Past Events

  • Dec
    4

    Michael S. Goodman ’74 Memorial Seminar Series.

    Speaker:Jacob Feldman (Professor Rutgers University - New Brunswick)

    Title: Complexity and information in category and feature learning

    Abstract: In this talk I will present two interrelated projects on category and feature learning, one on the role of complexity in the way we learning categories, and the other on the role of information measures in the way we learn the features that underlie categories. Conceptual complexity is known to impair concept learning, implying a kind of simplicity bias in the way we induce categories from examples. But past studies of complexity have been limited to discrete complexity measures, while the role of complexity in the general case—probabilistically defined categories in continuous feature spaces—is comparatively unexplored. I will discuss a series of experiments in which subjects were presented with probabilistic categories of various levels of intrinsic complexity. As with categories based on discrete features, subjects show a dramatic fall-off in performance with more complex category structures. But what is the right way to measure “complexity?” A comparison of measures suggests that a simple information-theoretic measure of category complexity best fits the data. In the second part of the talk I will discuss how category learning can influence the choice of underlying perceptual features. Category learning is known to induce “categorical perception” effects, in which discrimination performance along category-relevant or informative features measurably improves. But what is the right way to measure “informativeness?” A comparison of measures suggests again that a simple information-theoretic measure—the mutual information between the feature and the category variables—best fits the data. This finding suggests a “rational” basis for categorical perception, in which the precision of perceptual discrimination is tuned to the statistical structure of the environment

    Academic Calendar, University Dates & Events, Carney Institute for Brain Science, Neuroscience, Psychology & Cognitive Sciences
  • Dec
    4
    10:00am - 12:00pm

    CCV Office Hours

    Drop in on the Zoom meeting to ask members of CCV’s High-Performance Computing (HPC) team your questions about using Oscar or any other research computing topics you are interested in.

    Computing, HPC, Research
  • Growing Up in Neuroscience Webinar Series

    “Emotion Regulation and Aging: What I used to think, what I think now, and why”

    Derek Isaacowitz

    Northeastern University

    “How Older Adults Remember Emotional Events”

    Elizabeth Kensinger

    Boston College

    Register  to attend the webinar. Zoom details will be sent after registration.

    About Growing Up in Aging Neuroscience

    Any early-career researcher interested in aging neuroscience across a wide range of domains – including cognition, affect, memory, and everything in between – is invited to participate in Growing Up in Aging Neuroscience (GRAN). Our objective is to provide a setting in which junior researchers considering or pursuing a career in aging neuroscience can learn about the latest developments (and the people behind it). We have brought together world-class researchers across a wide range of career stages (from assistant to full professor) to present their work, as well as share their unique experiences relating to how they became investigators (inspired by the Growing Up in Science series), in hopes of encouraging junior researchers considering or pursuing a career in aging neuroscience.

    GRAN2020 will take place via a virtual and free webinar series in Fall 2020. To receive the zoom invite and passcode for each session, you will need to register for each session. Each session will be 1.5 hours long, with each speaker presenting their research and participating in a Q&A session at the end of each webinar.

    Abstracts

    “Emotion Regulation and Aging: What I used to think, what I think now, and why” (Derek Isaacowitz):

    For basically my whole career, I have been interested in trying to understand the mechanisms underlying older adults’ generally positive affective experience. Based on socioemotional selectivity theory, I spent a number of years investigating age-related positivity effects in attention, and whether these may play a role in older adults’ affective success. This then led to a wider investigation of emotion regulation strategies, including attentional deployment, that may vary by age. While our early work supported the age differences narrative, and sometimes the “older people are better” narrative, our more recent work has found much more similarity than differences among age groups in emotion regulation behavior, both in the lab and in everyday life. I will consider the implications of these findings for my own research trajectory as well as for the field in general.

    “How Older Adults Remember Emotional Events” (Elizabeth Kensinger):

    In this talk, I will first describe some similarities in the ways that young and older adults remember emotional events. In particular, across the adult lifespan individuals show emotional memory enhancements and also emotional memory trade-offs. I will then shift to focusing on ways in which age seems to affect the way that emotional experiences are remembered. In particular, older adults often appear to remember negative events less vividly, and with an increased focus on the silver linings. The negativity of emotional events also can fade over time for older adults. I will briefly describe how an expanded model of emotional memory that incorporates the role of the dorsomedial prefrontal cortex may elucidate these age-related differences.

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research
  • Neural mechanisms of human episodic memory formation
    Host: Dr. Wael Asaad
    Biology, Medicine, Public Health, Research
  •  

    Center for Translational Neuroscience  Special Seminar

     

    “Modeling late-onset neurodegenerative disorders with patient-derived neurons generated by neuronal reprogramming”

     

    Andrew S. Yoo, Ph.D.

    Associate Professor of Developmental Biology

    Washington University School of Medicine in St. Louis

     

    December 3, 2020 at 1:00 p.m.

     

    Zoom link: https://brown.zoom.us/j/97382218771?pwd=OWQ5OURXS0ZrKzVnWVluZW5kZ3AwZz09

    Passcode: 505338

     

    Host: Ashley E. Webb, Ph.D.

    Biology, Medicine, Public Health, CTN, Graduate School, Postgraduate Education, Research
  • Dec
    3

    Speaker:Jacob Feldman (Professor Rutgers University - New Brunswick)

    Title: Complexity and information in category and feature learning

    Abstract: In this talk I will present two interrelated projects on category and feature learning, one on the role of complexity in the way we learning categories, and the other on the role of information measures in the way we learn the features that underlie categories. Conceptual complexity is known to impair concept learning, implying a kind of simplicity bias in the way we induce categories from examples. But past studies of complexity have been limited to discrete complexity measures, while the role of complexity in the general case—probabilistically defined categories in continuous feature spaces—is comparatively unexplored. I will discuss a series of experiments in which subjects were presented with probabilistic categories of various levels of intrinsic complexity. As with categories based on discrete features, subjects show a dramatic fall-off in performance with more complex category structures. But what is the right way to measure “complexity?” A comparison of measures suggests that a simple information-theoretic measure of category complexity best fits the data. In the second part of the talk I will discuss how category learning can influence the choice of underlying perceptual features. Category learning is known to induce “categorical perception” effects, in which discrimination performance along category-relevant or informative features measurably improves. But what is the right way to measure “informativeness?” A comparison of measures suggests again that a simple information-theoretic measure—the mutual information between the feature and the category variables—best fits the data. This finding suggests a “rational” basis for categorical perception, in which the precision of perceptual discrimination is tuned to the statistical structure of the environment

    Academic Calendar, University Dates & Events, Carney Institute for Brain Science, Neuroscience, Psychology & Cognitive Sciences
  • “Geometry of Object Representation in Visual Hierarchies”

    Haim Sompolinsky, Ph.D.
    The Hebrew University  

    Abstract: Neurons in object representations in top stages of the visual hierarchy exhibit high selectivity to object identity as well as to identity-preserving variables, including location, orientation and scale. suggesting that changes in the object representations from low to high processing stages are related to changes in the geometry of object manifolds. Each manifold consists of the set of population responses to stimuli belonging to the same object.

    In my talk, I will present recent work that elucidates the relation between manifold geometry and object-identity computations. I will discuss two kinds of computations. The first is object classification. I will describe new measures of manifold radius and dimensions that predict the ability to support object classification (Chung et al., PRX, 2018). Based on these measures, we characterize the changes in manifold geometry as signals propagate across layers of Deep Convolutional Neural Networks (DCNNs). Recordings from neurons in various stages of the visual systems, have been similarly analyzed, allowing us to test the correspondence between DCNNs and the visual hierarchy in the visual cortex.

    In a recent unpublished work with Ben Sorscher (Stanford), we have studied the ability to learn new objects and object categories from just a few examples (the few shot learning problem). We show that feature layers in DCNNs exhibit a remarkable ability in few shot learning of new categories. To explain this performance, we develop a new theory of the geometry of concept formation, that delineates the salient geometric features that underlie rapid concept formation in artificial and brain sensory hierarchies.

    CCBS, Psychology & Cognitive Sciences, Research
  • BRENDEN LAKE

    Assistant Professor of Psychology and Data Science, NYU

    LEARNING THROUGH THE EYES OF A CHILD

    Young children have meaningful expectations about the world around them. What is the origin of this early knowledge? How much can be explained through generic learning mechanisms applied to sensory data, and how much requires more substantive innate inductive biases? Addressing this fundamental question in its full generality is infeasible, but we can hope to make real progress in more narrowly defined domains, such as the development of high-level visual categories, thanks to new datasets and progress in deep learning. We train large-scale neural networks through the eyes of a single developing child, using longitudinal baby headcam videos (Sullivan et al., 2020, PsyArxiv). Our results show how high-level visual representations emerge from a subset of one baby’s experience, through only self-supervised learning.

     
    Biography

    Brenden builds computational models of everyday cognitive abilities, focusing on problems that are easier for people than they are for machines. The human mind is the best-known solution to a diverse array of difficult computational problems: learning new concepts, learning new tasks, understanding scenes, learning a language, asking questions, forming explanations, amongst many others. Machines also struggle to simulate other facets of human intelligence, including creativity, curiosity, self-assessment, and commonsense reasoning.

    In this broad space of computational challenges, Brenden’s work has addressed a range of questions: How do people learn a new concept from just one or a few examples? How do people act creatively when designing new concepts? How do people learn qualitatively different forms of structure? How do people ask questions when searching for information?

    By studying these distinctively human endeavors, there is potential to advance both cognitive science and machine learning. In cognitive science, building a computational model is a test of understanding; if people outperform all existing algorithms on certain types of problems, we have more to understand about how people solve them. In machine learning, these cognitive abilities are both important open problems as well as opportunities to reverse engineer human solutions. By studying human solutions to difficult computational problems, Brenden aims to better understand humans and to build machines that learn in more powerful and more human-like ways.

     

    Follow Brenden on Twitter: @LakeBrenden

     

    DSI & CCMB Data Wednesday Seminar Series

    The Data Science Initiative (DSI) joins the Center for Computational and Molecular Biology (CCMB ) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science. Please check our events page for more information on these and other events of interest.

  • Regina Binda is inviting you to a scheduled Zoom meeting.

    Topic: Aging & Dementia Research Presentation
    Time: Dec 2, 2020 01:00 PM Eastern Time (US and Canada)

    Join Zoom Meeting
    https://brown.zoom.us/j/94840086825

    Meeting ID: 948 4008 6825
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    Meeting ID: 948 4008 6825

     

    Aging and Dementia Research Presentation

     Sponsored by: The Norman Prince Neurosciences Institute

    In Association with:

    The Rhode Island Hospital Alzheimer’s Disease & Memory Disorders Center

     “Pupil Measurement as a Novel,

    Non-Invasive Biomarker of Risk for Alzheimer’s Disease”

     Laura Korthauer, PhD

    Assistant Professor

    Department of Psychiatry and Human Behavior

     December 2, 2020

    1 to 2 PM

    Via Zoom

  • Dec
    2
    12:00pm - 1:30pm

    LingLangLunch

    Michael S. Goodman ’74 Memorial Seminar Series.

    Speaker: Misha Oraa Ali (PhD student, Brown)

    Title:  (When) do children learn the distributivity of “each”?

    Abstract: Since Inhelder and Piaget (1964), many studies show children making non-adult-like ‘quantifier spreading errors’ in understanding expressions like, “Each girl rode an elephant”. Young children often face difficulties binding the appropriate quantifier to the right noun. Since Brooks and Braine (1996), a parallel literature investigates children’s resolution of scope ambiguities: Does “each girl rode an elephant” mean one elephant, or one-per-girl? Both literatures presuppose that children know the logical words (“each”, “a”), and struggle with their composition. But children’s non-adult-like behavior is unsurprising if they just don’t know what “each” means. Additionally, many of these studies, which assume that children can indeed comprehend “each”, claim to be investigating children’s acquisition of distributivity (that the predicate applies to each individual member of the set) and universality (that the predicate applies to all members contained in a set, without exception) in their comprehension of quantifiers like “all”, “every”, and “each”. However, the question of when children actually acquire distributivity in their compositional toolkits also remains open. Understanding how children learn the meanings of function words like “each”, “every” and “all” will give us important insights into the compositional machinery underlying many aspects of cognition. These words have abstract logical meanings that children cannot learn by reference to anything in the world, the way they might learn words like “tiger” or “rutabaga”. My project aims to evaluate children’s understanding of the universal quantifiers “all”, “every” and “each” in English. While these words have similar, abstract meanings, they also differ in subtle ways. How do children acquire an understanding of these subtle differences in meanings, and at what age?

    Bio: Hi! I’m currently a PhD student in the Brown Language and Thought Lab , where I do research with Roman Feiman

    I was a research assistant at the Harvard Lab for Developmental Studies, working with Elizabeth Spelke and Rhea Howard. Before that, I was a research support associate in the LanguageLab (TedLab) with Edward A Gibson and Paula Rubio-Fernandez at the Department of Brain and Cognitive Sciences at MIT. I have also been involved with Project Prakash , an initiative from the Sinha Lab for Vision Research at MIT.

    I received my Bachelor’s degree in May 2017 from Mount Holyoke College , with a major in Neuroscience and Behaviour and a special minor in Graphic Narrative and Visual Storytelling. In college, I did my thesis research with Mara Breen where I used ERP to study auditory imagery and implicit prosody - specifically, by investigating the effects of rhythm and metrical structure on poetry during silent reading.

    Academic Calendar, University Dates & Events, Carney Institute for Brain Science, Neuroscience, Psychology & Cognitive Sciences
  • Dec
    2
    11:00am - 12:30pm

    DPHB Academic Grand Rounds

    Virtual

    Psychological and Developmental Impact of Trauma, Violence, and Racism: From Research to Service and Advocacy

    Maureen Allwood, Ph.D.
    Professor, Department of Psychology
    John Jay College of Criminal Justice

    City University of New York

    Wednesday, December 2, 2020 ◊ 11:00 am - 12:30 pm

    Course Link: https://cme-learning.brown.edu/DPHB-Series-2021

    Join December 2, 2020 Zoom Meeting: https://brown.zoom.us/j/97760079559

    Meeting ID: 977 6007 9559

    Password: dphb

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Physical & Earth Sciences, Psychology & Cognitive Sciences, Research, Social Sciences
  • Please join the Brown Contemplative Studies Initiative and the Center for Mindfulness for a lecture by Dr. Eric Garland, Ph.D., LCSW on “Mindfulness-Oriented Recovery Enhancement Heals Opioid Misuse and Chronic Pain by Restructuring Reward:  From Hedonic Pleasure to Self-Transcendent Meaning” on December 1st from 4 - 5:30 pm EST.  This event is free and open to the public.  However, you must register with [email protected] to receive the Zoom link. 

    Dr. Eric Garland, PhD, LCSW is Distinguished Endowed Chair in Research, Professor and Associate Dean for Research in the University of Utah College of Social Work, and Director of the Center on Mindfulness and Integrative Health Intervention Development (C-MIIND). Dr. Garland is the developer of an innovative mindfulness-based therapy founded on insights derived from cognitive, affective, and neurobiological science, called Mindfulness-Oriented Recovery Enhancement (MORE). As Principal Investigator or Co-Investigator, Dr. Garland has over 175 scientific publications and has received more than $50 million in research grants from the NIH, DOD, and PCORI to conduct translational research on biopsychosocial mechanisms implicated in addiction, emotion dysregulation, and chronic pain, including randomized controlled trials of MORE and other mindfulness-based interventions as treatments for opioid misuse and addiction. To complement his expertise in clinical research, Dr. Garland is a licensed psychotherapist with more than 15 years of clinical experience providing mind-body therapies for persons suffering from addictive behaviors, psychological disorders, and chronic pain. In 2019, he was appointed by NIH Director Dr. Francis Collins to the NIH HEAL Multi-disciplinary Working Group comprised of national experts on pain and addiction research to help guide the $1.1 billion HEAL initiative aimed at using science to halt the opioid crisis.

    Academic Calendar, University Dates & Events, Biology, Medicine, Public Health, Philosophy, Religious Studies, Psychology & Cognitive Sciences, Research
  • Dr. Sabrina Burmeister- University of North Carolina, Chapel Hill

     

    Talk Title: Frognition: Poison Frogs and the Ecology of Spatial Memory

  • Join the Carney Institute for Brain Science for a conversation focused on Alzheimer’s research at Brown University, featuring:

    • Stephen Salloway, Martin M. Zucker Professor of Psychiatry and Human Behavior at Brown, director of neurology and the Memory and Aging Program at Butler Hospital in Providence, RI
    • Ashley Webb, Richard and Edna Salomon Assistant Professor of Molecular Biology, Cell Biology and Biochemistry

    This event will be moderated by Diane Lipscombe, Reliance Dhirubhai Ambani Director of the Carney Institute, and Christopher Moore, associate director of the Carney Institute.

    Biology, Medicine, Public Health, CTN, Psychology & Cognitive Sciences, Research
  • Nov
    23
    3:00pm - 4:00pm

    Workshop - Advanced Slurm

    This workshop is for people who are already familiar with Slurm, but would like to use Slurm’s more powerful features. Topics covered include: dependencies for conditional execution of jobs, job arrays for parameter sweeps, dealing with hundreds or thousands of small tasks, how to limit the number of jobs running at once, and how to cancel multiple jobs.

    This will be a virtual workshop. Registered participants will receive an email with instructions for connecting via Zoom the day of the workshop.

    Registration:  Google Forms

    Computing, HPC, Research
  • Nov
    23
    12:00pm - 1:30pm

    Developmental Brown Bag

    Michael S. Goodman ’74 Memorial Seminar Series.

    Speaker:  Angeline Tsui (Postdoc Stanford University)

    Title : Language development of bilingual infants

    Abstract: Many children in the US and around the world are growing up as bilinguals: Nearly 22% of children in the US are learning two or more than two languages at home. The prevalence of bilingualism calls for research investigating the foundations of successful early bilingual language development. My research program answers this call by investigating how bilingual infants handle the demands of learning two languages and in what ways bilinguals differ from monolinguals in their language development. In my talk, I will present evidence that learners can use subtle cues to overcome the challenges of learning two languages in a bilingual environment. On the other hand, most of the time, bilingual infants do not differ from monolingual infants in their word learning, cognitive development and preferences for infant-directed speech. Together, these findings suggest that early bilingualism does not hinder children’s language development relative to their monolingual counterparts. Finally, I will discuss my current and future work extending this research program to real-world educational and clinical applications through the use of collaborative methods and large-scale data collection.

    Bio:  I am a postdoctoral scholar working with Michael C. Frank at Stanford University. My primary research focuses on bilingual infant word learning. I study (i) how young bilingual learners handle the demands of learning two languages in their environments and (ii) whether bilinguals and monolinguals differ in language development and what may drive the difference. More recently, I have focused on the use of large-scale, multi-site studies to examine whether key infancy research findings can be generalized to different populations. I also have a strong interest in advancing infancy research through open science and best research practices (e.g., appropriate use of statistical methods in data analysis).

    My secondary research interest is related to young children’s future-oriented behavior. In particular, I am interested in investigating what motivates children to save for their future and how to foster their saving behavior.

    Academic Calendar, University Dates & Events, Carney Institute for Brain Science, Neuroscience, Psychology & Cognitive Sciences
  • Data Club Reilly DOrdine 11-20-2020 2
    Nov
    20

    This event will require a password. If you are not part of the MCB Graduate Program and would like to participate, please contact the program coordinator for the password.

    Shanelle Reilly
    (Brossay Lab)

    “The characterization of MCMV specific Qa-1 restricted CD8 + T cells.”

    _______________________

    Alexandra D’Ordine
    ((Sedivy/Jogl Labs))


    “Developing small molecule inhibitors of the LINE-1 retrotransposon endonuclease domain to target age-associated disease.”

    Biology, Medicine, Public Health, Graduate School, Postgraduate Education, Physical & Earth Sciences, Research
  • Nov
    20
    2:00pm - 3:30pm

    Cognitive Seminar Series

    Michael S. Goodman ’74 Memorial Seminar Series.

    Speaker:Tomer Ullman (Assistant Professor Harvard)

    Title: Models of Core Knowledge (Physics, Really)

    Abstract: Even young children seem to have an early understanding of the world around them, and the people in it. Before children can reliably say “ball”, “wall”, or “Saul”, they expect balls to not go through walls, and for Saul to go right for a ball (if there’s no wall). What is the formal conceptual structure underlying this commonsense reasoning about objects and agents? I will raise several possibilities for models underlying core intuitive physics as a way of talking about models of core knowledge and intuitive theories more generally. In particular, I will present some recent ML work trying to capture early expectations about object solidly, cohesion, and permanence, that relies on a rough-derendering approach

    Academic Calendar, University Dates & Events, Carney Institute for Brain Science, Neuroscience, Psychology & Cognitive Sciences
  • Emerging areas such as virtual reality and machine learning present new opportunities and challenges for the research of data visualization. To these, we propose to follow user-centric approaches to understand users and then to develop computational approaches to further model and predict users’ behavior. In this talk, I will first introduce three completed studies where we assessed users’ cognition and perception and one study where we modeled human perception of visualization. I will then introduce detailed plans for two proposed projects; they aim to advance computational approaches to describe, quantify, and model users’ behavior centered around data visualization. The first project will apply machine learning to precisely model and predict human perception of correlation in scatterplots. I will present the background, the preliminary dataset, a data collection plan, and a list of plausible machine learning techniques. The outcome of this project would be a set of practices and observations that could automate visualization evaluation and modeling approaches. The second project will develop informative performance metrics based on users’ movements in a virtual reality environment and then use information theory to evaluate these metrics. I will present the datasets we intend to use, the example metrics proposed, and the evaluation plan. The outcome of this project would be a set of metrics that could facilitate future empirical studies in virtual reality. We anticipate that the end results of this thesis will improve user performance and enhance user experience in virtual reality and machine learning; and, in turn, new techniques and methods from virtual reality and machine learning will also cope with the user-centric computational approaches to data visualization.
    Host: Professor David H. Laidlaw
  • PLEASE NOTE THIS TALK IS FOR FACULTY ONLY.  

     

    THIS TALK HAS BEEN RESCHEDULED TO BEGIN AT 1:00 PM.

     

    BRENDA RUBENSTEIN

    Joukowsky Family Assistant Professor of Chemistry, Brown University

    LEARNING QUANTUM MECHANICS: HOW MACHINE LEARNING IS (AND IS NOT) TRANSFORMING THE PRACTICE OF QUANTUM MECHANICS

    Much of our ability to understand the quantum world, including how to design new materials, synthesize new molecules, and study puzzling emergent quantum phases, rests upon our ability to accurately solve the Schrodinger Equation. While the recipe for solving the Schrodinger Equation has been known for over a century, the cost of finding its exact solutions scales exponentially with system size, a fact which has frustrated the progress of quantum chemistry and physics for decades. The rise of data science, however, presents new and exciting opportunities for potentially accelerating the solution of the Schrodinger Equation - or foregoing its solution whatsoever by directly predicting quantum properties. In this talk, I will describe how machine learning is and isn’t transforming our ability to model quantum phenomena drawing upon examples from my own group’s research and the wider literature.

     
    Biography:

    Dr. Brenda Rubenstein is currently the Joukowsky Family Assistant Professor of Chemistry at Brown University. While the focus of her work is on developing new electronic structure methods, she is also deeply engaged in rethinking computing architectures. Prior to arriving at Brown, she was a Lawrence Distinguished Postdoctoral Fellow at Lawrence Livermore National Laboratory. She received her Sc.B.s in Chemical Physics and Applied Mathematics at Brown University, her M.Phil. in Computational Chemistry while a Churchill Scholar at the University of Cambridge, and her Ph.D. in Chemical Physics at Columbia University. Ask her about basketball - you may be surprised!

     

    Faculty for Faculty Research Talks

    This is an opportunity for faculty to share current data science-related research activities with other faculty colleagues in an informal and interdisciplinary environment. More about this series on our website .

  • Nov
    20
    10:00am - 12:00pm

    CCV Office Hours

    Drop in on the Zoom meeting to ask members of CCV’s High-Performance Computing (HPC) team your questions about using Oscar or any other research computing topics you are interested in.

    Computing, HPC, Research
  • Nov
    19
    Welcome to the 5th Annual CS Research Open House, hosted by the MURAs!

    This is a great way to get to know more about research in the department, no matter what year, concentration, or classes you’re in. Lots of groups are recruiting new researchers to join their team - this is the perfect opportunity to start thinking about research for next semester!

    This year, the event will be held remotely. There will be brief introductions from each group (recorded) followed by a breakout room Q&A (not recorded) that participants can enter and leave freely.

    As always, reach out to us at [email protected]  if you have any questions!

    RSVP: https://www.facebook.com/events/4834613723278028

    Participating Research Groups: https://docs.google.com/document/d/1i_ybxAEO6M_otXPd-TCn-KEJNTgQscx_IL8XxNqkbNE/preview

    Host: Meta Undergraduate Research Assistants (MURAs)

  • Carney Methods Meetups: Beyond the Brady Bunch Meeting

    Join the Carney Institute for Brain Science for a Carney Methods Meetups, an informal gathering focused on methods for brain science, on Thursday, November 19, at 2 p.m.

    Jason Ritt, Carney’s scientific director of quantitative neuroscience, David Sheinberg, professor of neuroscience, and Andrew Creamer, scientific data management specialist in the Brown Library, will lead an open discussion of tools and tricks to enhance virtual meetings for brainstorming, collaborative manuscript editing, poster presentation, social events, and others. Some tools like OBS studio  and spatial.chat  will be briefly demonstrated, and we invite further ideas from the community.

    Please note, this workshop requires you to be logged into Zoom through your Brown account. Click to learn more .

    Notes from previous Meetups  are available online.

    Biology, Medicine, Public Health, Psychology & Cognitive Sciences, Research, Teaching & Learning
  • Nov
    19

    Michael S. Goodman ’74 Memorial Seminar Series.  

    Speaker:Zhiyan Wang (CLPS Grad Student at Watanabe Lab)

    Title: Visual perceptual learning of faces- a study with Body Dysmorphic Disorder Patients


    Abstract: Visual perceptual learning (VPL) is defined as a long-term performance change on a visual task as a result of visual experiences(Watanabe & Sasaki, 2015). The majority of previous VPL researches have focused onlower-level visual features using artificial visual stimuli. However, themechanisms of VPL on natural stimuli remains elusive. Moreover, VPL has been studied as an intervention for patients with visual deficits that originated from lower-level visual areas (Polat et al., 2004) (e.g. amblyopia, glaucoma). There is a limited number of studies that explore the effect of VPL on patients whose deficits originated from high-level visual areas. In this talk, I will present a study that investigates VPL of faces with body dysmorphic disorder (BDD) patients. BDD patients are characterized by distressing preoccupation with slight defects in appearances. First, I will describe the behavioral changes for the BDD patients after VPL of faces. Second, I will discuss the neural mechanisms underlying the changes in association with VPL of faces. Our study demonstrates the effect of VPL on natural stimuli and explores the effect of VPL on patient populations with deficits from high-level visual areas.

    Academic Calendar, University Dates & Events, Carney Institute for Brain Science, Neuroscience, Psychology & Cognitive Sciences
  • “Mechanisms of neuronal wiring”

    Biology, Medicine, Public Health, Research
  • Please join Carney’s Center for Computational Brain Science (CCBS) on November 18 for a special seminar on “Differential Resilience of Neurons and Networks with Similar Behavior to Perturbation,” featuring Eve Marder, Ph.D., university professor and Victor and Gwendolyn Beinfield Professor of Biology at Brandeis University.

    Please note, you must be logged into Zoom through your Brown account to join this event. 

    Abstract:

    Both computational and experimental results in single neurons and small networks demonstrate that very similar network function can result from quite disparate sets of neuronal and network parameters. Using the crustacean stomatogastric nervous system, we study the influence of these differences in underlying structure on differential resilience of individuals to a variety of environmental perturbations, including changes in temperature, pH, potassium concentration and neuromodulation. We show that neurons with many different kinds of ion channels can smoothly move through different mechanisms in generating their activity patterns, thus extending their dynamic range.

    Biology, Medicine, Public Health, CCBS, Psychology & Cognitive Sciences, Research
  • NANDITA GARUD

    Assistant Professor, Ecology and Evolutionary Biology, UCLA

    RAPID ADAPTATION IN NATURAL POPULATIONS:  LESSONS FROM DROSOPHILA AND THE HUMAN MICROBIOME

    The availability of whole-genome data from natural populations has challenged many long-standing assumptions about molecular evolution. For example, it has long been assumed that natural selection is typically slow and infrequent. Using whole-genome data from both Drosophila and the human microbiome, I found evidence that rapid adaptation is much more pervasive than previously thought. In my talk, I will first describe a method I developed to detect soft sweeps, a signature of rapid adaptation, and its application to Drosophila and other, non-model organism data. Next, I will show that selective sweeps of genes and SNPs in bacteria in the human microbiome are common on 6-month time scales and that these sweeps likely originate in adaptive introgression from other species and strains in the microbiome. This suggests that complex ecological communities can play an important role in shaping evolution on short time scales. In sum, I will describe how we can leverage whole-genome data and novel statistics for uncovering the mode and tempo of adaptation in natural populations.

    DSI & CCMB Data Wednesday Seminar Series

    The Data Science Initiative (DSI) joins the Center for Computational and Molecular Biology (CCMB ) every Wednesday afternoon, 4 – 5 pm during the academic year to present lecturers in various mathematical and statistical fields worldwide, as well as local researchers on Brown’s campus. The aim is to provide students, staff, faculty, and visitors with an introduction to current research topics in the fields of data science, mathematics, statistics, and computer science. Please check our events page for more information on these and other events of interest.

  • Dr. Hansen is a Professor in the Program for Development, Aging and Regeneration at the Sanford Burnham Prebys Medical Discovery Institute (SBP), a non-profit research institute located in San Diego, CA, where she studies molecular mechanisms of aging with a focus on the cellular recycling process called autophagy. She obtained a Master of Science in biochemistry in 1998, and a doctorate in molecular biology in 2001, both from Copenhagen University, Denmark. Dr. Hansen subsequently carried out postdoctoral studies in the laboratory of Professor Cynthia Kenyon, Ph.D., at the University of California, San Francisco, a world leader in the genetics of aging. She started her laboratory at SBP in the fall of 2007, and currently serves as Associate Dean of Student Affairs in SBP’s accredited graduate program, and as Faculty Advisor on Postdoctoral Training for SBP’s ∼150 postdoctoral scholars. In recognition of her mentoring efforts, Dr. Hansen has received the 2017 Mentor Award from the USA National Postdoctoral Association.
    The event will require a password. If you are not affiliated with the Center on the Biology of Aging  and would like to participate, please contact the program coordinator for the password.
  • In this lecture, I show how considering the intersection of collaborative and social scenarios with other domains of computing can reveal end-user needs and result in innovative technical systems. I give examples of this approach from my work in gesture interaction, information retrieval, and accessibility, focusing particularly on the topics of creating more efficient and expressive augmentative and alternative communication technologies and of making social media more accessible to screen reader users. I close by identifying future opportunities for creating inclusive, accessible collaborative and social technologies.
     
    Meredith Ringel Morris is a Sr. Principal Researcher at Microsoft Research and Research Area Manager for Interaction, Accessibility, and Mixed Reality. She founded Microsoft Research’s Ability research group and is a member of the lab’s Leadership Team. She is also an Affiliate Professor at the University of Washington in the Allen School of Computer Science & Engineering and in The Information School. Dr. Morris is an expert in Human-Computer Interaction; in 2020, she was inducted into the ACM SIGCHI Academy in recognition of her research in collaborative and social computing. Her research on collaboration and social technologies has contributed new systems, methods, and insights to diverse areas of computing including gesture interaction, information retrieval, and accessibility. Dr. Morris earned her Sc.B. in Computer Science from Brown University and her M.S. and Ph.D. in Computer Science from Stanford University.
    Hosts: Professors Jeff Huang/Andy van Dam
  • “A dedicated brainstem circuit underlies REM sleep control—implication for Synucleinopathies”

    Jimmy Fraigne, Ph.D.

    Associate Professor, Department of Cell and Systems Biology

    University of Toronto

    Abstract:

    A specific brainstem circuit controls REM sleep. Degeneration of this circuit underlies REM sleep behavior disorder (RBD), which is characterized by excessive movement that often result in patient injuries. However, the most alarming aspect of RBD is that 80-90% of patients eventually develop a synucleinopathy like Parkinson’s disease or dementia with Lewy bodies.

    About the PSRIG seminar series:

    The PSRIG seminar series has been hosted by Dr. Mary Carskadon and her lab at Brown University for over 25 years. PSRIG has historically served as a venue for the local sleep research and sleep medicine community, and for trainees, to learn about current basic and clinical sleep research.

    This year, the series will be held virtually and involve a diverse lineup of speakers from various institutions both nationally and internationally. Seminars will be held at 12 p.m. on the 3rd Tuesday of each month. If you would like to be added to the PSRIG mailing list, please email Patricia Wong at [email protected] .

    Biology, Medicine, Public Health, Research