Calendar

Jan
31
Thu
Distinguished Lecture Series: Andreas Tolias, Baylor College of Medicine @ Clark 110
Jan 31 @ 3:00 pm

Abstract: Despite major advances in artificial intelligence through deep learning methods, computer algorithms remain vastly inferior to mammalian brains, and lack a fundamental feature of animal intelligence: they generalize poorly outside the domain of the data they have been trained on. This results in brittleness (e.g. adversarial attacks) and poor performance in transfer learning, few-shot learning, casual reasoning and scene understanding, as well as difficulty with lifelong and unsupervised learning – all important hallmarks of human intelligence. We conjecture that this gap is caused by the fact that current deep learning architectures are severely under-constrained, lacking key model biases found in the brain that are instantiated by the multitude of cell types, pervasive feedback, innately structured connectivity, specific non-linearities, and local learning rules. There is ample behavioral evidence that the brain performs approximate Bayesian inference under a generative model of the world (also known as inverse graphics or analysis by synthesis), so the brain must have evolved a strong and useful model bias that allows it to efficiently learn such a generative model. Therefore, our goal is to learn the brain’s model bias in order to engineer less artificial, and more intelligent, neural networks. Experimental neuroscience now has technologies that enable us to analyze how brain circuits work in great detail and with impressive breadth. Using tour-de-force experimental methods we have been collecting an unprecedented amount of neural responses (e.g. more than 1.5 million neuron-hours) from the visual cortex, and developed computational models that we use to extract principles of functional organization of the brain and learn the brain’s model biases.

 

Biography: Dr. Andreas Tolias’ research goal is to decipher brain’s mechanisms of intelligence. He studies how networks of neurons are structurally and functionally organized to process information. Research in his lab combines computational and machine learning approaches to electrophysiological (whole-cell and multi-electrode extracellular), multi-photon imaging, molecular and behavioral methods. He got his Ph.D. from MIT in Computational and Systems Neuroscience. The current focus of research in his lab is to reverse engineer neocortical intelligence. To this end his lab is deciphering the structure of microcircuits in visual cortex (define cell types and connectivity), elucidate the computations they perform and apply these principles to develop novel machine learning algorithms. He has trained numerous graduate students and postdoctoral fellows and enjoys mentoring immensely.

Feb
14
Thu
Valentines Day Celebration @ ECE Student Lounge
Feb 14 all-day

Valentine Day card making and treats available all day in the ECE Student Lounge!

Distinguished Lecture Series: Victor Klimov, Los Alamos National Laboratory (The Minkowski Lecture) @ Hodson Hall 210
Feb 14 @ 3:00 pm – 4:00 pm

Abstract: Chemically synthesized quantum dots (QDs) can potentially enable new classes of
highly flexible, spectrally tunable lasers processible from solutions [1,2]. Despite a considerable progress over the past years, colloidal-QD lasing, however, is still at the laboratory stage and an important challenge – realization of lasing with electrical injection – is still unresolved. A major complication, which hinders the progress in this field, is fast nonradiative Auger recombination of gain-active multicarrier species such as trions (charged excitons) and biexcitons [3,4]. Recently, we explored several approaches for mitigating the problem of Auger decay by taking advantage of a new generation of core/multi-shell QDs with a radially graded composition that allow for considerable (nearly complete) suppression of Auger recombination by “softening” the electron and hole confinement potentials [5,6]. Using these specially engineered QDs, we have been able to realize optical gain with direct-current electrical pumping [7], which has been a long-standing goal in the field of colloidal nanostructures. Further, we apply these dots to practically demonstrated the viability of a “zero-threshold-optical-gain” concept using not neutral but negatively charged particles wherein the pre-existing electrons block either partially or completely ground-state absorption [8]. Such charged QDs are optical-gain-ready without excitation and, in principle, can exhibit lasing at vanishingly small pump levels. All of these exciting recent developments demonstrate a considerable promise of colloidal nanomaterials for implementing solution-processible optically and electrically pumped laser devices operating across a wide range of wavelengths.

Feb
21
Thu
Proposal: Phillip Wilcox @ Shaffer Hall 2
Feb 21 @ 3:00 pm
Apr
5
Fri
Alumni Weekend Student Showcase @ Decker Quad Tent
Apr 5 @ 4:00 pm – 6:00 pm
Apr
11
Thu
Distinguished Lecture Series: Lihong Wang, California Institute of Technology (Kouwenhoven Lecture) @ Hodson Hall 210
Apr 11 @ 3:00 pm – 4:00 pm
May
7
Tue
Design Day 2019
May 7 all-day
ECE Networking Event
May 7 @ 5:00 pm – 7:00 pm
Sep
5
Thu
Thesis Proposal: Gary Li @ Olin Hall 305
Sep 5 @ 3:00 pm – 4:15 pm

Title: Dose Optimization for Pediatric Renal SPECT Imaging

Abstract: Like any real-world problem, the design of an imaging system always requires tradeoffs. For medical imaging modalities using ionization radiation, a major tradeoff is between diagnostic image quality (IQ) and risk to the patient from absorbed radiation dose. In nuclear medicine, reducing the radiation dose to the patient will always result in increased Poisson noise in the image. At the same time, reducing the radiation dose (RD), below some level at least, will always result in reduced risk of adverse effects to the patient. The overall goal of this research is to propose a rigorous IQ-RD tradeoff analysis method for pediatric nuclear medicine renal imaging. However, the methodologies developed in this proposal can also be applied to other nuclear medicine imaging applications and other important medical modalities involving ionization radiation such as computed tomography and planar X-rays.

Balancing the tradeoffs between RD and IQ is especially important for children, as they are often considered more vulnerable to radiation than adults. In nuclear medicine imaging, reducing the RD requires reducing the administered activity (AA). Lower AA results in increased Poisson noise in the images or requires longer acquisition durations to maintain the noise level. In pediatric nuclear medicine, it is desirable to use the lowest AA and the shortest acquisition duration that gives sufficient IQ for clinical diagnosis. In current clinical practice, AA for pediatric molecular imaging is often based on the North American consensus guidelines (U.S.) and the European pediatric dosage card (Europe). Both of these dosing guidelines involve scaling the adult AA by patient weight subject to upper and lower constraints on the AA. However, these guidelines were developed based on expert consensus or rough estimates (estimated count rates) of IQ rather than rigorous, objective measures of performance on the diagnostic task.

In this research, we propose a general framework for optimizing RD with task-based assessment of IQ. Here, IQ is defined as an objective measure of the user performing the diagnostic task that the images were acquired to answer. Specifically, we propose to establish relationships between AA, acquisition duration, measures of body habitus, and IQ for pediatric patients undergoing renal molecular imaging procedures. To investigate IQ as a function of renal defect detectability, we have developed a projection image database modeling imaging of 99mTc-DMSA, a renal function agent. The database uses a highly-realistic population of pediatric phantoms with anatomical and body morphological variations. Using the developed projection image database, we have explored patient factors that affect IQ and are currently in the process of determining relationships between IQ and AA (IQ-AA curve) in terms of these found factors. Our preliminary data have shown that the current weight-based guidelines, based on scaling the AA by patient weight, are not optimal in the sense that they do not give the same image quality for patients with the same weight. Furthermore, we have found that factors that are more local to the target organ may be more robust than weight for estimating the AA needed to provide a constant IQ across a population of patients. In the case of renal imaging, we have discovered that girth is more robust than weight in predicting AA needed to provide a desired IQ. In addition, in order to simulate a full clinical multi-slice detection task (just like what a nuclear medicine physician would do), we propose to develop a CNN-based model observer. We will perform human observer studies to verify and calibrate the developed model observers used to generate the IQ-AA curves. The results of this proposal will provide the data needed by standards bodies to develop improved dosing guidelines for pediatric molecular imaging that result in more consistent image quality and absorbed dose.

Sep
12
Thu
Thesis Proposal: Debojyoti Biswas @ Olin Hall 305
Sep 12 @ 3:00 pm – 4:15 pm

Title: Modeling Cellular Events: Chemotaxis and Aneuploidy

Abstract: Biology is the ‘study of complex natural things’ and the biologists are mostly interested in details of those complexity in a system.  But often a simpler mathematical model is proved to be very efficient in deciphering the underlying basic working principle of the system. Despite the usefulness, these models are often criticized for not being able to explain the sufficient details of the wide range of experimental observations of different cases of pharmacological/genetic perturbations.

To fill this gap, in the present study we are proposing biochemically detailed models of signaling events during chemotaxis in soil-living social amoebae Dictyostelium discoideum and the structural aneuploidy in mammalian cells. In the chemotaxis model, we replaced the existing activator-inhibitor system with a detailed stochastic reaction-diffusion model of different phospholipids and proteins. In the case of the structural aneuploidy, we came up with two possible models involving tumor protein, Tp53. We believe that these models will not only elucidate the role of different entities in the signaling network but also are able to explain their functions in response to various genetic mutations and drug treatments.

 

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