Invited talk at Interpreting, Explaining and Visualizing Deep Learning Invited talk at Reliable ML in the wild workshop ICML 2017 Invited talk at Human in the Loop Machine Learning workshop ICML 2017 Invited keynote at Visualization for AI Explainability at IEEE VISi 2018 Invited tutorial at Deep Learning Summer School at University of Toronto/Vector institute 2018 Invited talk at Interpretable Machine Learning for Computer Vision CVPR 2018 Invited talk at CodeX FutureLaw conference at Stanford law school 2018 Invited talk at Statistics and Data Science Conference (SDSCon) at Data Systems and Society at MIT Invited talk at NIPS 2018 workshop on Security in Machine Learning 2018 Invited interview with Parsing science podcast: designed to explain science for lay people. Invited talk at SimonвЂ™s workshop on Foundations of Deep Learning at UC Berkeley: Frontiers of Deep Learning Invited talk at SimonвЂ™s workshop on Foundations of Deep Learning at UC Berkeley: Emerging Challenges in Deep Learning Invited tutorial at Dagstuhl вЂњMachine Learning Meets Visualization to Make Artificial Intelligence InterpretableвЂќ 2019 Invited tutorial at Deep Learning for Science School at Lawrence Berkeley National Laboratory 2019 Invited lecture at UC Berkeley for seminar course Special Topics in Deep Learning Invited lecture at Stanford Dawn seminar 2019 Invited talk at Safe Artificial Intelligence for Automated Driving CVPR 19 Invited talk at Workshop on Explainable AI CVPR 19 Invited talk at Security and privacy in ML workshop ICML 19 Invited panel at Science meets Engineering of Deep Learning workshop Neurips 19

Invited panel at Minding the Gap workshop at Neurips 19 Invited talk at Human centered ML (HCML) workshop at Neurips 19 Invited talk at Neurips Expo 19, Google speaker Invited keynote at Visualization in Data Science (VDS) 2019 Invited talk at IAS on New Directions in Optimization, Statistics and Machine Learning April 15-17, 2020

Invited talk at Women in Data Science at Stanford 2020 Invited forum at Humans of AI, Stanford 2020 Guest lecture at Stanford class CS81SI 2020 Invited talk Robust machine learning group at Oxford, 2021 Invited talk Distinguished Speakers: Oxford Women in Computer Science Invited panel AAAI 2021 Explainable Agency in Artificial Intelligence Workshop MIT career forum: Academia vs Industry? 2021 Invited talk at Distinguished Speakers: Oxford Women in Computer Science Invited talk at ICLR-21 Workshop on Responsible AI Invited lecture at Machine Learning Summer School 2021 MLSS ICAPS 2021 International Workshop of Explainable AI Planning (XAIP) Invited talk at CVPR SAIAD 2021: Safe Artificial Intelligence for Automated Driving Invited talk at ICML 2021 Workshop on Algorithmic Recourse Invited tutorial at Annual Machine Learning Summer School 2021 (not an exhuastive list and not in a completely chronological order.) Tutorial on Interpretable machine learning at ICML 2017 ( slides, video). Machine Learning Summer School MLSS 2021 ( slides, video)Дeep Learning Summer school at University of Toronto, Vector institute in 2018 ( slides, video) Steering committee and area chair at FAccT conferenceДЇormer executive board member and VP of Women in Machine Learning.Д¬o-organizer of multi-year workshops of Human interpretability in ML (WHI) at ICMLДЄnd NIPS 2016 Worshop on Interpretable Machine Learning for Complex Systems. Senior Area chair/Area chair/Senior program committee NeurIPS 2017-now, ICML 2019-now, ICLR 2020-now, AISTATS 2020-now One of my work TCAV received UNESCO Netexplo award, was featured at Google I/O 19' and in Brian Christian's book on The Alignment Problem. The language that humans and machines communicate must be human-centered- higher-level, human-friendly concepts-so that it can make sense to everyone, regardless of how much they know about ML.ДЄt the G20 meeting in Argentina in 2018. Quanta magazine (written by John Pavlus) is a great description of what I do and why. I am interested in helping humans to communicate with complex machine learning models: not only by building tools (and tools to criticize them), but also studying their nature, compared to humans.

Kimberly beem