Author Archives: Joe Pater

Cognitive Science grant writing group forming

If you are writing an external grant this semester, or would like to support or learn from those who are, please consider joining the CogSci grant writing group. E-mail by the end of the day Sunday March 15th to join, or for more information. We will meet between biweekly and monthly. The primary focus will be to provide informal feedback to one and other on ideas and writing, but we may engage in more structured activities (e.g. workshops led by the staff at the research office) if there is interest.

Coppock in Linguistics Fri. 9/27 at 3:30

Elizabeth Coppock, Boston University, will present “Universals in Superlative Semantics” in the Linguistics colloquium series at 3:30 Fri. 9/27. An abstract follows. All are welcome!

Abstract: This talk reports on the results of a broad cross-linguistic study on the semantics of quantity words such as ‘many’ in the superlative (e.g. ‘most’). While some languages use such a form to express both a relative reading (as in ‘Gloria has visited the most continents’) and a proportional reading (as in ‘Gloria has visited most continents’), the vast majority do not allow the latter, though all allow the former. Absolute readings for the superlatives of ordinary gradable adjectives, in contrast, are universal. I propose an explanation for this cross-linguistic generalization, centered around two core assumptions: quantity words denote gradable predicates of degrees, while proportional readings involve a comparison class of individuals. Proportional readings, I suggest, arise in rare cases when the former assumption is violated.

Jefferson in Cognitive Brown Bag Weds. 9/25 at noon

Brett Jefferson will present on “Using systems factorial technology to understand automation” in Cognitive brown bag Weds. 9/25 at noon in Tobin 521B. Abstract below. All are welcome!

Abstract. Viewing the perception as a systems processing model provides insight and rigor to common questions we have about the way humans understand the world. In this talk, I will discuss one such modeling framework, Systems Factorial Technology, and how it is applied to understanding transparency in automation. Specifically, we apply the capacity coefficient to measure cognitive resource allocation when information about what an automated system is relying on is present to users.

Wallace in CICS Thurs. 9/26 at 11:45

who:  Byron Wallace (Northeastern)
when: Sept 26 (Thursday) 11:45a – 1:15p
where: Computer Science Building Rm 150
food: Athena’s Pizza

What does the evidence say?
Models to help make sense of the biomedical literature


How do we know if a particular medical intervention actually works better than the alternatives for a given condition and outcome? Ideally one would consult all available evidence from relevant trials that have been conducted to answer this question. Unfortunately, such results are primarily disseminated in natural language articles that describe the conduct and results of clinical trials. This imposes substantial burden on physicians and other domain experts trying to make sense of the evidence. In this talk I will discuss work on designing tasks, corpora, and models that aim to realize natural language technologies that can extract key attributes of clinical trials from articles describing them, and infer the reported findings regarding these. The hope is to use such methods to help domain experts (such as physicians) access and make sense of unstructured biomedical evidence.

More specifically, I will discuss models to automatically extract trial population characteristics (e.g., conditions), interventions/comparators (treatments), and outcomes studied in a given clinical trial; together these “PICO” elements compose well-formed clinical questions. I will then present ongoing work on corpora and models for inferring the comparative effectiveness of a given treatment, as compared to a specified comparator, and with respect to a particular outcome of interest. If successfully realized (a big if), such models would effectively facilitate real-time clinical question answering over reports of clinical trials, in turn enabling evidence-based care.
Byron Wallace is an assistant professor in the Khoury College of Computer Sciences at Northeastern University. He holds a PhD in Computer Science from Tufts University, where he was advised by Carla Brodley. He has previously held faculty positions at the University of Texas at Austin and at Brown University. His research is in machine learning and natural language processing, with an emphasis on their application in health informatics.

Wallace’s work has been supported by grants from the National Science Foundation (including a CAREER award), the National Institutes for Health, and the Army Research Office. He won the Tufts University 2012 Outstanding Graduate Researcher award, and his thesis work was recognized as The Runner Up for the 2013 ACM Special Interest Group on Knowledge Discovery and Data Mining (SIG KDD) Dissertation Award. He co-authored the winning submission for the Health Care Data Analytics Challenge at the 2015 IEEE International Conference on Healthcare Informatics, and co-authored the 2017 Distinguished Clinical Research Informatics Paper Award winner at the American Medical Informatics Association Joint Summits on Translational Sciences. He also received the 2018 Early Career Award from the Society for Research Synthesis.

Save the date: Michael Frank Thurs. Nov. 7th at 1 pm

Michael C. Frank of Stanford University ( will be presenting “Children’s language learning; developing understanding of social world” in a talk organized by the Developmental Science Initiative and co-sponsored by the Cognitive Science Initiative. It will take place Thursday November 7th from 1-2 pm at a location to be announced later.


Kim in CICS Thurs. 9/19 at 11:45

who:  Yoon Kim (Harvard University)

when: 09/19 (Thursday) 11:45a – 1:15p

where: Computer Science Building Rm 150

food: Athena’s Pizza

Neural Grammar Induction


Grammar induction is the task of inducing hierarchical syntactic structure from observed sentences alone. It is a longstanding problem in AI/NLP with potential scientific implications for understanding human language acquisition and engineering implications for improving machine learning systems. In this talk, I will discuss two recent works on unsupervised grammar induction with neural networks: (1) a method for learning a good generative model of language (i.e. language model) while at the same time inducing linguistically meaningful tree structures; (2) an approach to learning non-context free grammars by revisiting and extending the classical approach to grammar induction with probabilistic context-free grammars.


Yoon Kim is a fifth-year Ph.D. candidate in computer science at Harvard University. He is advised by Alexander Rush. He is supported by a Google Fellowship.

p-value discussion in Cognitive Brown Bag noon Weds. 9/18

From Jeff Starns

I’m writing to invite you to a discussion entitled “What should we do with p-values?” that will be held next Wed. (9/18) at noon in Tobin 423. Misuse of p-values (p hacking, interpreting p as the chance of error, etc.) is a common theme in discussions about the replication crisis, but you don’t see a lot of discussion on the *proper* use of p-values. We will explore the latter in an open discussion format. We’ll have some Psych 240 instructors to moderate, but we are trying for a conversation, not a lecture. All are welcome to come share their approach to using (or eschewing) p-values. Maybe we’ll be able to add some “Thou shalt”s to all the “Thou shalt not”s in the p-value rule book. This is technically the cognitive brownbag spot, but we would like to get people from other
areas joining the discussion. Hope to see you there!

Nosakhare in Machine Learning and Friends Lunch Thurs. 9/12

who: Ehimwenma Nosakhare
(Microsoft New England Research and Development Center)
when: Sept 12 (Thursday) 11:45a – 1:15p
where: Computer Science Building Rm 150
food: Athena’s Pizza

“Probabilistic Latent Variable Modeling for Predicting Future Well-Being and Assessing Behavioral Influences on Stress”


Health research has an increasing focus on promoting well-being and positive mental health, to prevent disease and to more effectively treat disorders. The availability of rich multi-modal datasets and advances in machine learning methods are now enabling data science research to begin to objectively assess well-being. However, most existing studies focus on detecting the current state or predicting the future state of well-being using stand-alone health behaviors. There is a need for methods that can handle a complex combination of health behaviors, as arise in real-world data.
Building on our previous work where we predict future well-being, in this talk, I’ll present a framework to 1) map multi-modal messy data collected in the “wild” to meaningful feature representations of health behavior, 2) uncover latent patterns comprising multiple health behaviors that best predict well-being, and 3) propose how these patterns may be used to recommend healthy behaviors to participants. We show how to use supervised latent Dirichlet allocation (sLDA) to model the observed behaviors, and we apply variational inference to uncover the latent patterns. Implementing and evaluating the model on 5,397 days of data from a group of 244 college students, we find that these latent patterns are indeed predictive of self-reported stress, one of the largest components affecting well-being. We investigate the modifiable behaviors present in these patterns and uncover some ways in which the factors work together to influence well-being.
This work contributes a new method using objective data analysis to help individuals monitor their well-being using real-world measurements. Insights from this study advance scientific knowledge on how combinations of daily modifiable human behaviors relate to human well-being.


Ehi Nosakhare is an AI Data Scientist at Microsoft’s New England Research and Development Center (NERD). She designs, develops and leads the implementation of machine learning solutions in application projects for Microsoft’s products and services. In August 2018, she earned her Ph.D. in Electrical Engineering and Computer Science (EECS) from the Massachusetts Institute of Technology (MIT), Cambridge, MA. Her PhD research focused on probabilistic latent variable models and applying them to understand subjective well-being. She is generally interested in developing interpretable ML models and using these models to solve real world problems, as a result, she is curious about the ethical implications of AI/ML. Ehi got her S.M. in EECS from MIT, and graduated with a B.Sc. in Electrical Engineering, summa cum laude, from Howard University, Washington DC. As a student, she completed internships at Microsoft and IBM T. J. Watson Research Center. She is a recipient of a best paper award at the NeurIPS ML for Healthcare Workshop. In 2017, she was an organizer for the Women in Machine Learning (WiML) workshop, co-located with NeurIPS. Ehi has been honored as a Tau Beta Pi Scholar and Fellow. In her spare time, she enjoys reading and re-learning to play the cello.