1. Overview of Purdue Computer Engineering
Last Update: March 2017

2. Computer Engineering (CE) at Purdue
23 faculty members (core), 10 more faculty members (associated)
Key Areas:
Software Systems
Compilers and High Performance Computing
Distributed Systems, Networking and Operating Systems
Dependability and Security
Computer Architecture and Embedded Systems
Microarchitectures
Interconnection networks
Low power design
AI, Robotics, and Visualization
Artificial Intelligence and Machine Learning
Graphics and Visualization
Robotics, Vision and Haptics

3. Why Purdue Computer Engineering?
Ranked #9 (Engineering), #8 (EE), #9 (CmpE) (US News and World Report, 2016)
Strong software focus is unique…
Many faculty with Ph.Ds from Computer Science Departments
Areas like OS, Networking, Compilers, Visualization, AI not common in Computer Engineering Departments
Strong reputation in hardware
Architecture, VLSI, Embedded Systems
Collaborations with sister areas/departments/centers
Communication & Signal Processing, VLSI, etc.
Computer Science Department
CERIAS (Security center), CRI (High Performance Computing)

4. Where do CE alumni go? (in recent years)
PhD MS
Universities (professors)
US* 8
Oversea 4
Research Labs 1
Startup
4(2 as founders)
Large Companies 30 13
* One is a department head.

5. CE Positions Only a representative sample Universities Research Labs
Companies
UNC Charlotte
Microsoft Research
Facebook
Mathworks
U Pittsburgh
PARC
Intel (12) HP
Oregon State
Sandia
Google (5)
Samsung
U Missouri
MIT Lincoln Labs
IBM (3)
Cisco
U Toronto
AT&T Labs
Microsoft (4)
Simulex
Arizona State
Lawrence Livermore
Qualcomm
Cary
MIT
MITRE
VMWare
Schlumberger
Stanford
Adobe
DE Shaw
Bangladesh U
Amazon
Accenture
Korea U
Saavn
Arctic Sand
Cal Poly
JP Chase
SensorHound

6. Degree Options Lots of flexibility in degree options, course selection
Ph.D (after Masters): courses
Direct Ph.D (after Bachelors): 12 courses
Masters:
Non-Thesis (10 courses)
Thesis Option (6 courses)
Some highlights about our Masters:
Courses same as Ph.D level courses
Easy to switch to Ph.D (with support from committee members)
Thesis option allows large-scale system building, exposure to research etc.
Details: See Ph.D Handbook:

7. Research Areas: Overview
Compilers: Eigenmann, Midkiff, Kulkarni, Siskind
Distributed Syst./Networking/Operating Syst: Bagchi, Hu, Rao, Lin, Zhang
Dependability/Security: Ghafoor, Bagchi, Lin, Zhang
Computer Architecture: Vijaykumar, Thottethodi, Rogers, Zhang
Embedded Systems: Raghunathan, Lu
Artificial Intelligence/Machine Learning: Givan, Siskind, Kak
Human Computer Interaction: Ebert, Quinn
Robotics/Vision: Kak, Lee, Park, Siskind

8. Internet Systems Lab (Sanjay Rao) Current Research Focus: Cloud Computing
Many Opportunities
Elasticity, Cost-savings, Geo-distribution
Big Challenges:
Data Privacy Concerns
Performance is variable:
Amazon: every 100ms latency cost 1% in sales
Google: 0.5 sec’s delay
 revenue drop by 20%
Data-center failures and outages

9. Internet Systems Lab (Prof. Sanjay Rao)
an ACL
Local Data Center
Cloud
Less
sensitive
data
frontend
Internet
(sensitive
databases)
Internet Systems Lab (Prof. Sanjay Rao)
Geo-distributed data storage
Balancing consistency, availability, performance
Hybrid Cloud Architectures
How to architect interactive geo-distributed cloud applications?
Adapt to performance variability, high availability
Mobile Applications and cloud:
When does cloud make sense for mobile applications?
Consider both energy efficiency (3G/LTE), and performance

10. ISL (Sanjay Rao) Other Research Areas:
Enterprise Network Management
Software Defined Networks
Internet Video Delivery (e.g., Youtube)
Collaborations with AT&T, Microsoft, Google
Research prototypes deployed in AT&T, Purdue
More Information:
URL:

11. Purdue University Visual Analytics Center (PURVAC) – Prof. David Ebert
Visual analytics (spatial, temporal, predictive, tools)
Illustrative visualization
Perceptually-motivated visualization
Procedural modeling of natural phenomena
Rendering (volume, procedural, …)

12. Visual Analytic Center of Excellence (VACCINE) Public Safety & Health Visual Analytics Research
Public safety visual analytics
Coast Guard Search And Rescue Visual Analytics
Social Media Visual Analytics
Crime Visual Analytics
Public health visual analytics
Decision support environment for epidemic modeling and responses
Pandemic influenza modeling and visualization tool
Cancer Care Engineering

13. Human-Powered Systems Lab Prof. Alex Quinn
We develop systems that bring humans and machines together
to automate processes in ways that neither could accomplish alone.

14. Human-Powered Systems Lab Prof. Alex Quinn翻 译
ترجمة
machine + human
translation
idea
generation
computer
vision
natural language
processing
Products supported by human-powered systems:
Google Maps
Apple Maps
Amazon
Facebook
Google Translate
Bing
USPS
Twitter

15. Dependable Computing Systems Research (DCSL) Prof. Saurabh Bagchi
URL: engineering.purdue.edu/~dcsl
We need computer systems that we can depend on in the face of
Naturally occurring faults – hardware malfunction, software bugs
Malicious intrusions – insider attack or external adversaries
To build these we need students who are not afraid to make and break real systems
Many project ideas and requirements come from industrial sponsors and partners
Why our students love it?
Internships at top industrial places
Multiple job offers from top places
No super-long PhD timelines
Work with industry: Google Faculty Award (2016), …
Collaborating ECE faculty: Eigenmann, Kulkarni, Lin, Quinn
Build end-to-end dependable platforms addressing node level issues + network once the message is on the wire.
We are looking at the gamut of dependability techniques –
Our focus is on application level and system level software
Animation shows a simulation we have built as part of an ongoing Department of Defense project for missile defense. We are simulating the distributed command and control system for missile defense and how to make it resilient to failures (links being jammed, nodes failing) or cybersecurity attacks.
The red lines denotes missiles being launched, the circular regions are command and control or ground stations, and the white lines are the interceptors to bring down the missiles.

16. Dependable Computing Systems Research (DCSL) Prof. Saurabh Bagchi
Distributed IDS, based on Bayesian Network – protecting Northrop Grumman’s networks
Framework for distributed intrusion tolerant system
How to build an adaptive infrastructure for diagnosing and recovering from failures in a distributed platform?
Application: Web services, DoD missile defense system
A sample distributed application like those at corporate entities. We deploy a plethora of detectors and provide the smart to piece together information received from each of the detectors to give high level actionable knowledge to the sys admins. We also deploy automated response techniques, say to prevent an insider from exfiltrating information. Northrop Grumman is using it to protect their clients’ networks.

17. Research Projects in DCSL
Black-box diagnosis
How to diagnose source of errors in large scale distributed computing platforms?
Application: Distributed web services, Mobile apps (with AT&T), Supercomputing clusters (with LLNL)
Dependable Cyber Physical Systems
How to build dependable system under resource constraints? Under strict timing constraints?
Application: Smart power grid monitoring (startup company: SensorHound Innovations), infrastructureless wireless networks, say in battlefields or in disaster relief (with USC/ISI)
Black box diagnosis: rather than cursing when your call gets dropped or data connection gets slow, you can work on solving it – predict when a failure is going to happen based on a machine learning classifier, and then take mitigation action such as, connecting to a different cell tower.
Dependable embedded wireless networks: When it is deployed in hard to reach places like a battlefield, how do you tell if someone has tampered with the devices. Our technology has been commercialized through a startup company, which is selling to Department of Defense.

18. Distributed Systems and Networking Lab (Prof. Y. Charlie Hu) (1/2)
Smartphone Energy Profiling
Characterizing and Modeling the Impact of Wireless Signal Strength on Smartphone Battery Drain (Sigmetrics 2013)
Where is the energy spent inside my app? Fine Grained Energy Accounting on Smartphones with Eprof (EuroSys 2012, Best Student Paper Award)
Fine-Grained Power Modeling for Smartphones Using System Call Tracing (EuroSys 2011)
Smartphone Energy Debugging (with Prof. Midkiff)
Hypnos: Understanding and Treating Sleep Conflicts in Smartphones (Eurosys 2013)
What is keeping my phone awake? Characterizing and Detecting No-Sleep Energy Bugs in Smartphone Apps (MobiSys 2012)
Bootstrapping Energy Debugging on Smartphones: A First Look at Energy Bugs in Mobile Devices (HotNets 2011)

19. Distributed Systems and Networking Lab (Prof. Y. Charlie Hu) (2/2)
Data Center Networking
Duet: Cloud Scale Load Balancing with Hardware and Software (SIGCOMM 2014)
The Only Constant is Change: Incorporating Time Varying Network Reservations in Data Centers (SIGCOMM 2012)
The TCP Outcast Problem: Exposing Unfairness in Data Center Networks (NSDI 2012)
Latency Inflation with MPLS-based Traffic Engineering (IMC 2011)
Optimizing Cost and Performance in Online Service Provider Networks (NSDI 2010)

20. Prof. Felix Xiaozhu Lin’s Research
System Software for Mobile/Wearable Computers
Buttery smooth, “jank-free” interactions
Energy efficient, cooler devices
Friendly, foolproof app programming paradigms
System Software for Data Processing in IoT
Unleashing the power of novel accelerators
Combating the memory wall
Securing the use of accelerators

21. What is High-Performance Computing (HPC)?
Systems and application technology that creates and uses the highest compute power
Technology that pushes this envelope is clearly HPC
Today’s performance level: ~100TFlops peak
(Cray Oak Ridge NL, 560,000 cores, TFlops peak)
Classical application areas: climate modeling, computational fluid dynamics, molecular dynamics, structural analysis, ….
Hardware and software systems that enable this technology
The Computing Research Institute is Purdue’s interdisciplinary Center for High-Performance Computing
Several CE faculty participate in CRI: Midkiff, Eigenmann, Ghafoor, Vijaykumar, Hu, Bagchi, Thottethodi, Raghunathan

22. Cyberinfrastructures
You can get involved in the largest project in the history of Purdue
NEES: Network for Earthquake Engineering Simulation
nees.org
Research topics:
Search, analyze, visualize, manipulate large data
Cloud computing services
Security
Collaboration, virtual organizations
Big Data capture, use, storage, management
Profs Rudi Eigenmann Saurabh Bagchi
Collaborating with Civil Engineering, Mechanical Engineering, Engineering Education and ~20 other Universities 22

23. Parallelism, Languages and Compilers Lab (Prof. Milind Kulkarni)
How do we make it easier for “Joe programmer” to write correct, efficient parallel and distributed programs?
Give him an intuitive programming model
Sequential, or close to it
Simple abstractions for data structures, algorithms
Develop languages, compilers and run-time techniques to support intuitive programming models
New languages and language features to support simple parallel programming models
Compiler techniques to optimize program
Reduce communication latency, improve locality
Run-time techniques to optimize execution
Build efficient run-time systems
Automatically tune run-time parameters

24. PLCL Projects
Compilers and runtime systems for optimizing irregular applications
New transformations and analyses to improve locality, vectorize programs
Runtime systems to support distributed execution or using GPUs
Automatic tuning of run-time behavior
How do you adapt to the input?
Optimizing scientific applications
Take advantage of domain-specific properties for optimization
Algebraic properties, data usage behaviors
Optimizing and parallelizing Computational Genomics applications
Make it easier to write fast genomics applications (joint work with Prof. Bagchi)
What are the key kernels that people use in computational genomics?

25. Selected Compiler Projects (Prof. Sam Midkiff)
URL:
Compilation for more efficient, easier, safer programming
Novel high level programming language design and optimization
Aspen (With Prof. Vijay Pai)
Novel models of how threads interact (memory models) to ease of programming and high performance
Optimization of programs with high level user-specified parallelism
Compiler monitoring of programs to detect and characterize errors
Compilation for high performance
Hybrid compile-time/run-time analysis and optimization methods
Optimization of code for multithreaded processors
Debugging massively parallel programs (Prof. Hu)

26. (Prof. Rudi Eigenmann, Prof. Sam Midkiff)
Auto-tuning Compilers: Turning computer applications into life-long learners
(Prof. Rudi Eigenmann, Prof. Sam Midkiff)
Today:
Goals:
Programs run “optimally”
Programs improve with age
Software evolves
Write a program
Compile it
Run/use it
Tomorrow:
Challenges:
Navigate a huge space of optimization options
Dynamically plug-in new code
Ensure the evolving program improves
Write a program
Compile, try new options automatically
Run/use program as it evolves

27. Prof. Xiaokang Qiu’s Research
Computer-Aided Programming
Research mission: Make software development easier, more reliable, and more productive.
Program Verification
How to verify a program?
How to verify a large program?
How to verify an intricate program?
How to locate a bug?
Program Synthesis
How to automatically optimize a program?
How to automatically update my app for the latest
version of Android?
How to automatically repair a buggy program?
Automatic grading for MOOC programming courses? no
yes
Program
Correct?

28. HELPS Lab (Prof. Yung-Hsiang Lu)
High-Efficiency, Low-Power Systems
Supercomputer on Your Hand: Combine convenience of mobile and the resources in cloud servers
Run complex programs, such as recognition, high-performance graphics, strategic games… on phone while performing heavy computing on server (cloud)
Problems
Which parts of programs should run on mobile or cloud?
How to handle intermittent connections?
How many servers should be used?
How to retrieve data from multiple sites (such as Youtube + Facebook + Picasa)?
How to protect privacy and data integrity?

29. HELPS Lab: Prof. Yung-Hsiang Lu
Integration of mobile robots and cloud computing
Robot mobiles' batteries can last only minutes (for demos).
Computing takes too long and wastes energy.
Robots: sensing and control. Cloud: computing.
Big Data.
Thousands of cameras streaming video continuously.
Most videos are not stored or analyzed.
Analyzing these videos require distributed computing. MB b
Mb/s
b/s
Mb/s
b/s
storage
data sources
distributed computers

30. Embedded Systems Lab (ESL) – Vijay Raghunathan
Webpage:
Embedded Systems: “Computers that are part of larger systems that you don’t normally think of as computers”
98% of all CPUs sold go into embedded systems
Often very resource constrained, distributed, wireless
71 embedded CPUs
2 million lines of code
BMW 745i

31. ESL: HW and SW Systems Built By Us
Heliomote solar-powered sensor node
Ultra-low voltage energy harvesting IC
Super capacitor based high-efficiency energy storage system
AVEKSHA system for non-intrusive debugging of embedded systems
RF triggered wakeup system
SPI-SNOOPER reliable wireless sensor node

32. Embedded Systems Lab (ESL)
Hardware and Software Architectures for Wireless Embedded Systems
New low-power hardware architectures
Environmental energy harvesting
Embedded systems that enable green computing
Wireless Sensor Networks
New programming paradigms
New techniques for reliable operation
System-on-Chip Design Methodologies

33. Overview of Research in Assistive Robotics Technology Lab (ARTLab)
Prof. C. S. George Lee
Understanding: Perception & Cognition
Mobility: Locomotion & Control
Collaboration & Interaction: HRI
Mobile Robots
Building Better Future with Robotics & Machine Learning!

34. Prof. T. N. Vijaykumar’s Research (1/2)
Power- and Reliability-related
Mitigating power and heat problems
Data center power and cooling
Fault Tolerance
Architecture support for hard errors & soft errors
Architectures to tolerate process variations
Network Hardware
High-speed router design
Hardware support for network management
Hardware support for network security

35. Prof. T. N. Vijaykumar’s Research (2/2)
Multicore architectures
Chip multiprocessor architecture
Heterogeneous multicores
Architecture support to make parallel programming easier
Quantum Computing
Quantum architectures
Microfluidics
Programmable Lab-on-a-Chip

36. On-Chip & Off-chip Interconnection Networks (Prof. Mithuna Thottethodi)
Node
Node
Node
Node R
CPUs
Mem R
Router
Computation Issues:
Architecture support for management of large scale systems
Memory hierarchy, interaction with application
Communication Issues:
Congestion control for communication intensive applications
Routing, switching, & arbitration with worst-case guarantees
Other Projects: - Storage area networks
- Architectures for biochemical analysis

37. (AKA Wukong Lab) – Building the Next-Generation Datacenters
Faculty: Yiying Zhang
Fields
Operating Systems
Distributed Systems
Datacenter Networking
Computer Architecture
System Security
Big Data
“I see myself as a generalist -- I am attracted to the biggest problem I can find, regardless of area” (currently in systems)
Lab spirit: Fun, Real, Impact

38. Selected Projects We are building a new OS, from scratch
For next-generation datacenter hardware architecture and modern big data applications
with hardware assist
New hardware model that is not monolithic computer
and new programming model
We are building a new datacenter network system
System software stack
Network topology, routing, and congestion control
We are defining a new memory layer
Distributed, shared, persistent
Many other exciting projects
Security, big data

39. Programmable Accelerator Architectures Prof. Tim Rogers
Single Core OoO Superscalar CPU
Better
(how to get here?)
Brawny (OoO) Multicore
Wimpy (In-order) Multicore
Ease of
Programming
16K thread, SIMT Accelerator
ASIC
Hardware Efficiency

40. Programmable Accelerator Architectures Prof. Tim Rogers
Current Focus: GPUs
Exploring novel architecture and software to…..
Ease of
Programming
16K thread, SIMT Accelerator
Hardware Efficiency

41. Architecture and Embedded Systems
Recap: Research Areas
Software Systems
Architecture and Embedded Systems
AI/Vision/Robotics
Compilers: Eigenmann, Midkiff, Kulkarni, Siskind, Qiu
Distributed Syst./Networking/Operating Syst: Bagchi, Hu, Rao, Lin, Zhang
Dependability/Security: Ghafoor, Bagchi, Lin, Zhang, Qiu
Computer Architecture: Vijaykumar, Thottethodi, Zhang
Embedded Systems: Raghunathan, Lu
Artificial Intelligence/Machine Learning: Givan, Siskind, Kak
Human Computer Interaction: Ebert, Quinn
Robotics/Vision: Kak, Lee, Park, Siskind