Official Story
Yael Niv received her MA in Psychobiology from Tel Aviv University and her PhD in Computational Neuroscience from the Hebrew University in Jerusalem, having conducted a major part of her thesis research at the Gatsby Computational Neuroscience Unit in UCL. She is currently a professor at Princeton University, at the Psychology Department and the Princeton Neuroscience Institute. Her lab studies the neural and computational processes underlying reinforcement learning and decision making, with a particular focus on how the cognitive processes of attention, memory and learning interact in constructing task representations that allow efficient learning and decision making. She is co-founder and co-director of the Rutgers-Princeton Center for Computational Cognitive Neuropsychiatry, where she is applying ideas from reinforcement learning to questions pertaining to psychiatric disorders within the new field of computational psychiatry.
Unofficial Story
Yael grew up in Israel, thinking she would work in a Kibbutz and be a farmer. Two weeks volunteering in a Kibbutz in high school convinced her otherwise. She nixed her second career aspiration, to be a teacher, because she was worried about being surrounded by burnt out colleagues. Instead, after her mandatory military service in which she learned software programming -- and decided she hated it and would never do it again -- she married both her passions for nature and teaching and became a guide in the Society for Preservation of Nature. That paid pennies, so she also applied for part time jobs in software programming, thinking she would do that only for 6 months, to save up some money. Amazingly, she discovered that with the right colleagues, even programming can be fun. When she found out that in university she has to commit to one discipline from the start, she balked. Luckily she was accepted (after being rejected the previous year) to an interdisciplinary program at Tel Aviv University, that allowed her to create a US-college-like experience, and to study computational neuroscience before there were any BA programs even remotely related to the brain. Through this direct-to-MA program, she joined a lab in computer science (housed in the medical school) and had a second advisor in psychobiology. Her MA was in the latter, not the former, because psychology demanded fewer mandatory courses. Her MA paper was reviewed in most journals in the field, and rejected from all. In the end it was published in Adaptive Behavior, at a point where she could no longer look at it. Having lived with her high-school sweetheart for years at this point, they decided to get married, but then Yael joined a neuromorphic engineering summer course (because her lab mates told her it was super fun, and involved a lot of volleyball, which she did not, and still does not, play). From the transatlantic distance she suddenly realized her partner was not supportive of her career, so she ditched him a month before the wedding. Unrelated (really!), but in the same course, she met the person who would later become her soulmate. Planning to do her PhD in Zurich where he was a researcher, she finally quit her temporary 6-month-max programming job after 7 years. She quite liked that job, and was leading a team of 10 people (kinda like a lab). The plan was to get the PhD and then return there and be promoted. Tragically, her partner died unexpectedly 2 years and a month after they met. She almost died too, as a result. But she survived, and diverted her PhD plans to London, which had been his favorite city. She was officially a student at the Interdisciplinary Center for Neural Computational at the Hebrew University in Jerusalem, though in practice spent her time between the Gatsby Computational Neuroscience Unit in London, working with Peter Dayan, and Tel Aviv University, working with Daphna Joel. In London, she met Nathaniel Daw, who was her clandestine boyfriend (since they were in the same lab). Their first paper together led to so many heated arguments, that they decided to avoid collaborating in the future. At Gatsby, Yael also learned for the first time that people doing a PhD consider staying in academia as faculty. Given that Nathaniel applied for faculty positions in USA, she applied for postdocs there too, joining Jonathan Cohen's lab at Princeton. Shortly thereafter, she realized her mistake: she loved Princeton, but would not be able to be faculty there as they do not hire their own postdocs. Luckily (amazingly luckily, really), a faculty position opened a couple of months after she arrived, and she was encouraged to apply as a newbie, and as a long shot. She got the job. It was actually the position that Nathaniel had turned down a year prior, when he chose NYU. And it was contingent on submitting her thesis, which she had not yet finished writing. As junior faculty, by the time she was told to make sure to not create any enemies before tenure, it was too late. Her Israeli outspokenness and strong sense of justice had led her to call out gender bias and make some enemies. Her activism and community organizing also led her to found (with others) the website biaswatchneuro, and the faculty-support mailing list NeuroTeam. Years later, she finds herself a full professor, no longer commuting from NYC as Nathaniel was finally lured to Princeton as a spousal hire, running a lab where she is fortunate to be working with amazing students and postdocs, and raising two boys, at least one of whom shares her oversized sense of empathy (reminding her that gender has nothing to do with personality). In her "other job", spurred by the 2016 election, she co-founded and runs the Princeton Progressive Action Group and is president of the Good Government Coalition of New Jersey. She can talk about these for hours, so don't get her started. It turns out that she is a teacher after all, and yes, some of her colleagues are burnt out, but all in all this is probably the best way to teach in a constantly invigorated environment. She misses home -- her friends, family, and the scenery and hikes of her home country -- terribly.