Across science—from astrophysics to molecular biology to economics—researchers are overwhelmed by the sheer amount of data they are collecting. But, this problem is better viewed as an opportunity since, with the right computing resources and algorithmic tools, scientists might unlock new insights from the swathes of data to carry their field forward.
AI4science (or artificial intelligence for science) is an initiative at Caltech led by Anima Anandkumar and Yisong Yue that aims to bring together AI researchers with experts from other disciplines to push modern AI tools into every area of science and engineering. Launched in the summer of 2018, the initiative organizes talks, courses, and tutorials aimed at training researchers from across the scientific spectrum in the theory and practice of machine learning algorithms. Weekly AI4science office hours also allow researchers to ask computer scientists for help—hopefully stimulating new interdisciplinary research at Caltech. Additionally, seed grants for researchers applying AI to new applications in the sciences are allocated yearly through the Carver Mead New Adventures program
Here are a few examples of Caltech professors transferring artificial intelligence and machine learning research to other disciplines.
To find out more about this new initiative, visit the AI4science website.
Joel Burdick, Richard L. and Dorothy M. Hayman Professor of Mechanical Engineering and Bioengineering, is applying machine learning algorithms (some designed by Caltech’s Professor Yisong Yue) to help patients with spinal cord injuries to walk again. The so-called “neural prosthesis” is a device which plugs into a human’s nerve endings, reads the electrical signals and uses them to control a mobility-assisting device.
Andrew Stuart, Bren Professor of Computing and Mathematical Sciences, is applying machine learning algorithms to build more fine-grained climate models. Working with Professor Tapio Schneider in Caltech’s Climate Dynamics Group, Stuart hopes to build better predictive models of the Earth’s changing climate.
Maria Spiropulu, Professor of Physics, is applying machine learning methods to try to make sense of the floods of data coming in from the Large Hadron Collider at CERN. Currently most of the roughly one petabyte of data collected every second at CERN must be thrown away, so even deciding which data to keep is an important problem in the hunt for signals in the dark.
The flagship programs of the AI4Science initiative are the selection of AI4Science Graduate and Postdoctoral Fellows and the Cloud Credit Grant program, both of which are sponsored by Amazon AWS. The recipients of this are listed below. Additionally, the AI4Science initiative sponsors awards through the
Carver Mead New Adventures Fund and organizes workshops, seminars, summer schools, and other events centered around AI and its applications.
AI4Science/Amazon AWS Fellows
|2021-2022||Ignacio Lopez-Gomez||Tapio Schneider|
|2021-2022||Rebecca Gallivan||Julia R. Greer|
|2021-2022||Andrew Charbonneau||Chiara Daraio|
|2021-2022||Zongyi Li||Anima Anandkumar|
|2021-2022||Bijan Mazaheri||Shuki Bruck / Leonard Schulman|
|2021-2022||Dmitry Burov||Andrew Stuart|
|2021-2022||Jennifer J. Sun||Pietro Perona / Yisong Yue|
|2021-2022||Frankcesco (Frank) Lanfranchi||Doris Tsao (Berkely) / Mikhail Shapiro|
|2020-2021||Carmen Amo Alonso||John Doyle|
|2020-2021||Sara Beery||Pietro Perona|
|2020-2021||Charles Guan||Richard A. Andersen|
|2020-2021||Kadina Johnston||Frances H. Arnold|
|2020-2021||Nikola Kovachki||Andrew Stuart|
|2020-2021||Zhuoran Qiao||Thomas F. Miller III|
|2020-2021||Guannan Qu||Steven Low / Adam Wierman|
|2020-2021||He Sun||Katie Bouman|
AI4Science/Amazon AWS Cloud Credit Recipients
|Year||Project Title||Award Recipients||Division|
|2021-2022||Multiphysical Single Protein Identification - Deep Learning for Multiphysical Single Protein Identification||Michael Roukes Group||Physics, Mathematics and Astronomy|
|2021-2022||Political Polarization, Geographic Sorting, and Novel Methods for Large Administrative Data||Claudia Kann & Daniel Ebanks||Humanities and Social Sciences|
|2021-2022||Dynamic Topic Modeling with Spectral Methods||Zhuofang Li||Social Science|
|2021-2022||Optimized Terapixel Scale Processing of Astronomical Images||Graham (Bruce) Berriman||IPAC|
|2021-2022||BBE Education||Justin Bois||Biology and Biological Engineering|
|2021-2022||Using Cloud Computing For The Detection And Prevention of Social Media Misinformation and Harassment||R. Michael Alvarez||Humanities and Social Sciences|
|2021-2022||Learning an Index of Economic Complexity||Frederick Eberhardt & Patrick Burauel||Humanities and Social Sciences|
|2021-2022||Harm Reduction in Tobacco Addiction: Pharmacokinetics of Nicotine for Smoking Cessation||Henry A. Lester||Biology and Biological Engineering|
|2021-2022||NUPACK: Molecular Programming in the Cloud||Niles A. Pierce||Biology and Biological Engineering|
|2021-2022||Preventing T cell exhaustion by engineering oscillatory circuits||Shirin Shivaei||Bioengineering|
|2021-2022||The ZTF AGN Catalog||Matthew J. Graham||Astronomy|
|2021-2022||Computational design of computational protein networks||Michael Elowitz||Biology and Biological Engineering|
|2020-2021||Text Similarity Query System to Improve Biological Curation of Scientific Articles at WormBase||Paul Sternberg/Hans-Michael Muller/Valerio Arnaboldi/Daniela Raciti/Kimberly Van Auken||Biology and Biological Engineering|
|2020-2021||Active Drug Delivery||John F. Brady/(Edmond) Tingtao Zhou/Zhiwei Peng||Chemistry and Chemical Engineering|
|2020-2021||Expanding the Definition of a Functional Protein with Ancestral Sequence Reconstruction for Semi-Supervised Transfer Learning in Protein Engineering||Bruce J. Wittmann/Kadina E. Johnston/Frances H. Arnold||Chemistry and Chemical Engineering|
|2020-2021||Discovery of Microscopic Control Strategies Involving Active Matter by Using State-of-the-Art Reinforcement Learning Techniques||Dominik Schildknecht/Enrique Amaya/Matt Thomson||Biology and Biological Engineering|
|2020-2021||Causal Discovery Methods for Astrophysical Data||Frederick Eberhardt/Eric Huff||Humanities and Social Sciences|
|2020-2021||Computational Lineage Motif Analysis of cell fate decision programs during differentiation||Michael Elowitz||Biology and Biological Engineering|
|2020-2021||Memory maps as long-term memory storage in recurrent neural networks||James Gornet/ Matt Thomson||Biology and Biological Engineering|
|2020-2021||Embedding of single cell drug perturbation experiments by graph neural networks||Jialong Jiang/Sisi Chen/Tahmineh Khazaei/Matt Thomson||Biology and Biological Engineering|
|2020-2021||Millisecond Astronomy with the Zwicky Transient Facility||Mansi Kasliwal/Igor Andreoni/Matthew Graham/Ashish Mahabal/Roger Smith/Stephen Kaye/Sterl Phinney/Richard Walters||Physics, Mathematics and Astronomy|
|2020-2021||Finding Rare Astrophysical Sources||Mahabal Ashish/Kevin Burdge/Michael Coughlin/Andrew Drake/Dmitry Duev/Matthew Graham/Lynne Hillenbrand/Przemek Mroz/Joannes Van Roestel||Physics, Mathematics and Astronomy|
|2020-2021||NUPACK: Molecular Programming in the Cloud||Niles Pierce||Biology and Biological Engineering|
|2020-2021||Development of a high throughput analysis pipeline for whole-brain light sheet imaging during behavior||Andrey Andreev/Amina Kinkhabwala/David Prober||Biology and Biological Engineering|
- Sign up for email@example.com to receive notifications about AI4science office hours, tutorials, seminars, and workshops.
- Apply for a seed grant through the Carver Mead New Adventures program
Giving to AI4science
Thanks to the generous donations of the IST Council members the AI4science initiative kicked off in Fall 2018. The ultimate goal is to able to establish a thriving interdisciplinary community of researchers applying AI to transform the sciences. A gift to the AI4science initiative is an investment in ideas that have the potential to revolutionize the sciences and push the boundaries of AI.