I am currently seeking AI research scientist positions in industry, available immediately, with no hard location constraints.
I am an AI Research Scientist with experience in interdisciplinary biological sciences and high-impact applications for social good. I have a deep interest in graphs/networks. My most recent interests have been in fairness and bias, particularly within complex, graph-structured data. My PhD (defended 2020) focused on data science methodologies for inferring and validating graphs derived from underlying data, with applications in social networks, mobile location privacy, and large-scale multimedia recommendation.
Worked with several Salesforce non-profit customers and external collaborators. Focused on projects relating to fairness and bias, particularly in AutoML platforms, and novel, structured environments such as graphs/networks.
Designed deep learning graph APIs on MXNet. Focused on structured computational models on graphs, and scalability. Part of the Amazon Web Services AI Platforms team. Mentored by Alex Smola.
Developed textual and device models for novelty detection and attribution in the Windows 10 user population. Part of Windows Core Data Science. Mentored by Marcello Hasegawa.
Formulated graph inference problems over several scientific research domains. Mentored by Brian Gallagher
Graph-based models for attribute inference and privacy preservation on real mobile device datasets.
Rule discovery for biometric sensor time series data for actionable analysis of film audiences. Developed methods to discover and visualize dynamic audience communities. Part of the User Analytics team, mentored by Fernando Silveira.
Model selection for graph structure inference and prediction. Focusing on ecology and populations biology domains. Under the direction of Professor Tanya Berger-Wolf.
Time series change detection and anomaly detection on large remote sensing datasets. Focused on incorporating spatial aspects for change significance testing, and domain-driven information retrieval. Part of the Global Observatory for Planetary Health and Resources project under the direction of Professor Vipin Kumar. M.S. thesis focused on efficient time-series subsequence similarity search in large datasets.
Thesis: Network Structure Inference: Methodology and Applications (Advisor: Prof. Tanya Berger-Wolf)
Thesis: Approximate Search on Massive Spatiotemporal Datasets (Advisor: Prof. Vipin Kumar)
2020: AAAI, ICDM, KDD, SDM
2019: AAAI, CIKM, ICDM, KDD, SDM
Conferences: AAAI, CIKM, ICDM, IJCAI, KDD, PAKDD, SDM
Journals: ACM CSUR, IMS AOAS, IEEE TKDE, KAIS