Haris Vikalo

Haris Vikalo Department of Electrical and Computer Engineering The University of Texas at Austin 1 University Station C0803 Austin, TX 78712-0240 Email: hvikalo@ece.utexas.edu Phone: + 1 512 232 7922

Research Interests

My research interests include signal processing, computational biology / bioinformatics, and communications. Recent research thrusts include:

• Base-calling algorithms for next-generation DNA sequencing systems. Novel sequencing technologies will provide significant improvements in many aspects of human condition, ultimately leading towards the understanding, diagnosis, treatment, and prevention of diseases. Reliable decision-making in such downstream applications is predicated upon accurate base-calling, i.e., precise identification of the order of nucleotides from noisy sequencing data. We explored trade-offs between performance and complexity of base-calling, developing algorithms that have both better accuracy and are orders of magnitude faster than state-of-the-art methods.
• Signal processing for real-time biosensor arrays. Real-time DNA microarrays observe the dynamics of binding between molecular targets and the biosensing elements that capture them. The paradigm shift in data acquisition, from measuring a single steady-state data point in conventional systems to temporally sampling dynamics of the binding process, requires new mathematical models and inference methods. We developed fast algorithms that enable reliable estimation of the number of molecular targets in real-time biosensor arrays, demonstrating significant performance gains over the conventional technology.
• Sensor and cognitive radio networks. For a rational use of the sensor network resources, the fusion center typically schedules only a subset of the available sensors for transmission, and the selected sensors transmit only partial information. Recently, we showed that in a variety of scenarios sensor selection can be cast as maximization of a submodular function over uniform matroids. It turns out that a computationally efficient greedy sensor selection algorithm is guaranteed to perform within (1-1/e) of the optimal solution. We extended these results to the scenario where the communication between sensors and the fusion center is unreliable.
• Algorithms for solving integer least-squares problems in communications. Complexity of the optimal signal processing in communication systems is a main obstacle to delivering the high data rates promised by theory. In a probabilistic setting, common to communications, it is possible to exploit statistics of the problem in order to design fast algorithms and analyze their complexity. An example of our work in this area includes the analysis of the expected complexity of an algorithm for optimal decoding in high-dimensional communication systems. The results reveal that optimal decoding, hitherto considered infeasible, can in fact often be implemented in practice.