My Research
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  • NLP
  • SpacioTemporal
  • Graph-ML
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CoreQuisite: Reinforcing CommonSense Knowledge Graphs with Contextual Requisites

Brief: In this work, we investigate the notion of context as the missing gem in modern commonsense resources.

CSKG: Consolidating Commonsense Knowledge

Brief: In this work, we investigate and consolidate various resources of commonsense knowledge into a first integrated commonsense knowledge graph.

DSBox: Data Scientist In A Box

Brief: End-to-End Automated Machine Learning (E2E-AutoML) seeks to automate the machine learning pipeline generation process. In this work we present DSBox, an expandable, multi-modal E2E-AutoML system, that can go directly from raw data to ML pipeline with minimal human supervision. We show DSBox is able to outperform human experts and beat state-of-the-art AutoML systems (ML-Plan, Auto-sklearn, TPOT) through evaluation on 468 diverse datasets with multiple media and task types.

Tabular Data to Knowledge Graph Matching

Brief: Aim of this research is to understand tabular data based on actual contents and the meta-data of table (e.g. styling, headers) and map the content to entities and relations from knowledge graphs such as dbpedia and wikidata.

Detecting Autism Spectrum Disorder (ASD): Learning Empirical Space for Spatio-temporal Data Analysis

Brief: Here, we investigated spatio-temporal data analysis methods in motor-based early ASD (autism spectrum disorder) detection application. The challenge here is processing the stream multi-modal (Motion, fMRI, EEG, and DTI) information with the goal of detecting early signs of ASD in the human subject. We propose two solutions, with the ability to achieve 80% accuracy. Additionally, we have extracted a set of entropy-based features that can help explain the decision-making process of the network and improve confidence in the results.

Adversarial Depth Estimation based on Left-Right Consistency

Brief: In this work, we build upon previous work on using Left-Right consistency for depth estimation. The conventional approach in such methods is using a series of hand-designed loss metrics to measure the consistency between the left-eye and right-image, which limits the capabilities of such models. In this work, we borrow from Generative Adversarial Networks (GAN) and propose a method for LR-Consistency that learns the consistency/loss function with goal of discriminating the inconsistent images.

Deep Leaning in Art History

Brief: In this work, we studied deep learning applications in elucidating the relationships among painting. The painting dataset consists of three periods of Pre-Baroque, Baroque, and post-baroque.

Deep Leaning and Empirical Topology in Music Style Detection

Brief: Investigate deep learning applications as the computational intelligence medium for classification of music pieces according to style and historical period, such as Baroque, Classical and Romantic periods.

Deep Learning Features in Atmospheric Chemistry

Brief: Big data local-to-global methods in analysis and prediction of dynamics in atmospheric chemistry spatiotemporal data.

Hardware Security Module

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Blokus-Duo AI Agent based on Monte-Carlo Tree Search (MCTS)

Brief: This research focuses on our proposed hardware architecture on a highly scalable, shared-memory, MonteCarlo Tree Search (MCTS) based Blokus-Duo solver. Our design is inspired from parallel MCTS algorithms and is potentially capable of obtaining maximum possible parallelism from MCTSalgorithm. We also combine MCTS with set of pruning heuristics to increase both the memory and Logic Element (LE) utilizations.

Maestro: A High Performance AES Encryption / Decryption System

Brief: This research focuses on our proposed hardware architecture on a highly scalable, shared-memory, MonteCarlo Tree Search (MCTS) based Blokus-Duo solver. Our design is inspired from parallel MCTS algorithms and is potentially capable of obtaining maximum possible parallelism from MCTSalgorithm. We also combine MCTS with set of pruning heuristics to increase both the memory and Logic Element (LE) utilization.

Embedded Stream Encryption

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Stochastic Methods and Monte-Carlo Tree Search (MCTS) Applications in High Level Synthesis

Brief: In this work, we address the problem of scheduling in high-level synthesis application. We propose a new scheduling algorithm based on Monte-Carlo (MC) simulation that uses MC Tree Search (MCTS)method as the heart of our scheme handles the schedulingto produce optimized scheduling with both time and resource constraints.

Subthreshold Memory Compiler

Brief: In this work, we investigated SRAM memory architectures to work in subthreshold voltage range for low-power applications. We have proposed a set of cell architectures that improve read, write, and hold stability compared to state-of-the-art cell architectures. Finally, we developed our in-house memory compiler tool that assembles such memory cells into full SRAM memory with arbitrary size.

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