2018 GOBOILER INTERNSHIP PROJECT LIST
MACHINE LEARNING
Project number: 1
Project title: Structured Prediction
Faculty member: Jean Honorio
More info: Learning and inference drives much of the research in diverse domains. For
instance, in computer vision and image understanding, complex models are required in order
to better represent real-world images. As such, one aims to obtain more representational
power by expressing images as the interaction of a large number of constituent elements.
While producing more realistic models, this also increases the computational cost of inference
and learning. In this project, you will be able to use a family of newly created
computationally-efficient randomized algorithms on several real-world image datasets.
E-mail: jhonorio@purdue.edu
INFORMATION SECURITY & PRIVACY
Project number: 2
Project title: Interconnecting Blockchains Together
Faculty member: Aniket Kate
More info: Cryptocurrencies such as Bitcoin and Ethereum have emerged as a paradigm shift
for the way payment systems work today. They rely on the blockchain, a technology that has
been proven useful in a vast number of applications other than monetary transactions. Many
companies today are tailoring the blockchain technology to their business logic and
successfully developing applications for credit settlement networks, supply chain, IoT and
beyond. However, these separate efforts are leading to incompatible individual systems. This
contrasts with our highly interconnected world and it is inevitable to see that soon these
blockchains will need to operate with each other, effectively forming a network of
blockchains where transactions can flow through a sequence of blockchains, similar how the
network of networks (i.e., the Internet) works today. Towards enabling the Internet of Value,
in this project, we will design and evaluate the cryptographic and distributed systems tools
required to move money the same way as the information/data moves today. Project
Webpage:
https://freedom.cs.purdue.edu/blockchains/
E-mail:	aniket@purdue.edu	
	
Project number: 3
Project title: Blockchain Hub
Faculty member: Bharat Bhargava
More info: Big data in various forms (including streaming) from multiple sources is received
by government agencies for dissemination to many stakeholders. To ensure integrity, trust,
immutability, and authenticity of information (cyber data, user data, and attack event data),
research is needed to advance blockchains and use them for provenance, trust, and analytics
of data. We propose an approach for tracking the provenance of data with enhanced
blockchain-based mechanisms to build trustworthiness of the data and ensure identities of
the parties/nodes.
E-mail:	bbshail@purdue.edu	
Project number: 4
Project title: V2V Safety and Security
Faculty member: Bharat Bhargava
More info: Evaluate V2V and its safety under various scenarios and attacks.
E-mail:	bbshail@purdue.edu
FORMAL METHODS/PROGRAMMING LANGUAGES
Project number: 5
Project title: Robustness of Probabilistic Programs
Faculty member: Roopsha Samanta
More info: The goal of this project is to develop foundational results and practical techniques
for the design of robust systems in the presence of stochasticity in the system or its
environment. An example problem is ensuring that the expected behavior of a probabilistic
program is “acceptable” when the input distribution is perturbed. The project will involve
development and/or implementation of algorithms for verification/repair/synthesis of robust
programs. The candidate must have a strong background in program analysis and probability
theory.
E-mail:	roopsha@purdue.edu
	
Project number: 6
Project title: Data-driven Repair of Web Applications
Faculty member: Roopsha Samanta
More info: PHP web applications are ubiquitous and error-prone. Bugs such as HTML
validation errors, access-control bugs, SQL injection and cross-site scripting (XSS) are
commonplace in PHP applications. Inspired by the success of data-driven repair of programs
written in C, Java or C#, the goal of this project is to develop a framework for data-driven
repair of real-world PHP-based server-side programs. The framework will learn probabilistic
models of repaired/correct programs from training data, which will then be used to produce
likely candidate fixes for buggy programs. The project will involve identification of opensource
repositories of PHP web applications and investigation of different choices of program
representations, probabilistic models and machine learning algorithms. The candidate must
have a strong background in program analysis and machine learning.
E-mail: roopsha@purdue.edu
	
MOBILE NETWORKING/MOBILE COMPUTING
Project number: 7
Project title: Supporting Immersive Virtual Reality Over 4G LTE
Faculty member: Chunyi Peng
More info: In recent years, we have witnessed a boom in mobile virtual reality (VR) to offer
panorama, immerse 3D experience for users. The most popular mobile VR form is empowered
by mobile phones with VR headsets (e.g., Google Cardboard and DayDream, Samsung Gear
VR), which contributes 98% of the sales. However, our current 4G LTE mobile network can’t
support ubiquitous and high-fidelity VR experience “anytime, anywhere” regardless of
whether they roam (e.g., in cars or on trains) or remain static in indoor/outdoor settings. The
biggest obstacle lies in network latency.
In this project, we study how to lower network latency in better support of mobile VR. We will
quantify the sources of latency, explore the potential improvement room in a standardcompatible
way and devise a solution which is ready to launch and improve VR experience
right away. In the next phase, we will move to clean-slate design to further support real-time
VR.
E-mail:	chunyi@purdue.edu
Project number: 8
Project title: Build IoT Applications for Autonomous Driving at Scale
Faculty member: Chunyi Peng
More info: We envision a fully automated driving experience in the years to come. V2X
(vehicle-to-anything) will play a critical role to make it happen. In this project, we conduct the
preliminary study to use IoT technique over cellular links (here, 4G LTE) to prototype it. We
will incorporate mobile edge computing and 4G networking tailored for critical
communication, massive communication, low power and low latency communication to
study its feasibility and disclose its challenges when it is extended to a large scale.
E-mail:	chunyi@purdue.edu
COMPUTER NETWORKING
Project number: 9
Project title: Investigation of a Scalable Data Center Network Architecture
Faculty member: Douglas Comer
More info: The move to cloud computing has changed the network traffic patterns in multitenant
data centers. Instead of primarily handling web traffic between a server in the data
center and a user at an arbitrary location on the Internet, data centers now experience
significant intra-center traffic that travels between pairs of servers within the data center (so
called “east-west” traffic). We are investigating a novel data center network architecture that
supports significant east-west traffic and allows a VM to retain its IP address as the VM
migrates from one physical server to another. An intern will help conduct experiments that
measure and assess the design, analyze the resulting measurements, and propose
modifications or extensions.
E-mail:	comer@cs.purdue.edu
Project number: 10
Project title: Assessing Mesh Routing Protocols for the Internet of Things
Faculty member: Douglas Comer
More info: Protocols for low-power wireless Internet of Things (IoT) devices use a mesh
networking approach. Devices that cannot reach a central base station connect to neighbors
and depend on neighbors to forward packets on their behalf. The standards body in charge of
Internet protocols, the IETF, has developed a routing protocol for use in such networks.
Named RPL (Routing over Low Power and Lossy Networks), the protocol uses neighbor
relationships to impose a tree topology on a set of nodes. We are constructing a testbed to
assess wireless protocols. An intern will help finish an implementation of RPL, and then use
the testbed to assess how RPL reacts to continuous changes in topology (nodes joining and
leaving the mesh, and nodes moving from one location to another).
E-mail:	comer@cs.purdue.edu
COMPILERS, ARTIFICIAL INTELLIGENCE & DATA MGMT.
Project number: 11
Project title: Flare: A High-Performance Big Data and AI System
Faculty member: Tiark Rompf
More info: Flare is a big data and machine learning platform developed here at Purdue
(flaredata.github.io). Flare can transparently accelerate pipelines implemented in Apache
Spark and TensorFlow, and provides speedups of 10x-100x, thanks to cutting edge compiler
technology. The goal of this summer research project will be to extend Flare in one of several
possible dimensions: implement code generation for distributed execution (using MPI or
similar), implement streaming abstractions for incremental data processing, implement code
generation for GPUs, implement a cost-based query optimizer, implement new internal
compiler optimizations, implement case studies based on state of the art AI kernels.
E-mail:	tiark@purdue.edu
COGNITIVE INTELLIGENCE & LEARNING
Project number: 12
Project title: Intelligence Autonomous Systems
Faculty member: Bharat Bhargava
More info: Intelligent Autonomous Systems (IAS) are highly cognitive, reflective, multitaskable,
and effective in knowledge discovery. Examples of IAS include software that is capable
of automatic reconfiguration, autonomous vehicles, network of sensors with reconfigurable
sensory platforms, and an unmanned aerial vehicle respecting privacy by deciding to turn off
its camera when pointing inside a private residence. Research is needed to build systems that
can monitor their environment and interactions, learn their capability, and adapt to meet the
mission objectives with limited or no human intervention. The systems should be fail-safe and
should allow for graceful degradations while continuing to meet the mission objectives.
The main objective is to realize a vision based on the following approaches.
(1) Employ machine learning techniques on sensor and provenance data to learn and
understand the underlying patterns of interaction, conduct forensics to detect anomalies, and
provide assistance in decision making by on-the-fly semantic and probabilistic reasoning.
(2) Apply advanced data analytics techniques to incomplete and hidden raw system data
(provenance data, error logs, etc.,) to discover new knowledge that contributes to the success
of the IAS mission.
(3) Enhance the autonomous system’s self-awareness, self-protection, self-healing, and selfoptimization
by learning from the knowledge discovered through data analytics.
(4) Utilize blockchain technology for storing provenance data for providing monitoring,
trust, and verification, using the WaxedPrune system developed for Northrup Grumman.
E-mail:	bbshail@purdue.edu