Stanford University
Showing 21,901-21,910 of 36,203 Results
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Tapan Mukerji
Professor (Research) of Energy Science Engineering, of Earth and Planetary Sciences and of Geophysics
Current Research and Scholarly InterestsMy students and I use theoretical, computational, and statistical models, to discover and understand fundamental relations between geophysical data and subsurface properties, to quantify uncertainty in our geomodels, and to address value of information for decision making under uncertainty.
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Kunal Mukherjee
Assistant Professor of Materials Science and Engineering
BioKunal Mukherjee is an assistant professor in Materials Science and Engineering at Stanford. He has been an assistant professor in the Materials department at UC Santa Barbara (2016-2020), held postdoctoral appointments at IBM TJ Watson Research Center (2016) and MIT (2015), and worked as a transceiver engineer at Finisar (2009-2010).
The Mukherjee group specializes in semiconductors that emit and detect light in the infrared. Our research enables better materials for data transmission, sensing, manufacturing, and environmental monitoring. We make high-quality thin films with IV-VI (PbSnSe) and III-V (GaAs-InAs/GaSb) material systems and spend much of our time understanding how imperfections in the crystalline structure such as dislocations and point defects impact their electronic and optical properties. This holds the key to directly integrating these semiconductors with silicon and germanium substrates for new hybrid circuits that combine infrared photonics and conventional electronics. -
Souradeep Mukherjee
Ph.D. Student in Earth and Planetary Sciences, admitted Autumn 2023
BioSouradeep is an Exploration Geologist-turned-AI Researcher developing decision-making algorithms to discover high-grade mineral deposits. With almost a decade of experience in on-field exploration of critical & strategic mineral deposits, he is now using his computer to unlock the full potential of mineral discovery.
Souradeep is currently a Doctoral Researcher at Stanford Mineral-X. His current work focuses on fusing multi-element soil geochemistry with 3D geological inference to build predictive models for subsurface intrusion-hosted Ni-Cu sulfide mineralization in the Curaçá Valley, Brazil. By leveraging machine learning, geostatistics, and compositional data analysis, he aims to reduce geological uncertainty and operational costs in mineral targeting, transforming how exploration is conducted in under-explored terrains.
Prior to Stanford, Souradeep held several technical and supervisory roles at the Atomic Minerals Directorate for Exploration and Research (AMD), Government of India, where he spearheaded multiple national-scale mineral exploration campaigns. He led the exploration and evaluation of uranium-bearing sediments in the Bhima Basin, the mapping of lithium-rich pegmatites in Karnataka, and the delineation of heavy mineral sand deposits across the east coast of India. His work included advanced geological mapping, structural interpretation, kinematic analysis, downhole geophysics, petrological microscopy, and geostatistical modeling—skills that now enrich his algorithmic thinking in research.
His research interests span compositional data analysis, geospatial modeling, machine learning for Earth systems, building intelligent decision making agents for mineral exploration and mineral prospectivity analysis. He is particularly passionate about bridging the gap between traditional geosciences and computational intelligence to power the next generation of resource discovery tools.
When he’s not deep into code or core samples, Souradeep contributes to scientific outreach, mentors young geoscientists, and advocates for data transparency and sustainability in the mining sector. He is also a writer, a poet and loves to write pieces on existential philosophy.
