Author: David Orenstein | The Picower Institute for Learning and Memory<\/p>\n
Starting on New Year\u2019s Day, when many people were still clinging to holiday revelry, scores of students and faculty members from about a dozen partner universities instead flipped open their laptops for MIT\u2019s\u00a0Quantitative Methods Workshop<\/a>, a jam-packed, weeklong introduction to how computational and mathematical techniques can be applied to neuroscience and biology research. But don\u2019t think of QMW as a \u201ccrash course.\u201d Instead the program\u2019s purpose is to help elevate each participant\u2019s scientific outlook, both through the skills and concepts it imparts and the community it creates.<\/p>\n \u201cIt broadens their horizons, it shows them significant applications they’ve never thought of, and introduces them to people whom as researchers they will come to know and perhaps collaborate with one day,\u201d says\u00a0Susan L. Epstein<\/a>, a Hunter College computer science professor and education coordinator of MIT\u2019s Center for Brains, Minds, and Machines, which hosts the program with the departments of Biology and Brain and Cognitive Sciences and The Picower Institute for Learning and Memory. \u201cIt is a model of interdisciplinary scholarship.\u201d<\/p>\n This year 83 undergraduates and faculty members from institutions that primarily serve groups underrepresented in STEM fields took part in the QMW, says organizer\u00a0Mandana Sassanfar<\/a>, senior lecturer and director of diversity and science outreach across the four hosting MIT entities. Since the workshop launched in 2010, it has engaged more than 1,000 participants, of whom more than 170 have gone on to participate in MIT Summer Research Programs (such as\u00a0MSRP-BIO<\/a>), and 39 have come to MIT for graduate school.<\/p>\n Individual goals, shared experience<\/strong><\/p>\n Undergraduates and faculty in various STEM disciplines often come to QMW to gain an understanding of, or expand their expertise in, computational and mathematical data analysis. Computer science- and statistics-minded participants come to learn more about how such techniques can be applied in life sciences fields. In lectures; in hands-on labs where they used the computer programming language Python to process, analyze, and visualize data; and in less formal settings such as tours and lunches with MIT faculty, participants worked and learned together, and informed each other\u2019s perspectives.<\/p>\n And regardless of their field of study, participants made connections with each other and with the MIT students and faculty who taught and spoke over the course of the week.<\/p>\n Hunter College computer science sophomore Vlad Vostrikov says that while he has already worked with machine learning and other programming concepts, he was interested to \u201cbranch out\u201d by seeing how they are used to analyze scientific datasets. He also valued the chance to learn the experiences of the graduate students who teach QMW\u2019s hands-on labs.<\/p>\n \u201cThis was a good way to explore computational biology and neuroscience,\u201d Vostrikov says. \u201cI also really enjoy hearing from the people who teach us. It\u2019s interesting to hear where they come from and what they are doing.\u201d<\/p>\n Jariatu Kargbo, a biology and chemistry sophomore at University of Maryland Baltimore County, says when she first learned of the QMW she wasn\u2019t sure it was for her. It seemed very computation-focused. But her advisor Holly Willoughby encouraged Kargbo to attend to learn about how programming could be useful in future research\u00a0\u2014 currently she is taking part in research on the retina at UMBC. More than that, Kargbo also realized it would be a good opportunity to make connections at MIT in advance of perhaps applying for MSRP this summer.<\/p>\n \u201cI thought this would be a great way to meet up with faculty and see what the environment is like here because I\u2019ve never been to MIT before,\u201d Kargbo says. \u201cIt\u2019s always good to meet other people in your field and grow your network.\u201d<\/p>\n QMW is not just for students. It\u2019s also for their professors, who said they can gain valuable professional education for their research and teaching.<\/p>\n Fayuan Wen<\/a>, an assistant professor of biology at Howard University, is no stranger to computational biology, having performed big data genetic analyses of sickle cell disease (SCD). But she\u2019s mostly worked with the R programming language and QMW\u2019s focus is on Python. As she looks ahead to projects in which she wants analyze genomic data to help predict disease outcomes in SCD and HIV, she says a QMW session delivered by biology graduate student Hannah Jacobs was perfectly on point.<\/p>\n \u201cThis workshop has the skills I want to have,\u201d Wen says.<\/p>\n Moreover, Wen says she is looking to start a machine-learning class in the Howard biology department and was inspired by some of the teaching materials she encountered at QMW \u2014 for example, online curriculum modules developed by Taylor Baum, an MIT graduate student in electrical engineering and computer science and Picower Institute labs, and\u00a0Paloma\u00a0S\u00e1nchez-J\u00e1uregui<\/a>, a coordinator who works with Sassanfar.<\/p>\n Tiziana Ligorio<\/a>, a Hunter College computer science doctoral lecturer who together with Epstein teaches a deep machine-learning class at the City University of New York campus, felt similarly. Rather than require a bunch of prerequisites that might drive students away from the class, Ligorio was looking to QMW\u2019s intense but introductory curriculum as a resource for designing a more inclusive way of getting students ready for the class.<\/p>\n Instructive interactions<\/strong><\/p>\n Each day runs from 9 a.m. to 5 p.m., including morning and afternoon lectures and hands-on sessions. Class topics ranged from statistical data analysis and machine learning to brain-computer interfaces, brain imaging, signal processing of neural activity data, and cryogenic electron microscopy.<\/p>\n \u201cThis workshop could not happen without dedicated instructors \u2014 grad students, postdocs, and faculty \u2014 who volunteer to\u00a0give lectures,\u00a0design and teach hands-on computer labs, and meet with students during the very first week of January,\u201d Saassanfar says.<\/p>\n The sessions surround student lunches with MIT faculty members. For example, at midday Jan. 2, assistant professor of biology Brady Weissbourd<\/a>, an investigator in the Picower Institute, sat down with seven students in one of Building 46\u2019s curved sofas to field questions about his neuroscience research in jellyfish and how he uses quantitative techniques as part of that work. He also described what it\u2019s like to be a professor, and other topics that came to the students\u2019 minds.<\/p>\n Then the participants all crossed Vassar Street to Building 26\u2019s Room 152, where they formed different but similarly sized groups for the hands-on lab \u201cMachine learning applications to studying the brain,\u201d taught by Baum. She guided the class through Python exercises she developed illustrating \u201csupervised\u201d and \u201cunsupervised\u201d forms of machine learning, including how the latter method can be used to discern what a person is seeing based on magnetic readings of brain activity.<\/p>\n As students worked through the exercises, tablemates helped each other by supplementing Baum\u2019s instruction. Ligorio, Vostrikov, and\u00a0Kayla Blincow<\/a>, assistant professor of biology at\u00a0the University of the Virgin Islands, for instance, all leapt to their feet to help at their tables.<\/p>\n At the end of the class, when Baum asked students what they had learned, they offered a litany of new knowledge. Survey data that Sassanfar and\u00a0S\u00e1nchez-J\u00e1uregui\u00a0use to anonymously track QMW outcomes, revealed many more such attestations of the value of the sessions. With a prompt asking how one might apply what they\u2019ve learned, one respondent wrote: \u201cPursue a research career or endeavor in which I apply the concepts of computer science and neuroscience together.\u201d<\/p>\n Enduring connections<\/strong><\/p>\n While some new QMW attendees might only be able to speculate about how they\u2019ll apply their new skills and relationships, Luis Miguel de Jes\u00fas Astacio could testify to how attending QMW as an undergraduate back in 2014 figured into a career where he is now a faculty member in physics at the University of Puerto Rico Rio Piedras Campus. After QMW, he returned to MIT that summer as a student in the lab of neuroscientist and Picower Professor\u00a0Susumu Tonegawa<\/a>. He came back again in 2016 to the lab of physicist and Francis Friedman Professor\u00a0Mehran Kardar<\/a>. What\u2019s endured for the decade has been his connection to Sassanfar. So while he was once a student at QMW, this year he was back with a cohort of undergraduates as a faculty member.<\/p>\n Michael Aldarondo-Jeffries<\/a>, director of academic advancement programs at the University of Central Florida, seconded the value of the networking that takes place at QMW. He has brought students for a decade, including four this year. What he\u2019s observed is that as students come together in settings like QMW or UCF\u2019s McNair program, which helps to prepare students for graduate school, they become inspired about a potential future as researchers.<\/p>\n \u201cThe thing that stands out is just the community that\u2019s formed,\u201d he says. \u201cFor many of the students, it’s the first time that they’re in a group that understands what they’re moving toward. They don\u2019t have to explain why they\u2019re excited to read papers on a Friday night.\u201d<\/p>\n Or why they are excited to spend a week including New Year\u2019s Day at MIT learning how to apply quantitative methods to life sciences data.<\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"