By: Utopia.
Jing Gao, a Research Scientist at Meta Platforms, Inc., has been making significant strides in the field of algorithm development for airline networks, promising substantial benefits for both travelers and airline operators globally. Her groundbreaking work integrates non-stationary and structured properties of airline networks, leading to more robust and efficient solutions that reduce travel costs by an average of 5%. This reduction translates to significant savings in expenses, shorter travel times, and reduced fuel consumption, all of which contribute to a more sustainable aviation industry.
Integrating Non-Stationary and Structured Properties
Dr. Gao’s unique approach involves the integration of non-stationary and structured properties in the airspace using real-life NOAA data. This methodology captures both the linear and exponential kernel structures of the airspace and the dynamic nature of airline networks and their inherent structured dependencies. “This allows my algorithms to adapt to changing conditions, such as fluctuating demand and varying operational constraints, resulting in more robust and efficient solutions,” Jing explains. The combination of these elements improves the accuracy and reliability of predictions, ultimately enhancing the overall effectiveness of the algorithms in optimizing airline operations.
Impact on Travel Costs
The 5% reduction in travel costs achieved through Jing’s algorithms primarily affects several key areas:
Environmental Sustainability
Dr. Gao’s algorithms also contribute significantly to environmental sustainability. By optimizing flight routes and schedules to minimize fuel consumption, her work helps lower carbon emissions. “By reducing unnecessary fuel usage, the algorithms support the aviation industry’s goals of achieving greater sustainability,” she notes. Additionally, these algorithms have applications beyond traditional aviation, such as in the operation of drones, further supporting environmental sustainability efforts.
Professional Development and Contributions
Dr. Gao’s professional journey includes chairing sessions and giving tech talks at multiple INFORMS Annual Meetings, the largest academic association for operations research. “These experiences have significantly influenced my research and professional development by exposing me to cutting-edge ideas and fostering connections with leading experts in the field,” Jing shares. Such experiences have provided valuable feedback on her work, inspired new research directions, and enhanced her presentation and communication skills, crucial for effectively disseminating research findings.
Leadership in Academic Community
As Vice President of the INFORMS Student Chapter at UMN, Jing initiated tech talks and fostered academic camaraderie among doctoral students in operations research. “These activities have benefited the doctoral student community by creating a platform for knowledge exchange, networking, and collaboration across different departments and universities,” she explains. Her efforts led the student chapter to win the 2021 and 2022 Student Chapter Annual Award as an honorable mention chapter. Jing advises those looking to enhance academic collaboration to seek opportunities for interdisciplinary interactions, encourage open communication across various areas and departments, and build a vibrant, supportive environment for idea sharing.
Innovative Contributions at Meta
At Meta, Dr. Gao played a crucial role in enhancing ads ranking models using large datasets. Her approach involved continuous iteration and rigorous testing, refining feature engineering, incorporating new data sources, and applying advanced machine learning techniques. “I focused on ranking accuracy and efficiency to enhance user engagement and information relevance,” she explains. This work led to more effective ad ranking and delivery, increased user engagement, and ultimately higher ad revenue, particularly benefiting small business owners.
During her tenure as a Machine Learning Engineer Intern at Meta, Jing collaborated on designing data frameworks and feature engineering, which was pivotal for developing and improving Sparse-NN models. “These collaborations enabled us to create robust data pipelines and identify key features that significantly enhanced model performance,” she notes. The advancements in these models resulted in over 1% NE gains, significantly improving ad scores and revenue. Jing continues to collaborate on enhancing these models across a broader range of advertising scenarios, contributing to substantial revenue growth and supporting economic development.
Dr. Gao’s contributions to both the airline industry and digital advertising demonstrate her commitment to innovation, efficiency, and sustainability. Her work not only advances technological prowess but also provides practical, impactful solutions with far-reaching benefits.
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Published by: Martin De Juan
