I'm excited about collaborating across disciplines at Northeastern to build more equitable models and understand the real human impacts of machine learning.

– Tomo Lazovich, Senior Research Scientist, EAI

Tomo Lazovich (they/them) is a senior research scientist at the Institute for Experiential AI at Northeastern University. Prior to joining the institute in 2023, they were a senior machine learning researcher at Twitter, developing a suite of metrics to measure inequality in outcomes for the ML Ethics, Transparency, and Accountability (META) team. 

Lazovich has a significant amount of experience as an interdisciplinary researcher and machine learning practitioner, with defined expertise in building technical solutions to complex problems from the ground up. Their work currently focuses on understanding the impacts of algorithmic systems, developing novel technical approaches that better account for model impacts, and imagining more just and equitable socio-technical infrastructures. 

They are also dedicated to bridging the gap between academia, industry practitioners, and policymakers to better operationalize responsible machine learning practice.

In their pre-Twitter days, Lazovich worked as an ML team lead at Lightmatter, adapting algorithms to the computer hardware startup’s novel photonics-based hybrid digital-analog architecture, and at non-profit engineering company Draper, building deep learning architectures for the identification and repair of bugs in source code as part of the DARPA-funded MUSE program. 

Lazovich holds a Ph.D. in Physics from Harvard University, where their thesis was based on the discovery and subsequent study of the Higgs Boson at the Large Hadron Collider in Switzerland. In addition, they are also currently a part-time JD candidate at Northeastern University, hoping to fuse their technical knowledge with legal expertise to build practical regulatory solutions for AI. In their free time, you can find them building LEGO projects, video and board gaming, or yelling at the TV about any number of sporting events.