News

The latest news, publications, awards, and happenings from the Bhargava Group.

Rohit Bhargava does a podcast with the News Gazette about Cancer
April 26, 2019

Paper - A fully automated, faster noise rejection approach to increasing the analytical capability of chemical imaging for digital histopathology

Collaborators between the University of Illinois at Urbana-Champaing, University of Illinois Chicago, and the University of Texas at Austin published this paper focusing on denoising images against the Minimum Noise Fraction (MNF).

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April 16, 2019

Paper - Relating strain hardening moduli in high density polyethylene copolymers to anisotropic molecular deformation using infrared spectroscopic imaging

The second latest paper co-authored with Chevron Phillips Chemical Company focuses on bulk strain hardening moduli of tensile polyolefin samples. Read the full article below.

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March 11, 2019

5 Amazing Projects That Will Change the Future of Healthcare

Rohit Bhargava's 3D printer work was featured in PCMag as one of the five amazing projects that will change the future of healthcare.

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March 09, 2019

2019 Beckman Open House

The Chemical Imaging and Structures Laboratory particpated in the biannual Beckman Open House on Friday, March 8 and Saturday, March 9. Visitors had the opportunity to build chemical structures out of marshmallows, learn about spectroscopy, and even have the chance to win a free-form 3D printed item!

Beckman Open House
March 06, 2019

Paper - A comparison of mid-infrared spectral regions on accuracy of tissue classification

Shachi Mittal and Dr. Bhargava's paper on IR Spectroscopy dives deeper into spectral regions that can provide high-accuracy classifiers other than just the fingerprint region.

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March 01, 2019

Shachi Mittal Selected as Beckman Fellow

Shachi Mittal were among the five researchers named as Beckman Institute Postdoctoral Fellows for 2019. Shachi expects to earn her Ph.D. in bioengineering in 2019. She hopes to improve the treatment of breast cancer by developing digital models using molecular imaging. Her work could provide more detailed diagnoses, identify breast cancer subtypes, and better predict patient outcomes for improved patient care.

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