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Oleg Aulov (Fed)
Computer Scientist
Dr. Oleg Aulov is a Computer Scientist at the National Institute of Standards and Technology's Information Technology Laboratory. Oleg’s research interests focus on performance measurement, metrics, and evaluation of the 3rd wave of AI systems targeting Human Language Technology.
Currently, Oleg is a PI on the evaluation of Knowledge Directed AI Reasoning over Schemas (KAIROS) Program, funded by DARPA. The program seeks to develop multimodal, multilingual reasoning systems capable of predicting real world events.
While at NIST, Oleg has contributed to the design of tasks and metrics for an array of past evaluations including: DARPA's Active Interpretation of Disparate Alternatives Evaluation; Speech Analytic Technologies Evaluation; Speaker Recognition Evaluation Vendor Testing (SREVT); Data Science Evaluation (DSE); and DARPA’s Low Resource Languages for Emergent Incidents Evaluation (LORELEI).
For his Ph.D. dissertation, Oleg researched ways of extracting quantifiable data from heterogeneous social media sources and assimilating it into a variety of geophysical models in order to improve analysis, forecast and situational awareness during catastrophic events such as the Gulf oil spill or Hurricane Sandy. Through this work Oleg developed the concept of a human sensor network.
Prior to pursuing his Ph.D. Oleg was a research assistant at NASA Goddard working on the Autonomous Objectively Optimized Observation Direction System (OOODS) for future constellations of earth observing satellites. Oleg implemented the sensor web simulator and the target scheduler components of the system - the observation targets were selected using the uncertainty matrix and the scheduling priorities were made using the information content matrix.
Building the Future grants:
Evaluation of temporal reasoning capabilities of 3rd wave of AI systems – 2021
Understanding Methods for Fusing Human Experts and AI Systems That Produce Explainable Answers – 2019
Building Blocks of Practical AI: Emerging Processors, Algorithms, and Frameworks – 2018
Deep Learning and Neuromorphic Computing – 2017
Developing Expertise in Deep Learning Neural Networks for ITL Science Programs – 2016
NSF grants:
NSF RAPID: Near Real-Time Quantifiable Social Media Data for Improved Modeling, Tracking and Mitigating the Spread of the Ebola Virus – 2015
NSF RAPID: Rapid Response for a Human Sensor Aware Fukushima Debris Monitor and Prediction System – 2012