Explore Computation + X research areas at Duke Computer Science.
Computation + Biology
Computational biochemistry and drug design
Research at Duke has led to computational structure-based protein design algorithms that could revolutionize therapeutic treatment. These algorithms will enable the design of proteins and other molecules to act on today's undruggable proteins and tomorrow's drug-resistant diseases. Examples include predicting future resistance mutations to new drugs in pathogens responsible for deadly nosocomial and community-acquired infections, the design of inhibitors of protein-protein interactions that address the underlying genetic defect in cystic fibrosis patients, and the discovery of broadly neutralizing antibodies against Human immunodeficiency virus (HIV).
The flood of high-throughput sequencing data necessitates computationally efficient algorithms for assembling genome sequences, comparing genome sequences, constructing evolutionary histories of genome sequences, and performing statistical inference on genome sequences. The same high-throughput sequencing technologies also allow us to understand the function of the genome and how processes like transcription, replication, packaging, and repair are dynamically regulated. Research at Duke has produced state-of-the-art methods and models for elucidating protein-DNA interactions, chromatin architecture, and transcriptional regulatory networks.
Recent advances in imaging technologies have revolutionized modern biology and medicine by providing researchers and health professionals with access to large amounts of image data at unprecedented levels of detail. Research at Duke has focused on the development of advanced computational tools for mining big data to extract patterns, for example, to elucidate the high-resolution structure of biomolecules under physiologically relevant conditions. This information can help biologists and physicians unveil the molecular mechanisms that regulate biological processes, providing more opportunities for the development of new medical treatments to combat disease.
DNA and molecular computing
The field of molecular computing makes use of chemical reactions to do computations at the molecular scale, potentially allowing for extreme parallelism. DNA computing makes use of nucleic acids such as DNA and RNA to do molecular computing and allows computations to be done in liquid environments such as in or near cells, where the scale of conventional computing devices are far too large.
Computation + Economics
Algorithmic game theory
The field of algorithmic game theory lies at the intersection of computer science and economics. It concerns itself with computational questions in the presence of self-interested agents. Researchers at Duke have studied fundamental questions in this emerging area, including auction theory, social choice theory, fair resource allocation, preference elicitation, and crowdsourcing, along with real-world implications to ethics, democracy, and society.
Computational social choice
The theory of social choice concerns how to make decisions based on the conflicting preferences of multiple parties (agents). In computational social choice, these problems are studied from a computational perspective: what are well-motivated and efficient algorithms for solving these problems? Research at Duke has made foundational contributions to this research area, including on voting, fair resource allocation, budget allocation, setting societal priorities, and many other topics.
Computation + Policy
Journalism is at a crossroads. Traditionally, we have relied on news organizations to hold governments, corporations, and individuals accountable to society. In recent years, there has been an alarming trend in the increasing amount of misinformation, compared with stagnant resources and talents devoted to investigative reporting and fact-checking. Computational journalism aims at bridging this divide, by inventing computational techniques and tools to increase effectiveness and broaden participation for journalism—especially public interest journalism, to help preserve its watchdog tradition. Duke Computer Science, in collaboration with the School of Public Policy and practicing journalists, has pioneered research on computational fact-checking to help combat outrageous lies as well as factually correct but still misleading claims. Learn more about the Computational Journalism Project.
Now that AI algorithms are widely deployed in the world, it is becoming clear that the decisions that they make often have a significant moral component. Many of these algorithms require an objective to be specified that they then pursue, but how should we determine the right objective? Research on this topic at Duke focuses on combining insights from computer science, philosophy, and the social sciences to establish well-founded methodologies for determining the objective.