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.

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.

JHU Spring Vacation
Mar 18 – Mar 24 all-day
Distinguished Lecture Series: Lihong Wang, California Institute of Technology (Kouwenhoven Lecture) @ Hodson Hall 210
Apr 11 @ 3:00 pm – 4:00 pm
Last Day of Classes
May 3 all-day
Design Day 2019
May 7 all-day
Final Examination Period
May 8 – May 16 all-day
ECE Graduation Lunch @ Hodson 2nd Floor Floor Lobby
May 22 @ 11:30 am – 1:30 pm
University Commencement
May 23 all-day
Special Virtual Seminar and Fireside Chat: Russ Poldrack, Stanford University @ Olin Hall 305
Oct 24 @ 3:00 pm – 4:15 pm
Special Virtual Seminar and Fireside Chat: Russ Poldrack, Stanford University @ Olin Hall 305

Note: This is a virtual seminar that will be broadcast in Olin Hall 305. Refreshments will be available outside Olin Hall 305 at 2:30 PM.

Title: Computational infrastructure to improve scientific reproducibility

Abstract: The massive increase in the dimensionality of scientific data and the proliferation of complex data analysis methods has raised increasing concerns about the reproducibility of scientific results in many domains of science. I will first present evidence that analytic flexibility in neuroimaging research is associated with surprising variability in scientific outcomes in the wild, even holding the raw data constant. These findings motivate the development of well-tested software tools for neuroimaging data processing and analysis. I will focus in particular on the role of software development tools such as containerization and continuous integration, which provide the potential to deliver automated and reproducible data analysis at scale. I will also discuss the challenging tradeoffs inherent in the usage of complex software by scientists, and the need for increased transparency and validation of scientific software.

Bio: Russell A. Poldrack is the Albert Ray Lang Professor in the Department of Psychology and Professor (by courtesy) of Computer Science at Stanford University, and Director of the Stanford Center for Reproducible Neuroscience. His research uses neuroimaging to understand the brain systems underlying decision making and executive function. His lab is also engaged in the development of neuroinformatics tools to help improve the reproducibility and transparency of neuroscience, including the and data sharing projects and the Cognitive Atlas ontology.

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