Harshith Bachimanchi – Soft Matter Lab

Harshith Bachimanchi is shortlisted as one of the RMS early-career award speakers at RMS annual general meeting 2024, London, UK on 2 October 2024

The three RMS Early Career Award speakers (l to r) Harshith Bachimanchi, Akaash Kumar and Liam Rooney. *(Image by RMS.)*

Harshith Bachimanchi is shortlisted as one of the RMS (Royal Microscopical Society) early-career award speakers at RMS AMG 2024 (RMS Annual General Meeting 2024) held in London, UK, on 2 October 2024.

In this meeting, Harshith presented his work on leveraging deep learning as a powerful tool to enhance the microscopic data analysis pipelines, to study microorganisms in unprecedented detail. Taking holographic microscopy as an example, he demonstrated that combining holography with deep learning can be used to follow marine micro-organisms through out their lifespan, continuously measuring their three-dimensional positions and dry mass. He also presented some recent results on using deep learning to transform microscopy images from one modality to another (For eg., from Holography to Bright-field and vice versa).

The articles related to his presentation can be found at the following links:
1. Microplankton life histories revealed by holographic microscopy and deep learning.
2. Deep-learning-powered data analysis in plankton ecology

The annual Early Career Award—for which Harshith is shortlisted as one of the potential candidates—recognises the achievements of an outstanding early career imaging scientist in their contribution to microscopy, image analysis, or cytometry.

From RMS:

We heard some fantastic talks earlier from the RMS #EarlyCareer Award speakers at our AGM.

Big thanks to Akaash Kumar @_Akaash_Kumar, Liam Rooney @lmr_1994 and Harshith Bachimanchi @harshi_b7! 👏👏👏 pic.twitter.com/l2yAjZCiGN

— RMS (@RoyalMicroSoc) October 2, 2024

Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial on ArXiv

Super-resolution by diffusion models: low-resolution images of microtubules (left) are transformed to high-resolution (right) by diffusion model. Dataset courtesy: BioSR Dataset. *(Image by H. Bachimamchi.)*

Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial
Harshith Bachimanchi, Giovanni Volpe
arXiv: 2409.16488

Diffusion models have emerged as a prominent technique in generative modeling with neural networks, making their mark in tasks like text-to-image translation and super-resolution. In this tutorial, we provide a comprehensive guide to build denoising diffusion probabilistic models (DDPMs) from scratch, with a specific focus on transforming low-resolution microscopy images into their corresponding high-resolution versions. We provide the theoretical background, mathematical derivations, and a detailed Python code implementation using PyTorch, along with techniques to enhance model performance.

Book “Deep Learning Crash Course” published at No Starch Press

The book Deep Learning Crash Course, authored by Giovanni Volpe, Benjamin Midtvedt, Jesús Pineda, Henrik Klein Moberg, Harshith Bachimanchi, Joana B. Pereira, and Carlo Manzo, has been published online by No Starch Press in July 2024.

Preorder Discount
A preorder discount is available: preorders with coupon code PREORDER will receive 25% off. Link: Preorder @ No Starch Press | Deep Learning Crash Course

Links
@ No Starch Press

Citation
Giovanni Volpe, Benjamin Midtvedt, Jesús Pineda, Henrik Klein Moberg, Harshith Bachimanchi, Joana B. Pereira, and Carlo Manzo. Deep Learning Crash Course. No Starch Press.
ISBN-13: 9781718503922

Harshith Bachimanchi won best early-career researcher presentation award at AIM 2024, La Ràpita, Spain

Committee and winners for the IOP award at AIM24. From left to right: Susan Cox, Wylie Ahmed, Celia Rowland (IOP), Harshith Bachimanchi, Blanca Zufiria Gerboles, Mirja Granfors, Carlotta Viana, Gajendra Pratap Singh, Giorgio Volpe. *(Photo by G. Volpe)*

Harshith Bachimanchi won the best early career researcher presentation award at AIM 2024 meeting (Artificial Intelligence for iMaging 2024) held in La Ràpita, Spain, from 26 May – 1 June 2024.

The award, consisting of a certificate, and a cash prize of 500 €, is sponsored by Journal of Physics: Photonics (JPhys Photonics) from IOP Publishing.

Harshith received the prize for his presentation on “Bringing microplankton to focus: Holography and deep learning”, where he demonstrated that the combination of holographic microscopy and deep learning can be used to follow the marine microorganisms throughout their lifespan, continuously measuring their three-dimensional positions and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended periods of time. He exemplified this by showing detailed descriptions of micro-zooplankton feeding events, cell divisions, and long-term monitoring of single cells from division to division.

The article related to this presentation can be found at the following link: Microplankton life histories revealed by holographic microscopy and deep learning.

Award Certificate. *(Image by H. Bachimanchi)*

Harshith Bachimanchi receives the prize. *(Photo by A. Callegari)*

Deep-learning-powered data analysis in plankton ecology published in Limnology and Oceanography Letters

Segmentation of two plankton species using deep learning (N. scintillans in blue, D. tertiolecta in green). *(Image by H. Bachimanchi.)*

Deep-learning-powered data analysis in plankton ecology
Harshith Bachimanchi, Matthew I. M. Pinder, Chloé Robert, Pierre De Wit, Jonathan Havenhand, Alexandra Kinnby, Daniel Midtvedt, Erik Selander, Giovanni Volpe
Limnology and Oceanography Letters (2024)
doi: 10.1002/lol2.10392
arXiv: 2309.08500

The implementation of deep learning algorithms has brought new perspectives to plankton ecology. Emerging as an alternative approach to established methods, deep learning offers objective schemes to investigate plankton organisms in diverse environments. We provide an overview of deep-learning-based methods including detection and classification of phytoplankton and zooplankton images, foraging and swimming behavior analysis, and finally ecological modeling. Deep learning has the potential to speed up the analysis and reduce the human experimental bias, thus enabling data acquisition at relevant temporal and spatial scales with improved reproducibility. We also discuss shortcomings and show how deep learning architectures have evolved to mitigate imprecise readouts. Finally, we suggest opportunities where deep learning is particularly likely to catalyze plankton research. The examples are accompanied by detailed tutorials and code samples that allow readers to apply the methods described in this review to their own data.

Presentation by H. Bachimanchi at International Forum for Computer vision in Ecology and Evolution, Lund University, 21 September 2023

Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)

Bringing microplankton to focus: Holography and deep learning
Harshith Bachimanchi
21 September 2023, 11:15 AM CEST

The marine microbial food web plays a central role in the global carbon cycle. However, our mechanistic understanding of the ocean is biased toward its larger constituents, while rates and biomass fluxes in the microbial food web are mainly inferred from indirect measurements and ensemble averages. Yet, resolution at the level of the individual microplankton is required to advance our understanding of the microbial food web. Here, we demonstrate that, by combining holographic microscopy with deep learning, we can follow microplanktons throughout their lifespan, continuously measuring their three-dimensional position and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended time periods. This permits us to reliably estimate growth rates, both in terms of dry mass increase and cell divisions, as well as to measure trophic interactions between species such as predation events. The individual resolution provides information about selectivity, individual feeding rates, and handling times for individual microplanktons. The method is particularly useful to detail the rates and routes of organic matter transfer in micro-zooplankton, the most important and least known group of primary consumers in the oceans. Studying individual interactions in idealized small systems provides insights that help us understand microbial food webs and ultimately larger-scale processes. We exemplify this by detailed descriptions of micro-zooplankton feeding events, cell divisions, and long-term monitoring of single cells from division to division.

The article related to this presentation can be found at the following link: Microplankton life histories revealed by holographic microscopy and deep learning.

The recorded presentation can be found here:

Bubble-propelled micromotors for ammonia generation published in Nanoscale

Bubble-propelled micromotors tracked by deep learning. *(Image by H. Bachimanchi.)*

Bubble-propelled micromotors for ammonia generation
Rebeca Ferrer Campos, Harshith Bachimanchi, Giovanni Volpe, Katherine Villa
Nanoscale (2023)
doi: 10.1039/D3NR03804A

Micromotors have emerged as promising tools for environmental remediation, thanks to their ability to autonomously navigate and perform specific tasks at the microscale. In this study, we present the development of MnO2 tubular micromotors modified with laccase for enhanced oxidation of organic pollutants by providing an additional oxidative catalytic pathway for pollutant removal. These modified micromotors exhibit efficient ammonia generation through the catalytic decomposition of urea, suggesting their potential application in the field of green energy generation. Compared to bare micromotors, the MnO2 micromotors modified with laccase exhibit a 20% increase in rhodamine B degradation. Moreover, the generation of ammonia increased from 2 to 31 ppm in only 15 min, evidencing their high catalytic activity. To enable precise tracking of the micromotors and measurement of their speed, a deep-learning-based tracking system was developed. Overall, this work expands the potential applicability of bio-catalytic tubular micromotors in the energy field.

Harshith Bachimanchi won best early career researcher presentation award at ETAI 2023, San Diego

The three award winners. From left to right: Mite Mijalkov, Harshith Bachimanchi, Marie Drouhin. *(Photo by G. Volpe.)*

Harshith Bachimanchi won the best early career researcher presentation (gold) award at Emerging Topics in Artificial Intelligence (ETAI) 2023 held in San Diego, California, USA, from 20 – 24 August 2023. The award, consisting of an invitation to a part of a perspective article of AI in neurosciences, is offered by the organisers of the conference, and SPIE Optics + Photonics.

In this work, Harshith presented his recent work on combining holographic microscopy and deep learning to study the marine microplankton. He demonstrated that the combination of holographic microscopy and deep learning can be used to follow the marine microorganisms throughout their lifespan, continuously measuring their three-dimensional positions and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended periods of time. This enables to reliably estimate growth rates, both in terms of dry mass increase and cell divisions, as well as to measure trophic interactions between species such as predation events. Studying individual interactions in idealized small systems provides insights that help us understand microbial food webs and ultimately larger-scale processes. He exemplified this by showing detailed descriptions of micro-zooplankton feeding events, cell divisions, and long-term monitoring of single cells from division to division.

The article related to this presentation can be found at the following link: Microplankton life histories revealed by holographic microscopy and deep learning.