Dr. Deblina Sarkar
Translational Fellow and Postdoctoral Associate
Massachusetts Institute of Technology
Abstract: Excessive power consumption and dissipation of electronics with technology scaling, is a serious threat to the Information Society as well as to the environment and especially smacks a hard blow to the future of energy-constrained applications such as medical implants and prosthetics. This impending energy crisis has roots in the thermal distribution of carriers, which poses fundamental limitation on energy scalability of the present transistors.
In this talk, I will demonstrate the quantum mechanical transistor, that I developed, which beats the fundamental thermal limitations of present transistors. I will describe how this can be achieved by unique integration of heterogeneous material technologies including an atomically thin material, to make the electron waves propagate (tunnel) efficiently through an energy barrier (like a ghost walking through a wall). This device is the world’s thinnest channel (6 atoms thick) sub-thermal tunnel-transistor. Thus, it has the potential to allow dimensional scalability to beyond Silicon scaling era and thereby to address the long-standing issue of simultaneous dimensional and power scalability.
Going beyond electronic computation, I will discuss about the biological computer: the brain, which can be thought of as an ultimate example of low power computational system. However, understanding the brain, requires deciphering the dense jungle of biomolecules that it is formed of. I will introduce the nextgeneration expansion microscopy technology, that I have developed, which helps to decipher the organization of biomolecular building blocks of brain by literally blowing out the brain by up to 100-fold. This technology reveals for the first time, a nanoscale trans-synaptic architecture in brain tissue and structural changes related to neurological diseases. I will conclude with my research vision for how extremely powerful technologies can be built by fusing diverse research fields and how seamless integration of nanoelectronics-bio hybrid systems in the brain (brain doping), can create unprecedented possibilities for probing and controlling the biological computer and in future, help us transcend beyond our biological limitations.
 D. Sarkar et. al., Nature, 526 (7571), 91, 2015;
 D. Sarkar et. al., Nano Lett., 15 (5), 2852, 2015;
 D. Sarkar et. al., ACS Nano., 8 (4), 3992, 2014;
 D. Sarkar et. al., Society for Neuroscience, 2016.
 D. Sarkar et. al., International Conference on Nanoscopy, 2018.
Biography: Deblina Sarkar is currently an MIT Translational Fellow and postdoctoral associate in the Synthetic Neurobiology group, while she had received her M.S. and Ph.D. in Electrical and Computer Engineering at UCSB. Her research aims to combine novel materials, nanoelectronics and synthetic biology to create a new paradigm for computational electronics and invent disruptive technologies for life-machine symbiosis. Her work has led to more than 40 publications till date (citations: 1927, h-index: 18, i-10 index: 26 according to Google Scholar), several of which have appeared in popular press worldwide. Her PhD dissertation was honored as one of the top 3 dissertations throughout USA and Canada in the field of Mathematics, Physical sciences and all departments of Engineering by the Council of Graduate Schools in the period 2014-2016. She was UCSB’s nominee for this nationwide contest, after winning the Lancaster Award for the best PhD Dissertation at UCSB in 2016. She is the recipient of numerous other awards and recognitions, including the U.S. Presidential Fellowship (2008), Outstanding Doctoral Candidate Fellowship (2008), being one of three researchers worldwide to win the prestigious IEEE EDS PhD Fellowship Award (2011), a “Bright Mind” invited speaker at the KAUST-NSF conference (2015), one of three winners of the Falling Walls Lab Young Innovator’s Award at San Diego (2015), recipient of “Materials Research Society’s Graduate Student Award” (2015), named a “Rising Star” in Electrical Engineering and Computer Science (2015), invited speaker at TEDx (2016) and recipient of MIT Translational Fellowship (2017).
Computational Medicine Night is a networking event geared towards undergraduates who are interested in learning about the academic discipline of Computational Medicine and the research conducted in ICM labs. The event showcases undergraduate research and provides a forum for interested students to ‘Meet & Eat’ with ICM faculty, students, and postdoctoral fellows, to gather information about the Computational Medicine Minor, and to ask questions. Click here for a detailed event agenda and list of presenters and panelists.
Please register here to attend. Registration deadline: Feb. 21.
“Inverter-based Control for Low Inertia Power Systems: Scale-free Stability Analysis, Performance Trade-offs, and Controller Design”
Implementing frequency response using grid-connected inverters is one of the popular alternatives to mitigate the dynamic degradation experienced in low inertia power systems. However, such solution faces several challenges as inverters do not intrinsically possess the natural response to power fluctuations that synchronous generators have. Thus, to synthetically generate “virtual” inertia, inverters need to take frequency measurements, which are usually noisy, and subsequently make changes in the output power, which is therefore delayed. As a result, it is not a priori clear the whether virtual inertia will indeed mitigate the degradation, or some alternative control strategy will be necessary. In this talk, we present a comprehensive analysis and design framework that provides the tools required to answer this question. First, we develop novel stability analysis tools for power systems, which allows for the decentralized design of inverter-based controllers. The method requires that each inverter satisfies a standard H-infinity design requirement that depends on the dynamics of the components and inverters at each bus, and the aggregate susceptance of the transmission lines connected to it. It is robust to network and delay uncertainty, and when no network information is available reduces to the standard passivity condition for stability. Second, by selecting relevant performance outputs and signal norms, we define system-wide performance metrics that explicitly quantify the effect of frequency measurements noise and power disturbances on the overall system performance. Using a novel modal decomposition, we derive closed-form expressions for system performance that explicitly capture the impact of network topology, generator and inverter control parameters, and machine rating heterogeneity. Finally, we leverage this framework to design a new dynamic droop control (iDroop) mechanism for grid-connected inverters that exploits classical lead/lag compensation to outperform standard droop control and virtual inertia alternatives in both joint noise and disturbance mitigation and delay robustness.