Rohit Goswami

Senior Project Associate

Education / Work History

  • Doctoral Researcher, University of Iceland (2020–present)
  • Senior Project Associate, IIT Kanpur (2019-2020)
  • Project Associate, IIT Kanpur, (2018-2019)
  • B. Tech., HBTU Kanpur, (2014-2018)

Research Interests

  • Machine Learning
  • Structure Determination
  • Algorithmic approaches to computational complexity reduction

Other Duties

  • Site Admin

Things I do

  • I am an avid open source supporter (FOSS). I am rather active on Github.
  • Sites I manage actively can be verified on keybase

Other projects are not relevant to this site.

Publications

These include only those published in our lab.

  1. Space Filling Curves: Heuristics For Semi Classical Lasing Computations. R. Goswami, A. Goswami, and D. Goswami, in 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC) (IEEE, 2019), pp. 1–4 [Abstract] [PDF] [BibTeX]

    Abstract: For semi classical lasing, the FDTD (finite difference time domain) formulation including nonlinearities is often used. We determine the computational efficiency of such schemes quantitatively and present a hueristic based on space filling curves to minimize complexity. The sparse matrix kernel is shown to be optimized by the utilization of Bi-directional Incremental Compressed Row Storage (BICRS). Extensions to high performance clusters and parallelization are also derived.

     BibTeX: @inproceedings{goswamiSpaceFillingCurves2019,
      langid = {english},
      location = {{New Delhi, India}},
      title = {Space {{Filling Curves}}: {{Heuristics For Semi Classical Lasing Computations}}},
      isbn = {978-90-825987-5-9},
      url = {https://my.pcloud.com/publink/show?code=XZ8vxr7ZHFxXSXuD2J4ntMcQoyPGXFodmegy},
      doi = {10/gf5mqk},
      shorttitle = {Space {{Filling Curves}}},
      eventtitle = {2019 {{URSI Asia}}-{{Pacific Radio Science Conference}} ({{AP}}-{{RASC}})},
      booktitle = {2019 {{URSI Asia}}-{{Pacific Radio Science Conference}} ({{AP}}-{{RASC}})},
      publisher = {{IEEE}},
      urldate = {2019-08-01},
      date = {2019-03},
      pages = {1-4},
      author = {Goswami, Rohit and Goswami, Amrita and Goswami, Debabrata}
    }
    
  2. Quantum Distributed Computing with Shaped Laser Pulses. R. Goswami and D. Goswami, in 13th International Conference on Fiber Optics and Photonics (OSA, 2016), p. W4C.3 [Abstract] [BibTeX]

    Abstract: Shaped laser pulses can control decoherence under quantum adiabatic method of logic operations to result in a possible scalable quantum computer by distributing the computing load on a set of optically adiabatic quantum computing nodes.

     BibTeX: @inproceedings{goswamiQuantumDistributedComputing2016,
      langid = {english},
      location = {{Kanpur}},
      title = {Quantum {{Distributed Computing}} with {{Shaped Laser Pulses}}},
      isbn = {978-1-943580-22-4},
      doi = {10/gf5mrr},
      eventtitle = {International {{Conference}} on {{Fibre Optics}} and {{Photonics}}},
      booktitle = {13th {{International Conference}} on {{Fiber Optics}} and {{Photonics}}},
      publisher = {{OSA}},
      date = {2016},
      pages = {W4C.3},
      author = {Goswami, Rohit and Goswami, Debabrata}
    }
    
  3. Quantum Distributed Computing with Shaped Laser Pulses. R. Goswami and D. Goswami, in 13th International Conference on Fiber Optics and Photonics (OSA, 2016), p. W4C.3 [Abstract] [BibTeX]

    Abstract: Shaped laser pulses can control decoherence under quantum adiabatic method of logic operations to result in a possible scalable quantum computer by distributing the computing load on a set of optically adiabatic quantum computing nodes.

     BibTeX: @inproceedings{goswamiQuantumDistributedComputing2017,
      title = {Quantum {{Distributed Computing}} with {{Shaped Laser Pulses}}},
      booktitle = {13th {{International Conference}} on {{Fiber Optics}} and {{Photonics}}},
      author = {Goswami, Rohit and Goswami, Debabrata},
      date = {2016},
      pages = {W4C.3},
      publisher = {{OSA}},
      location = {{Kanpur}},
      doi = {10/gf5mrr},
      annotation = {00000},
      eventtitle = {International {{Conference}} on {{Fibre Optics}} and {{Photonics}}},
      isbn = {978-1-943580-22-4},
      keywords = {_tablet},
      langid = {english}
    }
    
  4. Qubit Network Barriers to Deep Learning. R. Goswami, A. Goswami, and D. Goswami, in 2019 Workshop on Recent Advances in Photonics (WRAP) (2019), pp. 1–3 [Abstract] [BibTeX]

    Abstract: The popularity of artificial neural networks (ANNs) of great depth and Quantum computing have led to many speculations as to their convergence. We enumerate barriers to utilizing qubit networks for deep learning architectures. We also describe the criteria for an effective usage of qubit networks and then assert that the bottleneck in their implementation is a lack of quantum algorithms for utilizing the topology of a deep neural network.

     BibTeX: @inproceedings{goswamiQubitNetworkBarriers2019a,
      title = {Qubit {{Network Barriers}} to {{Deep Learning}}},
      booktitle = {2019 {{Workshop}} on {{Recent Advances}} in {{Photonics}} ({{WRAP}})},
      author = {Goswami, Rohit and Goswami, Amrita and Goswami, Debabrata},
      date = {2019-12},
      pages = {1--3},
      issn = {null},
      doi = {10.1109/WRAP47485.2019.9013687},
      eventtitle = {2019 {{Workshop}} on {{Recent Advances}} in {{Photonics}} ({{WRAP}})},
      keywords = {artificial neural network,deep learning,quantum architecture,quantum computing,qubits}
    }
    
  5. Semi-Supervised Approaches to Ultrafast Pulse Shaping. R. Goswami, A. Goswami, and D. Goswami, in ICOL-2019, K. Singh, A. K. Gupta, S. Khare, N. Dixit, and K. Pant, eds., Springer Proceedings in Physics (Springer, 2021), pp. 747–749 [Abstract] [PDF] [BibTeX]

    Abstract: Spatiotemporal control aspects of pulsed laser experiments rely on the ability to modulate the shape of the generated pulses efficiently. Drawing from current state-of-the-art theoretical aspects of computational simulations to reduce the sim-to-real bottlenecks, we devise a novel schematic for the generation of on-the-fly calibrated pulse trains with more accountability than existing techniques under the domain of optimal control theory. The techniques presented further diminish the divide between experiment and theory.

     BibTeX: @inproceedings{goswamiSemiSupervisedApproachesUltrafast2021,
      title = {Semi-{{Supervised Approaches}} to {{Ultrafast Pulse Shaping}}},
      booktitle = {{{ICOL}}-2019},
      author = {Goswami, Rohit and Goswami, Amrita and Goswami, Debabrata},
      editor = {Singh, Kehar and Gupta, A. K. and Khare, Sudhir and Dixit, Nimish and Pant, Kamal},
      date = {2021},
      pages = {747--749},
      publisher = {{Springer}},
      location = {{Singapore}},
      doi = {10.1007/978-981-15-9259-1_172},
      url = {10.1007/978-981-15-9259-1_172},
      isbn = {9789811592591},
      langid = {english},
      series = {Springer {{Proceedings}} in {{Physics}}}
    }
    
  6. Space Filling Curves: Heuristics For Semi Classical Lasing Computations. R. Goswami, A. Goswami, and D. Goswami, in 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC) (IEEE, 2019), pp. 1–4 [Abstract] [PDF] [BibTeX]

    Abstract: For semi classical lasing, the FDTD (finite difference time domain) formulation including nonlinearities is often used. We determine the computational efficiency of such schemes quantitatively and present a hueristic based on space filling curves to minimize complexity. The sparse matrix kernel is shown to be optimized by the utilization of Bi-directional Incremental Compressed Row Storage (BICRS). Extensions to high performance clusters and parallelization are also derived.

     BibTeX: @inproceedings{goswamiSpaceFillingCurves2020,
      title = {Space {{Filling Curves}}: {{Heuristics For Semi Classical Lasing Computations}}},
      shorttitle = {Space {{Filling Curves}}},
      booktitle = {2019 {{URSI Asia}}-{{Pacific Radio Science Conference}} ({{AP}}-{{RASC}})},
      author = {Goswami, Rohit and Goswami, Amrita and Goswami, Debabrata},
      date = {2019-03},
      pages = {1--4},
      publisher = {{IEEE}},
      location = {{New Delhi, India}},
      doi = {10/gf5mqk},
      url = {https://my.pcloud.com/publink/show?code=XZ8vxr7ZHFxXSXuD2J4ntMcQoyPGXFodmegy},
      urldate = {2019-08-01},
      annotation = {00000},
      eventtitle = {2019 {{URSI Asia}}-{{Pacific Radio Science Conference}} ({{AP}}-{{RASC}})},
      isbn = {978-90-825987-5-9},
      keywords = {_tablet},
      langid = {english}
    }
    
  7. Ultrafast Insights for Predictive Fragrance Compounding. D. Goswami, R. Goswami, A. Kumar Rawat, and D. Chakrabarty, in (2020) [PDF] [BibTeX]
     BibTeX: @inproceedings{goswamiUltrafastInsightsPredictive2020,
      title = {Ultrafast Insights for Predictive Fragrance Compounding},
      author = {Goswami, Debabrata and Goswami, Rohit and Kumar Rawat, Ashwini and Chakrabarty, Debojit},
      date = {2020-04-29},
      doi = {10.1021/scimeetings.0c03998},
      url = {https://www.morressier.com/article/5e73d6ce139645f83c2299c8},
      urldate = {2020-07-11},
      eventtitle = {{{ACS Spring}} 2020 {{National Meeting}} \& {{Expo}}}
    }