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 innovation, execution and teamwork. Our goal is to enable students to pursue their futuristic innovative ideas.

Learn about our other QIF programs

Selected Abstracts

Application Process

Areas of Interest

Participating Universities

Timeline

University	Innovation	Students	Recommenders
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	Real-Time Machine Learning Incorporating Radio-Frequency Fingerprint Augmentation for Secure Radar Sensing	Yuyi Shen, Jiachen Xu	Vanessa Chen
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
Cornell	Continuously tracking full facial expressions with acoustic sensing on minimally-obtrusive wearables	Ke Li, Ruidong Zhang	Cheng Zhang
Cornell	Logic-Intensive Networks for Secure and Efficient Learning	Jordan Dotzel, Yichi Zhang	Zhiru Zhang
Georgia Tech	ANALYSER: A Hardware-Software Co-design Framework to Overcome StochasticImperfection of Mixed Signal DNN Accelerators	Payman Behnam, Uday Kamal	Saibal Mukhopadhyay
Georgia Tech	Design for Compute-in-Memory Accelerators without ADCs	Hongwu Jiang, Wantong Li	Shimeng Yu
Georgia Tech	On-line Hardware based Malware Detection using Integrated Machine learning Accelerators	Nael Mizanur Rahman, Harshit Kumar	Saibal Mukhopadhyay
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 P. 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	Data-driven optimization for fast and agile trajectory generation	Gilhyun Ryou, Ezra Tal	Sertac Karaman
MIT	Energy-Efficient System Design for Video Understanding on the Edge	Miaorong Wang, Ji Lin	Song Han, Anantha Chandrakasan
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
MIT	Privacy-Preserving Collaborative Inference	Abhishek Singh, Praneeth Vepakomma	Ramesh Raskar, Mehdi Bennis
MIT	Sequential tailoring: interleaving learning and decision-making by optimizing unsupervised objectives	Ferran Alet, Maria Bauza	Leslie Pack Kaelbling, Alberto Rodriguez
MIT	Towards Understanding Implicit Biases in Deep Learning	Jacob Huh, Hyojin Bahng	Pulkit Agrawal, Phillip Isola
Princeton	Non-uniform Spectrally Agile Arrays, Multi-band Antenna and Transmitter Front-ends for Spectrum Sharing in mm-Wave Wireless Links	Zheng Liu, Hooman Saeidi	Kaushik Sengupta
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
Purdue University	Next-generation Virtual Machine Enclaves	Adil Ahmad, Sishuai Gong	Pedro Fonseca, Byoungyoung Lee
Purdue University	Simily: Precise Control-flow Graph Embedding Space for Measuring Code Similarity	Charitha Saumya Gusthinna Waduge, Kirshanthan Sundararajah	Milind Kulkarni
Stanford	Implicit Representations for Compositional, Generalizable Scene Understanding	Honging 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
Stanford / MIT	Efficient & Robust Scene Understanding using Neuro-Symbolic AI	Sumith Kulal, Jiayuan Mao	Jiajun Wu
UCB	Algorithms for Safe and Data Efficient Reinforcement Learning	Ashwin Balakrishna, Brijen Thananjeyan	Ken Goldberg, Joseph E. Gonzalez
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	Learning Decentralized Autonomous Vehicle Controllers for Traffic Smoothing	Eugene Vinitsky, Abdul Rahman Kriedieh	Alexandre Bayen, Dan Work
UCB	Practical Lifting for Verification of Trusted Platform Software	Kevin Cheang, Federico Mora	Sanjit A. Seshia, Alvin Cheung
UCLA	A Distributed Privacy-Preserving Machine Learning Framework Using 5G Network	Hao-Jen Chien, Shashank Balla	Nader Sehatbakhsh
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
UCLA	Breaking one-time authentication in 4G/5G: New attacks and countermeasures	Zhaowei Tan, Boyan Ding	Songwu Lu
UCLA	High Resolution and Wideband ADC for Sub-6 GHz receiver systems	Avantika Singh, Jiazhang Song	Sudhakar Pamarti
UCSD	Adapting Articulated 3D Reconstructions at Test-Time	Jiteng Mu, Brandon Leung	Nuno Vasconcelos, Xiaolong Wang
UCSD	An Energy Efficient 5G LNA Exploiting Current Reuse	Abhishek Agarwal, Tyler Hack	Drew Hall
UCSD	Analog Neuromorphic Computing System based on 2D Oxide Materials	Chi-Hsin Huang, Hsuan (Amanda) Chang	Kenji Nomura
UCSD	Best-of-Class Digital PLL Frequency Synthesis IC Development	Eslam Helal, Amr Eissa	Ian Galton
UCSD	Controllable Spatiotemporal Deep Generative Model	Rui Wang, Weitang Liu	Rose Yu
UCSD	COOPRAD: Cooperative Radar Perception	Kshitiz Bansal, Aditya Arun	Dinesh Bharadia
UCSD	Deep Early-Fusion Approaches for Autonomous Driving: A Holistic Framework, Models & Analysis	Akshay Rangesh, Nachiket Deo	Mohan M. Trivedi
UCSD	Deep Reinforcement Learning for detailed placement optimization	Uday Mallappa , Chester Holtz	Chung-Kuan Cheng
UCSD	Dual Contradistinctive Variational Transformer	Yifan Xu, Zheng Ding	Zhuowen Tu
UCSD	End-to-End Scalable AI Accelerator with Hierarchical Reprogrammable Interconnects	Sangheon Oh, Gopabandhu Hota	Duygu Kuzum, Gert Cauwenberghs
UCSD	Generative 3D Modeling and its Applications	Chih-Hui Ho, Simeng Zheng	Nuno Vasconcelos, Paul H. Siegel
UCSD	Grounding neural language models with human sensorimotor judgments	James Michaelov, Sean Trott	Benjamin Bergen
UCSD	Hardware-friendly Neural Architecture Search for Multi-device System	Cheng Fu, Huili Chen	Farinaz Koushanfar
UCSD	Headset Facial Expression Tracking with Deep Learning of Skewed Angle Cameras	Yinan Xuan, Varun Viswanath	Edward Wang
UCSD	High-sensitive Wearable Biosensor using Oxide Semiconductor	Yong Zhang, Alex Lee	Kenji Nomura
UCSD	Learning Language Representations for Voice-based Conversational Agents for Older Adults	Khalil Mrini, Chen Chen	Ndapa T. Nakashole, Nadir Weibel
UCSD	Learning-Aided Estimation for Situational Awareness in Future Wireless Networks	Mingchao Liang, Wenyu Zhang	Florian Meyer
UCSD	Lightweight Human Digitization	Kunyao Chen, Bang Du	Truong Nguyen
UCSD	Neural Network-based Program Decompiler	Cheng Fu, Huili Chen	Jishen Zhao, Farinaz Koushanfar
UCSD	Towards a Systems Perspective on Neural Network Robustness	Malhar Jere, Xinqiao Zhang	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
UIUC	Fine-grained and Generic Acceleration for Data- and Compute-intensive Workloads in the Cloud	Yifan Yuan, Dong Kai Wang	Nam Sung Kim
UIUC	Improved Security and Efficiency in Mobile Systems via Software Debloating	Chaitra Niddodi, Hsuan-Chi Kuo	Sibin Mohan, Tianyin Xu
UIUC	Monolithic Machine Learning-based Plastic Systems for Patient Monitoring	Nathaniel Bleier, Muhammad Husnain Mubarik	Rakesh Kumar
UMD	Adaptive Massively Parallel Algorithms for Large-Scale Graph Mining with Low Communication	Marina Knittel, Hamed Saleh	MohammadTaghi Hajiaghayi
UMD	Toward battery-free continuous sensing for on-body health monitoring	Nakul Garg, Yang Bai	Nirupam Roy, Kanad Basu
UMD	WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic	Renkun Ni, Hong-min Chu	Tom Goldstein
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
USC	Planning and Learning for Environmental Adaptive Sampling	Christopher Denniston, Gautam Salhotra	Gaurav Sukhatme
USC	Secure Privacy-Preserving Machine Learning at the Edge	Jinhyun So, Saurav Prakash	Salman Avestimehr
UT Austin	Automating Layout Synthesis for Analog and Mixed-Signal Circuits with Human and Machine Intelligence	Keren Zhu, Mingjie Liu	David Z. Pan
UT Austin	Domain Adaptation Meets Meta-Learning: New Perspectives and Techniques	Liam Collins, Matthew Faw	Sanjay Shakkottai, Constantine Caramanis
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 G. Andrews
Virginia Tech	A Sample-Efficient Deep Reinforcement Learning Framework for 5G Dynamic Spectrum Access and Dynamic Spectrum Sharing	Hao-Hsuan Chang, Jiarui Xu	Lingjia Liu
Virginia Tech	Deep Reinforcement Learning Meets Resource Allocation in Radio Access Network (RAN) Slicing	EMADELDIN Abbas Mazied ABDRABOU, Nima Mohammadi	Lingjia Liu
Virginia Tech	mmWave Vehicular Communications: A Road to the Future paved with Better Information and Better Beams!	Tarun Cousik, Biplav Choudhury	Jeffrey H Reed, Vijay K Shah
Washington	Closing the Gap: From Innovation to Real-world Impact	Matthew Wallingford, Aditya Kusupati	Ali Farhadi
Washington	Cross-Modality Supervision for Object Detection and Tracking 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 Automotive Object Recognition in Mutual Radar Interference	Sian Jin, Xiangyu Gao	Sumit Roy
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
Washington / CMU	Merging with Confidence: Formally Safe Intention Estimation and Merging Behavior Planning under Uncertainty	Anqi Li, Yiwei Lyu	Byron Boots, John M. Dolan
Wisconsin-Madison	Autonomous Vehicle Domain Adaptation Using Fairness	Harrison Rosenberg, Brian Tang	Kassem Fawaz
Wisconsin-Madison	Model Backtracking on the Edge	Liu Yang, Shashank Rajput	Dimitris Papailiopoulos, Kangwook Lee
Wisconsin-Madison	PriMon: Monitoring Privacy Compliance at Run-Time for Smart Devices	Rishabh Khandelwal, Jingjie Li	Kassem Fawaz, Younghyun Kim

APPLICATION:

Each team should submit an application by the specified deadline (see Timeline tab) that must include:

1. One page abstract of innovation proposal

2. Letter from one or two faculty members recommending the innovation
a. Why the proposal is innovative
b. Why the proposal is important
c. Why the current team is likely to succeed in their proposal

3. Each student's CV

4. Signed copy of QIF rules (both students)

SUBMISSIONS SHOULD BE IN A ZIP FILE

The submission portal will open two weeks before the deadline.

PROPOSAL PHASE:

Selected teams will be notified directly to participate in the next phase by submitting a final proposal that must include a Three-page innovation proposal (plus one for reference/bibliography) including:

a. Introduction and problem definition

b. Innovation proposal and relation to the state of the art

c. One-year horizon of the project, even if the proposal is a multi-year project

d. Strength of the team to achieve the proposal milestones

Please upload your proposal in zip format to the QIF submission portal for updates by the specified deadline. Use your access code received in the confirmation email when you submitted your abstract.

SELECTION OF FINALISTS AND WINNERS:

Finalist teams will be selected to participate in the final presentation phase of the Program. Each finalist team must prepare a presentation slide deck for the judges. Presentation must be in PowerPoint or PDF format. The presentation generally includes

The idea
The differentiating factors from state of the art
The execution plan / strength of the team

Each Finalist team must also prepare a poster for a poster session to be presented to the judges.

Winning teams will be chosen from these finalists after they present to the judging panel.

ADDITIONAL INFO:

• Applications must be submitted online by the specified deadline (see Timeline tab). All proposals and presentations will be reviewed internally by a team of Qualcomm researchers.

• The official rules for the Qualcomm Innovation Fellowship are available for download and set forth the Program’s governing guidelines.

• All QIF-related information will be announced on this webpage. Please check back regularly for updates.

Qualcomm Research is a division of Qualcomm Technologies, Inc.

More Info?
See FAQs for more details

Do you need further Information?

Please direct your questions to: innovation.fellowship@qualcomm.com

Qualcomm Research is a division of Qualcomm Technologies, Inc.

We invite teams to submit proposals in the following areas. We also welcome proposals outside of the sub-areas listed below:

Advanced Semiconductor Electronics

Ultra-low (uW) power embedded platform for edge computing (ULP architectures and designs, HW accelerators, power generation and management, novel memories, security)
Novel materials and heterogeneous integration (2D semiconductors, GaAs, GaN, etc.)
CMOS package integration (thermal-aware designs or circuits, advanced packaging techniques, antenna-in-package etc.)
RF / analog ASICs and architectures (Sub-6GHz 5G power amplifiers, mmWave RFIC and Data Converters for 5G NR, adaptive RF signal processing algorithms, etc.)
Advanced antenna (mmWave and phase-array antennas), novel antenna materials, structures and implementations
Power Management ASICs (wide bandwidth SMPS, wide bandwidth envelope tracker, embedded regulation)
ASIC implementation methodology development for improved Performance, Power, Area, Yield. On-die power grid analysis and optimization, dynamic and static power optimizations, silicon-driven static timing analysis, design optimization for yield improvement
Low-power high-speed Data Converters
Low-power high-bandwidth SERDES for die to die and chip to chip communication
Low-power high-bandwidth memory interface (DRAM and FLASH) circuits
Ultra-low power ML HW design using mixed-signal techniques (such as Compute-In-Memory and analog neural network)
Mixed-signal HW design using ML techniques
Digital wireless transceivers
Interference/jammer cancellation and concurrent transceivers/coexistence

Advances in Communication Techniques and Theory

Reliable low latency communications for licensed/unlicensed in mid band and mmW spectrum
Machine learning designs for wireless communications systems and algorithms
Joint communication and sensing/positioning techniques for licensed and unlicensed spectrum
Vehicle-vehicle, vehicle to infrastructure, and vehicle-pedestrian communications design
Cooperative sensing and communication for radar and other sensors
Low energy networks (Wi-Fi, Bluetooth LE, 802.15.4, Zigbee, etc.)
Low-power signal-processing algorithms for mmW massive-MIMO communication systems
Advanced low power HW/FW/SW modem implementation approaches
New signal-processing techniques and use-cases using RF sensing (wireless channel capture)
Iterative detection and decoding algorithms
Non coherent communication for low power IoT
Communication systems design in THz bands

Autonomous Driving

Advanced sensors and sensor fusion
Imaging radar
Computer vision for autonomy
Sensor fusion with deep learning
Behavior planning with uncertainty

Machine Learning

Natural language processing and voice user interface
Computer vision and geometric deep learning
Reinforcement and continual learning
ML for combinatorial optimization
Graph neural networks
On-device training and model personalization
Intermediate representation for machine learning workloads/compilers
Transfer learning and Knowledge distillation
Extreme energy efficient inference hardware accelerators for ML loads and lower complexity algorithms and convolutional nets
Deep generative modeling and unsupervised learning
Bayesian deep learning and uncertainty estimation
Federated learning

Multimedia Computing

Real Time 3D perception, mapping, reconstruction, and geometry interpretation
Eye-tracking devices and algorithms
Hand skeleton and multimodal human body pose estimation and tracking
Low power/complexity rendering systems
Lighting/illumination modelling
Multi-focal, near eye displays
High efficiency video coding techniques
Deep learning based image and video compression (intra and inter prediction, in-loop filters, transforms, entropy coding)
Deep learning based optimized video encoding
Perceptually optimized video coding
Image and video quality assessment
6DoF video compression, Point Cloud compression
Few-shot learning in human activity classification
Dynamic texture and shape synthesis in the wild
Learning universal networks for multiple tasks in computer vision, e.g. detecting, tracking, segmenting objects and scenes in videos

Processor Architecture and SOC Design

Novel processor architectures, microarchitectures, extensions, and accelerators
Novel processor architectures, microarchitectures, extensions, and accelerators
Multimedia and gaming architectures (not limited to GPU, GPGPU, VLIW, DSP, etc.)
Novel architectures for artificial intelligence, edge training, inference and processing in memory
Security features of CPUs and accelerators at the instruction set, execution, storage (including caches) and SOC levels
Machine learning CAD for VLSI HW design

Secure System Design

Isolation technologies: Virtualization, enclaves, and software sandboxing
Key management for IoT: Establishing trust between embedded devices
Machine learning model security against adversarial attacks
Protocol security: Analysis and verification of communication protocols such as 3GPP for 5G to discover security issues both in standards and their implementations
SoC security: Security of heterogeneous systems on chip
Software-based exploitation of hardware vulnerabilities, such as micro-architectural, side-channels and glitch attacks, and proper countermeasures
User authentication: Biometric and behavioral authentication of users by mobile and embedded devices
Vulnerability detection: New tools and techniques for finding exploitable vulnerabilities in Software and/or Hardware, with a focus on embedded systems
Solutions for data provenance, privacy and security

Semiconductor Test, Quality and Yield Learning

5G test techniques for mmWave and sub6 devices and system
Defect-oriented testing and fault modeling in deep sub-micron process nodes
Applications of Data Analytics, Machine Learning and AI in Test
Test Challenges for 2.5D/3D Systems in Packages

Qualcomm is inviting applications for the Qualcomm Innovation Fellowship 2020 from PhD students in the Electrical Engineering and Computer Science (and related) departments at:

United States

California Institute of Technology
Carnegie Mellon University (CMU)
Columbia University
Cornell University
Georgia Institute of Technology
Massachusetts Institute of Technology (MIT)
Princeton University
Purdue University
Rutgers University
Stanford University
UC Berkeley (UCB)
UC Los Angeles (UCLA)
UC San Diego (UCSD)
UC Santa Barbara (UCSB)
University of Illinois at Urbana-Champaign (UIUC)
University of Maryland, College Park
University of Michigan
University of Southern California (USC)
University of Texas at Austin
University of Washington
University of Wisconsin-Madison
Virginia Polytechnic Institute and State University

Canada

University of Montreal
University of Toronto

Info Sessions are scheduled in October 2020 – details in the ‘Info Sessions’ section

Submission site opens: November 2020

Application submission deadline: December 1, 2020 (10:00AM PST)

Selection announcement: December 18, 2020

Proposal submission deadline: January 25, 2021 (10:00AM PST)

Finalists’ announcement: March 2021

Finalist presentations submission deadline: March 2021

Finalist presentations: April 2021

Winners’ announcement: May 2021

QIF Winners’ Day: October 2021

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:

Winners

Finalists

Qualcomm Innovation Fellowship Finalists' Day

Oct 19, 2020

1:35

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