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News & Highlights

News, awards, and milestones from the lab, alongside seminal contributions from the group's history.

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Seminal work

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2018

  • ARPA-E Funds our lab to lead a project (Rapidly Viable Sustained Grid) with partners NREL, UIUC, UAF, Dynapower and Aelios Tech.

    The University of Minnesota (UMN) will develop a net-load management framework that rapidly identifies neighborhood-units to support grid infrastructure and enable ultrafast coordinated management. UMN’s project will rethink power recovery from near blackout conditions with a focus on rapid energization and maximizing power duration. This project’s approach could fundamentally change the way large contingencies are managed. It would transition power systems and critical infrastructure from fragile to robust using intelligent, self-organizing control for coordinating resources, enhancing resiliency and increasing the use of renewable energy sources. The communication and control layer coupled with rapid decision-making methods for managing local sources and loads will coordinate power resources and leverage renewable energy. This framework will support the grid in contingencies such as failure of aging infrastructure or catastrophic weather events.

    Energy Foundations

  • Govind Saraswat joins NREL

    Govind joins NREL and is the NREL lead for the ARPA-E project, Rapidly Viable Sustained Grid

    Energy

  • NSF funds Energy Efficiency in Computing Logical Operations: Fundamental Limits with and Without Feedback

    NSF ECCS 1809194 grant on the fundamental thermodynamic limits of memory erasure and logical computation — the theoretical foundation of the lab's Landauer-bound work.

    Foundations

  • Saurav graduates with PhD and joins Apple

    Saurav graduates with a PhD from the Salapaka Lab and joins Apple.

    Energy Single-Molecule Biophysics Foundations Machine Learning

  • Shreyas graduates with PhD and joins Becton Dickinson

    Shreyas graduates with a PhD from the Salapaka Lab and joins Becton Dickinson.

    Single-Molecule Biophysics Foundations

2017

2015

  • ARPA-E Funds Our Group to lead a project (A Robust Distributed Framework for Flexible Power Grids) with partners NREL, UIUC, and UTK.

    The University of Minnesota will develop a comprehensive approach that addresses the challenges to system reliability and power quality presented by widespread stochastic renewable power generation. By developing techniques for both centralized cloud-based and distributed peer-to-peer networks, the proposed system will enable coordinated response of many local units to adjust consumption and generation of energy, satisfy physical constraints, and provide ancillary services requested by a grid operator. The project will apply concepts from nonlinear and robust control theory to design self-organizing power systems that effectively respond to the grid events and variability. A key feature enabled by the proposed methodology is a flexible plug and play architecture wherein devices and small power networks can easily engage or disengage from other power networks or the grid.

    Energy Foundations

  • Prof. Salapaka delivers a semi-plenary at MTNS: Reconstruction of interconnectedness in networks of dynamical systems based on passive observations

    Abstract: Determining interrelatedness structure of various entities from multiple time series data is of significant interest to many areas. Knowledge of such a structure can aid in identifying cause and effect relationships, clustering of similar entities, identification of representative elements and model reduction. In this talk, a methodology for identifying the interrelatedness structure of dynamically related time series data based on passive observations will be presented. The framework will allow for the presence of loops in the connectivity structure of the network. The quality of the reconstruction will be quantified. Results on the how the sparsity of multivariate Wiener filter, the Granger filter and the causal Wiener filter depend on the network structure will be presented. Connections to graphical models with notions of independence posed by d-separation will be highlighted.

    Foundations

2014

  • CyberPhyscial Systems project funded: CPS: Synergy: Collaborative Research: Learning from cells to create transportation infrastructure at the micron scale (2015)

    Cells, to carry out many important functions, employ an elaborate transport network with bio-molecular components forming roadways as well as vehicles. The transport is achieved with remarkable robustness under a very uncertain environment. The main goal of this proposal is to understand how biology achieves such functionality and leveraging the knowledge toward realizing effective engineered transport mechanisms for micron sized cargo. The realization of a robust infrastructure that enables simultaneous transport of many micron and smaller sized particles will have a transformative impact on a vast range of areas such as medicine, drug development, electronics, and bio-materials. A key challenge here is to probe the mechanisms often at the nanometer scale as the bio-molecular components are at tens of nanometer scale. The main tools for addressing these challenges come from an engineering perspective that is guided by existing insights from biology. The proposal will bring together researchers from engineering and biology and it provides an integrated environment for students. Moreover, it is known that an impaired transport mechanism can underlie many neurodegenerative maladies, and as the research here pertains to studying intracellular transport, discoveries hold the potential for shedding light on what causes the impaired transport.

    Foundations Machine Learning Nanoscience Single-Molecule Biophysics

2012

  • Contribution and detection of higher eigenmodes during dynamic atomic force microscopy

    We have demonstrated that the conventional approach of discerning higher mode participation via amplitude and phase demodulation is not suitable for high bandwidth applications. Furthermore, we have developed a method where the higher mode participation is reconstructed with high fidelity, and presented a scheme for high bandwidth detection of higher modes when their participation becomes significant. These methods are shown to outperform the traditional amplitude-phase demodulation schemes in terms of speed, resolution, and fidelity. The framework developed is tested on simulations and the method's utility for first two modes is demonstrated experimentally. The results have appeared in Applied Physics Letters.

    Nanoscience

  • Fastest Optical force clamp for probing of motor proteins and real-time step detection

    We have developed a methodology that circumvents the limitations of existing active force clamps with the use of experimentally determined models for various components of the optical tweezing system. This paradigm makes it possible to probe motor proteins at higher speeds and also allows for real-time step estimation for step sizes as small as 8 nm with dwell time of 5 ms or higher without sacrificing force regulation. The results have appeared in Applied Physics Letters.

    Nanoscience Single-Molecule Biophysics

2011

  • Dr. Donatello Materassi joins LIDS, MIT

    Donatello who was a research associate at NDSL has moved on to join LIDS, MIT as a postdoctoral researcher. While at NDSL he pioneered work on network topology reconstruction, less conservative tools for nonlinear systems, modeling of intracellular transport and single molecule research.

    Foundations Single-Molecule Biophysics

  • Wiener Filter on Linear Dynamic Graphs is local

    We have shown that in a linear dynamic graph, where nodes represent stochastic processes and links represent LTI channels that model the influence of a node on another node, the Wiener filter and other filters are local. This insight can be used to reconstruct the topology of a network of stochastic processes connected via dynamical links. These results have interesting connections to machine learning and theory of causations.

    Foundations Machine Learning

2010

  • Restrictions on the use of Zames-Falb Multiplier relaxed

    We have shown that the monotonicity condition of the Zames-Falb multiplier can be effectively relaxed thereby significantly extending its applicability. The results have appeared in IEEE Transactions on Automatic Control.

    Foundations

  • Step Detection Method for Single Molecule Investigation

    We report a new method that detects steps and other events in single molecule data. It does not assume any knowledge of step sizes, estimates the noise statistics from data and is a learning based algorithm. We also provide new measures for performance step detection methods which show the efficacy of our approach. Matlab script files with support documentation is available for download.

    Machine Learning Single-Molecule Biophysics

2009

  • Quantitative material property determination using Dynamic Mode AFM: A first

    In this work we have developed a new real-time methodology for simultaneous determination of both elastic and dissipative properties of material using tapping-mode Atomic Force Microscopy. This will substantially enhance the toolkit for soft matter research; this is the first reportage of such a capability.

    Nanoscience