Girmaw Abebe Tadesse
Speaker at AMLD Africa 2024
Girmaw is a Principal Research Scientist and Manager at Microsoft AI for Good Research Lab which aims to develop AI solutions for critical problems across sectors including agriculture, healthcare, biodiversity, etc. Prior to that he was a Staff Research Scientist at IBM Research Africa working on detecting and characterizing systematic deviations in data and machine learning models. At IBM Research, Girmaw led multiple projects in trustworthy AI including evaluation of generative models, representation analysis in academic materials and data-driven insight extraction from public healthy surveys, with active collaborations with external institutions such as Bill & Melinda Gates Foundation, Stanford University, Oxford University and Harvard University. Previously, Girmaw also worked as a Postdoctoral Researcher at the University of Oxford, where he primarily developed deep learning techniques to assist diagnosis of multiple diseases, with collaborations with clinicians and hospitals in China and Vietnam. Girmaw completed his PhD at Queen Mary University of London, under the Erasmus Mundus Double Doctorate Program in Interactive and Cognitive Environments, with a focus on computer vision and machine learning algorithms for human activity recognition using wearable cameras. He has interned/worked in various research groups across Europe, including the UPC-BarcelonaTech (Spain), KU Leuven (Belgium), and INESC-ID (Portugal). Girmaw is an Executive Member for IEEE Kenya Section, and he is currently serving as a reviewer and program committee member for multiple top-tier AI focused journals and conferences.
At AMLD Africa
United States International University Africa, Nairobi, Kenya
Theory-to-Practice: Ensuring positive impact from ML solutions
With a growing adoption of machine learning (ML) solutions across different sectors, it is critical to ensure its positive societal impact. In this work, I will share key practices including the role of domain experts in the process and data valuation for potential systematic deviations to achieve trustworthy ML. Similarly, I reflect on our recent works across climate/sustainability and healthcare including automated identification and characterization of systematic deviations for various tasks, including data quality understanding, temporal drift, treatment effects analysis, and new class detection. Furthermore, I will also share how our generalized approach helps to evaluate capabilities of generative models in domain-agnostic and interpretable ways. I argue that similar data-centric analysis should also extend to traditional data sources beyond curated ML datasets.
