I currently work at Meta and lead the machine learning effort for Threads. I previously led Instagram's Home Feed team building large scale recommendation systems and worked on Meta Generative AI team focusing on video generations.
Before doing software and machine learning, I was a physicist and a semiconductor chip designer. I received my Ph.D. at the University of Washington and spent several years commercializing my research through a hardware startup Elenion, later acquired by Nokia.
I hold 9 US patents and published more than 80 peer-reviewed academic papersGoogle Scholar.
You can contact me on LinkedIn or by email at
Ways I can help
Want help with something? Here are ways I'm available to help:
For small startups or non-profit:
- I'm open to short-term consulting or advisory roles at small startups or non-profit organizations solving technical or organizational problems.
- The domains I'm most familiar with are:
- (1) machine learning/AI, especially in search, NLP, recommendations systems and Generative AI.
- (2) semiconductor, chip and system design, (exotic) applications of photonics and electronics (e.g. communications, sensing/bio-sensing, quantum computing).
- I'm happy to help people who are switching careers into software or machine learning, e.g. by providing critic to resume or LinkedIn profile.
Facebook / Meta
Ran led Instagram's Feed team working on the ranking and delivery of Home Feed -the largest product surface of Instagram and one of the largest recommendation system on the planet serving billions of users every day.
Ran worked on Meta's Generative AI, focusing on image and video generation before heading back to Instagram to lead the machine learning effort for Threads app shortly after its launch in July 2023.
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 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
Aperio.AI was a solid-state LIDAR 3D sensing startup. The thesis was to use Silicon Photonics (SiPh) for LIDAR sensing along with integrated front-end circuits and computer vision algorithms to alleviate the stringent requirements for high-definition near real-time LIDAR sensing needs for applications in autonomous vehicles. This approach ultimately did not deliver the orders of magnitude improvements over conventional methods and as the startup was shut down.
PhD and Elenion
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.
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.