Elmore ECE
Emerging Frontiers Center
Crossroads of Quantum and AI


Vision


The rapid emergence of the next-generation technologies is currently fueled by the most remarkable rise of Quantum Information Science and Technologies (QIST), novel large scale manufacturing approaches, new concepts in Cognitive Systems as well as Big Data and Artificial Intelligence (AI). Merging these powerful approaches promises to solve fundamental scientific problems and unlock new applications with breakthrough functionalities. On this path, there is a critical need to explore both the untapped potential of AI-empowered manufacturing and quantum systems as well as the new generation of robust AI systems, including neuromorphic computing and beyond-CMOS approaches, and physics-driven AI. By combining historically disconnected research areas of quantum, AI, secure cognitive systems and manufacturing, this Center aims at advancing both the “physical” and the “virtual” parts of the emerging technologies.

The Center builds on the team’s seminal, complementary contributions in the areas of machine learning, nano- and quantum photonics, neuromorphic computing, large-scale manufacturing and big data. With the team’s many “world’s firsts” in the above areas and unique, integrated EE/CISE expertise, the team is uniquely positioned to achieve preeminence in the proposed effort. The Center activities are expected to attract an increasing attention in the coming years at the government and corporate levels, thus offering a great potential for Purdue ECE to become the Leader in merging AI, quantum and beyond.

Team


 

Principal Investigators

  • Alexandra Boltasseva
    PI
    Ron and Dotty Garvin Tonjes Professor of Electrical and Computer Engineering
  • Jan P. Allebach
    Co-PI
    Hewlett-Packard Distinguished Professor of Electrical and Computer Engineering
  • Christopher Brinton
    Co-PI
    Assistant Professor of Electrical and Computer Engineering
  • Kaushik Roy
    Co-PI
    Edward G. Tiedemann Jr. Distinguished Professor of Electrical and Computer Engineering
  • Xiaoqian (Joy) Wang
    Co-PI
    Assistant Professor of Electrical and Computer Engineering

Participants

  • Muhammad Ashraful Alam
    Jai N. Gupta Professor of Electrical and Computer Engineering
  • George Chiu
    Assistant Dean of Global Engineering Programs and Partnerships and Professor of Mechanical Engineering
  • Sumeet Gupta
    Elmore Associate Professor of Electrical and Computer Engineering
  • Sabre Kais
    Professor - Physical/Theoretical and Computer Science
  • Milind Kulkarni
    Associate Professor of Electrical and Computer Engineering; Associate Head of Teaching and Learning
  • Anand Raghunathan
    Silicon Valley Professor of Electrical and Computer Engineering
  • Ali Shakouri
    Mary Jo and Robert L. Kirk Director of Birck Nanotechnology Center Professor of Electrical and Computer Engineering
  • Vladimir M. Shalaev
    Bob and Anne Burnett Distinguished Professor of Electrical and Computer Engineering

Administrators

Partners


Birck Nanotechnology Center

Birck Nanotechnology Center is home to 300 resident students, faculty and staff who have access to all shared labs. The 186,000 sq ft. facility includes a 25,000 sq. ft. ISO Class 3-4-5-6 (Class 1-10-100-1000) nano¬fabrication cleanroom – the Scifres Nanofabrication Laboratory – that includes a 2,500 sq. ft. ISO Class 6 (Class 1000) pharmaceutical-grade biomolecular cleanroom. Researchers from six academic colleges are at the forefront of nano electronics, photonics, energy, micro electro-mechanical systems, nanobio technology and nano¬manufacturing and are helping to develop a local high tech ecosystem in collaboration with Discovery Park District and Wabash Heartland Innovation Network (WHIN) community internet of things (IoT) testbed.

Purdue Quantum Science and Engineering Institute

The Purdue Quantum Science and Engineering Institute was established at Purdue University in order to foster the development of practical and impactful aspects of quantum science. The Institute focuses on discovering and studying new materials and basic physical quantum systems that will be best suited for integration into tomorrow's technology. It encourages interdisciplinary collaboration leading to the design and realization of industry-friendly quantum devices with enhanced functionality and performance close to the fundamental limits in order to produce systems based on these devices that will impact a vast community of users. Finally, we work to train the next generation of quantum scientists and engineers in order to meet the growing quantum workforce demands.

Quantum Science Center

The Road Ahead. In order to accelerate innovation, researchers need new technologies to accurately predict, detect, and model complex phenomena such as energy generation and efficiency, national security, new materials discovery, and fundamental physics. This opportunity now exists by developing a new generation of technologies that exploits quantum mechanics to deliver much-needed advances in computation and sensing. The Quantum Science Center (QSC) located at ORNL is dedicated to overcoming key roadblocks in quantum state resilience, controllability, and ultimately the scalability of quantum technologies to realize the quantum future. Integral to the activities of the Quantum Science Center is the development of the next generation of scientists and engineers; by actively engaging students and postdoctoral associates in research activities, the Center offers a rich environment for professional development. Further, by working in close conjunction with industry from its inception, the Quantum Science Center strongly couples its basic science foundation and technology development pathways to transition applications to the private sector.

More News

Resources


Introduction to Machine Learning Problem Framing

A one-hour course that introduce common machine learning terms and describes examples of machine learning problem-solving in practice.

Machine Learning Crash Course

Google's practical introduction to machine learning, including lectures on various machine learning algorithms. This course also provides hand-on exercises using TensorFlow in real-world case studies.

Intro to Deep Learning with PyTorch

The course by Facebook Artificial Intelligence introduces the basics to build deep learning models using PyTorch in AI applications such as style transfer and text generation.

Machine Learning Course from Stanford

This course provides an overview of various algorithms for machine learning and data mining. The course introduces mathematical and statistical background of the algorithms along with application exercises.

An Introduction to Statistical Learning by Gareth M. James, Daniela Witten, Trevor Hastie, Robert Tibshirani

The book introduces several data analysis tools with application in R.

The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, Jerome Friedman

This book covers the mathematical and statistical concepts of various approaches in data science.