Presidential Young Investigators Award: Fast Neural Network Like Optimization Techniques

侦探他目前对大规模并行、扰动和集体计算、人工神经系统、学习机、联想神经记忆、光子计算和光神经网络实现的研究感兴趣。在神经网络领域，他计划研究各种联想神经记忆架构和相关记录/学习算法的性能。他为联想记忆引入了新的高性能记录/学习算法，目前正在研究各种记录/学习算法的神经记忆动力学。在计算和神经网络实验室，他和他的学生正在开发用于聚类和无监督模式分类的无监督学习自优化神经网络架构。在另一个项目中，神经网络架构将用于解决二维和高维的复杂最优寻径问题。他还将研究人工神经系统的光学实现。他拥有首个集成光阈值门（“光神经元”）的专利，并开发了光纤跨条网络制造技术，这在光神经网络实现中具有潜在的用途。他最近完成了一个基于光纤的高性能动态联想神经记忆原型的设计和实现。他对神经网络在信号处理和并行计算中的应用也很感兴趣。在与韦恩州立大学神经学系的研究人员的共同努力下，他正在开发一种基于神经网络的技术，用于临床诊断目的的临床肌电图（EMG）的自动量化。他未来的研究方向是高性能动态人工神经网络的自适应无监督合成技术。他还将研究通过自优化神经结构来加速多层神经网络学习的方法。他未来的研究还将涉及提出和实现大规模高性能光纤互联动态联想神经记忆和其他神经网络范例。

The P.I.'s currently interested in research in massively parallel, disturbed, and collective computations, artificial neural systems, learning machines, associative neural memories, photonic computing, and optical neural network implementations. In the area of neural networks, he plans to investigate the performance of various associative neural memory architectures and related recording/learning algorithms. He has introduced new high-performance recording/learning algorithms for associative memories, and is currently investigating the dynamics of such neural memories for various recording/learning algorithms. In the Computation and Neural Networks Laboratory, he and his students are developing unsupervised learning self-optimizing neural net architectures for clustering and unsupervised pattern classification. In another project, neural net architectures will be employed in solving complex optimal path finding problems in two- and higher-dimensions. He will also be investigating the optical implementations of artificial neural systems. He holds a patent for the first integrated optical threshold gate ("optical neuron") and has developed fiber optic cross-bar network fabrication techniques, which are potentially useful in optical neural network implementations. He has recently completed the design and implementation of a fiber optic-base high-performance dynamic associative neural memory prototype. He is also interested in the application of neural networks in signal processing and parallel computing. In a joint effort with researchers at Wayne State's Neurology Department, he is developing a neural-network-based technique for automatic quantification of the clinical Electromyogram (EMG) for clinical diagnostic purposes. His future research will focus on adaptive unsupervised synthesis techniques for high- performance dynamic artificial neural networks. He will also be investigating ways of speeding up learning in multiple-layer neural nets through self-optimizing neural architectures. His future research will also involve proposing and implementing large scale high- performance fiber optic interconnected dynamic associative neural memories and other neural network paradigms.