Artificial Intelligence in Spectroscopy: A SciX Interview with Ishan Barman
As a preview to SciX 2023, Spectroscopy magazine sat down with Ishan Barman of Johns Hopkins University to ask him about his thoughts on how artificial intelligence may impact spectroscopic research going forward.
Barman spoke with us before the SciX Conference in Sparks, Nevada, where he is set to give a talk titled, “From Spectroscopy to Solutions: Transformative Biophotonics in Disease Detection and Monitoring,” and accept The Coblentz Society Clara Craver Award as the 2023 recipient on Monday October 9, 2023, at 11:30 am in Sierra 5 at the Golden Nugget Casino in Sparks, Nevada.
Dr. Barman, an Associate Professor at Johns Hopkins University, specializes in Mechanical Engineering, Oncology, and Radiology. He earned his undergraduate degree from IIT Kharagpur and a Ph.D. from MIT, where he pioneered Raman spectroscopy for transcutaneous blood analysis. His work established key techniques for in vivo spectroscopy, addressing issues like tissue turbidity and non-linear chemometric analysis. As a postdoc at MIT, he extended Raman and reflectance spectroscopy to guide breast biopsies and diagnose lesions with microcalcifications. Barman joined Johns Hopkins University in 2014, rising to the position of Associate Professor with tenure in 2019.
In this interview clip, Barman answers the following question:
- In your view, how can artificial intelligence (AI) be used effectively in spectroscopy, and where do you see the application of AI heading in this field?
This interview segment is the first of several conducted in conjunction to the SciX 2023 conference. You can see our latest conference coverage, including our additional video interviews conducted at SciX, at the following link: https://www.spectroscopyonline.com/conferences/scix
AI, Deep Learning, and Machine Learning in the Dynamic World of Spectroscopy
December 2nd 2024Over the past two years Spectroscopy Magazine has increased our coverage of artificial intelligence (AI), deep learning (DL), and machine learning (ML) and the mathematical approaches relevant to the AI topic. In this article we summarize AI coverage and provide the reference links for a series of selected articles specifically examining these subjects. The resources highlighted in this overview article include those from the Analytically Speaking podcasts, the Chemometrics in Spectroscopy column, and various feature articles and news stories published in Spectroscopy. Here, we provide active links to each of the full articles or podcasts resident on the Spectroscopy website.
Diffuse Reflectance Spectroscopy to Advance Tree-Level NSC Analysis
November 28th 2024Researchers have developed a novel method combining near-infrared (NIR) and mid-infrared (MIR) diffuse reflectance spectroscopy with advanced data fusion techniques to improve the accuracy of non-structural carbohydrate estimation in diverse tree tissues, advancing carbon cycle research.
Mass Spectrometry for Forensic Analysis: An Interview with Glen Jackson
November 27th 2024As part of “The Future of Forensic Analysis” content series, Spectroscopy sat down with Glen P. Jackson of West Virginia University to talk about the historical development of mass spectrometry in forensic analysis.
