QIF 2021 North America

2021 QIF North America Program Details

We believe that research and development is the key to harnessing the power of imagination and to discovering new possibilities. We are excited to announce a new kind of Fellowship that promotes Qualcomm’s core values of innovationexecution and teamwork. Our goal is to enable students to pursue their futuristic innovative ideas.

Finalist Selections
Finalist Instructions
Selected Abstracts
Application Process
Areas of Interest
Participating Universities

The following 43 proposals have been selected as Finalists. Congratulations!


University Innovation Title Students Recommendor(s)
Caltech Blending machine learning and control in autonomous systems Gautam Goel, Taylan Kargin Babak Hassibi
CMU Collaboration and Competition: Distributed Multi-agent Reinforcement Learning for IoT Jinhang Zuo, Yi Hu Carlee Joe-Wong, Bob Iannucci
CMU Open World 3D Dynamic Reconstruction For Smart Cities Gengshan Yang, Dinesh Reddy Narapureddy Srinivasa Narasimhan, Deva Ramanan
CMU Providing Architectural Support for Building Privacy-Sensitive Smart Home Apps Haojian Jin, Han Zhang Jason Hong, Yuvraj Agarwal
CMU Safe Adaptive Learning and Control for Autonomous Driving Charles Noren, Weiye Zhao Changliu Liu
CMU Zero-Shot Accented Speech Recognition Patrick Conrey, Mark Lindsey Bhiksha Raj
Georgia Tech Playing it Safe: Towards Low-Cost and Generalized Memory Safety Gururaj Saileshwar, Yonghae Kim Moinuddin K Qureshi, Hyesoon Kim
Georgia Tech Revolutionizing EDA Flow through Advanced Machine Learning Algorithms Yi-Chen Lu, Anthony Agnesina Sung Kyu Lim
Michigan Deep Systems Consolidation for Task-Incremental Learning Ekdeep Singh Lubana, Puja Trivedi Robert Dick, Danai Koutra
Michigan / Washington Machine Learning Designs for Universal Decoders Mohammad Vahid Jamali, Xiyang Liu Sewoong Oh, Hessam Mahdavifar
MIT Algorithm-Hardware Co-Design for Efficient LiDAR-Based Autonomous Driving Zhijian Liu, Yujun Lin Song Han
MIT Long Term Multi-Agent Hybrid Prediction through Online Factored Concept Inference and Continual Hierarchical Learning Xin Huang, Meng Feng Brian Williams
MIT On-Device NLP Inference and Training with Algorithm-Hardware Co-Design Hanrui Wang, Han Cai Song Han, Hae-Seung Lee
Princeton Optimization Inspired Neural Architectures for 3D Reconstruction Zachary Teed, Ankit Goyal Jia Deng
Princeton Quantum Computation for Wireless Networks Sai Srikar Kasi, Minsung Kim Kyle Jamieson
Purdue University A Generalized Framework for Optimizing ML Workload Acceleration in Processing-in/near Memory Architectures Mustafa Ali, Tanvi Sharma Kaushik Roy
Purdue University High Performance, Energy-Efficient, and Accurate in-Memory Computing Accelerators for ML Workloads Mustafa Ali, Dong Eun Kim Kaushik Roy
Stanford Implicit Representations for Compositional, Generalizable Scene Understanding Hongxing Yu, Michelle Guo Jiajun Wu
Stanford Predicting hand-object interaction for improved haptic feedback in extended reality Mike Salvato, Negin Heravi Allison Okamura, Jeannette Bohg
Stanford Towards Human-like Compositional Generalization in Machines Eric Mitchell, Shikhar Murty Chelsea Finn, Chris Manning
UCB Asynchronous Neural Accelerator for NP-Hard Optimization Philip Canoza, Saavan Patel Sayeef Salahuddin, Jan M. Rabaey
UCB High-Speed Spatial Light Modulators for Holographic Near Eye Displays Cem Yalcin, Nathan Tessema Ersumo Rikky Muller, Laura Waller
UCB Parallel Algorithms for Safe and Data Efficient Reinforcement Learning with Opportunistic Risk-Taking Ashwin Balakrishna, Brijen Thananjeyan Ken Goldberg, Joseph E. Gonzalez
UCB Practical Lifting for Veri fication of Trusted Platform Software Kevin Cheang, Federico Mora Sanjit Seshia, Alvin Cheung
UCLA A Programmable Receiver with Adaptive Blocker Suppression Vinod Kurian Jacob, Avantika Singh Sudhakar Pamarti
UCLA A Stochastic Compute-In-Memory Neural Network Accelerator with Variable Precision Tuning Jiyue Yang, Tianmu Li Sudhakar Pamarti, Puneet Gupta
UCSD Best-of-Class Digital PLL Frequency Synthesis IC Development Eslam Helal, Amr Eissa Ian Galton
UCSD Deep Early-Fusion Approaches for Autonomous Driving: A Holistic Framework, Models & Analysis Akshay Rangesh, Nachiket Deo Mohan Trivedi
UCSD Dual Contradistinctive Variational Transformer Yifan Xu, Zheng Ding Zhuowen Tu
UCSD Generative 3D Modeling and its Applications Chih-Hui Ho, Simeng Zheng Nuno Vasconcelos, Paul H. Siegel
UCSD Learning-Aided Estimation for Situational Awareness in Future Wireless Networks Mingchao Liang, Wenyu Zhang Florian Meyer
UCSD Neural Network-based Program Decompiler Cheng Fu, Huili Chen Jishen Zhao, Farinaz Koushanfar
UCSD Transforming Microarchitectural Predictors with the Power of Neural Transformers Fatemehsadat Mireshghallah, Mohammadkazem Taram Taylor Berg-Kirkpatrick, Dean Tullsen
UIUC Commutated-Circuit-and-Acoustic-Delay-Line-based Adaptive Canceller (CADELAC) Hyungjoo Seo, Steffen Link Songbin Gong, Jin Zhou
University of Toronto Learning Long-Range 3D Object Detectors for High-Speed Autonomous Driving Anas Mahmoud, Juan Carrillo Steven Waslander
USC Federated Deep Learning: On-device Learning of CV and NLP with Transformers and CNNs Chaoyang He, Saurav Prakash Salman Avestimehr
UT Austin Efficient Heterogeneous Federated Learning with Lottery Tickets Allen Farcas, Xiaohan Chen Radu Marculescu, Atlas Wang
UT Austin Federated Generative Learning for Channel Estimation in mmWave and THz systems Akash Doshi, Manan Gupta Jeffrey Andrews
Washington Cross-Modality Supervision for Object Detection under Adverse Autonomous Driving Scenarios Haotian Zhang, Yizhou Wang Jenq-Neng Hwang
Washington Decentralized Cooperative 5G: Enabling Efficient Handover and Spectrum Coordination Sudheesh Singanamalla, Sachin Nayak Kurtis Heimerl
Washington Enhanced Self-Interference Suppression with Phase Noise Cancellation for use in Full-duplex Radios Yi-Hsiang Huang, Xichen Li Chris Rudell, Visvesh Sathe
Washington Hybrid Molecular-Electronic Computing for Ubiquitous Biological Sensing Applications Jason Hoffman, Alyssa La Fleur Shwetak Patel, Georg Seelig
Wisconsin-Madison PriMon: Monitoring Privacy Compliance at Run-Time for Smart Devices Rishabh Khandelwal, Jingjie Li Kassem Fawaz, Younghyun Kim

Fellowship Winners and Finalists

The Qualcomm Innovation Fellowship began in 2009, and has continued to grow with the addition of more universities, more candidates, and expansion to our research centers internationally. Take a look at a list of all our fellowship winners and finalists from years past:

Qualcomm Innovation Fellowship Finalists' Day

Oct 19, 2020


©2021 Qualcomm Technologies, Inc. and/or its affiliated companies.

References to "Qualcomm" may mean Qualcomm Incorporated, or subsidiaries or business units within the Qualcomm corporate structure, as applicable.

Qualcomm Incorporated includes Qualcomm's licensing business, QTL, and the vast majority of its patent portfolio. Qualcomm Technologies, Inc., a wholly-owned subsidiary of Qualcomm Incorporated, operates, along with its subsidiaries, substantially all of Qualcomm's engineering, research and development functions, and substantially all of its products and services businesses. Qualcomm products referenced on this page are products of Qualcomm Technologies, Inc. and/or its subsidiaries.

Materials that are as of a specific date, including but not limited to press releases, presentations, blog posts and webcasts, may have been superseded by subsequent events or disclosures.

Nothing in these materials is an offer to sell any of the components or devices referenced herein.