The Gradient is a digital magazine covering research and trends in artificial intelligence and machine learning. We provide accessible and technically informed overviews of the what's going on AI, as well as a platform for perspectives on recent developments and long-term trends. In short, The Gradient points in the direction of the field.
We are a non-profit and volunteer-run effort run by researchers in the AI community. We were founded in 2017 by a group of students and researchers at the Stanford Artificial Intelligence Laboratory (SAIL).
- For more on our mission, see our Editor’s Note.
- Keep up with us on Twitter.
- Contact us at editor [at] thegradient.pub.
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Hugh Zhang (he/him) is a graduate student at Harvard EconCS and a cofounder of the Gradient. His recent research interests include generative models, AI policy, game theory, and multi-agent reinforcement learning. In his spare time, he enjoys writing, playing Go and eating burgers at In-N-Out. Follow him on Twitter.
Andrey Kurenkov (he/him) is a PhD student with the Stanford Vision and Learning Lab. His work primarily focuses on applying deep reinforcement learning for robotic manipulation, with several publications utilizing supervised learning and imitation learning as well. Besides being a cofounder of The Gradient, he also founded the publication Skynet Today, created the Last Week in AI newsletter, and is a co-host of the Let's Talk AI podcast.
Justin Landay (they/them) completed their undergraduate and masters degrees from George Washington University, publishing numerous papers on machine learning applications in nuclear physics. They are now a Senior Data Scientist at Etsy, focusing on using deep learning to identify and mitigate disruptive and fraudulent behavior.
Kiran Vaidhya is a PhD candidate at the Diagnostic Image Analysis Group of Radboudumc in the Netherlands. His primary research interests revolve around AI in medical image analysis. For his PhD research, he works on developing deep learning algorithms for lung cancer screening with chest CT images. In his spare time, he enjoys playing tennis, sketching, stargazing, and reading about astronomy.
Bradly Alicea has a PhD from Michigan State University. With interests centered upon computational science, developmental biology, and cognitive systems, he is currently Head Scientist and Founder of Orthogonal Research and a Senior Contributor at the OpenWorm Foundation. Bradly is also the manager of open-source community activities at Rokwire and administrator of Synthetic Daisies blog.
Liam Li has a PhD in Machine Learning from Carnegie Mellon University, where he was advised by Ameet Talwalkar. Since then, he has joined Determined AI as a machine learning engineer to build the leading platform for deep learning, enabling users to be vastly more productive and happier! He also continues to be involved in the AutoML community and frequently volunteer as a reviewer for top ML conferences.
Jessica Dai is a Machine Learning Engineer at Arthur AI. So far, her research interests have involved fairness (and more recently, explainability) in machine learning; more broadly, she is interested in bridging the gap between research and practice in these areas. In addition to her work at The Gradient, she is also on the editorial team at Reboot; in her spare time, she enjoys live music, ballet, and fiction. Follow her on Twitter.
Ather Fawaz is a software engineer at noon.com working in the ad-tech space. He takes a keen interest in deep learning (particularly GANs) and quantum computing, and has covered new research in these areas at Neowin.net. He's also authored a beginner-friendly course on quantum computing at educative.io. In his spare time, you'll find him engrossed in the world of papercraft, books, Formula 1 racing, and football. You can follow him on Twitter.
Daniel Bashir is a Machine Learning Engineer. His research interests have involved the intersection of machine learning and information theory. In 2021 he wrote the book "Towards Machine Literacy" to give an accessible introduction to a range of issues in AI ethics and governance. In his spare time, he enjoys reading fiction, traveling, cooking, and exercising. Follow him on Twitter.
Marco Cognetta is a PhD student at the Tokyo Institute of Technology and a PhD Student Researcher at Google Tokyo. He is interested in federated learning, interpretability, and high school level computer science education. You can find him on Twitter.
Derek Lim (he/him) is a PhD student at MIT CSAIL, working on geometric deep learning. He has recently been working on equivariant neural networks and graph neural networks, with a mix of theory and practice. His favorite animal is the duck. Follow him on Twitter.