Volunteer-based physical crowdsourcing systems connect individuals to make unique contributions to solve local and communal problems and enable new services. A key challenge in enabling such systems is attracting enough willing volunteers who can make useful contributions to achieve desired system goals. While most volunteer-based systems provide volunteers flexibility to...
Algorithmically-driven social platforms present a challenge for self-presentation and identity management by obscuring audiences behind algorithmic mechanisms. Users are increasingly aware of this and actively adapting through folk theorization, but we do not know how users are coping with the constant change endemic to these platforms. We also do not...
In this thesis we study two problems, one in unsupervised learning - k-means clustering and the other in a supervised learning setting with the presence of adversarial perturbations. We do a beyond-worst case style analysis and show that in either case instances that are resilient to adversarial perturbations are also...
Commonsense inference is a critical capability of modern artificial intelligence (AI) systems. The machines need commonsense knowledge to perform tasks exactly like human being does. Learning commonsense inference from text has been a long standing challenge in the field of natural language processing due to reporting bias -- people do...
This dissertation explores the design and evaluation of augmentative and alternative communication (AAC) technologies for people with aphasia. Humans use speech and language to communicate their thoughts and opinions as well as express their individuality, autonomy and agency (George Armitage Miller 1951; Ahearn 2001). Speech and language are important tools...
Millions of people freelance in the growing online gig economy, making it important to advance pay equity and support freelancers in earning their livelihoods online. Compared to offline employment, freelancing introduces at least two challenges that threaten freelancers’ ability to secure work and the equitability of the gig economy: 1)...
Supervised learning model is one of the most fundamental machine learning models. It can provide powerful capability of prediction by learning complex patterns hidden in many, sometimes thousands, predictors. It can also be used as a building block of other machine learning tasks, like unsupervised learning and reinforcement learning. Such...
The language Esterel has found success in many safety-critical applications, from aircraft landing gear to digital signal processors. Its unique combination of powerful control operations, deterministic concurrency, and real time execution bounds are indispensable to programmer in these kinds of safety-critical domains. However these features lead to an interesting facet...
The world is awash in data and much of artificial intelligence focuses on learning models of the underlying structure in this data or the mechanisms governing its evolution. Both neural and symbolic models have weaknesses that make these models sub-optimal from a use perspective. Much of this data is in...
Three-dimensional (3D) imaging has been widely used in academic research and industrial applications. Compared to 2D representations, 3D imaging can yield more information about geometric structures of an object such as small surface variations that are difficult to perceive otherwise. 3D image contents provide additional information that is complementary to...