Upamanyu Madhow

About

Upamanyu Madhow is a Distinguished Professor in the Department of Electrical and Computer Engineering at UC Santa Barbara. His research interests include next-generation communication, sensing, and inference infrastructures centered around millimeter wave systems, signal processing algorithms, and robust machine learning. He is associated with the Wireless Communication and Sensornets Lab, where his work focuses on advancing communication technologies and sensing systems. His contact information includes a phone number, email, and office location at Harold Frank Hall, indicating his active engagement in research and academic activities within the university.

Research topics

• Artificial Intelligence
• Machine Learning
• Computer Science
• Telecommunications
• Materials science
• Real-time computing
• Statistics
• Electrical engineering
• Electronic engineering
• Mathematics

Selected publications

• Wireless Fingerprinting via Deep Learning: The Impact of Confounding Factors

  2014 48th Asilomar Conference on Signals, Systems and Computers · 2021 · 49 citations

  Senior authorCorresponding

  Can we distinguish between two wireless transmitters sending exactly the same message, using the same protocol? The opportunity for doing so arises due to subtle nonlinear variations across transmitters, even those made by the same manufacturer. Since these effects are difficult to model explicitly, we investigate learning device fingerprints using complex-valued deep neural networks (DNNs) that take as input the complex baseband signal at the receiver. We ask whether such fingerprints can be ma…

  PublisherDOI

• A 146.7 GHz Transceiver with 5 GBaud Data Transmission using a Low-Cost Series-Fed Patch Antenna Array through Wirebonding Integration

  2020 · 17 citations

  We present a fully-packaged two-channel transmitter and a fully-packaged four-channel receiver using previously reported four-channel transceivers, designed in 45 nm CMOS SOI. We first present a low -cost antenna and packaging technologies to integrate with the transceivers. 8-element series-fed linear microstrip patch antenna arrays fabricated on an Isola Astra MT77 (${\mathcal{E}_r} = 3$, tanℽ=0.0017) printed circuit board (PCB) showed 13.6 dB gain, 9° E- plane and 65° H-plane 3-dB beam-widths…

  DOI

• Towards robust, interpretable neural networks via Hebbian/anti-Hebbian learning: A software framework for training with feature-based costs

  Software Impacts · 2022-07-03 · 5 citations

  articleOpen accessSenior authorCorresponding

  Conventional deep neural network (DNN) training with an end-to-end cost function is unable to exert control on, or to provide guarantees regarding the features extracted by the layers of a DNN. Thus, despite the pervasive impact of DNNs, there remain significant concerns regarding their (lack of) interpretability and robustness. In this work, we develop a software framework in which end-to-end costs can be supplemented with costs which depend on layer-wise activations, permitting more fine-grain…

  PublisherOA PDFDOI

• Neuro-Inspired Deep Neural Networks with Sparse, Strong Activations

  2022 IEEE International Conference on Image Processing (ICIP) · 2022-10-16 · 5 citations

  articleSenior author

  While end-to-end training of Deep Neural Networks (DNNs) yields state of the art performance in an increasing array of applications, it does not provide insight into, or control over, the features being extracted. We report here on a promising neuro-inspired approach to DNNs with sparser and stronger activations. We use standard stochastic gradient training, supplementing the end-to-end discriminative cost function with layer-wise costs promoting Hebbian ("fire together," "wire together") update…

  PublisherDOI

• Generalized Likelihood Ratio Test for Adversarially Robust Hypothesis Testing

  arXiv (Cornell University) · 2021-12-04 · 4 citations

  articleOpen access

  Machine learning models are known to be susceptible to adversarial attacks which can cause misclassification by introducing small but well designed perturbations. In this paper, we consider a classical hypothesis testing problem in order to develop fundamental insight into defending against such adversarial perturbations. We interpret an adversarial perturbation as a nuisance parameter, and propose a defense based on applying the generalized likelihood ratio test (GLRT) to the resulting composit…

  PublisherOA PDFDOI

Recent grants

• TCHCS: COLLABORATIVE RESEARCH: Millimeter-wave MIMO: A New Architecture for Integrated 10-40 Gigabit Wireless/Optical Hybrid Networks

  NSF · $270k · 2006–2010

• ITR: Cross-Layer Optimization For 4G Wireless Networks: Heavy-Tailed Traffic, Multiuser Channels, and Pseudocells

  NSF · $462k · 2002–2007

• NeTS: Large: Collaborative Research: GigaNets: A Path to Experimental Research in Millimeter Wave Networking

  NSF · $1.6M · 2015–2020

Frequent coauthors

• M.J.W. Rodwell

  Gillette Children's Specialty Healthcare

  38 shared

• Raghuraman Mudumbai

  University of Iowa

  35 shared

• G. Barriac

  Market Matters

  27 shared

• Maryam Eslami Rasekh

  University of California, Santa Barbara

  27 shared

• João P. Hespanha

  25 shared

• B.S. Manjunath

  University of California, Santa Barbara

  19 shared

• Sriram Venkateswaran

  Roche (Switzerland)

  18 shared

• Zhinus Marzi

  17 shared

Similar researchers at University of California, Santa Barbara

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