My research focuses on developing innovative ways to control Heating, Ventilation, and Air Conditioning (HVAC) and schedule occupancy flows in smart buildings to reduce our ecological footprint (and energy bills). We look at the potential for integrating building operations with room booking and meeting scheduling. Specifically, we improve on the effectiveness of energy-aware room-booking and occupancy scheduling approaches, by allowing the scheduling decisions to rely on an explicit model of the building's occupancy-based HVAC control. From computational standpoint, this is a challenging topic as HVAC models are inherently non-linear non-convex, and occupancy scheduling models additionally introduce discrete variables capturing the time slot and location at which each activity is scheduled. The mechanism needs to tradeoff minimizing energy cost against addressing occupancy thermal comfort and control feasibility in a highly dynamic and uncertain system.

Today’s data centers are designed with multi-core CPUs where multiple virtual machines (VMs) can be co-located into one physical machine or distribute multiple computing tasks onto one physical machine. The result is co-tenancy, resource sharing and competition. Modeling and predicting such co-run interference becomes crucial for job scheduling and Quality of Service assurance. Co-locating interference can be characterized into two components, sensitivity and pressure, where sensitivity characterizes how an application’s own performance is affected by a co-run application, and pressure characterizes how much contentiousness an application exerts/brings onto the memory subsystem. Previous studies show that with simple models, sensitivity and pressure can be accurately characterized for a single machine. We extend the models to consider cross-architecture sensitivity (across different machines).

Entity Resolution (ER) concerns identifying logically equivalent pairs of entities that may be syntactically disparate. Although ER is a long-standing problem in the artificial intelligence community, the growth of Linked Open Data, a collection of semi-structured datasets published and inter-connected on the Web, mandates a new approach. The thesis is that building a viable Entity Resolution solution for serving Big Data needs requires simultaneously resolving challenges of automation, heterogeneity, scalability and domain independence. The dissertation aims to build such a system and evaluate it on real-world datasets published already as Linked Open Data.

We must properly model attacks and the preferences of the electorate for the computational study of attacks on elections to give us insight into the hardness of attacks in practice. Theoretical and empirical analysis are equally important methods to understand election attacks. I discuss my recent work on domain restrictions on partial preferences and on new election attacks. I propose further study into modeling realistic election attacks and the advancement of the current state of empirical analysis of their hardness by using more advanced statistical techniques.

In the proposed thesis, we study Distributed Constraint Optimization Problems (DCOPs), which are problems where several agents coordinate with each other to optimize a global cost function. The use of DCOPs has gained momentum, due to their capability of addressing complex and naturally distributed problems. A majority of the work in DCOP addresses the resolution problem by detaching the model from the resolution process, where they assume that each agent controls exclusively one variable of the problem (Burke et al. 2006). This assumption often is not reflected in the model specifications, and may lead to inefficient communication requirements. Another limitation of current DCOP resolution methods is their inability to capitalize on the presence of structural information, which may allow incoherent/unnecessary data to reticulate among the agents (Yokoo 2001). The purpose of the proposed dissertation is to study how to adapt and integrate insights gained from centralized solving techniques in order to enhance DCOP performance and scalability, enabling their use for the resolution of real-world complex problems. To do so, we hypothesize that one can exploit the DCOP structure in both problem modeling and problem resolution phases.

The Web contains a large amount of information in the form of videos that remains inaccessible to the visually impaired people. We identify a class of videos whose information content can be approximately encoded as an audio, thereby increasing the amount of accessible videos. We propose a model to automatically identify such videos. Our model jointly relies on the textual metadata and visual content of the video. We use this model to re-rank Youtube video search results based on accessibility of the video. We present preliminary results by conducting a user study with visually impaired people to measure the effectiveness of our system.

Much work has been done on understanding and predicting human mobility in time. In this work, we are interested in obtaining a set of users who are spatio-temporally most similar to a query user. We propose an efficient way of user data representation called Spatio-Temporal Signatures to keep track of complete record of user movement. We define a measure called Spatio-Temporal similarity for comparing a given pair of users. Although computing exact pairwise Spatio-Temporal similarities between query user with all users is inefficient, we show that with our hybrid pruning scheme the most similar users can be obtained in logarithmic time with in a (1+\epsilon) factor approximation of the optimal. We are developing a framework to test our models against a real dataset of urban users.

This paper briefly describes how the neural structure of fear conditioning has inspired to develop a computational intelligence model that is referred to as the brain emotional learning-inspired model (BELIM). The model is applied to predict long step ahead of solar activity and geomagnetic storms.

According to the website AcronymFinder.com which is one of the world's largest and most comprehensive dictionaries of acronyms, an average of 37 new human-edited acronym definitions are added every day. There are 379,918 acronyms with 4,766,899 definitions on that site up to now, and each acronym has 12.5 definitions on average. It is a very important research topic to identify what exactly an acronym means in a given context for document comprehension as well as for document retrieval. In this paper, we propose two word embedding based models for acronym disambiguation. Word embedding is to represent words in a continuous and multidimensional vector space, so that it is easy to calculate the semantic similarity between words by calculating the vector distance. We evaluate the models on MSH Dataset and ScienceWISE Dataset, and both models outperform the state-of-art methods on accuracy. The experimental results show that word embedding helps to improve acronym disambiguation.

SLA, I propose a data-driven approach inspired by Conversation Analysis (CA) to create models of linguistic repair. Conversational agents for educational purposes, specifically I use the data set of instant messaging dialogues in German for Second Language Acquisition (SLA) use different approaches described in (Danilava et al. 2013). The corpus consists to support language learning through conversation. of 72 free conversations produced by 9 learners and CSIEC chatbot (Jia 2009) can correct spelling errors.