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Electrical & Computer Engineering

Students may apply to one or more of the below projects, indicating this in their statement of interest, or they may apply for "Electrical & Computer Engineering: General," indicating in their statement of interest their skills and background and some faculty with whom they would be interested in working.  ECE Faculty List

Title Name Email Project Name Project Description Requirements
Prof. Kevin J. Webb webb@purdue.edu Optomechanics with Nanostructured Material The use of light for mechanical control is of broad importance in science and technology. While mirrors and cavities are pervasive in optomechanics, planar mirrors with homogenized materials
experience a force only in the direction of the incident light. Consequently, moving a planar surface in the opposite direction requires a restoring force that may not exist or may not have suitable
characteristics. More generally, being able to control the motion of material, depending on the spatial and spectral properties of the incident light, would offer new dimensions in applications as diverse
as silicon photonics and molecular biology. We have shown experimentally that the force on nanostructured material can exceed that on a perfect mirror.  Fundamental is the role of optical
resonances, and how to excite them with the incident light. Our goals in this project are to explore the interplay between nanostructured material and optical force magnitude and direction control. Simulations indicate that it is possible to pull a structure, and we propose a demonstration using laser illumination. New dimensions in optomechanical control and evaluation of the underlying theory as a design framework will result from this project, thereby opening avenues for new technology that exploits optical force concepts.
A suitable background would include undergraduate classes in mathematics and physics, including electromagnetics. The project may involve experimental (laser experiments and device fabrication), theoretical, or computational work, including a combination, depending on the interest of the participant. Interested students might, for example, come from Engineering or Physics departments. 
Prof. Kevin J. Webb webb@purdue.edu Super-Resolution Optical Imaging Optical super-resolution imaging implies accessing far-subwavelength information, down to the nanometer length scale. From microscopy to semiconductor material and mask inspection, the scientific and technological impact of advances in super-resolution imaging is enormous. We propose two avenues in this project. One relates to the generation and control of spatially varying laser light to develop a super-resolution microscope that does not require fluorescence nor a vacuum system. Upon development of the experimental capability, this will be used in microscopy to pursue neuroscience research. The other avenue involves material inspection and imaging in multiply scattering media where laser speckle occurs. We have proposed a theory and have experimental results for a method to coherently image small objects using motion, and in this case the object is moving in a speckled field. This aspect of the project involves the development of a means to extract information and hence either enable image formation or identification. There is also the potential for deep-tissue biomedical applications.  A suitable background would include undergraduate classes in mathematics and physics, including electromagnetics. The project may involve experimental, theoretical, or computational work,
including a combination, depending on the interest of the participant. Interested students might, for example, come from Engineering or Physics departments.
Prof. Kevin J. Webb webb@purdue.edu Optical Imaging of Neural Activity for Neuroscience Applications Current approaches to directly measure neuronal activity in the intact brain involves invasive techniques that provide only local data or methods that are effective only near the surface. While deeper brain activity can be measured indirectly via blood oxygen, these methods do not facilitate investigations of brain function that directly assess molecular and circuit mechanisms. The research objective in this roject is to pursue synergistic enabling steps that will ultimately lead to a means to noninvasively image a substantial portion of the living brain at high spatial and temporal resolution. Based on strong preliminary data, we expect to resolve temporal heavily scattered light from fluorescence reporters to approximately 10 microns through several centimeters of brain tissue within the skull with sub-millisecond temporal resolution. The rationale for this research is that these significant technical advances are key steps to identifying molecular and circuit level mechanisms of brain function in normal and disease states. To prepare for imaging neuron activity in live animals, we propose: (i) a theoretical-experimental localization approach will focus on development of a computational framework for super-resolution optical imaging through highly scattering media; and (ii) a combined theoretical-experimental approach will focus on local neuron activity and communication using microscope measurements with neuron-specific fluorescent reporters.  This project will enable non-invasive imaging of brain activity, and is expected to lead to new understanding of the brain. For example, disruptions in synapse function and/or neuronal network communication are common features in many neural disorders. A suitable background would include undergraduate classes in biology and neuroscience and/or mathematics and physics, depending on the emphasis in the project. The research may involve experimental, theoretical, or computational work, or some combination, depending on the interest of the participant. Interested students might, for example, come from Engineering, Physics, Biology, or Chemistry departments. 
Prof. Kevin J. Webb webb@purdue.edu Optical Imaging of Neural Activity for Neuroscience Applications Current approaches to directly measure neuronal activity in the intact brain involves invasive techniques that provide only local data or methods that are effective only near the surface. While deeper brain activity can be measured indirectly via blood oxygen, these methods do not facilitate investigations of brain function that directly assess molecular and circuit mechanisms. The research objective in this roject is to pursue synergistic enabling steps that will ultimately lead to a means to noninvasively image a substantial portion of the living brain at high spatial and temporal resolution. Based on strong preliminary data, we expect to resolve temporal heavily scattered light from fluorescence reporters to approximately 10 microns through several centimeters of brain tissue within the skull with sub-millisecond temporal resolution. The rationale for this research is that these significant technical advances are key steps to identifying molecular and circuit level mechanisms of brain function in normal and disease states. To prepare for imaging neuron activity in live animals, we propose: (i) a theoretical-experimental localization approach will focus on development of a computational framework for super-resolution optical imaging through highly scattering media; and (ii) a combined theoretical-experimental approach will focus on local neuron activity and communication using microscope measurements with neuron-specific fluorescent reporters.  This project will enable non-invasive imaging of brain activity, and is expected to lead to new understanding of the brain. For example, disruptions in synapse function and/or neuronal network communication are common features in many neural disorders. A suitable background would include undergraduate classes in biology and neuroscience and/or mathematics and physics, depending on the emphasis in the project. The research may involve experimental, theoretical, or computational work, or some combination, depending on the interest of the participant. Interested students might, for example, come from Engineering, Physics, Biology, or Chemistry departments. 
Assoc. Prof. Peter Bermel pbermel@purdue.edu Photonic Computing via Neuromorphic Architectures With ever-increasing demands of big data processing and data centers for increased storage and reduced energy consumption, researchers are interesting in going beyond traditional, so-called von Neumann architectures with new types of computing devices. A key driving concept for recent work has been brain- inspired computing, where one uses the physical architecture of the brain as a basis for designing new hardware to mimic its key properties. While this can be done in software already, via deep neural network programming, it is widely believed that dedicated hardware can potentially be orders of magnitude more efficient. While several different schemes have been explored, such as electronic, spintronic methods, increasing interest has been paid to photonic methods over the last few years. In this PURE project, we will focus on a specific implementation of this concept: novel designs of nanophotonic neuromorphic hardware accelerators for artificial neural networks. These will employ photonic waveguides coupled to phase-changing surfaces to perform biomimetic thresholding akin to the operations of individual neurons, which will be connected in a network to perform computing tasks. Required Skills:
1)Basic understanding of Transport Equations (Drift-Diffusion Models)
2)Basic Understanding of Numerical Electro-Magnetic methods and EM Wave propagation in dielectric media
3)Programming proficiency in MATLAB/Python

Desired Skills:
1)Exposure to MEEP electromagnetics simulation via the finite-difference time-domain (FDTD) method
2)Exposure to HSPICE, TCAD, LABVIEW or similar circuit simulation environment
Asst. Prof. Tillmann Kubis tkubis@purdue.edu Machine Learning to Optimize Quantum Transport Modeling Understanding electronic physics in state-of-the-art semiconductor nanodevices requires quantum transport simulations – as e.g. performed in the Kubis team with Purdue’s nanodevice simulation tool NEMO5 and its nonequilibrium Green’s function framework. When N is the number of degrees of freedom in a discretized nanodevice (e.g. N=105 atomic orbitals), the numerical load for solving quantum transport scales as N3. Thus, reducing N to the minimally required degrees of freedom has critical impact on the simulation costs. In math science, such reductions are known as “low-rank approximations” (also called “mode space approach” in physics and engineering). The quality of low-rank approximations heavily depends on the quality of the reduced-rank basis set. While there are several algorithms to determine such basis sets, they all require human interaction to ensure sufficient quality. In this project, machine learning algorithms will automatically generate basis sets and improve them until a sufficiently low-dimensional basis set is found that still perverse the predictive power of quantum transport algorithms. Prior experience in machine learning and/or quantum transport is helpful but not required. The related knowledge will get trained during the project’s runtime.  Some experience in scripting (perl, python, etc) and programming (C++) will be very useful with a basic knowledge of tools existing in machine learning (optional). Basic knowledge in quantum physics and semiconductor physics (e.g. bandstructures) will be preferred.
Prof. Yung-Hsiang Lu yunglu@purdue.edu Protect Privacy at Cameras The project investigates methods to ensure data security and protect privacy in video streams. The data is processed at the cameras extracting aggregate information. Faces are detected and encrypted so that identifiable information never leaves cameras. The data is protected to prevent unauthorized use and may be recovered by authorities with decryption keys for post-event forensic analysis (such as identifying crime suspects). A major challenge is making data processing efficient so that the proposed methods can be performed using an embedded computer in every camera. video processing, computer programming
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