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.

Distinguished Lecture Series: Lihong Wang, California Institute of Technology (Kouwenhoven Lecture) @ Hodson Hall 210
Apr 11 @ 3:00 pm – 4:00 pm
Design Day 2019
May 7 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.

Distinguished Lecture Series: Reimund Gerhard, University of Potsdam @ Olin Hall 305
Nov 14 @ 3:00 pm – 4:00 pm
Distinguished Lecture Series: Reimund Gerhard, University of Potsdam @ Olin Hall 305

Title: Electrets (Dielectrics with quasi-permanent Charges or Dipoles) – A long history and a bright future

Abstract: The history of electrets can be traced back to Thales of Miletus (approx. 624-546 B.C.E.) who reported that pieces of amber (“electron”) attract or repel each other. The science of fundamental electrical phenomena is closely intertwined with the development of electrets which came under such terms as “electrics”, “electrophores”, “charged/poled dielectrics”, etc. until about one century ago. Modern electret research started with Oliver Heaviside (1850-1925), who defined the concept of a “permanently electrized body” and proposed the name “electret” in 1885, and Mototarô Eguchi, who experimentally investigated carnauba wax electrets at the Higher Naval College in Tokyo around 1920. Today, we see a wide range of electret types, electret materials, and electret applications, which are being investigated and developed all over the world in a truly global endeavour. A classification of electrets will be followed by a few examples of useful electret effects and exciting device applications – mainly in the area of electro-mechanical and electro-acoustical transduction which started with the invention of the electret microphone by Sessler and West in the early 1960s. Furthermore, possible synergies between electret research and ultra-high-voltage DC electrical insulation will be mentioned.

Bio: Reimund Gerhard is a Professor of Physics and Astronomy at the University of Potsdam and the current President of the IEEE Dielectrics and Electrical Insulation Society (DEIS). He graduated from the Technical University of Darmstadt as Diplom-Physiker in 1978 and earned his PhD (Doktor-Ingenieur) in Communications Engineering from TU Darmstadt in 1984. From 1985 to 1994, Gerhard was a Research Scientist and Project Manager at the Heinrich-Hertz Institute for Communications Technology (now the Fraunhofer Institute) in Berlin, Germany. He was appointed as a Professor at the University of Potsdam in 1994. From 2004 to 2012, Gerhard served as the Chairman of the Joint Board for the Master-of-Science Program in Polymer Science of FU Berlin, HU Berlin, TU Berlin, and the University of Potsdam. He also served as the Dean of the Faculty of Science at the University of Potsdam from 2008 to 2012, eventually serving as a Senator of the University of Potsdam from 2014 to 2016.

Prof. Gerhard has received many awards and honors over his long career, including an Award (ITG-Preis) from the Information Technology Society (ITG) in the VDE, a silver medal from the Foundation Werner-von-Siemens-Ring, a First Prize Technology Transfer Award Brandenburg, Whitehead Memorial Lecturer of the IEEE CEIDP, and the Award of the EuroEAP Society “for his fundamental scientific contributions in the field of transducers based on dielectric polymers.” He is a Fellow of the American Physical Society (APS) and the Institute of Electrical and Electronics Engineers (IEEE). His research interests include polymer electrets with quasi-permanent space charge, ferro- or piezoelectrets (polymer films with electrically charged cavities), ferroelectric polymers with piezo- and pyroelectric properties, polymer composites with novel property combinations, physical mechanisms of dipole orientation and charge storage, electrically deformable dielectric elastomers (sometimes also called “electro-electrets”), as well as the physics of musical instruments.

Research Interests: 

  • Global or patterned electric charging or poling of dielectric polymer films (electrets)
  • Thermal (pyroelectrical) and acoustical (piezoelectrical) probing of electric-field profiles
  • Dielectric spectroscopy over large temperature and frequency ranges and at high voltages
  • Dipole orientation, ferroelectricity (switching, hysteresis, etc.), quasi-static and dynamic pyroelectricity, direct and inverse piezoelectricity in polymer films (including ferro-electrets)
  • Charge storage and transport and their molecular mechanisms in dielectric polymers
  • Dielectric elastomers (electro-electrets) and their applications in sensors and actuators
  • Demonstration and assessment of applications-relevant electro-mechanical, mechanoelectrical, and thermo-electrical transducer properties for device applications
  • Investigation of musical instruments (organs, pianos, violins) with use of polymer sensors

Note: There will be a reception after the lecture.

55th Annual Conference on Information Sciences and Systems (CISS 2021)
Mar 24 – Mar 26 all-day

55th Annual Conference on Information Sciences and Systems (CISS)

March 24, 25, & 26, 2021

Hosted by the
Department of Electrical and Computer Engineering, Johns Hopkins University
and Technical Co-sponsorship by the IEEE Information Theory Society

CISS 2021 is a forum for scientists, engineers, and academics to present their latest research results and developments in multiple areas of Information Sciences and Systems. Authors will present unpublished papers describing theoretical advances, applications, and ideas in the fields of:

  • Information Theory
  • Communications
  • Energy Systems
  • Signal Processing
  • Image Processing,
  • Coding, Systems and Control
  • Optimization
  • Quantum Systems
  • Machine Learning
  • Security and Privacy
  • Statistical Inference
  • Biological Systems
  • Neuroscience
Back to top