QIF 2020 North America

2020 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
Timeline

The following 42 proposals have been selected as Finalists. Congratulations!

University Innovation Title Students
CMU Hardware-Aware Multimodal 3D Object Detection for On-Device Augmented Reality Applications Ting-Wu Chin, Ahmet Inci
CMU Safe Adaptive Learning and Control for Dynamical Systems Charles Noren, Weiye Zhao
CMU 3D Multi-Agent Social Interaction Understanding and Diverse Future Behavior Forecasting for Next General AI Systems Xinshuo Weng, Ye Yuan
CMU Ad Hoc Spatially-Anchored Augmented Reality Interfaces Karan Ahuja, Sujeath Pareddy
Columbia Protecting Heterogeneous SoCs with Security Sockets Davide Giri, Luca Piccolboni
Columbia Practical Software Security on Heterogeneous System on Chips Mohamed Hassan, Evgeny Manzhosov
Cornell Synthesis-Driven ISA Extensions for DSPs Alexa VanHattum, Rachit Nigam
Georgia Tech Towards Building Machine-Learning Powered EDA Flow and Methodologies Anthony Agnesina, Yi-Chen Lu
MIT Side-Channel Security Analysis of Embedded Machine Learning Implementations and Efficient Software / Hardware Countermeasures 3Saurav Maji, Utsav Banerjee
MIT Aerial Drone Sensing and Intelligence Favyen Bastani, Songtao He
MIT Privacy-Preserving Video Analytics with Untrusted Queries Francis Cangialosi, Neil Agarwal
Princeton Exploring In-Memory Computing for Architectural and Technology Scaling Peter Deaville, Rakshit Pathak
Purdue University EM/Power Statistical and Machine-Learning Side-Channel Attacks & Generic Low-Overhead Synthesizable Circuit-Level Countermeasures Debayan Das, Baibhab Chatterjee
Purdue University Enabling Edge Intelligence with In-Memory Accelerators for Ultra-low Precision Deep Neural Networks Niharika Thakuria, Sourjya Roy
Purdue University Toward energy-efficient and accurate in-memory analog computing systems for machine learning workloads Indranil Chakraborty, Mustafa Fayez Ahmed Ali
Stanford Peer Pressure: On-Device Learning from Soft Decisions Ilai Bistritz, Ariana Mann
Stanford Autoregressive Generation that Adapts to Computational Constraints Yang Song, Rui Shu
UCB Designing Analog Mixed Signal Circuits with Machine Learning Keertana Settaluri, Kourosh Hakhamaneshi
UCLA Robust Crossbar Persistent Memories for In-Memory Learning Acceleration Zehui Chen, Siyi Yang
UCLA Autonomous Driving with Smartphones using Online Expectation-Maximization and Controllable Stereoscopic Vision Tsang-Kai Chang, Kenny Chen
UCSB N-face InGaN/AlGaN RF power HEMTs with a relaxed InGaN channel for mm-wave power amplifier Shubhra Pasayat, Weiyi Li
UCSB Learning to Encourage Cooperative Behavior in Multi-agent Systems Daniel Lazar, Erdem Biyik
UCSB Practical algorithms for near-capacity massive MAC: Iterative schemes based on principled convex-relaxations Ganesh Ramachandra Kini, Orestis Paraskevas
UCSD LEGOS: AI for Cross-Domain Multi-Tenant Acceleration of Autonomous Systems Byung Hoon Ahn, Soroush Ghodrati
UCSD Physically-Motivated Deep Inverse Rendering from Sparse Inputs Sai Bi, Zhengqin Li
UCSD Vertical Hybrid Power Delivery for High-Performance Processors and Digital Systems Casey Hardy, Abdullah Abdulslam
UCSD Finding and Eliminating Timing Side-Channels in Crypto Code with Pitchfork Sunjay Cauligi, Craig Disselkoen
UCSD Temporospatial Fusion of Radar, Vision and LIDAR data for Autonomous Driving Dominique Meyer, Hengyuang Zhang
UCSD Doing More with Less:Sparse Sensing for Millimeter Wave Channel Estimation Pulak Sarangi, Rohan Ramchandra Pote
UCSD Learning-Based 3D Mesh Reconstruction Minghua Liu, Xiaoshuai Zhang
UCSD Toward Personalized and Multimodal Conversational Recommender Systems Shuyang Li, Bodhisattwa Prasad Majumder
UIUC High Resolution Millimeter Wave Imaging Using Deep Adversarial Learning Suraj Jog, Junfeng Guan
UMD Provably robust neural networks using curvature regularization Sahil Singla, Yogesh Balaji
USC High-Capacity Mode-Division-Multiplexed Wireless Communications Within and Beyond Millimeter-Wave Band Runzhou Zhang, Huibin Zhou
UT Austin mmWave and TeraHz Channel Estimation and Beamforming using Deep Generative Networks Akash Doshi, Ajil Jalal
UT Austin Radar-to-radar interference: System level analysis and solutions Khurram Mazher, Andrew Graff
Virginia Tech Physics-Driven Machine Learning and Data Fusion for Semiconductor Test, Quality and Yield Learning Yinan Wang, Tim Lutz
Virginia Tech A Low-Power Hybrid Neural Processing Architecture for Mobile Edge Intelligent Computing Yibin Liang, Kangjun Bai
Virginia Tech Hybrid Reinforcement Learning for Autonomous Vehicles in Adversarial Environments Ian Garrett, Leila Amanzadeh
Washington Source Separation with Deep Generative Priors John Thickstun, Vivek Jayaram
Wisconsin-Madison Comprehensively Accelerating Sequence-based Neural Networks Preyesh Dalmia, Suchita Pati
Wisconsin-Madison Ultra Low-Power Machine Learning at the Edge Tianen Chen, Setareh Behroozi

The Finals presentations are a major part of the winner selection. Both members of each finalist team are expected to present at the Finals.

About the Finals presentations

Prepare a 12 - minute presentation

You must submit your presentation slides in PDF and PowerPoint format, a video-recording of your presentation and poster in PDF only format by 10:00AM PST on Thursday May 14th, 2020.

The submission site will be open on May 1st, 2020. Details of the format of the slides and poster are below. Use your assigned confirmation number for the submission.

 

SUBMISSION SHOULD BE A SINGLE ZIP FILE CONTAINING THE THREE OR FOUR DOCUMENTS: PRESENTATION SLIDES, PDF OF SLIDES _FORPRINT, VIDEO RECORDING, FIRST POSTER, AND OPTIONAL SECOND POSTER.

 

About the slides

12-slides maximum, including the cover page. The presentation slides generally include:

The idea
The differentiating factors from state of the art
The execution plan and strength of the team
We do not recommend adding slides with references. If you do, they would also count towards the 12-slide limit.

Please do not use animations purely to get around the 12-slide limit.

Please ensure that all external media is embedded in the PDF or PowerPoint file itself, e.g., videos. The PDF/PowerPoint file must be self-contained.

In addition to your presentation slides, please provide a for-print PDF (8.5"X11" landscape) of your presentation (max. 12 pages). The name of the for-print PDF should end with "_forprint.pdf". It should have the same content (sans animations) as your presentation.

 

About the poster

Each team may submit up to two posters (PDF format required, for-print 24"x36").

The submitted posters will be used for the interactive session with the judges, which is a part of the Finals event.

We received 188 abstracts this year. After a careful selection process, the following have been chosen to proceed to the Proposal phase of the QIF 2020. Congratulations!

University Submission No. Innovation Title
CMU S2019-15933 Resource-Efficient Distributed Statistical Inference in Networks
CMU S2019-15947 Hardware-Aware Multimodal 3D Object Detection for On-Device Augmented Reality Applications
CMU S2019-15998 Developing a Programmable RRAM-Based Platform for High-Performance and High-Efficiency Processing-in-Memory
CMU S2019-16019 Redundancy-driven neural compression
CMU S2019-16039 Safe Adaptive Learning and Control for Dynamical Systems
CMU S2019-16054 A Highly Energy Efficient TNN-based Sensory Processing Unit for Image Segmentation
CMU S2019-16076 Re-imagining the system stack for ultra-low-power image sensing
CMU S2019-16087 Generating Free-form Animations by Coalescing Narrative and Dialogue
CMU S2019-16092 3D Multi-Agent Social Interaction Understanding and Diverse Future Behavior Forecasting for Next General AI Systems
CMU S2019-16100 Ad Hoc Spatially-Anchored Augmented Reality Interfaces
Columbia S2019-15943 Deep Unfolded Neural Network for Wireless Communication: Algorithm and Hardware Co-design
Columbia S2019-16010 Protecting Heterogeneous SoCs with Security Sockets
Columbia S2019-16052 Adversarial Learning for Typical Nuisances
Columbia S2019-16095 Practical Software Security on Heterogeneous System on Chips
Cornell S2019-16050 Synthesis-Driven ISA Extensions for DSPs
Georgia Tech S2019-15959 Privacy-Preserving and Attack-Resilient Federated Learning
Georgia Tech S2019-16049 Evolving architecture recommendation system for custom hardware design
Georgia Tech S2019-16111 Towards Building Machine-Learning Powered EDA Flow and Methodologies
Michigan S2019-16014 A Deep Neural Network Assisted Terahertz Communication Link with Rapid In-Field Performance Optimization
Michigan S2019-16094 Energy Efficient Biologically Inspired Embedded Machine Vision
MIT S2019-15940 Side-Channel Security Analysis of Embedded Machine Learning Implementations and Efficient Software / Hardware Countermeasures
MIT S2019-15992 Distributed Ledgers On the “Edge” for Trustworthy Cellular-V2X
MIT S2019-16001 Aerial Drone Sensing and Intelligence
MIT S2019-16091 Learning Transferable Strategies via Skills and Memory
MIT S2019-16105 Privacy-Preserving Video Analytics with Untrusted Queries
MIT S2019-16110 Learning Generalizable and Scalable Reinforcement Learning Agents for Transportation Systems
Princeton S2019-15941 Exploring In-Memory Computing for Architectural and Technology Scaling
Princeton S2019-15950 Exploiting Channels for Energy-efficient Physical Layer Security with Spatio-Temporal Modulated Arrays in Future mm-Wave Wireless Links
Purdue University S2019-15929 Systematic Security Analysis of Narrowband Internet of Things (NB-IoT) and LTE-M
Purdue University S2019-15951 EM/Power Statistical and Machine-Learning Side-Channel Attacks & Generic Low-Overhead Synthesizable Circuit-Level Countermeasures
Purdue University S2019-16007 Hardware Security with Embedded Spintronics for Internet of Things
Purdue University S2019-16018 Enabling Edge Intelligence with In-Memory Accelerators for Ultra-low Precision Deep Neural Networks
Purdue University S2019-16021 Optimization of Autonomous Vehicle Routes with Flexible Objectives
Purdue University S2019-16036 Toward energy-efficient and accurate in-memory analog computing systems for machine learning workloads
Purdue University S2019-16048 MixNN: Significance based Tensor Bit-Width Allocation using PCA for Edge Inference
Purdue University S2019-16051 Accelerating Irregular Machine Learning Models
Purdue University S2019-16060 Hardware-Aware Adversarial Attacks on Deep Neural Networks
Purdue University S2019-16069 Detecting Deepfakes Using Audio-Video Inconsistencies
Purdue University S2019-16080 Next-generation Hardware Enclaves
Stanford S2019-15995 Peer Pressure: On-Device Learning from Soft Decisions
Stanford S2019-16029 Autoregressive Generation that Adapts to Computational Constraints
UCB S2019-15956 Monolithic Fast-Scanning Lidar through Wavevector-Division Multiplexing
UCB S2019-15957 High-Speed Spatial Light Modulators for Holographic Near Eye Displays
UCB S2019-15963 Real-World Scalability of Zero-knowledge Proofs
UCB S2019-15979 Interactive Computer Vision Techniques for Authoring AR Tutorials
UCB S2019-16063 Designing Analog Mixed Signal Circuits with Machine Learning
UCB S2019-16116 Hardware-Software Co-Design for Agile Verification of Trusted Execution Environments
UCLA S2019-15946 Turning Any Object into Robots
UCLA S2019-15990 Microgesture: Human-computer Interaction at the Micro Scale​
UCLA S2019-16017 Ultra-High-Resolution Transportation Mode Recognition and Localization using Phonon-Engineered Optomechanical Inertial Sensors
UCLA S2019-16057 Robust Crossbar Persistent Memories for In-Memory Learning Acceleration
UCLA S2019-16113 Autonomous Driving with Smartphones using Online Expectation-Maximization and Controllable Stereoscopic Vision
UCSB S2019-15931 Stochastic Bounds for Robust Adversarial Learning in Vehicle Navigation
UCSB S2019-15952 N-face InGaN/AlGaN RF power HEMTs with a relaxed InGaN channel for mm-wave power amplifier
UCSB S2019-15969 Learning to Encourage Cooperative Behavior in Multi-agent Systems
UCSB S2019-15989 Practical algorithms for near-capacity massive MAC: Iterative schemes based on principled convex-relaxations
UCSB S2019-16015 Efficient, Robust, and Private Federated Learning
UCSB S2019-16037 Reinforcement Learning for Safety-Critical Systems
UCSB S2019-16042 Energy-Efficient and End-to-End Training of Tensorized Neural Networks on Resource-Constrained Platforms
UCSB S2019-16102 Device-free Cardiac Activity Monitoring Via WiGig
UCSB S2019-16106 Machine Learning Approaches for High Average Efficiency Power Amplifier Design
UCSD S2019-15935 LEGOS: AI for Cross-Domain Multi-Tenant Acceleration of Autonomous Systems
UCSD S2019-15937 Physically-Motivated Deep Inverse Rendering from Sparse Inputs
UCSD S2019-15954 mmTags: A Low-Cost Solution to Enhance Radar Vision
UCSD S2019-15962 Robust and Efficient MIMO mmWave Communication for Systems with Nonlinearities
UCSD S2019-15966 Intent Recognition for Pedestrians and Cars
UCSD S2019-15975 Automated Generation of 3D Labeled Human Pose and Motion Dataset
UCSD S2019-15978 Effective deep learning techniques for wireless communication
UCSD S2019-15983 CommRad: Concurrent Communication and Radar for Vehicles
UCSD S2019-15985 DeepPHY: Towards Secure Physical Layer Communications using Deep Learning
UCSD S2019-15988 Real-Time Reinforcement Learning on Edge Devices
UCSD S2019-16004 W-air glasses - Non-invasive continuous respiratory sensing device
UCSD S2019-16031 A Decentralized Bayesian Learning Framework for Supervised and Unsupervised Learning
UCSD S2019-16035 Vertical Hybrid Power Delivery for High-Performance Processors and Digital Systems
UCSD S2019-16047 Controllable Speech Synthesis using Hardware Software Co-design
UCSD S2019-16064 Finding and Eliminating Timing Side-Channels in Crypto Code with Pitchfork
UCSD S2019-16077 Temporospatial Fusion of Radar, Vision and LIDAR data for Autonomous Driving
UCSD S2019-16084 Doing More with Less:Sparse Sensing for Millimeter Wave Channel Estimation
UCSD S2019-16089 Learning-Based 3D Mesh Reconstruction
UCSD S2019-16109 Toward Personalized and Multimodal Conversational Recommender Systems
UCSD S2019-16114 Context Assisted Robust Localization using Wireless Sensing
UIUC S2019-15942 Agile RF Front-Ends Using Lithium Niobate MEMS Filters and N-Path Switched-LC Circuits
UIUC S2019-15997 Hardware Accelerators for Novel Real-time Forecasting Algorithms
UIUC S2019-16012 Protecting keys in the wild using Modern Cryptography
UIUC S2019-16086 High Resolution Millimeter Wave Imaging Using Deep Adversarial Learning
UMD S2019-15930 Provably robust neural networks using curvature regularization
UMD S2019-15948 PRGFlyt: Unified Active Perception Architecture for UAV Autonomy
University of Toronto S2019-15932 Analysis and Rehabilitation of Hand Function Using Egocentric Video
University of Toronto S2019-15934 Emo-PupilNet: A Deep Generative Model for Continuous Behavior Analysis using Pupillometry
University of Toronto S2019-16000 An Inverse Reinforcement Learning-Based Approach to Smart Traffic Management System
University of Toronto S2019-16068 Guided deep learning for the prediction of brain activation patterns in a driver for handoff in semi-autonomous vehicles
University of Toronto S2019-16081 Active Learning for Generative Adversarial Networks in Cybersecurity
USC S2019-15964 Learning Human-Aware Manipulation Plans by Watching YouTube Videos
USC S2019-15976 High-Capacity Mode-Division-Multiplexed Wireless Communications Within and Beyond Millimeter-Wave Band
USC S2019-16030 FLWiEN: A Fast, Robust and Secure Framework for Federated Learning in Wireless Edge Networks
USC S2019-16046 Energy and Area Efficient Beyond CMOS CNN Accelerators Through Architecture Specific Sensitivity-driven Pruning
USC S2019-16112 Data Trustworthiness Quantification and Propagation in Self-driving Car Systems
USC S2019-16115 Specification and Formal Verification of Federated Machine Learning Systems
UT Austin S2019-15996 mmWave and TeraHz Channel Estimation and Beamforming using Deep Generative Networks
UT Austin S2019-16022 Radar-to-radar interference: System level analysis and solutions
Virginia Tech S2019-15928 Physics-Driven Machine Learning and Data Fusion for Semiconductor Test, Quality and Yield Learning
Virginia Tech S2019-15953 Adaptive Integrated Power Supply with Neural Network Based Learning and Control for IoT Edge Computing Applications
Virginia Tech S2019-15972 Experienced Deep Reinforcement Learning for Reliable Communications
Virginia Tech S2019-15991 A Low-Power Hybrid Neural Processing Architecture for Mobile Edge Intelligent Computing
Virginia Tech S2019-16009 Real-time Detection and Adaptive Mitigation of Side-Channel Leakage in SoC
Virginia Tech S2019-16070 Designing Adaptive Information Display and Access Techniques for All-Day Wearable AR Glasses
Virginia Tech S2019-16079 Hardware Acceleration for OS-level Memory Compression
Virginia Tech S2019-16098 Dynamic spectrum sharing by Distributed Reinforcement Learning on Mobile Edges
Virginia Tech S2019-16104 Hybrid Reinforcement Learning for Autonomous Vehicles in Adversarial Environments
Washington S2019-16041 Source Separation with Deep Generative Priors
Washington S2019-16083 Gatekeeper: A low-power wake-up module for wireless transceivers
Washington S2019-16118 Automating Tensor Program Synthesis for Novel Architecture
Wisconsin-Madison S2019-15999 Accelerating Machine Learning Inference with Approximate Caching on Accelerators
Wisconsin-Madison S2019-16062 Comprehensively Accelerating Sequence-based Neural Networks
Wisconsin-Madison S2019-16066 Ultra Low-Power Machine Learning at the Edge

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 and make a 12-minute video recording of them presenting the slides 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 (millimeter-wave 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

 

 

Advances in Communication Techniques and Theory
 

• Reliable low latency communications for low unlicensed and mmWave spectrum

• Machine learning designs for wireless communications systems and algorithms

• Advanced communication and positioning techniques for licensed and unlicensed spectrum

• Vehicle-vehicle and vehicle-pedestrian communications design

• 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

 

 

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

• Computer vision

• Reinforcement and continual learning

• On-device training

• Intermediate representation for machine learning workloads/compilers

• Transfer learning and Knowledge distillation

• Novel compute architectures for ML tasks, e.g. in-memory compute, analog compute

• 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 and quantum machine 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

 

 

Processor Architecture and Implementation
 

• 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: DRM for learning models

• Protocol security: Analysis and verification of communication protocols

• SoC security: Security of heterogeneous systems on chip

• Software-based exploitation of hardware vulnerabilities: Micro-architectural attacks, side-channels, and associated 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, Trojans and Backdoors, with a focus on embedded systems

• Solutions for data provenance, privacy and security

 

Semiconductor Test, Quality and Yield Learning

• 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

Due to COVID-19 national emergency, the Finals event is postponed until further notice. Please stay tuned for updates.

Submission site opens: November 12, 2019

Application submission deadline: December 9, 2019 (10:00AM PST)

Selection announcement: December 19, 2019

Proposal submission deadline: January 27, 2020 (10:00AM PST)

Finalists’ announcement: March 30, 2020

Finalist presentations submission deadline: May 14, 2020 (10:00 AM PST)

Finalist presentations: Postponed Due to COVID-19

Winners’ announcement: Postponed Due to COVID-19

QIF Winners’ Day: October 2020

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

May 14, 2018

1:35

©2020 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.