Publications

Inference of species trees plays a crucial role in advancing our understanding of evolutionary relationships and has immense significance for diverse biological and medical applications. Extensive genome sequencing efforts are currently in progress across a broad spectrum of life forms, holding the potential to unravel the intricate branching patterns within the tree of life. However, estimating species trees starting from raw genome sequences is quite challenging, and the current cutting-edge methodologies require a series of error-prone steps that are neither entirely automated nor standardized. In this paper, we present ROADIES, a novel pipeline for species tree inference from raw genome assemblies that is fully automated, easy to use, scalable, free from reference bias, and provides flexibility to adjust the tradeoff between accuracy and runtime. The ROADIES pipeline eliminates the need to align whole genomes, choose a single reference species, or pre-select loci such as functional genes found using cumbersome annotation steps. Moreover, it leverages recent advances in phylogenetic inference to allow multi-copy genes, eliminating the need to detect orthology. Using the genomic datasets released from large-scale sequencing consortia across three diverse life forms (placental mammals, pomace flies, and birds), we show that ROADIES infers species trees that are comparable in quality with the state-of-the-art approaches but in a fraction of the time. By incorporating optimal approaches and automating all steps from assembled genomes to species and gene trees, ROADIES is poised to improve the accuracy, scalability, and reproducibility of phylogenomic analyses.

The automatic surgical video processing for anomaly detection is becoming important nowadays since manual feedback from surgeons is subject to errors and time-consuming. It is necessary to have more precise and detailed detection and recognition of organs and tissues as we move into more complete understanding of the surgical videos. Thus, automatic localization and detection of organs have now become a prerequisite in surgical video analysis. In this paper, a convolutional neural network (CNN) is designed to evaluate laparoscopic and endoscopic surgical videos. The neural network will detect and localize the organs for the given videos which lay the path for automatic post-surgical processing.

The Clifford+T gate library consisting of Hadamard, T, and CNOT gates has attracted much interest in quantum circuit synthesis, particularly due to its applicability to fault tolerant realizations. Since fault tolerant implementations of the T gate have very high latency, recent work in this area is aiming at minimizing the number of T stages, referred to as the T-depth. In this paper, we present an approach to exploit additional ancilla qubits in the mapping of reversible circuits consistina of multiple controlled Toffoli gates (MCT gates) into califford+T quantum circuits, with the primary optimization objective to minimize the T-depth. Our proposed approach takes advantage of and generalizes earlier work on corresponding mapping algorithms. An experimental evaluation shows that our approach leads to a significant T-depth reduction compared to earlier approaches.