Michael S. Goodman ’74 Memorial Seminar Series.

Speaker: Lisa Fazio - Associate Professor- Vanderbilt

Title: Understanding the Effects of Repetition on Belief

Abstract: Repetition increases belief in false statements. This illusory truth effect occurs with many different types of statements (e.g., trivia facts, news headlines, advertisements), and even occurs when the false statement contradicts participants’ prior knowledge. I will present a series of studies demonstrating that the effects of repetition are widespread – occurring for even very implausible statements, occurring in naturalistic settings, and occurring across development. However, the effects are not inevitable, I will also discuss situations where the repetition has only a minimal effect on belief.

Speaker: Babak Hemmatian Borujeni , Brown University

Title: Taking the High Road: A Big Data Investigation of Natural Discourse in the Emerging U.S. Consensus about Marijuana Legalization

Abstract: U.S. support for marijuana legalization grew from 38% to 65% in 2008-2019. To find the discourse features that preceded and followed the shift, I curated a comprehensive corpus of Reddit comments from the same period. Neural networks trained on human annotations of attitude and persuasion attempts separated strategic use of narratives from non-argumentative discourse. Two narrative frames considered important to persuasion in past research were studied: anecdotal vs. generalized content. I operationalized anecdotal frames based on three linguistic clause-level features: Whether the clause is about a generic kind, if it represents a reliable state or an event, and whether any events are bounded in time. A corpus of Reddit and news was annotated for these features and more, neural networks based on which estimated anecdotal properties in the broader Reddit dataset. Anecdotal themes were less prevalent but present in most comments, particularly in arguments favoring legalization. Nationally, a surge in anecdotes within non-argumentative discourse happened over time as a consequence of attitude shifts. Generalized discourse was a potential cause with major surges around the 2012 and 2016 legal milestones. Attempts to associate generalized discourse with legal changes were complicated by marijuana’s varied status across the U.S. I therefore inferred user locations and compared the rate of anecdotal themes before and after legalization in comments from pioneering states. More generalized frames set the stage for each successful legalization bid. The particular content, however, varied between the two milestones. Character judgments were prominent in 2012, while crimes and politics took center-stage in 2016. The generalized precedents of legalization in the two periods shared argumentative and moralistic focus but had distinctive clause-level profiles. Meanwhile, legal and medical arguments were sidelined, meaning the novel consensus was not informed by much of the relevant information. Together, my findings present generalized argument framing as a harbinger of attitude shift toward hot-button topics, and anecdotal non-argumentative framing as a consequence of it. The machine learning pipeline that made this insight possible is novel for social media research but general-purpose, allowing similar abstract narrative frames to be broken down into theory-driven constituents, and studied in quantitative detail.

Advisor: Steven Sloman

~ All Are Invited ~

If you are not a part of the CLPS Department and would like to attend virtually, please contact [email protected] , at least 24 hours ahead of time.

The Joint Seminar Series on Clinical Trials in Aging is co-hosted by the Interventional Studies in Aging Center (ISAC) in the Hinda and Arthur Marcus Institute for Aging Research at Hebrew SeniorLIfe and the Brown University Center for Long-Term Care Quality & Innovation (Q&I). Lectures focus on issues in the design and execution of clinical trials, including trials currently in the field.

Our guest speaker for September 29 is Kendra Plourde, PhD, Yale University School of Medicine.  Dr. Plourde’s presentation is entitled, “Sample Size Calculation for Stepped Wedge Cluster Randomized Trials with Subclusters.”

The stepped wedge cluster randomized trial (SW-CRT) is an increasingly popular design for evaluating health service delivery or policy interventions. Especially when embedded in healthcare delivery systems, many SW-CRTs may have
subclusters nested in clusters, within which outcomes are collected longitudinally. Dr. Plourde will present sample size calculations for multilevel SW-CRTs that differentiate within-and between-period correlations at each level of clustering.

All are welcome!

Michael S. Goodman ’74 Memorial Seminar Series.

Speaker: Dr. Alessandra Sciutti, Italian Institute of Technology (IIT)

Title: Establishing shared perception with a robot

Abstract: For robots to become an effective component of our society, it is necessary that these agents become primarily cognitive systems, endowed with a cognitive architecture that enables them to adapt, predict, and pro-actively interact with the environment and communicate with the human partners. Human communication depends on mutual understanding: I know how to communicate because I entertain a model of you, which enables me to select an effective way to convey to you what I want and to have an intuition of your internal states – what you need, fear or desire. Such intuition enables me to perceive properties that would be otherwise not accessible to my perception, as goals, emotions or effort. Our contribution to the roadmap toward cognitive systems leverages on the use of a humanoid robot (iCub) to test some of our assumptions on how to build a cognitive interactive agent. We attempt at modeling the minimal skills necessary for cognitive development, focusing on the visual features that enable to recognize the presence of other agents in the scene, to allow action matching across different visual perspectives and to foster automatic speed adaptation in human-robot interactive repetitive tasks. In a dual approach, we are trying to understand how to modulate robot behavior to elicit better human understanding and to express different characteristics of the interaction: from the mood to the level of commitment. This approach is propaedeutic to the creation of a cognitive system, by helping in the definition of what is relevant to attend to, starting from signals originating from the intrinsic characteristics of the human body. We believe that only a structured effort toward cognition will in the future allow for more humane machines, able to see the world and people as we do and engage with them in a meaningful manner.

This is a drop-in session where CCV staff members will be available to answer questions about Brown’s research computing resources (Oscar, Stronghold, Globus) and help with any high-performance computing (HPC) issues you might have.

Join Advance-CTR and the Data Science Initiative at Brown for this 5-part series exploring machine learning, its methodology, and application in biomedicine and health. The purpose of this series is to serve as an introduction to machine learning for researchers, clinician scientists, and others who may be interested in using these methods in their research.

Friday, October 1, 2021:

Carsten Eickhoff, PhD: “An Introduction to Machine Learning” 

This talk will introduce the basic components of machine learning systems and research papers and introduce the notion of casting inference problems in terms of features and labels. We will discuss supervised classification and regression techniques as well as altogether unsupervised learning schemes. We will have a look at reinforcement learning, model optimization and evaluation and close with a discussion of model generality and the curse of dimensionality.

About the Speaker

Dr. Eickhoff is an Assistant Professor of Medical and Computer Science at Brown University where he leads the Biomedical AI Lab, specializing in the development of data science and information retrieval techniques with the goal of improving patient safety, individual health and quality of medical care. Prior to joining Brown, he graduated from The University of Edinburgh and TU Delft, and was a postdoctoral fellow at ETH Zurich and Harvard University. Carsten has published more than 100 articles in computer science conferences (SIGIR, EMNLP, NAACL, WWW, KDD, WSDM, CIKM) and clinical journals (Nature Digital Medicine, The Lancet - Respiratory Medicine, Radiology, European Heart Journal). His research has been supported by the NSF, NIH, DARPA, IARPA, Google, Amazon, Microsoft and others. Aside from his academic endeavors, he is a founder and board member of several deep technology startups in the health sector that strive to translate technological innovation to improved safety and quality of life for patients. 

Michael S. Goodman ’74 Memorial Seminar Series.

Speaker: Jae-Young Son - PhD Student - Brown
Title: Cognitive maps of social features enable flexible inference in social networks
Abstract: In order to navigate a complex web of relationships, an individual must learn and represent the connections between people in a social network. However, the sheer size and complexity of the social world makes it impossible to acquire firsthand knowledge of all relations within a network, suggesting that people must make inferences about unobserved relationships to fill in the gaps. Across three studies (n = 328), we show that people can encode information about social features (e.g., hobbies, clubs) and subsequently deploy this knowledge to infer the existence of unobserved friendships in the network. Using computational models, we test various feature-based mechanisms that could support such inferences. We find that people’s ability to successfully generalize depends on two representational strategies: a simple but inflexible similarity heuristic that leverages homophily, and a complex but flexible cognitive map that encodes the statistical relationships between social features and friendships. Together, our studies reveal that people can build cognitive maps encoding arbitrary patterns of latent relations in many abstract feature spaces, allowing social networks to be represented in a flexible format.

Bevil R. Conway, Senior Investigator with the National Eye Institute at the National Institutes of Health (NIH), visits the Pembroke Center to present the lecture “Color Coded: in neuroscience and culture.” In the lecture, associated with the 2021-22 Pembroke Seminar “Color,” Conway examines the dogma that color vision plays a modest role in encoding and recognizing objects, and uses arguments from neuroscience, culture, and personal history to argue that color plays a fundamental role in perception and cognition.

Sponsored by the Marshall Woods Lectureships Foundation of Fine Arts.

This is a drop-in session where CCV staff members will be available to answer questions about Brown’s research computing resources (Oscar, Stronghold, Globus) and help with any high-performance computing (HPC) issues you might have.

The Department of Behavioral and Social Sciences is pleased to bring the i-BSHS (Innovations in Behavioral and Social Health Sciences) Seminar Series to the Brown University School of Public Health. The i-BSHS lecture series fosters collaborative discussion on innovative behavioral and social science-based approaches to improving population health.The first guest speaker this year is Angela Haeny, Ph.D. This event is co-sponsored by the C.V. Starr Foundation and the Brown University Center for the Study of Race and Ethnicity in America. 

Dr. Angela Haeny is an Assistant Professor of Psychiatry at Yale School of Medicine and a licensed Clinical Psychologist with specialty in substance use disorders. She received her undergraduate degree in Psychology and Addiction Studies from the University of Minnesota, received her doctorate from the University of Missouri, completed her internship in Clinical and Community Psychology at Yale School of Medicine, and completed a NIDA T32 postdoctoral fellowship through the Division of Prevention and Community Research at Yale School of Medicine. Dr. Haeny is committed to eliminating racial disparities and enhancing diversity, equity, and inclusion, which cuts across all aspects of her work. Her research involves addressing race-related stress to improve drug and alcohol treatment outcomes among Black adults.

Please note that this virtual event, including attendees’ Zoom video, audio and screen name, and questions or chats, will be recorded. All or portions of the event recording may be shared through Brown University’s digital channels. Individuals who do not want their identities to be captured are solely responsible for turning off their camera, muting their microphone and/or adjusting their screen name accordingly. By attending this event, you consent to your name, voice, and/or image being recorded and to Brown University reproducing, distributing and otherwise displaying the recording, within its sole discretion.

Join Advance-CTR and the Data Science Initiative at Brown for this 5-part series exploring machine learning, its methodology, and application in biomedicine and health. The purpose of this series is to serve as an introduction to machine learning for researchers, clinician scientists, and others who may be interested in using these methods in their research.

Friday, October 8, 2021: 

Roberta De Vito, PhD: “Cross-Study Machine Learning Techniques: Reproducibility and Differences Across Studies”

Biostatistics and computational biology are increasingly facing the urgent challenge of efficiently dealing with a large amount of experimental data. In particular, high-throughput assays are transforming the study of biology, as they generate a rich, complex, and diverse collection of high-dimensional data sets. Through compelling statistical analysis, these large data sets lead to discoveries, advances and knowledge that were never accessible before, via compelling statistical analysis. Building such systematic knowledge is a cumulative process which requires analyses that integrate multiple sources, studies, and technologies. The increased availability of ensembles of studies on related clinical populations, technologies, and genomic features poses four categories of important multi-study statistical questions: 1) To what extent is biological signal reproducibly shared across different studies? 2) How can this global signal be extracted? 3) How can we detect and quantify local signals that may be masked by strong global signals? 4) How do these global and local signals manifest differently in different data types? We will answer these four questions by introducing a novel class of methodologies for the joint analysis of different studies. The goal is to separately identify and estimate 1) common factors reproduced across multiple studies, and 2) study-specific factors. We present different medical and biological applications. In all the cases, we clarify the benefits of a joint analysis compared to the standard methods. Our method could accelerate the pace at which we can combine unsupervised analysis across different studies, and understand the cross-study reproducibility of signal in multivariate data.

About the Speaker

Roberta De Vito is a statistician with a passion for teaching and developing statistical tools for cancer research and disorder risk, with particular focus on epidemiology and genomics. Currently, she is Assistant Professor in the department of Biostatistics and at the Data Science Initiative at Brown University. She completed her Ph.D. in Statistical Science at the University of Padua, advised by Giovanni Parmigiani at Harvard University where she developed her thesis work. The main research interest is latent variable model, Bayesian non parametric, variable selection via sparsity prior, machine learning and big data with particular focus on genomics and epidemiology. She was a postdoc at Princeton University in Barbara Engelhardt’s group where she developed Bayesian and latent variable discrete model in high-dimensional biological and epidemiological data. Her passion for teaching developed at Princeton University where she taught some classes and had the opportunity to mentor Master and PhD students. Some of her previous mentees are now pursuing successful research careers in biostatistics and data science also across Ivy League universities, like Harvard University, Princeton and MIT. Her website https://rdevito.github.io/web/ provides complete details.

How does uncertainty influence our choices and how we make decisions? 

Join the Carney Institute for Brain Science for a conversation about uncertainty and decision-making, featuring:

• Emily Oster, Royce Family Professor of Teaching Excellence and Professor of Economics, whose academic work focuses on health economics and statistical methods
• Amitai Shenhav, Assistant Professor of Cognitive, Linguistic and Psychological Sciences, whose research is focused on the neuroscience of motivation, decision-making and cognitive control

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

Join Advance-CTR and the Data Science Initiative at Brown for this 5-part series exploring machine learning, its methodology, and application in biomedicine and health. The purpose of this series is to serve as an introduction to machine learning for researchers, clinician scientists, and others who may be interested in using these methods in their research.

Wednesday, October 13, 2021:

Yichi Zhang, PhD: “Interpretable Individualized Treatment Rules Using Decision Lists”

Precision medicine is currently a topic of great interest in clinical science. One typical way to formalize precision medicine is through an individualized treatment rule, which is a sequence of rules, one per each stage of intervention, that map up-to-date patient information to a recommended treatment. An optimal individualized treatment rule is defined as maximizing the mean of some cumulative clinical outcome if applied to a population of interest. In many settings, estimation of an optimal individualized treatment rule is an exploratory analysis intended to generate new hypotheses for subsequent research and not to directly dictate treatment to new patients. In such settings, a rule that is interpretable in a domain context may be of greater value than an unintelligible one built using “black-box” methods. In this talk, I will present a causal inference framework for estimating an optimal individualized treatment rule and discuss its connection to reinforcement learning. Then, I will describe an estimator of an optimal and interpretable rule, which is expressible as a list of “if-then” statements that can be presented as either a paragraph or as a simple flowchart that is immediately interpretable to domain experts. The proposed method will be illustrated using a clinical trial dataset.

About the Speaker

Yichi Zhang is an assistant professor in the Department of Computer Science and Statistics at the University of Rhode Island. He received his PhD in Statistics from North Carolina State University and received postdoctoral training at Harvard School of Public Health. His research focuses on data-driven decision-making and sequential causal inference with applications to biomedical problems.

This is a drop-in session where CCV staff members will be available to answer questions about Brown’s research computing resources (Oscar, Stronghold, Globus) and help with any high-performance computing (HPC) issues you might have.

Innovate to stop overdose! The 2021 COBRE Overdose Hackathon seeks innovators to create solutions to help address preventable drug overdose.

We would like to invite you to participate in the virtual COBRE Overdose Hackathon which will be held on Friday, October 15th through Sunday, October 17th, 2021 with a kick-off event that can be joined virtually on the platform Airmeet or in-person at District Hall in Providence from 4-8 pm (Located at 225 Dyer Street). Virtual networking and matchmaking opportunities will be made available throughout the event, and a series of amazing speakers will share community, healthcare, and policy perspectives related to the overdose problem, including Dr. Brandon Marshall from Brown School of Public Health and Dr. Kim Sue from the National Harm Reduction Coalition!

The top three teams will receive $400 per person and will have the opportunity to receive funding to further develop their idea!

Join Advance-CTR and the Data Science Initiative at Brown for this 5-part series exploring machine learning, its methodology, and application in biomedicine and health. The purpose of this series is to serve as an introduction to machine learning for researchers, clinician scientists, and others who may be interested in using these methods in their research.

Wednesday, October 20, 2021

Ruotao Zhang, MSc: “Role of Calibration in Uncertainty-based Referral for Deep Learning”

The uncertainty in predictions from deep neural network analysis of medical imaging is challenging to assess but potentially important to include in subsequent decision making. Using data from diabetic retinopathy detection, we present an empirical evaluation of model performance and the impact of uncertainty-based referral, an approach that prioritizes referral of observations based on the magnitude of a measure of uncertainty. We consider several configurations of network architecture, method for uncertainty estimation, and training data size. We identify a strong relationship between the effectiveness of uncertainty-based referral and having a well-calibrated model. This is especially relevant as complex deep neural networks tend to have high calibration errors. Finally, we provide evidence that post-calibration of the neural network can improve uncertainty-based referral.

Dilum Aluthge, MD, PhD student: “Supervised Machine Learning Workflows for Electronic Health Records”

Supervised machine learning can be used to develop clinical decision support systems for use in electronic health records (EHRs). The first portion of the talk will provide an overview of the supervised machine learning workflow. The second portion will present an example application ofclassification using EHR data, specifically the problem list and medication list from a patient’s chart.

Optional Readings: 

1. Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. N Engl J Med. 2019 Apr 4;380(14):1347-1358. doi: 10.1056/NEJMra1814259. PMID: 30943338.

2. Sinha I, Aluthge DP, Chen ES, Sarkar IN, Ahn SH. Machine Learning Offers Exciting Potential for Predicting Postprocedural Outcomes: A Framework for Developing Random Forest Models in IR. J Vasc Interv Radiol. 2020 Jun;31(6):1018-1024.e4. doi: 10.1016/j.jvir.2019.11.030. Epub 2020 May 4. PMID: 32376173.

About the Speakers

Ruotao Zhang is a PhD student in the Department of Biostatisticsunder the supervision of Dr Steingrimsson and Dr Gatsonis. Before coming to the US, he worked as a data scientist at China Resources Holdings. Ruotao graduated from University of Oxford with a MSc in Applied Statistics, and before that he obtained a BSc in Mathematics from Imperial College London. His research interests focus on statistical machine learning methods with application to biomedical data. 

Dilum Aluthge is an MD/PhD student at the Brown Center for Biomedical Informatics, Center for Computational Molecular Biology and the Warren Alpert Medical School. His advisors are Dr. Neil Sarkar and Dr. Liz Chen. His research focuses on the theoretical concepts of learning health systems as well as the practical considerations of their implementation. Specific areas of interest include machine learning, clinical decision support, health information exchange, standards and interoperability, and physiologic reserve. Dilum earned his Bachelor of Science in Applied Mathematics at Brown. He is the co-creator of the PredictMD machine learning framework, which is implemented in the Julia programming language. He is also the founder of the JuliaHealth open source organization.

This is a drop-in session where CCV staff members will be available to answer questions about Brown’s research computing resources (Oscar, Stronghold, Globus) and help with any high-performance computing (HPC) issues you might have.

Join Advance-CTR and the Data Science Initiative at Brown for this 5-part series exploring machine learning, its methodology, and application in biomedicine and health. The purpose of this series is to serve as an introduction to machine learning for researchers, clinician scientists, and others who may be interested in using these methods in their research.

Wednesday, October 27: 

Joe Hogan, ScD: Title TBA

This is a drop-in session where CCV staff members will be available to answer questions about Brown’s research computing resources (Oscar, Stronghold, Globus) and help with any high-performance computing (HPC) issues you might have.

Please join us for our Fall 2021 Implementation Science Seminar series, featuring speakers at Brown University, Providence VA and University of California San Diego, for presentations on sustainability, implementation strategies, hybrid trial designs, implementation blueprints and policy implementation. The sessions will run on Tuesdays from 10-11 am between November 2 and December 7, 2021.

Tuesday, November 2

Rani Elwy, PhD: “How to Plan for and Assess Sustainability of Evidence-Based Practices”

This is a drop-in session where CCV staff members will be available to answer questions about Brown’s research computing resources (Oscar, Stronghold, Globus) and help with any high-performance computing (HPC) issues you might have.

This is a drop-in session where CCV staff members will be available to answer questions about Brown’s research computing resources (Oscar, Stronghold, Globus) and help with any high-performance computing (HPC) issues you might have.

This is a drop-in session where CCV staff members will be available to answer questions about Brown’s research computing resources (Oscar, Stronghold, Globus) and help with any high-performance computing (HPC) issues you might have.

This is a drop-in session where CCV staff members will be available to answer questions about Brown’s research computing resources (Oscar, Stronghold, Globus) and help with any high-performance computing (HPC) issues you might have.

This is a drop-in session where CCV staff members will be available to answer questions about Brown’s research computing resources (Oscar, Stronghold, Globus) and help with any high-performance computing (HPC) issues you might have.

This is a drop-in session where CCV staff members will be available to answer questions about Brown’s research computing resources (Oscar, Stronghold, Globus) and help with any high-performance computing (HPC) issues you might have.