Posts by Collection

portfolio

publications

Multivariate Time-Series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection

Published in IEEE Sensors, 2018

We use a deep autoencoder approach to learn representations of multivariate physiological signals that can be hashed and used to compute similarities between patients to assist in predicting critical events.

Recommended citation: Dhamala, J., Azuh, E., Al-Dujaili, A., Rubin, J., & O’Reilly, U. M. (2018). Multivariate time-series similarity assessment via unsupervised representation learning and stratified locality sensitive hashing: Application to early acute hypotensive episode detection. IEEE Sensors Letters, 3(1), 1-4. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8506445

Towards Bilingual Lexicon Discovery From Visually Grounded Speech Audio

Published in Interspeech, 2019

This work presented a multimodal approach to learn bilingual lexicon directly from speech signals in two languages without the need for text by using vision as an interlingua. The approach starts a line of inquiry that can build word level translation between a pair of languages using say youtube videos that have similar objects but with speech in the two languages.

Recommended citation: Azuh, Emmanuel, David Harwath, and James R. Glass. "Towards Bilingual Lexicon Discovery From Visually Grounded Speech Audio." INTERSPEECH. 2019. http://groups.csail.mit.edu/sls/publications/2019/EmmanuelAzuh_Interspeech-2019.PDF

When Borders Blur - Overcoming Political Limits with Computing in Truly Global Societies

Published in Computing Within Limits, 2021

In the face of ecological and political limits, we propose a computational approach to opening borders to migrants in an automated way while ensuring safety for the host nation.

Recommended citation: Mensah, E. A., Singanamalla, S., Anderson, R., & Heimerl, K. (2021). When Borders Blur-Overcoming Political Limits with Computing in Truly Global Societies. https://computingwithinlimits.org/2021/papers/limits21-mensah.pdf

talks

teaching

Deep Learning Practicum (MIT 6.S198)

Undergraduate course, MIT, 2018

Created the computer vision component of a practical deep learning class launched in spring 2018, led recitations and mentored student teams in their end of semester projects.

CSE550 - Computer Systems (for All)

Class, University of Washington, Computer Science Department, 2021

Prepared teaching material and assisted learning for graduate students taking the systems-for-all breadth course.

CSEP561 - Networking Systems

Class, University of Washington, Computer Science Department, 2022

Sole TA for a professional masters students computer networking course of about 50 students, responsible for office hours and grading as well as running a lecture on machine learning approaches for networking and networking systems for machine learning.

CSE 446/546 - UW Undergraduate Machine Learning

Teaching Assistant, University of Washington, Computer Science Department, 2023

Served as one of the Teaching Assistants for UW Undergraduate Machine Learning course. Responsibilities included leading section, holding office hours to assist with debugging pytorch code and explaining ML fundamental concepts, and grading assignments and quizzes.