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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About Me
Ghanaian student pursing a PhD in the Univeristy of Washington.
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Posts
Animal Monitoring Chronicles (Part 1)
Published:
Low Resource Wildlife Monitoring
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
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
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 492 R - Guided Undergrad Research Project
Mentorship, University of Washington, Computer Science Department, 2023
Mentoring three UW undergraduates in an Undergraduate Research Program course as part of my research work on low resource ML for ecology.
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.