2018 in Review

This post reviews my experiences in 2018. I welcomed the year in the gorgeous beaches of Goa and am now ending it in the wilderness of South Africa. My highlights of 2018 are the following:

Joining NVIDIA: I joined NVIDIA in September and started a new research group on core AI/ML. I am hiring at full pace and have started many new projects. I am also excited about many new launches from NVIDIA over the last few months:

  1. Rapids: Apache open-source multi-GPU ML library.
  2. Clara: Platform for medical imaging.
  3. Physx: Open source 3D simulation framework.

Honor of being the youngest named chair professor at CaltechI was one of the six faculty members that Caltech recognized during the 2017-18 academic year. This is the Institute’s most distinguished award for individual faculty.

Launching AWS Ground Truth: Before leaving AWS, I was working on the ground truth service which got launched during ReInvent conference in November. Data is a big barrier to adoption of AI. The availability of private workforce and not just the public crowd on Mturk will be a game changer in many applications. My team did the prototyping and many research projects on active learning, crowdsourcing and building intelligence into the data collection process.

Exciting research directions:

  1. Autonomous Systems: CAST at Caltech was launched in October 2017 to develop foundations for autonomy. This has been an exciting new area of research for me. We got a DARPA Physics of AI project funded that infuses physics into AI algorithms. The first paper to come out of this project has been the neural lander that uses neural networks to improve landing of drones while guaranteeing stability. Check out its videos here.
  2. AI4Science at Caltech: Along with Yuxin Chen and Yisong Yue, I launched AI4Science initiative at Caltech. The goal is to do truly integrated research that brings about new advances in many scientific domains. Some great use cases are high energy physics, earthquake detection, spinal cord therapy etc.
  3. Core ML research: We have pushed for a holistic view of AI as data + algorithms + systems.
    • Active learning and crowdsourcing for intelligent data gathering that significantly reduces data requirements.
    • Neural rendering model combines generation and prediction in a single model for semi-supervised learning of images.
    • SignSGD yields drastic gradient compression with almost no loss in accuracy.
    • Symbols + Numbers: Instead of indulging in pointless Twitter debates over which is better, can we just unify both? We combine symbolic expressions and numerical data in a common framework for neural programming.
    • Principled approaches in reinforcement learning: We develop efficient Bayesian DQN that improves exploration in high dimensions.  We derive new trust-region policy optimization for partially observable models with guaranteed monotonic improvement. We show negative results for combining model-based and model-free RL frameworks.
    • Domain adaptation: We derive generalization bounds when there are shifts in label distribution between source and target. This is applicable for AI cloud services where training distribution can have different proportions of categories from the serving distribution.
    • Tensorly: The open-source framework that allows you to write tensor algorithms in Python and choosing any of the backends: PyTorch, TensorFlow, NumPy or MxNet. It has many new features now and is now part of PyTorch ecosystem.

On academic job market: My graduating student Kamyar Azzizadenesheli has done ground-breaking work in reinforcement learning (some of which I outlined above). Hire him!

Having grandkids: academically speaking 😉 It is great to see my former student Furong Huang and my former postdoc Rose Yu thrive in their faculty careers.

Outreach and Democratization of AI: It has been very fulfilling to educate the public about AI around the world. I gave my first TEDx talkI shared the stage with so many luminaries such as his holiness Dalai Lama. It was special to speak to a large crowd of Chinese women entrepreneurs at the Mulan event.

2018 NYTimes GoodTech award: for raising awareness about diversity and inclusion. 2018 has been a defining year for me and for many #womeninTech. A large part of my energy went into fighting vicious sexism in our research communities. It is impossible to distill this into few sentences. I have had to fend off numerous pushbacks, trolls and threats. But the positive part has been truly uplifting: countless women have hugged me and said that I am speaking on their behalf. I have found numerous male allies who have pledged to fight sexism and racism.

I want to end the year in a positive light. I hope for a great 2019! I know it is not going to be easy, but I won’t give up. Stay strong and fight for what you truly believe in!