👋 Hi there, thanks for visiting!
I'm Ran. I currently work at Instagram as an Engineering Manager, supporting a machine learning team that handles the ranking of Instagram's home feed. Previously, I worked on Amazon SageMaker and Amazon Kendra at AWS AI. Before switching to software, I did my PhD on semiconductor physics and commercialized the research through a hardware startup.
Longer version about my journey so far 👇
PhD and OpSIS
Ran Ding received his Ph.D. in EE from the University of Washington (UW), Seattle in 2014. With support from Intel, his Ph.D. research contributed to the creation of an open-access foundry platform OpSISNature article, which made silicon photonics technology accessible to more than 150 research and industry institutions. For example, researchers at CalTech and MIT used chips fabricated at OpSIS for cutting-edge research in 3D LiDAR sensing, quantum computing, and deep learning computation acceleration.
- A Silicon Platform for High-Speed Photonics Systems (OFC'12, invited talk)
- A Compact Low-Power 320-Gb/s WDM Transmitter Based on Silicon Microrings
- 100-Gb/s NRZ optical transceiver analog front-end in 130-nm SiGe BiCMOS (OI'14)
During his PhD, Ran co-founded a fabless (Design-as-a-Service) startup commercializing silicon photonics technology. The startup was acquired in 2014 and later rebrandedOVUM article as Elenion, where he continued to lead a 15-person core technical group and delivered the companies first commercial productIMRA in 2017. Nokia acquired Elenion in March 2020.
In 2017, I left my job as a Director at Elenion and switched career to software, you can read more about it here.
Ran joined AWS AI to work on infinitely scalable machine learning algorithms and platforms in Amazon SageMaker. His main focus was neural variational inference and its applications in Natural Language Processing (NLP)AWS blog, webinar. Later on, he became a founding member of Amazon Kendra, making state-of-the-art deep learning based semantic search and question answering available to the public through this new service.
- Coherence-Aware Neural Topic Modeling (EMNLP'18)
- Weakly Semi-Supervisited Neural Topic Models (ICLR'19, workshop)
- Topic Modeling with Wasserstein Autoencoders (ACL'19)
- Technical reports: Generative Models, Language Modeling, Neural Variational Inference
Ran kickstarted Compass’ machine learning effort and built its first machine learning team. The team delivered several machine learning services and features end-to-end (real-time recommenders, search ranking, valuation, CRM). In the process, the team built the necessary machine learning infrastructure (event-driven feature store, training and serving pipeline) and defined machine learning development process.
Ran currently supports the Instagram Feed Ranking team.
Ran's main interests in machine learning include natural language processing (NLP) (language modeling, topic models, semantic matching, text classification, etc), deep generative models (VAE, GAN, adversarially regularized autoencoders), and learning methods (weak supervision, transfer learning, meta/few-shot learning, domain adaptation).
Ran currently holds 9 US patents and has authored and co-authored more than 80 peer-reviewed academic papersGoogle Scholar.