The Department of Computer Science at the University of Iowa would like to share our research and our passion for this work with you and your students. To this end, we would like to introduce our speakers bureau, a list of faculty in our department who would love to present their work to your students! Our goal is to provide a single point of contact to set up faculty talks from our department. We also provide a list of possible talk topics, so you have an idea what we can offer. Ideally, by making it easier to organize colloquia and other presentations at your school, we will see you more often!
Below is a list of our faculty who have volunteered, as well as sample talks they already have prepared. If you would like to set up a talk, please send an e-mail to Suely Oliveira with ideal dates and times and any preferred speakers or talks, and she will work to find a speaker available to fit your schedule. If you aim for a particular speaker, feel free to coordinate directly with them (though please copy firstname.lastname@example.org, too).
Among other institutions, our faculty have visited St. Ambrose, Carleton, Coe, Cornell College, Drake, Grinnell, U. of Illinois Chicago, ISU, Knox, Luther, Truman State U., Wartburg, U. of Wisconsin La Crosse, as well as K-12 establishments.
Speakers and Example Talk Topics
Bijaya Adhikari, Ph.D.
Data Driven Epidemic Forecasting
The ongoing COVID-19 pandemic has highlighted the necessity of epidemic forecasting. An accurate forecasting framework acts as a powerful tool to combat infectious outbreaks by giving valuable lead-time for preparation. In this talk, we present our data driven frameworks for epidemic forecasting for various targets associated with influenza and COVID outbreaks. Our data driven approaches are carefully designed with the true nature of the disease in mind. Hence, they help in addressing several challenges of real time forecasting such as interpretability, handling noisy signals, and principled propagation of uncertainty. Results from real time forecasting challenges show that our approaches are among the state-of-the-art.
Octav Chipara, Ph.D.
Developing and Deploying Mobile Sensing Applications
Mobile sensing applications are an emerging class of mobile applications that take advantage of the increasing sensing, computational, storage, and networking capabilities of mobile devices. Chipara’s research focuses on the systems, networking, and software engineering aspects of developing mobile health (mHealth) systems that continuously monitor and infer the health status of patients. His work combines the design of communication protocols, middleware, and programming tools with large-scale real-world deployments of working systems.
Juan Pablo Hourcade, Ph.D.
Human factors, computing, and children
Human factors have been involved in key changes in the history of computing, making processes more efficient, broadening who can use computers, and expanding the applications of computing. These transformations have led to computing being ubiquitous in our lives, impacting even young children, not always in a positive way. In this talk, Hourcade presents a vision for children’s technologies that contrasts with prevailing industry approaches and presents an example of a technology that moves in his proposed direction. He concludes by discussing key guidelines for designing technologies for children. Hourcade is an ACM Distinguished Speaker.
Mehrdad Moharrami, Ph.D.
A Policy Gradient Algorithm for the Risk-Sensitive Exponential Cost MDP
We study the risk-sensitive exponential cost MDP formulation and develop a trajectory-based gradient algorithm to find the stationary point of the cost associated with a set of parameterized policies. We derive a formula that can be used to compute the policy gradient from (state, action, cost) information collected from sample paths of the MDP for each fixed parameterized policy. Unlike the traditional average-cost problem, standard stochastic approximation theory cannot be used to exploit this formula. To address the issue, we introduce a truncated and smooth version of the risk-sensitive cost and show that this new cost criterion can be used to approximate the risk-sensitive cost and its gradient uniformly under some mild assumptions.
J. Garrett Morris, Ph.D.
Extensibility in programming language design
Extensibility is an evergreen problem in programming and programming language design. The goal is simple: specifications of data should support the addition of both new kinds of data and new operations on data. Despite this problem having been identified as early as 1975, modern languages lack effective solutions. Object-oriented languages require programmers to adopt unintuitive patterns like visitors, while functional languages rely on encodings of data types. Lower-level languages, where similar problems arise, rely on textual substitution. I will present recent work which proposes a unified approach to extensible data specifications. I will show how this work both encompasses existing approaches to extensible data specifications, and captures examples inexpressible in all existing systems. Our approach naturally generalizes, providing a single account of extensible objects, effects, and bit-level specifications.
Rishab Nithyanand, Ph.D.
Tussling with Anonymity and Privacy on the Internet
In recent years the Internet has integrated itself into the critical infrastructure and its users have become increasingly dependent on it for commerce, communication, and social and political organization. This has resulted in the emergence of Internet stakeholders that have competing and contradictory interests. For example, given that the Internet economy is fueled by user data and targeted advertising, content providers aim to maximize their ability to gather user data and personally identifiable information (PII). This goal contradicts the interests of parties lobbying for consumer and privacy protection on the Internet. Effective regulation and resolution of such tussles by Internet governing authorities are hampered by the opacity of the Internet and the inability to uncover the behaviors of competing parties. In this talk I will focus on my most recent work that uncovers the state of two such tussles: (1) anonymity vs. accountability and (2) privacy vs. profitability.
Suely Oliveira, Ph.D.
New approaches for Machine Learning and Data Science
On her research Oliveira employs algorithms from continuous optimization in the field of Machine Learning, AI and Data Sciences. In the last twenty years, she has concentrated on models for clustering and neural networks to achieve accurate and fast algorithms in applications ranging from genetics to education. An important aspect for efficient methods is speed. Mathematics and parallel computing turned out to be excellent tools for her ongoing research in Machine Learning allowing her to work on mathematical foundations along with novel applications.
Sriram Pemmaraju, Ph.D.
The Power of Randomization in Algorithms
Over the last two decades, randomization has been recognized as a powerful tool in the design of algorithms. There are now many instances where randomization improves either the simplicity or the speed (or both) of algorithms. Also, randomization has become indispensable in new computational settings, for example in the context of online or streaming algorithms or in the context of wireless networks. This talk will provide accessible examples of the power of randomization in the design of algorithms starting with classical examples such as primality testing algorithms to recent examples of data streaming algorithms.
Sourya Roy, Ph.D.
Some recent advances in pseudorandomness and its applications
Pseudorandomness is a key concept in theoretical computer science, influencing areas like derandomization, coding theory, and cryptography. In this presentation, I'll share some of our recent work in advancing the understanding of pseudorandomness.
Alberto Maria Segre, Ph.D.
Computational epidemiology lies at the intersection of computer science, engineering, statistics, and health care. Our goal is to inform public and hospital policy decisions on topics such as: disease surveillance, disease prevention measures, and outbreak containment. Covers our work, including computational models, simulations, and visualization for the spread of disease.
Supreeth Shastri, Ph.D.
Legalizing Personal-data Systems
In recent years, many societies have recognized the privacy and protection of personal data as a fundamental right of the people. While regulations such as GDPR (in Europe) and CCPA (in California) are already in effect, modern computing systems are struggling to comply with them. In this talk, I will show how these laws invalidate computing principles and practices that have years of precedence, and why it is challenging to legalize personal-data systems. The overarching goal of my research has been to bridge this gap between (legal) intention and (computing) implementation. I will share the work that my collaborators and I have been doing in this space, and identify some of the interesting open problems.
Rahul Singh, Ph.D.
Mapping the Protein Universe
What does the Universe of all molecules look like? In this talk, I will cover research being conducted in my lab that seeks to answer this fundamental question by considering biologically relevant molecules such as proteins and enzymes that underlie the processes of life. We will show how data about the structure of thousands of such large-molecules can be leveraged to produce a map of the protein Universe. We will also look into the design of novel algorithms and human-data interaction strategies to analyze and assimilate information from such maps.
Padmini Srinivasan, Ph.D.
What is Text Mining?
Text mining is an exciting area of study that underlies a wide variety of entrepreneurial initiatives especially with Web data. For example, when applied to social media such as Twitter and YouTube we can find out interesting details such as the hottest topics, the most influential posters, which subjects attract the most attention from the public, how long does it take for an idea to spread etc. This talk highlights the many opportunities that arise with text mining.
Aaron Stump, Ph.D.
A Type-Based Approach to Verified Software
Discusses recent work to provide lighter-weight verification using strongly typed languages. Instead of relying on proving correctness, this approach expresses properties in the code using a rich type system. One goal is to support a continuum, where developers can select how much of their code to verify. Recent research examples are discussed.
Denise Szecsei, Ph.D.
Programming Humanoid Robots
Learn about the capabilities of Nao humanoid robots and see them in action. Get a brief introduction to what is involved in programming them, and watch the robots dance, act, and perform magic tricks. For more information, visit the CS Performing Robots page.
Cesare Tinelli, Ph.D.
Verifying the Correctness of Programs
An introductory talk on formal verification of software, including: motivation for program verification; comparison of formal verification versus testing; and an overview of modern techniques for automating verification. Focuses on Iowa's research in the area.