Reza Zamani, Rachit Shrivastava, Vidya Chhabria receive doctoral dissertation fellowships
Congratulations to Reza Zamani, Rachit Shrivastava, and Vidya Chhabria for winning the Graduate School’s 2021-2022 doctoral dissertation fellowship (DDF) award. The fellowship gives the University's most accomplished doctoral candidates an opportunity to devote full-time effort to an outstanding research project by providing time to finalize and write a dissertation during the fellowship year.
Reza Zamani has received the fellowship for his research titled “Introducing Cost-effective Approaches for Fabricating and Characterizing Multifunctional Magnetic Nanowires”. He is working under the guidance of Professor Bethanie Stadler. Originally from Sirjan, a town in the southern part of Iran, Zamani earned his bachelor’s degree in mechanical engineering from Isfahan University of Technology, and his master’s degree in electrical engineering from University of New Mexico, Albuquerque. He is currently pursuing his doctoral degree in electrical engineering, with a minor in biomedical engineering.
Zamani’s fellowship-winning research is directed at the synthesis, characterization, and surface chemistry modifications of novel magnetic nanowires to advance nanomedicine. His work addresses challenges in quantitative biolabeling, selective detection and stimulation of cancer cells in multimodal therapeutic platforms, drug delivery, and cryopreservation applications. Specifically, his dissertation focuses on long-standing issues such as magnetic nanowires agglomeration, surface biofunctionalization, colloidal stability in biological media, and selective and quantitative detection of biological entities.
Nanomedicine often requires a significant amount of nanoparticles with controlled morphology and properties that cannot be achieved using the current state of the art. Zamani has introduced a scalable approach for the mass production of elongated nanoparticles by harnessing the current distribution during the template-assisted electrodeposition technique. His approach not only substantially reduces the synthesis cost and time, but also significantly enhances monodispersity and fabrication yield. The scalable approach can unlimitedly be scaled up for several magnitudes of orders higher fabrication yields.
Usually high-yielding fabrication methods do not allow for perfectly identical nanoparticles, leading to variation in properties and functionalities. However, in this context, Zamani has devised a novel, fast, and universal characterization technique named the projection method that can potentially be used for characterizing hysteretic behaviors of magnetic nanoparticles. The projection method not only speeds up the extraction of magnetic signatures by a factor of 50X to 100X but also removes the trade-off between accuracy and characterization speed, thus benefiting both research development and industrial quality control levels.
Rachit Shrivastava has received the doctoral dissertation fellowship for his research titled, “Regulation of Intracellular Transport by Molecular Motors: Theory, Instrumentation and DNA based Single Molecule Methods.” He is completing his research under Professor Murti Salapaka’s guidance. Shrivastava earned his bachelor’s degree in energy engineering and his master’s degree in energy systems engineering from the Indian Institute of Technology Bombay, India. After graduation, he worked as an R&D engineer with Powergrid Corporation, an Indian bulk power transmission utility, where he was on the team responsible for commissioning the world's highest voltage level substation. He joined ECE’s PhD program in 2016.
Shrivastava’s interest lies in understanding the operation of cellular transportation infrastructure. To this end, he leverages mathematical and state-of-the-art simulation techniques and combines them with novel DNA-based single molecule experimental techniques, and high resolution optical instrumentation to get a holistic picture of the working of molecular motors.
Molecular motors are nanometer sized proteins which transport material from one part of the cell to another. These molecular motor proteins work in teams to transport important cargo inside the cells of living organisms while walking on tracks made of protein filaments. Failure in the intracellular transportation network results in illnesses such as Huntington’s and Alzheimer’s. Shrivastava’s doctoral research aims to combine instrumentation, mathematics, and biochemistry techniques to identify key regulating factors for transport by molecular motors. The goal is to understand the genesis of diseases and aid in the discovery of cures, and to achieve this he has developed a novel biochemical experimental assay to probe a single molecular motor at a time with high fidelity using optical tweezers. Optical tweezers are instruments that use a highly focused laser to apply pico-newton (one trillionth of a newton) level forces on cargos and trace their positions with high resolution in time and space.
Shrivastava is also currently working on building a novel hybrid instrument by combining optical tweezers with total internal reflection fluorescence microscope (TIRF) for simultaneous force spectroscopy and single photon imaging of molecular motors. To understand the aspects of intracellular transport which are difficult to control experimentally, he is developing a simulation toolbox which simulates the transportation of a single cargo by multiple molecular motors under different cellular conditions.
Vidya Chhabria’s fellowship winning dissertation is titled “Machine-Learning-based Open-source Chip Design Tools for Power Management”. She is working on her research under the guidance of Professor Sachin Sapatnekar. Chhabria earned her bachelor’s degree in India where her senior design project experience cemented her interest in the field of digital logic design. It allows her to combine her enthusiasm for algorithms and hardware design, which together enable the development of complex systems with diverse applications.
The focus of Chhabria’s doctoral research is the establishment of a machine learning (ML) paradigm for addressing power-related issues to aid industry chip designers in decreasing chip design times and design cost. The integrated circuit (IC) industry faces three persistent challenges: the spiraling cost of electronic design automation (EDA) software tools, the heuristic nature of EDA tools that often deliver suboptimal solutions, and the high computation times of EDA software, which increases IC time-to-market. The situation has worsened over time and EDA tools have struggled to keep up with industry demands. Chhabria addresses these challenges in the context of power-related problems by building new algorithms or software that are open-source and machine learning (ML)-based. The open-source aspect makes software freely available to the public and drives the transition from research to product, speeding up the process. The application of ML to chip design, supported by decades worth of design data, has opened the doors to solving EDA problems at unprecedented levels of accuracy and speed. Chhabria’s research demonstrates that ML can bring within reach goals that were previously thought impossible: automated chip design in 24 hours with no humans in the loop.
Chhabria has previously been the recipient of the Louise T. Dosdall fellowship awarded by the University’s Graduate School, and the Cadence Women in Technology scholarship.