Shahriar Shariat

Learn More
This paper presents an approach for sequence alignment based on canonical correlation analysis(CCA). We show that a novel set of constraints imposed on traditional CCA leads to canonical solutions with the time warping property, i.e., non-decreasing monotonicity in time. This formulation generalizes the more traditional dynamic time warping (DTW) solutions(More)
Real-Time Bidding allows an advertiser to purchase media inventory through an auction system that unfolds in the order of milliseconds. Media providers are increasingly being integrated into such programmatic buying platforms. It is typical for a contemporary Real-Time Bidding system to receive millions of bid requests per second at peak time, and have a(More)
The problem of human activity recognition is a central problem in many real-world applications. In this paper we propose a fast and effective segmental alignment-based method that is able to classify activities and interactions in complex environments. We empirically show that such model is able to recover the alignment that leads to improved similarity(More)
An evolutionarily ancient skill we possess is the ability to distinguish between food and non-food. Our goal here is to identify the neural correlates of visually driven 'edible-inedible' perceptual distinction. We also investigate correlates of the finer-grained likability assessment. Our stimuli depicted food or non-food items with sub-classes of(More)
Online media provides opportunities for marketers through which they can deliver effective brand messages to a wide range of audiences at scale. Advertising technology platforms enable advertisers to reach their target audience by delivering ad impressions to online users in real time. In order to identify the best marketing message for a user and to(More)
Traditional pairwise sequence alignment is based on matching individual samples from two sequences , under time monotonicity constraints. However, in some instances matching two segments of points may be preferred and can result in increased noise robustness. This paper presents an approach to segmental sequence alignment based on adaptive pairwise(More)
Bayesian networks are popular in the classification literature. The simplest kind of Bayesian network, i.e. naïve Bayesian network, has gained the interest of many researchers because of quick learning and inferring. However, when there are lots of classes to be inferred from a similar set of evidences, one may prefer to have a united network. In this paper(More)
Traditional pairwise sequence alignment is based on matching individual samples from two sequences, under time monotonicity constraints. However, in many application settings, matching subsequences (segments) instead of individual samples may bring in additional robustness to noise or local non-causal perturbations. This paper presents an approach to(More)
Online media offers opportunities to marketers to deliver brand messages to a large audience. Advertising technology platforms enables the advertisers to find the proper group of audiences and deliver ad impressions to them in real time. The recent growth of the real time bidding has posed a significant challenge on monitoring such a complicated system.(More)
This paper presents a systematic application of machine learning techniques for classifying high-density EEG signals elicited by face and non-face stimuli. The two stimuli used here are derived from the vase-faces illusion and share the same defining contours, differing only slightly in stimulus space. This emphasizes activity differences related to(More)