Zeinab Zolaktaf's Homepage

Experience

Amazon - Applied Scientist (2022–Present), Vancouver, Canada

Architected an internal LLM experimentation and evaluation toolkit that significantly cut time-to-production for new ML applications and became the foundation for multiple production generative AI applications across teams
Built reusable, scalable reference-based LLM judges for fact-checking, used across many projects
Used agentic workflows to ramp up in an unfamiliar domain and deliver an LLM judge for item analysis - approximating continuous variables via prompting

Elpha Secure - Senior Applied Scientist (2021–2022), Vancouver, Canada

Led cross-team efforts building ML products for cybersecurity, mainly anomaly detection and classification
Designed an ML pipeline to speed up experimentation and deployment
Automated model deployment with lightweight, scalable Kubernetes scripts

Olyns - Machine Learning Consultant (2021), San Francisco, USA

Verified existing ML models and supported their deployment on AWS

Georgian - Applied Data Scientist (2020–2021), Toronto, Canada

Performed deep error analysis on an existing ML product to help business and engineering stakeholders understand its behavior
Designed NLP models for hierarchical text classification in the financial and marketing domain

EhsAI - NLP and Machine Learning Scientist (2019–2020), Vancouver, Canada

Built NLP deep learning models for document intelligence in the environment, health, and safety domain
Designed data augmentation and synthetic data generation methods to reduce manual labeling
Built an ML framework for deploying and evaluating pretrained language models for classification tasks

PhD, University of British Columbia (2012–2019), Vancouver, Canada

Designed recommender systems for data exploration, published at ICDE and UMAP
Built deep learning models (LSTM/CNN) to predict SQL query properties before execution, published at SIGMOD
Developed statistical methods for estimating SQL query answer properties in data integration settings, published at EDBT

MSc, Dalhousie University (2009–2012), Halifax, Canada

Built a statistical topic model for question answering and automatic tagging in community Q&A archives
Studied expertise modeling to route new questions to the right experts

BSc in Computer Software Engineering, Isfahan University (2004–2008), Isfahan, Iran

Built autonomous soccer-playing agents for the “UI-AI” RoboCup team over three years - my entry point into AI

Publications

Workload-Aware Query Recommendation Using Deep Learning (EDBT, 2023)

This paper studies how workload context can improve recommendations for database queries. It uses deep learning to suggest useful queries while accounting for the patterns and needs represented in a user's existing workload.

Facilitating SQL Query Composition and Analysis (SIGMOD, 2020)

This work presents techniques for helping people construct and understand SQL queries. It focuses on supporting the full interaction around a query, from composing a statement to examining its behavior and results.

Improvement of SQL Recommendation on Scientific Database (SSDBM, 2019)

This paper investigates how SQL recommendations can be improved for scientific databases. The work considers the specialized workloads and query patterns found in scientific data analysis to make recommendations more useful to researchers.

A Generic Top-N Recommendation Framework for Balancing Accuracy, Novelty, and Coverage (ICDE, 2018)

This paper introduces a general framework for designing Top-N recommenders that balance several competing goals. In addition to accuracy, it considers whether recommendations are novel and whether they provide broad coverage of the available items.

Bridging the Gap Between User-Centric and Offline Evaluation of Recommendation Systems (UMAP, 2018)

This work examines the relationship between offline recommender-system metrics and the experience of real users. It highlights why evaluation should connect algorithmic measurements with user-centered outcomes rather than relying on a single offline score.

Facilitating User Interaction With Data (PhD@VLDB, 2017)

This research explores ways to make data systems more approachable for people who need to ask questions of data. It brings together query composition, recommendation, and answer analysis to support users throughout an interactive data exploration process.

Extracting Aggregate Answer Statistics for Integration (EDBT, 2015)

This paper studies how aggregate statistics about answers can be extracted and used to support data integration. The approach helps systems reason about query answers and combine information in a way that is useful for subsequent analysis.

Finding Expert Users in Community Question Answering (WWW, 2012)

This work investigates how to identify expert contributors in community question-answering archives. It uses signals from user activity and answered questions to help distinguish knowledgeable participants who can provide reliable guidance.

Modeling Community Question Answering Archives (NeurIPS Workshop, 2011)

This paper models the content and activity found in community question-answering archives. The goal is to better understand how questions, answers, and contributors relate to one another in these collaborative knowledge spaces.

Theses

Facilitating User Interaction With Data (PhD Thesis, UBC, 2019)

This dissertation develops methods for helping users explore data, compose queries, and interpret query answers. It combines user-centered interaction design with recommendations and analysis techniques for making database systems easier to use.

Probabilistic Modeling in Community-Based Question Answering Services (MSc Thesis, Dalhousie, 2012)

This thesis applies probabilistic modeling to community-based question-answering services. It studies the people and content in these systems to improve understanding of expertise, participation, and the organization of shared knowledge.

Posters and Demos

Facilitating Data User Interaction With Data (Microsoft Research AI Breakthroughs Workshop, 2019)

This demonstration presents an interactive approach to helping people work with data. It emphasizes practical user interaction and shows how research ideas can support more direct and productive data exploration.

Facilitating Data Exploration, Query Composition, and Query Answer Analysis (NWDS, 2018)

This demo brings together several stages of a data workflow in one user-facing experience. It supports exploring data, composing queries, and examining answers so that users can move more easily from an initial question to an informed result.

Personalized Top-N Recommendation for Promoting Long-Items (WIML/NeurIPS, 2017)

This work considers how personalized Top-N recommendation can help surface longer or less frequently selected items. It focuses on recommendation strategies that account for user interests while improving visibility beyond the most obvious short-list choices.

Talks

Generative AI Tutorial - Amazon Machine Learning Conference (AMLC)
Facilitating SQL Query Composition and Analysis - SIGMOD Conference, Portland, OR (2020)
Facilitating User Interaction With Data - Huawei Noah's Ark Lab, Toronto (2019)
Facilitating User Interaction With Data - Thomson Reuters, Toronto (2019)
Bridging the Gap Between User-Centric and Offline Evaluation of Recommendation Systems - UMAP Workshop, Singapore (2018)
A Generic Top-N Recommendation Framework for Balancing Accuracy, Novelty, and Coverage - ICDE, Paris (2018)
Facilitating User Interaction With Data - PhD@VLDB Workshop, Munich (2017)
Extracting Aggregate Answer Statistics for Integration - EDBT, Brussels (2015)