Scholar
Navid Anjum Aadit
Google Scholar ID: KXA0nl4AAAAJ
PhD Candidate, University of California, Santa Barbara
Citations & Impact
Citations
811
H-index
14
i10-index
15
Publications
20
Co-authors
21
list available
Publications
Resume (English only)
Academic Achievements
- - Publications:
- - Nature Electronics (2022): Massively parallel probabilistic computing with sparse Ising machines
- - Nature Communications (2024): All-to-all reconfigurability with sparse & higher-order Ising machines
- - VLSI Symposium (2023): Accelerating Adaptive Parallel Tempering with FPGA-based p-bits
- - IEDM (2022): Experimental evaluation of simulated quantum annealing with MTJ-augmented p-bits
- - IEDM (2021): Computing with invertible logic: Combinatorial optimization with probabilistic bits
- - Awards:
- - Misha Mahowald Prize (2025)
- - Bell Labs Prize (Bronze, 2023)
- - UCSB Graduate Division — PhD Dissertation Fellowship (2025)
Research Experience
- - Probabilistic Computing: Working on p-bits and extreme-scale distributed architectures
- - Ising/Boltzmann Machines: Building on CMOS/FPGA platforms
- - Hardware-Software Co-Design: Developing for scalable multi-chip systems
- - Applications: Combinatorial optimization, energy-based machine learning, AI sampling, and quantum-inspired non-local algorithms
- - Recent Work: Scaling p-computers across multi-FPGA systems with delay-tolerant communication and balanced partitioning to sustain solution quality at unprecedented sizes
Education
- - Degree: PhD
- - Institution: University of California, Santa Barbara
- - Major: Electrical & Computer Engineering
- - Expected Graduation: Dec 2025
Background
- - Research Interests: Probabilistic computing, Ising/Boltzmann machines, FPGA/CMOS, ML/AI
- - Field of Study: Electrical & Computer Engineering
- - Bio: PhD candidate in the Department of ECE at UC Santa Barbara. Focuses on probabilistic computing, extreme-scale distributed architectures, building Ising/Boltzmann machines on CMOS/FPGA platforms, and developing hardware–software co-design for scalable multi-chip systems. Applications include combinatorial optimization, energy-based machine learning, AI sampling, and quantum-inspired non-local algorithms.
