This Q&A highlight features Crescentia Jung, an Honorable Mention in the 2022 CRA Outstanding Undergraduate Researchers award program. Crescentia graduated from the University of Wisconsin-Madison and is now at Cornell University pursuing a Ph.D. in Information Science.
Computing Research News
This Q&A highlight features Naitian Zhou, an Honorable Mention in the 2022 CRA Outstanding Undergraduate Researchers award program. Naitian graduated from the University of Michigan and is now an Information Science PhD student at the University of California, Berkeley.
This Q&A highlight features Tiana Fitzgerald, an Honorable Mention in the 2022 CRA Outstanding Undergraduate Researchers award program. Tiana graduated from Princeton University and is now an Engineering Analyst at Goldman Sachs.
My research explores algorithmic methods for determining whether a pair of species are likely to have coevolved and, if so, finding the “best” scenarios that explain their evolutionary histories. This work explores the computational complexity of these reconciliation problems, seeks to develop efficient reconciliation algorithms where possible, and, ultimately, to implement these algorithms in practical tools for biologists and educators.
The computational complexity of these reconciliation problems depends on the particular biological events that we seek to model. We’ve shown that in some models the reconciliation problem is not only NP-hard but it’s even difficult to find an approximately optimal solution. In other cases, the reconciliation problem can be solved by efficient polynomial time algorithms.
My current work focuses on support for critical literacy and efforts to foster new paths for equity in the sciences.
I currently serve as chair of the faculty in the College of Information and Computer Sciences at the University of Massachusetts Amherst. This means that, like most chairs, I divide my time between administrative work supporting our college’s faculty, advancing our college, and conducting my own research as co-director of the Center for Intelligent Information Retrieval.
Increasingly, jobs rely on the ability to use computers to interpret, understand, and trust data. For example, my students and I have worked with ornithologists who cannot understand the representations of their bird sightings, civil engineers who cannot easily use their own building data, finance experts who cannot trace money between companies and their subsidiaries, and an XML document company whose clients cannot understand data that appears outside of their reports. In each case, the data users have been hampered because their data is exceedingly difficult to understand and trust, even though the users are experts in their fields. One reason for this difficulty is that the organization of the data is often designed for computers, not for people (i.e., for storage, not accessibility). Another reason is that data often come from different sources, leaving users with the challenge of integrating data that they neither understand nor trust.
My computer science research career started during my college internship at Bell Laboratories in Murray Hill, New Jersey, during the early 1970s in the center that later produced UNIX and the portable C compiler. This experience taught me that computing was broader than the introduction to scientific programming in my undergraduate studies in applied math. (There was no computer science undergraduate major at the time.) For most of my career, I was interested in deriving descriptions of program execution behaviors from code in order, for example, to optimize program time and/or memory performance, to validate desirable properties such as correctness or data security, or to refactor code for ease of maintenance.
Increasing diversity in computing has been my passion throughout my career, mostly through my informal mentoring of female CS students at Rutgers and Virginia Tech, participating in CRA-W mentoring workshops, and leading efforts in CS at Virginia Tech College of Engineering to join with NCWIT to increase the gender diversity of our CS students.
Research shows that it takes 25 minutes to reach full productivity after an interruption, yet we are interrupted every 3 minutes. And even without external interruptions, our focus is fragmented. We look at any given desktop window for an average of only 40 seconds, constantly self-interrupting to check email or Facebook. We also try to complete multiple tasks at once, even though we all know that multitasking typically fails. Our tendency to be easily distracted kept our hunter-and-gatherer ancestors alive when they needed to attend to potential predators, but now, in the safety of our offices, it is amazing we manage to get anything done. Chances are you won’t even read this entire article in one go.
As a researcher, I am fascinated by the challenge of advancing the high-level foundations of computer software (programming models, compilers, and runtimes) to productively exploit the latest advances in computing systems. While there has been a long tradition of research in this area since the dawn of computing, the rapid evolution of hardware has continuously fueled a need for new software technologies as old approaches quickly become obsolete. Current explorations of new hardware directions that go beyond Moore’s law have further amplified the motivation for this research direction.
For the past 30 years I have had two passions – machine learning (ML) that makes a difference in the real world and increasing diversity in computer science (CS). For the first 26 years, I focused on my first passion and developed new approaches to ML though applications to remote sensing, neuroscience, digital libraries, astrophysics, content-based image retrieval of medical images, computational biology, chemistry, evidence-based medicine, detecting lesions in the MRIs of epilepsy patients, and predicting disease progression for MS patients. For the last four years, my focus has been on my second passion: increasing diversity in CS.
My research sits at the intersection of Natural Language Processing (NLP) and speech processing. I have focused on identifying the role of prosodic information in speech and using this knowledge to produce more realistic Text-to-Speech Synthesis (TTS) systems; to detect many types of speaker state, including the classic emotions, such as anger, disgust, fear, happiness, sadness, and surprise; and derived emotions, such as confidence and uncertainty, deception, trust, and charisma. I’ve also studied human-machine and human-human behavior in Spoken Dialogue Systems (SDS) and Human-Computer Interaction (HCI).