Almost 15% of global deaths in 2008 were attributed to Coronary Heart Disease (CHD). While major risk factors for CHD are widely known, lack of comprehensive data on relevant risk factors has limited the ability to predict risk of developing CHD in large populations. In this study, we explore the application of the Framingham Risk Model to predict CHD risk using a limited set of attributes present in a health risk assessment (HRA) dataset from a digital health company. HRAs often fail to capture all the needed attributes of the Framingham Model, such as LDL and HDL cholesterol values that significantly affect CHD risk. Hence, we enhance our analysis with the National Health and Nutrition Examination Survey (NHANES) data from the Centers for Disease Control (CDC), the United States public health agency. Our preliminary findings indicate that HRA data can be successfully used as input for the Framingham Risk Model in predicting risk of CHD utilizing NHANES data to predict missing attributes, thus extending the use of HRAs for disease risk prediction.

With the growing popularity of search engines among consumers, advertising on search engines has also grown considerably. We study optimal bidding strategies for advertisers in sponsored search auctions. In general, these auctions are run as variants of second-price auctions but have been shown to be incentive incompatible. Thus, advertisers have to be strategic about bidding. Uncertainty in the decision-making environment, budget constraints and the presence of a large portfolio of keywords makes the bid optimization problem non-trivial. In addition, there keywords are not independent. In this paper, we formulate and solve the advertiser's decision problem. We propose two bidding policies in our paper. The first policy ignores the interaction between keywords and is referred to as the "myopic" policy in this paper. We extend this bidding policy to incorporate interaction between keywords, and refer to this policy as the "forward-looking" policy since it entails decision making over several time horizons. Depending on the advertiser's intent, level of sophistication and nature of the products being advertised, the advertiser might choose the myopic or the forward-looking policy. This paper makes three main contributions. The first contribution is towards improving managerial practice. Advertisers spend billions of dollars on sponsored search. The techniques described in the paper can help increase the Return on Investment (RoI) for advertisers and SEM firms, as demonstrated in our field implementation. The second key contribution is that our approach represents a significant step forward for the academic literature on bidding in multi-slot auctions. All the papers to date have studied the problem either in a deterministic setting or in a single-slot setting and have relied on heuristic solution techniques due to the complexity of the optimization problem. In contrast, we compute optimal bids in the more realistic stochastic multi-slot setting. The third contribution of this paper is that it is the first paper on bidding in sponsored search to incorporate the interdependence between keywords into a multi-period bidding problem. The interdependence in keyword performance, commonly referred to as spillovers, is a well-documented feature of sponsored search (Rutz and Bucklin, 2011) but has not been considered in the bidding literature. The interactions between keywords are modeled in the form of positive spillovers from generic keywords into branded keywords. The spillovers are estimated using a dynamic linear model framework. An approximate dynamic program is formulated to solve the advertiser's bidding problem. To validate our approaches, we estimate the parameters of the models using data from an advertiser's sponsored search campaign and use the bids proposed by the models in a field experiment. The "myopic" policy outperforms the advertiser's policy by 75.38% and the "forward looking" policy improves the ROI by another 7.87%. The results of the field implementation show that the proposed bidding techniques are very effective in practice.

There has been significant recent interest in studying consumer behavior in sponsored search advertising (SSA). Researchers have typically used daily data from search engines containing measures such as average bid, average ad position, total impressions, clicks and cost for each keyword in the advertiser's campaign. A variety of random utility models have been estimated using such data and the results have helped researchers explore the factors that drive consumer click and conversion propensities. However, virtually every analysis of this kind has ignored the intra-day variation in ad position. We show that estimating random utility models on aggregated (daily) data without accounting for this variation will lead to systematically biased estimates -- specifically, the impact of ad position on click-through rate (CTR) is attenuated and the predicted CTR is higher than the actual CTR. First, we prove that the average daily position of an ad is less in convex order than the actual position of the ad for an impression. Using this result, we analytically demonstrate the existence of the aggregation bias. Second, using a large disaggregate dataset from a major search engine containing 8 million impressions, we empirically validate our findings for both the traditional logit model and the Hierarchical Bayesian models that are commonly used in the SSA literature. Third, we build a game-theoretic model to analyze the effect of the bias on the equilibrium of the SSA auction.We find that advertisers bid lower in SSA auctions as a result of the bias, which always leads to lower search-engine revenue. We also find that an advertiser can always increase his payoff when he unilaterally switches to complete data from aggregate data. Finally, we empirically quantify the losses experienced by the search engine and the advertisers and find that the search engine loses over 17% of its revenue on average. We also observe that an advertiser loses around 6% of his payoffs due to data aggregation. Our findings raise serious concerns for SSA practitioners and also question the adequacy of the data standards that have become common in SSA. Finally, we provide recommendations for aggregate datasets that do not suffer from the bias.

An important problem in search engine advertising is key-word1 generation. In the past, advertisers have preferred to bid for keywords that tend to have high search volumes and hence are more expensive. An alternate strategy involves bidding for several related but low volume, inexpensive terms that generate the same amount of traffic cumulatively but are much cheaper. This paper seeks to establish a mathematical formulation of this problem and suggests a method for generation of several terms from a seed keyword. This approach uses a web based kernel function to establish semantic similarity between terms. The similarity graph is then traversed to generate keywords that are related but cheaper.

In this paper we describe a method for the synthesis of robotic controllers using evolutionary techniques. A modified version of the recurrent neural network used for controlling the robots is evolved using genetic algorithm using the variable length genotype approach where the genotype encodes the network. It has been discovered that the separation of modalities in the network like vision and touch, for the first few layers helps in faster evolution as well as in the development of faster controller networks. The structure of the network that emerges from this kind of evolution is similar to brain-like networks. Performance of plastic versus non-plastic individuals has also been explored. Gene-blocking technique has been used for developing several behaviors in the same evolution cycle. The final controller developed at the end of the evolutionary process was tested on a Khepera.

A novel integration of methods for person authentication and tracking is proposed for real time security systems. The implementation of the idea for this real time implementation follows a three step procedure-face detection, recognition and content-based tracking. Instead of analyzing continuous videos we sample the frame based on a method derived from Shannon's information theory model. The Face-detector detects multi-viewed faces in a video using feature-based kernel methods in a reduced feature space obtained using ICA. The identified "face regions" are then passed on to the face recognition system which is based on Active Appearance Models (AAM). Once the subject is recognized, it can be tracked in the video using kernel based object tracking method. Several space reduction techniques have been used like ICA, PCA and skin-color segmentation.