy your bagged lunch, but I have yet to do that with any part of an AI model. Every error code and missing dependency added to the frustration.

Slowly but surely, however, the pieces began to come together. In a matter of weeks I had my first working deep learning model, which brought insights I never expected. Before long, I was developing AI tools to identify various geologic landforms from publicly available topographic data. Rather than spending weeks mapping, I delineated the very same structures in a few afternoons. Automation not only eliminated the sampling bias of manual mapping, but it freed me to ask deeper questions about the thousands of natural features I was finding.

It’s now been just over a year since I received that email from NSF. That feeling of disappointment has faded, and I feel empowered. There is still much to learn, but I have gained the confidence to learn tools I never imagined I would use. Most important, I have learned that this evolving world doesn’t have to be the end for the science I love. I can embrace the blocks of code without losing the rocks that brought me here in the first place.

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How I hold space for crying in the workplace

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From ScienceMag:

As my colleague stood there crying in my office, telling me the details of how he had just been forced to retire, I was mortified. I was an assistant professor a year into my tenure, and he was “a name”—someone well known in the field. I felt bad for him. But as a noncrier, I was unsure where to look as he sobbed, or what to say to “fix” it. Like most men of my generation, I was socialized early on to stay composed and just deal with a problem; crying was not acceptable, because it would be perceived as weak and vulnerable. I was shocked that my colleague was allowing his tears to fall in front of me, and the panic and confusion I felt stayed with me long after.

The experience with the retiring faculty member was 2 decades ago. But I found myself in similar situations years later, in the context of mentoring conversations where I was the more senior person. In one, an early-career colleague asked about a possible career pivot. I offered what I thought was practical advice: She wasn’t ready yet. The conversation moved on. But 40 minutes later she became visibly upset and began to cry. As I sat there trying to resume the conversation, I felt the same flicker of discomfort and uncertainty about how to respond that I had experienced years earlier.

Around the same time, a colleague injured his hand in a door after a meeting in my office. He was in pain and visibly upset. This time, the tears did not unsettle me at all; they made sense as part of a problem I knew how to address. I checked he was OK and moved directly to the institutional safety procedures I had been trained to follow. Afterward, though, I faced an unsettling question: Why was my response so different when the tears reflected physical pain, not emotional distress?

Thinking about the two situations, I realized I needed to allow more space for emotional crying, and to be open to what it signaled. The cues that my early-career colleague was upset had been there—hesitation, small shifts in tone, moments of uncertainty. The crying simply made visible what had already been building. I began to wonder whether I had delivered my message too directly and what I might have done differently to lessen the emotional impact it had on her. And instead of seeing her crying as a problem, I could have taken it as an opportunity to better understand her as a person.

In academia we are rarely trained for moments when a colleague becomes upset in front of us. But this is no less important than knowing how to deal with cries of physical pain. Now, when someone I work with starts to cry, I know not to brush it aside or immediately move toward making the problem go away. I try to slow the conversation and allow space for the reaction. I pay closer attention to what led up to that moment, including what I said and how I said it. Only afterward do I return to the issue that prompted it.

I still feel traces of my earlier discomfort. But I no longer see it as something to eliminate, because the stakes are high during these moments. They can lead to a more open and honest exchange and signal an important shift in the conversation—from routine discussion to something more consequential. Tears can mark the points in work life where decisions matter most: whether to stay or leave, whether progress is enough, whether a path still feels viable.

I sometimes think back to that early encounter in my office. Today, I would tell my junior self that I don’t need to fix the person who is crying, or to run away from the emotional reaction. The tears should remind me that my job as a faculty member and colleague is not just about leading for better performance, but also about embracing the whole person behind the moment.

Sometimes, the most useful thing to do is not to stop the tears, but to notice what they might be telling us about a person—and about the work lives we share.

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To my younger self, the overwhelmed new professor: You will make it

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From ScienceMag:

I see you there, months into your new professor position. Sitting in your empty office, staring out the window, crying. The sunlight hits your face. You are wondering how you are going to make it through. Are you good enough? Smart enough? You are tired. You feel stretched to the point of breaking. The waves of emotions are rolling over you. I am here, your future self, to tell you that you are enough. You don’t break. You put one foot in front of the other, and you not only make it through, but you have fun.

Yes. The university gave you an empty office, an empty lab, a budget number, and wished you luck. They will check back in 5 years to decide whether you meet their ambiguous threshold for getting tenure and keeping your job.

They assume somehow your Ph.D. has prepared you to equip an empty lab. Who knew there were 1 million options for something as simple as an oven! All you ever did was use the oven in your adviser’s lab. You have no idea what features you need. And what about pipettes? Balances? The water system? It is overwhelming.

And of course, because you designed and executed a successful Ph.D. research project, they assume you know how to develop courses and teach them. And write grant proposals. And manage budgets. And mentor students.

I am here to tell you that you figure it out. The Ph.D. taught you how to solve problems and persevere. To evaluate and adjust. You do just that.

A turning point comes early one morning. You are exhausted after pulling your second all-nighter to get a proposal submitted. Your husband and 2-year-old are sleeping upstairs. At this moment, you decide that if this job requires you to give up sleep, give up your health, give up time with your daughter, ruin your marriage, it is not worth it.

You draw boundaries. You determine what you are willing to give, and decide that if it is not enough then, when they check back in 5 years, they can fire you. You will find something else to do. Anything that won’t require you to self-destruct.

You stop working on the weekends. You start picking your kid up from day care. You give her your full attention in the evening. You get 8-plus hours of sleep every night. With time, you regain your fitness. You make friends and find joy exploring the mountains with them. You have a second kid. You start reading fiction again. You and your husband get a home-cooked meal on the table roughly 70% of the time. You consider this a success.

The first class you develop overwhelms the students and buries them in homework. The evaluations are abysmal. But you recalibrate your expectations and gradually get the hang of teaching. Your evaluations steadily improve. At some point a student tells you your class is their favorite.

Your initial proposals don’t get funded, and you are certain if you get any more constructive criticism of your ideas and writing, you will explode. But eventually you have more funded projects than you feel comfortable managing. Your work takes you to fascinating places: a permafrost bog in Alaska, rice fields in Cambodia, and the Peruvian rainforest. You never enjoy managing budgets, but you figure it out well enough to keep your research program running.

You take what you know about parenting two dramatically different kids and apply it to mentoring students. You try to support each one by recognizing their unique motivations and needs. It is not without challenges. But most of them graduate and go on to do amazing things. One even nominates you for a mentoring award, which you win.

You realize the flexibility of the job is a godsend for coping with the expected and unexpected interruptions of parenting. You enjoy your life both at work and outside it.

In 5 years, the university checks in again. You manage to meet their mushy expectations. You get tenure and keep your job. What you gave within your boundaries was enough. And, even though you had no idea what kind of oven you needed, the one you bought for the lab has performed perfectly well. The water system is another story.

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Instead of banning AI, I made a classroom contract with my students

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From ScienceMag:

I sat in the heavy silence of my office, waiting for an undergraduate computer science student whose academic path I was about to derail. I had noticed a glaring red flag in his final report for my class: It answered questions I hadn’t even asked. It was clear that artificial intelligence (AI) had invented details to fill the gaps. My student had presented a chatbot’s output as his own work, and I could not accept that. But the meeting that was about to take place made me realize a flat ban on AI use was not the answer.

I had worked with earlier forms of AI for almost 2 decades. After a Ph.D. in computer science, I moved to the private sector, teaching machines to catch financial fraudsters and optimize marketing expenditures. Yet, I never left the classroom, teaching two courses at a local university. I felt confident I was preparing my students for the future.

My dual career in tech and academia felt safe and rewarding. Then generative AI rewrote the playbook. Suddenly, I wasn’t just designing software to assist humans; I was watching technology threaten to bypass human input entirely. AI could now generate complex code in seconds. A machine was claiming intellectual territory I had thought was mine.

For the first time, my career path felt like an anxious race to outrun my own obsolescence. So I adapted, spending hours integrating new generative AI tools into my daily routine to summarize emails, draft documents, and automate tedious coding tasks. It wasn’t just professional upskilling; it was self-defense. But in the classroom, I maintained a strict no-AI policy.

At this crossroads, the student I intended to fail walked into my office. He didn’t try to hide that he had used AI to generate much of his assignment. Instead, he admitted his anxiety. He felt that mastering these tools was essential for his future career, yet he had no idea how—or even whether—he was allowed to use them. The more we talked, the more I saw my own doubts mirrored in him. My indignation gave way to a nagging sense of hypocrisy. I used AI daily—how could I expect my students to avoid it entirely? Outlawing AI had felt comfortable—a neat wall built to preserve a familiar order. I hid behind rigid rules because I was terrified to admit the ground beneath my academic feet had shifted.

Instead of failing the student, I gave him a chance to rewrite his report. But as the office door closed behind him, the full weight of our encounter settled on me. I realized that instead of building higher walls, I needed to dismantle them.

At my next lecture, I projected a blank screen onto the wall and invited my students to negotiate an “AI contract.” At first, they were guarded, but as I shared my own experience with AI, the classroom dynamic shifted. We stopped playing cat and mouse and became partners. Students opened up about their AI use and began to ask questions. After some debate, we drew a line separating mechanical churning from actual thinking. Automating repetitive tasks or literature searches was acceptable. Bypassing critical analysis was not: System architecture and design would remain strictly human tasks.

I have since made cocreating an AI contract a standard part of my classes. I moderate the discussion, but a consensus always emerges naturally. Although the terms differ slightly for each class, the contracts all insist that critical analysis should remain human.

The challenge of AI has prompted me to make other changes as well. Since the release of ChatGPT, student reports have grown steadily longer. I often felt I was evaluating a machine’s capacity for volume, not a student’s capacity for insight. So, I now limit written papers to two pages and base students’ grades more heavily on oral discussions of their work, which allows me to probe their thought process and challenge their decisions.

After doing these things for four semesters, I’ve seen benefits beyond reducing AI use. Defining the AI contract has become a powerful icebreaker, creating a collaborative atmosphere. The short reports force students to synthesize their knowledge, and oral defenses sharpen their argumentation skills.

Our real duty as teachers has always been challenging students to think. The task now is finding ways to engage them in the painful, essential work of critical thinking in a world where machines are willing to do it for them.

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