Advances in robotic control and sensing have propelled the rise of automated scientific laboratories capable of high-throughput experiments. However, automated scientific laboratories are currently limited by human intuition in their ability to efficiently design and interpret experiments in high-dimensional spaces, throttling scientific discovery. We present AutoSciLab, a machine learning framework for driving autonomous scientific experiments, forming a surrogate researcher purposed for scientific discovery in high-dimensional spaces. AutoSciLab autonomously follows the scientific method in four steps: (i) generating high-dimensional experiments (x) using a variational autoencoder (ii) selecting optimal experiments by forming hypotheses using active learning (iii) distilling the experimental results to discover relevant low-dimensional latent variables (z) with a ‘directional autoencoder’ and (iv) learning a human interpretable equation connecting the discovered latent variables with a quantity of interest (y = f (z)), using a neural network equation learner. We validate the generalizability of AutoSciLab by rediscovering a) the principles of projectile motion and b) the phase-transitions within the spin-states of the Ising model (NP-hard problem). Applying our framework to an open-ended nanophotonics problem, AutoSciLab discovers a new way to steer incoherent light emission beyond current state-of-the-art, defining a new structure(material)-property(light-emission) relationship governing the physical process using closed-loop noisy experimental feedback.

Wireless eavesdropping on phone conversations has become a major security and safety concern, especially with advancements toward 5G and beyond featuring higher frequencies and higher sensing resolution. As demonstrated recently, attackers can remotely detect even micron-scale acoustic vibrations emanating from a smartphone's earpiece via off-the-shelf millimeter-wave radar for audio information eavesdropping, all without the victim ever noticing. Here, we present a new architecture, MiSINFO, that not only thwarts such attacks but also enables the victim to counter-attack by spoofing of eavesdroppers with audio misinformation. With emerging attacks targeting the physical medium, i.e., acoustic signals, which cannot be protected by digital encryption and are the weakest segment of the communication chain, MiSINFO aims to systematically modify the eavesdroppers' fundamental sensing observations, concealing native signals while encoding alternate synthetic data. MiSINFO incorporates a low-profile, reconfigurable metasurface and double-inference principles to dynamically generate artificial audio-vibration signatures, injecting deceptive misinformation. We design, implement, and experimentally evaluate MiSINFO. Our results reveal that eavesdroppers detect none of the original words emitted by the speaker, while the injected misinformation is reconstructed with a low average word error rate of 2.29%. Our work represents the first such eavesdropping countermeasure which not only prevents attackers from accurately decoding the true signal but also uses a false signal to fool them into believing that they have succeeded. This approach transforms defensive measures from merely reactive to proactively deceptive, giving the defender an advantage and the capability to delude attackers into trusting false information.

Wireless eavesdropping on phone conversations has become a major security concern as attackers repurpose advanced wireless capabilities in 5G and beyond featuring higher frequencies and higher sensing resolution. Recent studies have demonstrated that attackers can exploit off-the-shelf millimeter-wave radars to covertly detect even micron-scale vibrations of smartphones caused by the earpiece during the phone conversation, eavesdropping on audio information without the victim ever noticing. In our IEEE S&P'25 paper, we present a new architecture that not only thwarts such attacks but also injects false signatures to fool eavesdroppers into believing they have succeeded. Here, we demonstrate the eavesdropping countermeasure technique that enables the user to hide his private acoustic signals and simultaneously inject an alternative signal via a low-profile, reconfigurable metasurface. We present a metasurface-based audio encoding method that generates artificial audio-vibration signatures to send deceptive audio information to eavesdroppers. We showcase experimental audio samples from both the attack and the proposed countermeasure, which transforms defensive strategies from merely reactive to proactively deceptive.

The next generation of wireless networks will utilize highly directional beams to overcome the path loss at high frequencies, requiring angle inference during link establishment. Furthermore, the integration of location-based services into the wireless infrastructure is rapidly increasing, bringing in a significant demand for an integrated fast localization scheme. In this work, we present a first-of-its-kind one-shot angular localization method that is carried out with a re-configurable architecture that unlocks ISAC functionality. Specifically, we use an electrically tunable metasurface with broadband response to generate wavefronts that are randomized across the angular space with diverse wideband amplitude and phase observations, corresponding to a collection of angle-unique one-shot beacons. Our results show down to 0.26° mean absolute error at 20 dB SNR, an order of magnitude improvement over the recently proposed one-shot solutions based on leaky-wave antennas (LWAs), in addition to having wider area coverage and less stringent bandwidth requirements.

Spatiotemporal control over incoherent light sources is critically important for applications such as displays, remote sensing, clean energy, and illumination. Incoherent light emission made up of randomized wavefronts is incompatible with known beam steering techniques that rely on coherent electromagnetic wave interference. The emerging field of tunable dielectric metasurfaces consisting of sub-wavelength arrays of optical nanoresonators has recently enabled active re-direction of incoherent light (photoluminescence, PL) emission. This was achieved by illuminating (pumping) the metasurface with a pump laser reflecting off a programmable spatial light modulator (SLM) with sawtooth grating patterns as input. Achieving efficient beam steering requires the generation of optimal pump patterns programmed into the SLM to maximize the PL emitted towards a given direction. Given the innumerable possibilities and the lack of a theoretical physical framework to guide the exploration of pump patterns, we use an active learning algorithm running a closed loop optical experiment with a generative model to explore and optimize novel pump patterns. We achieve up to an order of magnitude enhancement in the steering efficiency by using pump patterns that are generated by a variational auto-encoder, with minimal number of experiments. The results presented in this paper highlight the unique ability of generative models and active learning to dramatically improve steering efficiency by finding novel optical pump patterns that are beyond human intuition. Our combination of advanced machine learning techniques driving closed loop nanophotonic experiments might pave the way to derive the underlying physics of emergent light-matter phenomena.