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Dr. Dharanidhar Dang
Principal Investigator

Dr. Dharanidhar Dang

Assistant Professor, Department of Computer Engineering, College of AI, Cyber and Computing (CAICC), UTSA

Silicon Photonics AI Hardware Computer Architecture Neuromorphic Computing Memristive Design VLSI Bioinformatics
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About

Dr. Dharanidhar Dang directs LUCENT, the Lab for Unconventional Computing using Emerging Nano-Technologies at UTSA. His research focuses on energy-efficient and sustainable AI accelerators built from emerging nano-technologies — silicon photonics, memristive devices, and the architectures and design tools that make them practical.

Before joining UTSA, Dr. Dang was a postdoctoral researcher at the University of California, San Diego, working at the intersection of computing and bioinformatics with contributions to papers in Nature Communications Biology and Frontiers in Physiology. He holds four patents and publishes regularly at venues including DAC, ICCAD, ICCD, DATE, VLSID, GLSVLSI, ISVLSI, and IEEE Transactions on Computers.

At UTSA, Dr. Dang teaches courses in embedded systems for AI and C++ data structures — see classes taught.

Research interests

  • Silicon photonics — photonic devices, integrated circuits, and architectures for AI acceleration.
  • AI hardware — energy-efficient accelerators for training and inference, especially under sustainability constraints.
  • Computer architecture — system-level co-design across compute, memory, and interconnect.
  • Neuromorphic computing — bio-inspired analog and photonic compute primitives.
  • Memristive design — non-volatile memory-based in-memory computing.
  • VLSI and mixed-signal design — drivers, ADCs/DACs, and the interface circuits that bridge analog primitives to digital systems.
  • Bioinformatics — computing methods for genomics and physiology, from prior postdoctoral work.

Education

  • PhD, Computer Engineering — Texas A&M University, 2018. Advisor: Dr. Rabi Mahapatra.
  • B.Tech, Instrumentation & Electronics Engineering — CET Bhubaneswar, 2010.

Awards & recognition

  • AAI Fellowship, 2019
  • TEES Travel Grants
  • Intel Youth Enterprise Winner, 2012
  • Intel Embedded Challenge Gold Medal, 2011

Selected publications

For the full list, see the publications page or Google Scholar.

Teaching

  • Embedded Systems in AI (Fall 2025, Spring 2026)
  • C++ Data Structures (Spring 2026)