Emmanuel Azuh Mensah

Emmanuel Azuh Mensah

PhD candidate, UW Allen School. I build efficient, on-device ML for the field. Open to industry ML / research roles.

Publications

Embedded Arena: Iterative Optimization via Hardware Feedback

Published in NeurIPS 2026 (Spotlight), 2026

A collaborative project (equal-contribution leads Zhihan Zhang, Alexander Le Metzger, and Jiuyang Lyu) building a hardware-in-the-loop agentic system that co-optimizes models for heterogeneous microcontrollers under hard memory, power, and temperature constraints — 250× compression for vision (<3.3% accuracy loss) and 400× for audio, enabling battery-free operation on a commercial MCU via solar harvesting.

Recommended citation: Zhang, Z.*, Le Metzger, A.*, Lyu, J.*, Chang, C.-C., Shao, J., Liu, Y., Azuh Mensah, E., Wang, E., Heimerl, K., Abowd, G. D., Patel, S., Jaques, N., & Iyer, V. "Embedded Arena: Iterative Optimization via Hardware Feedback." NeurIPS 2026 (Spotlight). (* equal contribution) https://www.alphaxiv.org/abs/2606.16190

The Promise and Peril of On-Device AI for Conservation Work

Published in CHI Conference on Human Factors in Computing Systems (CHI 2026), 2026

A field study across Pacific Northwest & Namibia conservancies with an on-device transcription→LLM prototype on EarthRanger, analyzing when on-device AI actually helps conservation field staff.

Recommended citation: Dong, C., Azuh Mensah, E., Ranganathan, V., & Heimerl, K. (2026). The Promise and Peril of On-Device AI for Conservation Work. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems. https://dl.acm.org/doi/10.1145/3772318.3791359

Wildfire and Forest Management: Opportunities for HCI Research

Published in ACM Transactions on Computer-Human Interaction (TOCHI), 2026

Interviews with wildfire and forest-management practitioners reveal socio-technical challenges in adopting geospatial technologies — fragmented data, knowledge-sharing barriers, and model-bias concerns — and where HCI can help.

Recommended citation: Migineishvili, N., Grunde-McLaughlin, M., Azuh, E., Wood, S., Just, R., & Reinecke, K. (2026). Wildfire and Forest Management: Opportunities for HCI Research. ACM Transactions on Computer-Human Interaction. https://dl.acm.org/doi/10.1145/3765288

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

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

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