TILOS in the NEWS
February 17, 2022 | Intel Labs
Congratulations to Professor Tajana Rosing for Intel's 2021 Outstanding Researcher Award !!!
Tajana has received this award for the project "MLWiNS: Hyper-Dimensional Computing for Scalable Intelligence Beyond the Edge."
January 19, 2022 | ACM Media Center
Congratulations to Professor David Z. Pan and to Professor Tajana Rosing on their elevation to ACM Fellow !!!
David's citation: "For contributions to electronic design automation, including design for manufacturing and physical design".
Tajana's citation: "For contributions to power, thermal, and reliability management".
December 23, 2021 | ACM News Release
Congratulations to Professor Yusu Wang on her recognition as ACM Distinguished Member for outstanding scientific contributions to computing!
December 6-14, 2021 | NeurIPS Proceedings
(Works acknowledging TILOS support are denoted by **)
- Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures Yuan Cao, Quanquan Gu, Mikhail Belkin
- Multiple Descent: Design Your Own Generalization Curve Lin Chen, Yifei Min, Mikhail Belkin, Amin Karbasi
- NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs Evan McCarty, Qi Zhao, Anastasios Sidiropoulos, Yusu Wang
- Fair Classification with Adversarial Perturbations L. Elisa Celis, Anay Mehrotra, Nisheeth Vishnoi
- Coresets for Time Series Clustering Lingxiao Huang, K Sudhir, Nisheeth Vishnoi
- Support Recovery of Sparse Signals from a Mixture of Linear Measurements Soumyabrata Pal, Arya Mazumdar, Venkata Gandikota
- Fuzzy Clustering with Similarity Queries Wasim Huleihel, Arya Mazumdar, Soumyabrata Pal
- L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Zixuan Jiang, Ray Chen, David Pan
- Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks Chenning Yu, Sicun Gao
- Adaptive Sampling for Minimax Fair Classification Shubhanshu Shekhar, Greg Fields, Mohammad Ghavamzadeh, Tara Javidi
- NovelD: A Simple yet Effective Exploration Criterion Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian
- Multi-Person 3D Motion Prediction with Multi-Range Transformers Jiashun Wang, Huazhe Xu, Medhini Narasimhan, Xiaolong Wang **
- Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation Nicklas Hansen, Hao Su, Xiaolong Wang **
- An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks Shashank Rajput, Kartik Sreenivasan, Dimitris Papailiopoulos, Amin Karbasi
- Submodular + Concave Siddharth Mitra, Moran Feldman, Amin Karbasi
- Parallelizing Thompson Sampling Amin Karbasi, Vahab Mirrokni, Mohammad Shadravan
- Model-Based Domain Generalization Alexander Robey, George Pappas, Hamed Hassani
- Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients Aritra Mitra, Rayana Jaafar, George Pappas, Hamed Hassani
- Adversarial Robustness with Semi-Infinite Constrained Learning Alexander Robey, Luiz Chamon, George Pappas, Hamed Hassani, Alejandro Ribeiro
- Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates Alp Yurtsever, Alex Gu, Suvrit Sra
- Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification Alkis Gotovos, Rebekka Burkholz, John Quackenbush, Stefanie Jegelka
- Measuring Generalization with Optimal Transport Ching-Yao Chuang, Youssef Mroueh, Kristjan Greenewald, Antonio Torralba, Stefanie Jegelka
- Can contrastive learning avoid shortcut solutions? Joshua Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, Suvrit Sra
- What training reveals about neural network complexity Andreas Loukas, Marinos Poiitis, Stefanie Jegelka
October 11, 2021 | Quanta Magazine
(Anil Ananthaswamy's article in Quanta Magazine discusses kernel methods and their relevance, including some of Misha Belkin's recent work.)
In the machine learning world, the sizes of artificial neural networks and their outsize successes are creating conceptual conundrums. When a network named Alexnet won an annual image recognition competition in 2021, it had about 60 million parameters. These parameters, fine-tuned during training, allowed AlexNet to recognize images that it had never seen before. Two years later, a network named VGG wowed the competition with more than 130 million such parameters. Some artificial neural networks or ANNs, now have billions of parameters.
August 27, 2021 | UC San Diego News Center
The National Science Foundation (NSF) recently announced that the Halıcıoğlu Data Science Institute (HDSI) at UC San Diego is the future home of The Institute for Learning-enabled Optimization at Scale, or TILOS. The $20M Artificial Intelligence (AI) hub will foster research and focus on “making impossible optimizations possible” at both scale and in practice.
August 2, 2021 | National University
LA JOLLA, CALIF — As part of an initiative to establish a series of artificial intelligence research institutes nationwide, the National Science Foundation (NSF) has awarded a $20 million grant to a partnership of prestigious universities led by University of California, San Diego, and including National University, Yale University, the Massachusetts Institute of Technology, the University of Pennsylvania, and the University of Texas at Austin.
July 29, 2021 | San Diego Union Tribune
A new research institute led by UC San Diego has been awarded $20 million from the National Science Foundation to pursue foundational breakthroughs in artificial intelligence — an emerging battleground for the U.S. in the global race for technology leadership.
July 29, 2021 | KPBS Science and Technology
The National Science Foundation is investing $20 million in artificial intelligence research at UC San Diego.
UC San Diego’s Jacobs School of Engineering will host one of 11 artificial intelligence institutes with an investment totaling $220 million.
July 29, 2021 | Penn Engineering Today
Through a $20 Million grant, the National Science Foundation (NSF) has established the Institute for Learning-enabled Optimization at Scale, or TILOS. As one of TILOS’ partner institutions, Penn Engineering will contribute to its research on how algorithm-based systems can learn and improve upon themselves as they work.
July 29, 2021 | University of Texas at Austin
The National Science Foundation just announced 11 new artificial intelligence institutes across the nation, and researchers from the Cockrell School of Engineering at The University of Texas at Austin will play prominent roles in two of them.
The investment from NSF, $220 million in total, underscores the importance of AI in research today. The goals of these institutes include helping older adults lead more independent lives and improving the quality of their care; transforming AI into a more accessible “plug-and-play” technology; creating solutions to improve agriculture and food supply chains; enhancing adult online learning by introducing AI as a foundational element; and supporting underrepresented students in elementary to postdoctoral STEM education to improve equity and representation in AI research.
July 29, 2021 | MIT Computer Science & Artificial Intelligence Lab
The National Science Foundation (NSF) announced today an investment of $220 million to establish 11 artificial intelligence (AI) institutes, each receiving $20 million over five years. One of these, The Institute for Learning-enabled Optimization at Scale (TILOS), will be led by the University of California San Diego in partnership with the Massachusetts Institute of Technology; San Diego-based National University; the University of Pennsylvania; the University of Texas atAustin; and Yale University. TILOS is also partially supported by Intel Corporation.
July 29, 2021 | Source: Yale School of Engineering & Applied Science
Yale researchers are taking a major role in two artificial intelligence (AI) institutes, each funded by the National Science Foundation (NSF).
The NSF announced today that it is investing $220 million to establish 11 AI institutes, each receiving $20 million over five years. Yale researchers are taking leadership positions in two of these institutes, one of which will focus on optimization, and another dedicated to edge computing and computer network systems. Eight Yale scientists in total are members of the institutes.
July 29, 2021 | Source: Newswise
The National Science Foundation (NSF) announced today an investment of $220 million to establish 11 artificial intelligence (AI) institutes, each receiving $20 million over five years. One of these, The Institute for Learning-enabled Optimization at Scale (TILOS), will be led by the University of California San Diego in partnership with the Massachusetts Institute of Technology; San Diego-based National University; the University of Pennsylvania; the University of Texas at Austin; and Yale University. TILOS is also partially supported by Intel Corporation.
July 29, 2021 | Source: Intel
Intel Labs has a long standing partnership with the NSF involving several programs aimed at advancing AI technology and innovation. The two organizations started working together decades ago when co-founding the Semiconductor Research Corporation (SRC), the world's leading microelectronics research consortium. Since then, they have established several exciting collaborations and programs spanning industry, government, and academia.
The Institute will pursue foundational breakthroughs at the nexus of artificial intelligence and optimization to transform chip design, robotics, and communication networks.
July 29, 2021 | Source: Jacobs School of Engineering News
SAN DIEGO — The National Science Foundation (NSF) announced today an investment of $220 million to establish 11 artificial intelligence (AI) institutes, each receiving $20 million over five years. One of these, The Institute for Learning-enabled Optimization at Scale (TILOS), will be led by the University of California San Diego in partnership with the Massachusetts Institute of Technology; San Diego-based National University; the University of Pennsylvania; the University of Texas at Austin; and Yale University. TILOS is also partially supported by Intel Corporation.
July 29, 2021 | Source: NSF AI Institutes Team
WASHINGTON — Today, the U.S. National Science Foundation announced the establishment of 11 new NSF National Artificial Intelligence Research Institutes, building on the first round of seven institutes funded in 2020. The combined investment of $220 million expands the reach of these institutes to include a total of 40 states and the District of Columbia.