The Illinois Institute of Technology College of Computing’s Dr. Frederica Darema Lecture Series in Computer Science is funded by an endowment to help advance female and minority early-stage computer science researchers at U.S. academic institutions.
“Opportunities to succeed as an academic in the areas such as computer sciences, are not readily available to everyone,” says Dr. Frederica Darema (M.S. PHYS ’72). “This fund enhances opportunities to showcase some of the most talented academics and researchers, those who may not have such opportunities otherwise. The more we lift up these excellent junior faculty, the more we will be able to encourage women and people of color to pursue an academic career in computer science.”
The lecture series is designed to encourage women and individuals from under-represented groups to pursue academic careers in computer sciences, and to focus on providing speaking opportunities for tenure track assistant professors (or the equivalent) at U.S. institutions in their fourth to sixth year. Lectureships may also be awarded to exceptional junior researchers in U.S. federal or industrial research laboratories in the third to fifth years of their careers, following doctoral/postdoctoral studies.
Events presented in the Dr. Frederica Darema Lecture Series in Computer Science are open to students, faculty, researchers, and others interested in learning about the latest research in computer sciences.
ABOUT FREDERICA DAREMA
Dr. Frederica Darema (M.S. PHYS ’72) is a Greek American physicist, with a career that has spanned physics, and computer and computational sciences. In 1984, she proposed the Single Program, Multiple Data (SPMD) programming model, the predominant model for supercomputing over the last 35 years. She proposed the Dynamic Data Driven Application Systems (DDDAS) computational paradigm in 2000. For her scientific contributions, she was elected as an Institute of Electrical and Electronic Engineers (IEEE) Fellow in 2004.
Darema earned her B.S. degree from the School of Physics and Mathematics of the University of Athens - Greece, and M.S. and Ph. D. degrees in Theoretical Nuclear Physics from the Illinois Institute of Technology and then the University of California at Davis, respectively. She attended Illinois Tech and UC Davis as a Fulbright Scholar, and was also a Distinguished Scholar at UC Davis.
After serving in physics research associate positions at the University of Pittsburgh and Brookhaven National Laboratory, she received an APS Industrial Fellowship in 1980, and became a technical staff member in the Nuclear Sciences Department at Schlumberger Doll Research. In 1982, she joined the IBM Thomas J. Watson Research Center as a research staff member in the Computer Sciences Department, and subsequently established and became the manager of a research group at IBM Research, on parallel applications.
Darema joined the National Science Foundation in 1994, where she served in executive level positions and led the New Generation Software and Dynamic Data Driven Application Systems programs. From 1996-1998 she completed an assignment at the Defense Advanced Research Project Agency (DARPA). She recently retired as SES Director of the Air Force Office of Scientific Research, where she led a staff of 200, PhD scientists and engineers, and administrators, in managing over half-billion dollars of research funding.
NOMINATE A SPEAKER
To nominate a speaker for the Dr. Frederica Darema Lecture Series in Computer Science, please contact Associate Professor of Computer Science and Engineering Zhiling Lan at Illinois Tech’s College of Computing.
April 24, 2023
High-performance computing (HPC) systems, or supercomputers, are big and complex. They integrate the most advanced computing, memory, storage, and networking technologies to meet the computational needs of our greatest scientific and engineering endeavors. However, designing and configuring these systems is challenging due to the complex inter-operation of tightly coupled components.
Simulation-based co-design has become the industry standard in evaluating and optimizing supercomputer designs and configurations. These simulation models, while they abstract the full complexities of the real-world, still require a significant amount of time and computing resources to execute. To enable predicting longer timescales, finer-grain activities, and larger-scale phenomena, we require faster model executions. Parallel discrete events simulations (PDES) and techniques such as machine-learning based surrogate modeling can improve time to prediction, but challenges remain due to model scale and complexity. This talk discusses some of the key challenges with PDES in designing large-scale scientific infrastructure, such as integrating multi-scale models, and explores opportunities for cross-disciplinary collaborations.
Kevin A. Brown is an Argonne Scholar – Walter Massey Fellow in the Mathematics and Computer Science Division at Argonne National Laboratory.
Brown received his B.Sc. from the University of Technology, Jamaica (UTech) and his M.Sc. and Ph.D. from the Tokyo Institute of Technology. He has previously worked in industry as a Systems Administrator and has also worked in other national research laboratories in Japan, Spain, and the USA.
At Argonne, Brown investigates new networking technologies and designs for next-generation supercomputers. He has worked on topics such as Big Data, performance analysis and visualization, and network simulation and modeling.
November 3, 2022
Title: Designing Intelligent Interactions to Support Aging Well
Abstract: The growing ubiquity of consumer-facing intelligent technologies such as Alexa and Siri has led to increased interest in how these tools can support continued independence and health over the lifespan. In this talk, I will discuss opportunities for AI-enabled technologies to support older adults as they age and their concerns about adopting these technologies into their homes. I will also discuss potential collaborative strategies and AI-enabled tools developed through participatory design that may address older adults' needs and overcome the hesitancy of adoption.
Speaker Bio: Aqueasha Martin-Hammond is an Assistant Professor of Human-Computer Interaction in the School of Informatics and Computing at Indiana University - Purdue University Indianapolis (IUPUI). She earned a Ph.D. in Computer Science from Clemson University, an M.S. in Computer Science from the University of Alabama, Birmingham, and a B.S. in Computer Science from Tougaloo College. She conducts research and teaches in Human-Computer Interaction, where she examines the intersection of the areas of aging, health, and intelligent technology design. She employs user-centered and participatory design methodologies to investigate the design of existing and novel intelligent technologies such as conversational assistants and AI-enabled tools to support aging through improved access to health information and wellness resources at home and in the local community. More broadly, she investigates ways to enhance the usability and accessibility of technologies for older adults across their lifespan and explores the benefits and barriers of adopting these technologies in home and community environments. Her work is funded by Google, the National Science Foundation (NSF), and the National Institutes of Health (NIH). She is also a recipient of an NSF CAREER award.
April 27, 2022
As artificial intelligence (AI) is transforming work and society, it is ever more important to ensure
that AI systems are fair and trustworthy, and support critical values and priorities in
organizations and communities. I argue that making AI procedurally fair and participatory is
critical to achieving the goal. We should enable procedural justice— the perceived fairness of
the decision-making process—in AI through transparency and participation, and design useful
and empowering AI by learning from the creativity and lived experience of people who use or
are affected by AI. In this talk, I will present empirical work that elucidates the importance of
procedural justice in trustworthy AI. I will then describe a series of novel design work that
enables stakeholder participation throughout the AI design and deployment cycle: i) the
WeBuildAI framework, a novel participatory framework that involves stakeholders in algorithmic
design, and ii) participatory design methods for envisioning new forms of algorithmic
management and worker well-being metrics.
Min Kyung Lee is an assistant professor in the School of Information at the University of Texas
at Austin. Dr. Lee is a human-computer interaction researcher, and has extensive experiences
in developing theories, methods and tools for human-centered AI and deploying them in practice
through collaboration with real-world stakeholders and organizations. She proposed a
participatory framework that empowers community members to design matching algorithms that
govern their own communities. She also conducted one of the first studies investigating public
perceptions of algorithmic fairness and the impacts of algorithmic management. Her current
research is inspired by and complements her previous work on social robots for long-term
interaction, seamless human-robot handovers, and telepresence robots.
Dr. Lee is a Siebel Scholar and has received the Allen Newell Award for Research Excellence,
research grants from NSF and Uptake, and five best paper awards and honorable mentions and
two demo/video awards in venues such as CHI, CSCW, DIS, HRI and MobiSys. She is an
Associate Editor of Human-Computer Interaction and a Senior Associate Editor of ACM
Transactions on Human-Robot Interaction. Her work has been featured in media outlets such as
the New York Times, New Scientist, Washington Post, MIT Technology Review and CBS. She
received a PhD and a MS in Human-Computer Interaction and an MDes in Interaction Design
from Carnegie Mellon University and a BS from KAIST.
November 3, 2021
Access Passcode: uT*0G*WB
Imagine a world where the Internet caters to all types of users
and hosts trustworthy content. Right now, this world seems far off for many reasons. For instance, this world would require us to think more broadly of user needs beyond an `average’ tech-savvy adult user—one who is assumed to be always online with a reliable Internet connection. Moreover, this world would require us to host content that is not misleading or manipulative in some way—content that can be evaluated at face value by various users. To achieve this lofty goal, we first need to deeply understand and catalogue different types of Internet users’ needs and also develop ways to assess and
make misleading online content more apparent to end-users.
In this talk, I will present a set of case studies from my research lab that helps further the goal of a trustworthy Internet for all. I will describe various projects geared at understanding a wide variety of Internet users’ needs for online privacy and security in different contexts from children to those in developing contexts. I will also describe work to determine
different kinds of misleading content online such as `dark patterns’ and disguised advertisements and show solutions to help users to better evaluate this content. These case studies will demonstrate how important it is to study the privacy and security needs of those who do not fit the “average” user mold and demonstrate possible solutions for helping users gain more trust in information on the Internet. I conclude with open questions for imagining an Internet which is more trustworthy and inclusive to all people.
Marshini Chetty, assistant professor in the Department of Computer Science at the University of Chicago, directs the AIR Lab. She specializes in human-computer interaction, usable privacy and security, and ubiquitous computing. Her work has won best paper and honorable mention awards at SOUPS, CHI, and CSCW, and she was a co-recipient of the Annual
Privacy Papers for Policymakers award. Her research has been featured in the New York Times, CNN, Washington Journal, BBC, Chicago Tribune, The Guardian, WIRED,
and Slashdot. She has received generous funding from the National Science Foundation, through grants and a CAREER award, as well as the National Security Agency, Facebook, and multiple Google Faculty Research Awards.
Marshini started her journey in the USA after she completed her MSc., BSc.(Hons), and BSc. in Computer Science at the University of Cape Town, South Africa (her beautiful home country). She received her PhD in Human-Centered Computing from Georgia Institute of Technology. Marshini subsequently completed a postdoctoral fellowship at Georgia Tech’s College of Computing with W. Keith Edwards. Following another postdoctoral fellowship at Research ICT Africa, she also held faculty positions at University of Maryland, College Park, and Princeton University before moving to Chicago.
February 2, 2021
In current times, data is considered synonymous with knowledge, profit, power, and entertainment, requiring development of new techniques to extract useful information and insights from data. In this talk, I will describe some concepts and techniques in interpretable data analysis from the viewpoint of a database researcher. First, I will talk about our work on explaining query answers, in terms of "intervention" or how changes in the input data changes the output of a query, and "context" or how input data not contributing to the answers of interest can help explain them. Then I will talk about true causal inference from observational data without randomized controlled experiments and how database techniques can
help with causal inference for large complex data.
Sudeepa Roy is an Assistant Professor in Computer Science at
Duke University. She works in the area of databases, with a focus on foundational aspects of big data analysis, which includes causality and explanations for big data, data provenance, probabilistic databases, and applications of database techniques in other domains. Prior to Duke, she did a postdoc at the University of Washington, and obtained her Ph.D. from the University of Pennsylvania. She has served on the program committees of a number of premier conferences and workshops including SIGMOD, VLDB, PODS, and ICDT. She is a recipient of an NSF CAREER Award and a Google PhD Fellowship in Structured Data.
December 5, 2019
The ability to automatically understand what is perceived in visual data is in increasingly high demand. However, despite tremendous performance improvement in recent years, state-of-the-art deep visual models learned using large-scale benchmark datasets still fail to generalize to the diverse visual world. In this talk I will discuss a general purpose semi-supervised learning algorithm, domain adversarial learning, which facilitates transfer of information between different visual environments and across different semantic tasks thereby enabling recognition models to generalize to previously unseen worlds, such as from simulated to real-world driving imagery. I’ll also touch on the pervasiveness of dataset bias and how this bias can adversely affect underrepresented subpopulations
Judy Hoffman is an Assistant Professor in the School of Interactive Computing at Georgia Tech. Previously, she was a Research Scientist at Facebook AI Research. She received her PhD in Electrical Engineering and Computer Science from UC Berkeley in 2016.