| Welcome to Shivani Rao's webpage |
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Pinterest (July 2024 - now), ML Manager, Notification Delivery
- Managed a team of 13 AI/ML engineers and platform engineers + 2 contractors at Pinterest, focusing on developing and maintaining Notification systems and models for user engagement.
- Led AI/ML projects including delivery budget optimization, personalized Delivery Time optimization, and final stage ranking using transformer-based models and constrained optimization.
- Achieved increased user engagement and re-engagement through innovative AI/ML solutions, contributing to Pinterest's growth in the competitive tech industry.
LinkedIn (Aug 2022 - July 2024), ML Manager, Knowledge Graph
Leading two teams with a total 11 AI/ML engineers and researchers on understanding our members and their relationship to entities in the LinkedIn ecosystem. Utilizing a variety of techniques including but not limited to Entity Resolution, Relationship extraction, Member activity understanding, Prompt engineering to aid in labeled data collection and identifying gaps in taxonomy
Leading two teams with a total 11 AI/ML engineers and researchers on understanding our members and their relationship to entities in the LinkedIn ecosystem. Utilizing a variety of techniques including but not limited to Entity Resolution, Relationship extraction, Member activity understanding, Prompt engineering to aid in labeled data collection and identifying gaps in taxonomy
LinkedIn (May 2021 - Aug 2022), ML Manager, Jobs Fraud AI
After serving several years as an Individual Contributor in the field of Online Education (Learning AI) and Ads (Revenue objective), I forayed into the world of management in a new area of application of AI - i.e. Trust. Led a team of 5 AI/ML engineers in fighting Jobs Abuse on LinkedIn Platform. Utilizing both behavioral and content based signals to build online and offline AI models to prevent and mitigate Jobs Fraud on LinkedIn platform. To learn more about what my team does please visit this blog
After serving several years as an Individual Contributor in the field of Online Education (Learning AI) and Ads (Revenue objective), I forayed into the world of management in a new area of application of AI - i.e. Trust. Led a team of 5 AI/ML engineers in fighting Jobs Abuse on LinkedIn Platform. Utilizing both behavioral and content based signals to build online and offline AI models to prevent and mitigate Jobs Fraud on LinkedIn platform. To learn more about what my team does please visit this blog
LinkedIn (Dec 2019 - May 2021), Ads
In the Ads AI, I work on the problem of response prediction, in particular, predicting view-through rate of video-ads. We are investing heavily in building and leverage embeddings derived from the frames of the videos to help predict response on those videos. In particular, we are investigating using content embeddings derived from text, image/video of the ad to help in variety of prediction tasks in the Ads domain. To learn more about these applications please visit the KDD tutorial. More recently, my work has shifted to focus on building response prediction pipelines from scratch for the Stories product, where we find the most relevant ad to show in the Stories to drive revenue for LinkedIn.
In the Ads AI, I work on the problem of response prediction, in particular, predicting view-through rate of video-ads. We are investing heavily in building and leverage embeddings derived from the frames of the videos to help predict response on those videos. In particular, we are investigating using content embeddings derived from text, image/video of the ad to help in variety of prediction tasks in the Ads domain. To learn more about these applications please visit the KDD tutorial. More recently, my work has shifted to focus on building response prediction pipelines from scratch for the Stories product, where we find the most relevant ad to show in the Stories to drive revenue for LinkedIn.
LinkedIn (Nov 2015 - Dec 2019), Learning AI
As a founding member of the LinkedIn Learning AI team, I forayed into the field of Machine Learning for Ed-Tech (Education Technology). I helped build several key components of the course-recommendation pipeline, from cold-start models to interaction feature mining using gradient boosted trees, tagging courses with skill using text understanding, to building Online REST end points to serve recommendations, end-to-end testing of these offline and online pipelines by running large scale experiments on the LinkedIn's A/B testing platform. My work spanned areas of Supervised and Unsupervised model building, Recommender systems, and text understanding. You can visit the blogs I authored (Personalized Course Recommendations, Micro-Content for Micro-learning) or read the papers I published (Learning to be Relevant: Evolution of a Course Recommendation System at CIKM 2019, Video to Skill Tagging using Transcripts under Weak Supervision at NuerIPS Workshop in 2017)
As a founding member of the LinkedIn Learning AI team, I forayed into the field of Machine Learning for Ed-Tech (Education Technology). I helped build several key components of the course-recommendation pipeline, from cold-start models to interaction feature mining using gradient boosted trees, tagging courses with skill using text understanding, to building Online REST end points to serve recommendations, end-to-end testing of these offline and online pipelines by running large scale experiments on the LinkedIn's A/B testing platform. My work spanned areas of Supervised and Unsupervised model building, Recommender systems, and text understanding. You can visit the blogs I authored (Personalized Course Recommendations, Micro-Content for Micro-learning) or read the papers I published (Learning to be Relevant: Evolution of a Course Recommendation System at CIKM 2019, Video to Skill Tagging using Transcripts under Weak Supervision at NuerIPS Workshop in 2017)
Box Inc (Nov 2013 - Oct 2015), Software Engineer, ML Team
At Box, I was involved in building the Machine Learning platform for improving user experience through data intelligence. My work involved analyzing usage data and content to come up with suitable ML algorithms for various product use cases. I was also involved in developing scalable and reliable software for these ML algorithms in Apache Spark. For a brief period of time, I was involved in supporting and building the data- analytics Infrastructure at Box, and thus, have accrued basic experience in technologies like Kafka, RedShift, Storm, Hive, Hadoop and ETL. Click here to learn more about my work at Box.
At Box, I was involved in building the Machine Learning platform for improving user experience through data intelligence. My work involved analyzing usage data and content to come up with suitable ML algorithms for various product use cases. I was also involved in developing scalable and reliable software for these ML algorithms in Apache Spark. For a brief period of time, I was involved in supporting and building the data- analytics Infrastructure at Box, and thus, have accrued basic experience in technologies like Kafka, RedShift, Storm, Hive, Hadoop and ETL. Click here to learn more about my work at Box.
Google Inc (May 2010- Aug 2010), Software Engineering Intern
Streetview panaromic views are heavily dependent on the pose of the vehicle as captured by the GPS. In big cities, the GPS data is grossly erroneous. In such situations, images can be used to correct the pose of the vehicle offline. The pose can be corrected by using a computer vision algorithm called loopclosing. The candidate intersections are pre-computed using GPS information and transform compute to align the vehicle pose at these intersections. The alignment is used to perform optimization in order to estimate the true location of the vehicle. My internship at Google was to develop and test appearance-based loop-closing algorithms on street view data, and suggest evaluation measures of performance of the algorithms.
Streetview panaromic views are heavily dependent on the pose of the vehicle as captured by the GPS. In big cities, the GPS data is grossly erroneous. In such situations, images can be used to correct the pose of the vehicle offline. The pose can be corrected by using a computer vision algorithm called loopclosing. The candidate intersections are pre-computed using GPS information and transform compute to align the vehicle pose at these intersections. The alignment is used to perform optimization in order to estimate the true location of the vehicle. My internship at Google was to develop and test appearance-based loop-closing algorithms on street view data, and suggest evaluation measures of performance of the algorithms.
Nvidia (Aug 2006- June 2007), Software Engineering
After the successful completion of MS in IIT Madras, I joined the video decode display team at Nvidia Graphics India. The work involved writing and modifying driver code at the kernel level in order to implement functionalities like post processing, image scaling, in-loop de-blocking using the GPU (Graphics processor unit). The job required one to learn or be aware of the various video-coding standards like MPEG-2, MPEG-4, WMV, H.264 and VC, knowledge of Windows OS like Windows XP, Windows Vista, co-processor architecture, low-level C programming
After the successful completion of MS in IIT Madras, I joined the video decode display team at Nvidia Graphics India. The work involved writing and modifying driver code at the kernel level in order to implement functionalities like post processing, image scaling, in-loop de-blocking using the GPU (Graphics processor unit). The job required one to learn or be aware of the various video-coding standards like MPEG-2, MPEG-4, WMV, H.264 and VC, knowledge of Windows OS like Windows XP, Windows Vista, co-processor architecture, low-level C programming
