Alireza Vahid
Rochester Institute of Technology, NY·Engineering
Modern wireless and data technologies face two challenges: getting more out of limited resources, and handling information that no longer arrives in neat order. These projects apply information theory, the math of how much information can be sent and stored, along with signal processing and hardware design.
- Fields
Engineering
Computer Science
Mathematics & Statistics
- Topics
information theory
coding theory
out-of-order data
- Grants
- 3 known grants, through Sep 2028
What they work on
- Develop algorithms that recover sets of similar gene sequences from unordered DNA sequencing reads, to detect antimicrobial resistance genes in microbial communities.
- Determine fundamental limits and design near-capacity codes for molecular DNA data storage, accounting for fragmentation, symbol-level noise and synthesis cost.
- Reconstruct full spatial, temporal and directional radio activity maps from sparse samples collected by low-cost SweepSpace sensors with sweeping phased array antennas.
- Find spectrum holes for safely adding new transmissions, and characterize missed-detection and false-positive risks for single sensors and sensor networks.
- Build computational antenna (CompTenna) hardware, a computational model and a simulator for programmable analog computing at mmWave and sub-THz frequencies.
- Use CompTenna for energy-efficient single- and multi-user beamforming, distributed interference management, and joint sensing and communication.
More about the research
The work spans three problems: making sense of scrambled DNA data, tracking crowded radio airwaves, and building antennas that compute.
Why it matters
The coding methods could make DNA-based data storage cheaper and more reliable. The algorithms could help detect antimicrobial resistance genes in microbial samples. Better spectrum sensing could let more wireless devices share limited airwaves safely without added spectrum cost. Computational antennas could expand the capacity and sensing abilities of next-generation mobile networks.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Collaborative Research: NSF-BSF: CIF: Small: An Information Theory for Out-of-Order Data: Applications in Genomics, Molecular Storage, and Beyond
NSF · Oct 2025 – Sep 2028
Collaborative Research: SII-NRDZ: SweepSpace: Enabling Autonomous Fine-Grained Spatial Spectrum Sensing and Sharing
NSF · Feb 2024 – Sep 2027
Collaborative Research: CNS Core: Medium: Programmable Computational Antennas for Sensing and Communications
NSF · Oct 2023 – Sep 2026
Closest labs
Dinesh Bharadia
University of California-San Diego, CA·Engineering
Develop real-time reinforcement learning algorithms that schedule cellular radio resources by application priority, using the structure of the optimal policy to learn quickly and at scale.
Taiyun Chi
Rice University, TX·Engineering·Early-career
Build a fully packaged implanted neural interface with more than ten times the channel count of current systems, combining a low-power application-specific integrated circuit for recording, stimulation and digitization with a flexible intracortical probe and a high-speed digital backend.
Yasaman Ghasempour
Princeton University, NJ·Engineering·Early-career
Steer narrow terahertz beams electronically using a new multi-slit antenna, in which the beam's angle depends on the signal frequency and the slit geometry, to overcome the high signal loss at frequencies above 100 GHz.
The school
Rochester Institute of Technology: 9 labs on PI Pages, $78M a year on research, 65 research doctorates a year.
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