Events Calendar

The Computer Science (CS) department welcomes prospective students interested in our graduate programs to our annual Prospective Student Visit Day on Fri, Nov 6. We are looking for strong students with diverse backgrounds to join our MCS and PhD programs. A wide variety of research areas is represented by our world-class faculty including algorithms, computational epidemiology, distributed computing, human-computer interaction, machine learning, massive data algorithms and technology, mobile computing, networks, programming languages, text mining, security, and virtual reality. If you intend to join us, please RSVP our graduate coordinator Ms. Sheryl Semler at sheryl-semler@uiowa.edu, by November 1.

An important part of the prospective Student Visit Day is the 6^th Iowa Computer Science Graduate Research Symposium (2020), in which senior PhD students will present talks showcasing their current research. This will be followed by a "keynote" by a CS faculty member. Talks are intended for a wide audience with interest in CS, including CS juniors and seniors. The talks presented by current CS graduate students at the Symposium are excellent examples of the exciting CS research taking place here on the UI campus!

Schedule 

Speakers 

Mitziu Echeverria

Title: Empowering Cellular Devices to Detect Control-Plane Attacks using Runtime Verification

Abstract: 

In the current cellular ecosystem, users are mostly unaware of vulnerable cellular activities (e.g., paging with sensitive identifier in clear or bypassing authentication) on the device as no general in-device warning systems exist to date. To change this undesired status quo, this paper takes the first step and develops a general and lightweight approach dubbed PHOENIX for effectively monitoring a (mobile) device’s cellular traffic against existing cellular network attacks and unsafe practices (e.g., use of null encryption) of the network operators. PHOENIX automatically synthesizes vulnerability signatures by solving variants of the language learning from the informant problem, and then efficiently monitors device’s control-plane cellular traffic for those vulnerability signatures and warns the presence of vulnerabilities at runtime. PHOENIX can identify all the 15 representative n-day attacks and unsafe practices of 4G LTE networks considered in our evaluation with a high packet processing speed (~68000 packets/second) while inducing only a moderate amount of energy (~4mW) overhead on the device.

3^rd year PhD student | Advisor: Omar Chowdhury | Areas of research: Computer Privacy and Security, IoT, Security, Mobile Security

Wanxin Wang

Title: The Numerical Inverse Kinematics Methods and Virtual Reality Environment

Abstract:

Inverse kinematics (IK) has a long historical usage for robotic control, computer animation editing, and film making; virtual reality is boosted by the low-cost home VR product from industry, and it is an affordable product for an average consumer with an acceptable tracking fidelity in aspect for the entertainment. Using IK in VR environment to animate posture in real time could be used for simulating human movement, and is used for phycology study in Hank, potentially could be used for other humanoid simulation purpose as in other fields.

IK works in VR environment, requires real time computation, which is a different requirement to the historical IK usage for computational speed. In this talk, I will focus on numerical IK algorithm, and the opportunities in VR environment that could be exploited for optimizing the IK computational speed in a high level.

The talk will start from introduction of IK problem, and VR environment, and then move focus on numerical IK methods (Jacobian method, and gradient based optimization method) in a brief high level. Then the two approaches that exploit the VR environment and the trait human physical movement respectively, will be discussed.

3^rd Year PhD student | Advisor: Joe Kearney | Areas of research: Distributed Computing, Driving simulation, Computer Graphics, Algorithms

Christopher Jenkins

Title: Efficient Lambda Encodings for Mendler-style Coinductive Types in Cedille

Abstract:

In the calculus of dependent lambda eliminations (CDLE), it is possible to define inductive datatypes via lambda encodings that feature constant-time destructors and a course-of-values induction scheme. This paper begins to address the missing derivations for the dual, coinductive types. Our derivation utilizes new methods within CDLE, as there are seemingly fundamental difficulties in adapting previous known approaches for deriving inductive types. The lambda encodings we present implementing coinductive types feature constant-time constructors and a course-of-values corecursion scheme. Coinductive type families are also supported, enabling proofs for many standard coinductive properties such as stream bisimulation. All work is mechanically verified by the Cedille tool, an implementation of CDLE.

4^th year PhD student | Advisor: Aaron Stump | Areas of research: Programming Language Theory, Specifically (Dependent) Type Theory, Inductive Definitions, Recursion Schemes, Elaboration, Bidirectional Type Inference

Keynote Speaker Peng Jiang

Title: Accelerating Sparse-Matrix Dense-Matrix Multiplication on GPUs

Abstract:

Sparse matrix computations are not amenable to GPU processing due to their irregular memory access patterns and limited data reuses. Previous works have focused on accelerating sparse matrix-vector (SpMV) or sparse matrix-matrix multiplication (SpGEMM). Recently, sparse-matrix dense-matrix multiplication (SpMM) has arisen in various machine learning applications. Unfortunately, previous techniques for SpMV and SpGEMM fail to achieve good performance for SpMM. In this talk, I will introduce the current research progress in accelerating SpMM on GPUs as well as its application in accelerating CNN inference with sparse convolutions.

Bio:

Dr. Peng Jiang is an assistant professor at the University of Iowa. His research interests are broadly in software systems, with a focus on compiler and runtime techniques for parallel computing and performance optimization. Before joining UIowa, he obtained his PhD at the Ohio State University where he worked on improving the performance of applications in various domains including graph algorithms, scientific simulations, data analysis, and machine learning. His work has been published in top venues in parallel computing, compiler, and machine learning areas.