Zuha Agha
Engineering Manager 2 at Apple
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolZuha Agha is an Engineering Manager with a decade of experience building and leading machine learning teams in the San Francisco Bay Area, currently driving Siri Search web ranking and document understanding at Apple. She progressed from hands-on ML engineering to management, bringing deep expertise in query/document relevance, feature engineering, and production model delivery. Her open-source work with Numenta—contributing to HTM implementations and the Numenta Anomaly Benchmark—highlights a strong background in anomaly detection and real-world streaming analytics. Zuha blends research experience (CVPR-related visual recognition work) with practical systems skills from graphics and large-scale search, and she has a proven record of improving algorithm performance and shipping robust ML systems. Notably, she has successfully translated neuroscience-inspired models into applied anomaly detectors and integrated them with domain examples like NYC taxi data.
10 years of coding experience
6 years of employment as a software developer
Bachelor of Science (BS) Computer Science, Bachelor of Science (BS) Computer Science at Lahore University of Management SciencesMaster’s Degree Computer Science, Master’s Degree Computer Science at University of Pittsburgh
English, Urdu, French
Github Skills (13)
hierarchical-temporal-memory10
algorithms10
machine-learning10
time-series10
anomaly-detection10
python10
data-science10
numpy10
data-analysis10
scikit-learn9
scikit9
unit-testing9
json8
Programming languages (2)
Github contributions (5)
numenta/NAB
May 2016 - Aug 2016
The Numenta Anomaly Benchmark
Role in this project:
Data Scientist
Data ScientistContributions:71 commits, 12 PRs, 6 pushes in 2 months
Contributions summary:Zuha primarily contributed to the development of anomaly detection algorithms within the Numenta Anomaly Benchmark (NAB) repository. Their work involved creating and refining various anomaly detection models, specifically implementing a new TM detector subclass, a Gaussian sliding window detector, and integrating the Numenta TM detector. The user also modified existing detectors, optimized the code, and ensured backward compatibility. These contributions centered on improving the benchmark's ability to identify anomalies.
benchmarkanomalymachine-learningnumenta
numenta/nupic-legacy
May 2016 - Aug 2016
Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.
Role in this project:Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:16 commits, 2 PRs, 8 comments in 3 months
Contributions summary:Zuha primarily focused on modifying and extending the `nupic-legacy` repository's temporal memory implementation, particularly within the context of anomaly detection. Their contributions included adding parameters for different temporal memory subclasses, modifying variable names related to temporal memory, and adding new unit tests for specific temporal memory configurations. The user also worked on integrating a NYC taxi example for anomaly detection, demonstrating their involvement in applying the project's core technology to real-world data analysis. This implies a focus on both back-end logic and the application of HTM models.
htmmemoryneurosciencedeep-learninghierarchical
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