The fund operates through a highly competitive process that weighs several explicit criteria, including the availability of intellectual property protection, marketability, commercial potential, technical feasibility and the breadth of a project’s impact. Since its launch in 2011, the program has invested more than 5.1 million dollars and advanced the commercial readiness of 99 innovations born at SUNY campuses. That seed money has catalyzed an additional 41 million dollars in follow-on investment from government agencies, industry licensees and early-stage investors, a leverage ratio that underscores why university systems increasingly treat translational funding as core infrastructure rather than a peripheral perk. Chancellor King framed the program in sweeping terms, describing SUNY as a national leader in interdisciplinary research that develops state-of-the-art technologies, saves lives, transforms industries and serves the public good. The Board of Trustees echoed the sentiment, noting that SUNY research uses revolutionary breakthroughs to fuel economies and empower communities throughout New York State and beyond.

Among the most technically ambitious awards is a project at the University at Albany led by Dr. Spyros Galis, who is building a scalable platform for quantum sensing and imaging that operates at room temperature. Current quantum photonic devices typically require cryogenic cooling to function properly, a constraint that has limited their widespread adoption since refrigeration hardware adds cost, bulk, complexity and substantial energy consumption to any deployment. By re-engineering quantum photonic platforms to work without cooling, the approach promises to reduce all of those barriers simultaneously, opening the door to real-world applications in sensing and imaging that would be impractical if each device needed a cryostat. Quantum sensors exploit delicate quantum states to measure physical quantities with extraordinary precision, and removing the thermal bottleneck is widely regarded as a prerequisite for moving such instruments out of specialized laboratories and into hospitals, factories and field instruments.

Also at Albany, Dr. Gary Saulnier is developing a product called CurrentView, a bedside monitoring system that provides real-time, non-invasive, three-dimensional monitoring of pulmonary perfusion and ventilation. The clinical target is the care of neonatal and pediatric patients with congenital heart disease and other cardiopulmonary conditions, populations in which complete, continuous data can be decisive for treatment quality. The system uses non-invasive electrical measurements to give caregivers a continuous assessment of lung function, drastically improving their ability to make data-informed decisions at the bedside. In intensive care settings for the smallest patients, where repeated imaging or invasive sampling carries real risk, a continuous non-invasive window into how blood and air are moving through the lungs represents a meaningful shift in the information available to clinicians during critical moments.

At Binghamton University, Dr. Nancy Guo has developed ClinSegAI, a secure artificial intelligence platform designed to predict molecular biomarkers directly from standard pathology imaging. Turnaround time for treatment decisions remains a major roadblock to efficient cancer care, because molecular characterization of a tumor often requires additional laboratory workflows that delay therapy. ClinSegAI applies machine learning to detect biomarkers within routine histopathology images, rapidly reducing the time needed to inform treatment decisions and marking what the university describes as a major improvement in the standard of care. Because the model works from images that pathologists already produce, it could in principle slot into existing clinical workflows rather than demanding new tissue collection, and the platform’s emphasis on security addresses the data governance concerns that frequently slow the adoption of medical AI systems.

Binghamton’s second award goes to Dr. John Fetse, who is conducting in vivo validation of engineered lipid nanoparticles for RNA therapeutic delivery. RNA therapeutics offer promising treatment avenues for a wide variety of diseases, but current lipid nanoparticle methods suffer from poor delivery efficiency and raise toxicity concerns that limit proper dosing. Fetse’s innovation incorporates amino acids directly into the lipid molecules themselves, a chemical modification that reduces toxicity risk while improving delivery rates. The approach matters because delivery remains the central bottleneck of the entire RNA medicine field: therapeutic RNA is fragile, quickly degraded, and must be escorted into the right cells in sufficient quantities for a meaningful dose while avoiding harmful accumulation elsewhere. Validating such formulations in living organisms is the essential step toward demonstrating that the chemistry performs outside the controlled conditions of cell culture.

The University at Buffalo is home to two projects that pair semiconductor engineering with human health. Dr. Uttam Singisetti is developing gallium oxide semiconductor transistors for electric vehicle and AI power applications, responding to surging demand from electrified transport and the data center boom for electronics that deliver electricity more efficiently. Gallium oxide is a wide-bandgap material, and transistors built from it can handle high voltages and switch power with less wasted energy than conventional silicon devices, improving performance while lowering costs. The second Buffalo project, led by Dr. Margarita L. Dubocovich, targets advanced phase circadian disorders, in which the body’s internal biological clock is misaligned with a person’s daily schedule or environment. Such misalignment elevates the risk of sleep disruption, cardiovascular disease, depression, cancer and chronic pain. Dubocovich is working toward a first-in-class, orally administered therapeutic that realigns the underlying clock mechanism and restores healthy rhythms, rather than merely masking symptoms with sedatives or stimulants.

At Stony Brook University, Dr. M. Hassan Arbab has developed a handheld, portable terahertz spectral imaging scanner for the diagnosis and triage of skin burns. Burn patients frequently undergo multiple reconstructive surgeries because current clinical techniques assess burn depth with only around 60 to 65 percent accuracy, forcing surgeons to make consequential decisions with incomplete information. Arbab’s device achieves 93 to 95 percent or better accuracy, a dramatic expansion of diagnostic capability that allows clinicians to determine which burns will heal on their own and which require intervention. Terahertz radiation sits between microwave and infrared frequencies and is sensitive to the water content and structural changes in tissue, which makes it well suited to distinguishing viable from non-viable skin without contact or ionizing radiation. Stony Brook President Andrea Goldsmith highlighted the device as an exemplar of the campus’s record of translating research breakthroughs into real-world medical diagnostics.

The final award supports Dr. Yamin Li at SUNY Upstate Medical University, who is developing a lung-targeting drug formulation for sepsis-induced acute respiratory distress syndrome, a life-threatening condition causing lung injury and breathing difficulty that affects roughly three million patients annually. Li’s formulation uses lipid nanoparticles engineered to target the lung, aiming to reduce mortality, shorten intensive care unit stays and decrease ventilator use for patients with the condition. The project addresses a critical unmet need, since treatment options for ARDS remain largely supportive. Campus leaders across the system framed the awards in similar terms: University at Albany President Havidán Rodríguez emphasized transforming promising ideas into real-world impact, Binghamton’s Anne D’Alleva pointed to safer and more effective treatments, Buffalo’s Caroline Attardo Genco cited the strength of the university’s innovation ecosystem in semiconductors and the life sciences, and Upstate’s Dr. Mantosh Dewan described the research mission as improving the human condition. Together, the nine projects illustrate how modest, well-targeted seed funding can push a diverse portfolio of early-stage technologies toward the market readiness that attracts the investors and partners needed to bring them to patients.

Subject of Research: SUNY Technology Accelerator Fund seed grants for commercializing university research technologies

Article Title: SUNY Chancellor King announces funding for groundbreaking technologies to improve lives and protect New Yorkers

Article References: SUNY Chancellor King announces funding for groundbreaking technologies to improve lives and protect New Yorkers. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: SUNY, Technology Accelerator Fund, quantum sensing, artificial intelligence, cancer diagnostics, lipid nanoparticles, RNA therapeutics, gallium oxide semiconductors, terahertz imaging, circadian rhythm disorders, ARDS, technology commercialization

The idea was proposed by David Klyshko in 1988 and verified experimentally in 1995 by Pittman and colleagues, who used pairs of entangled photons to form images of an object that neither photon had individually interacted with in a conventional way. For years, ghost imaging seemed inseparable from quantum entanglement. But researchers soon discovered that classical light sources—thermal lamps, polarized lasers, and laser beams passed through rotating diffusers to create pseudo-thermal speckle—could do the same job. The pivotal moment came in 2008 with computational ghost imaging, which dispensed with the reference arm entirely. Instead of measuring a reference beam, the system projects known patterns onto the object and reconstructs the image from the bucket signal alone, using the patterns as a computational reference. That simplification turned ghost imaging into a practical single-pixel imaging technology.

Mathematically, the reconstruction is straightforward. The image value at each position is obtained by correlating the bucket-detector readings with the known or measured intensity patterns, subtracting the product of the mean signals to isolate genuine fluctuations. Refinements followed quickly. Differential ghost imaging weights the correlation by the total source intensity, suppressing background noise and improving results for highly transparent or reflective objects. Normalized ghost imaging standardizes the reference intensity distribution, making the reconstruction insensitive to absolute brightness fluctuations. Compressive sensing, also introduced in 2008, exploits the fact that most natural images are sparse in some mathematical basis: by acquiring far fewer random measurements than the Nyquist sampling theorem would demand and solving a nonlinear optimization that favors sparse solutions, researchers can reconstruct images from dramatically reduced data. Deep learning entered the field in 2017, when Lyu and colleagues trained a neural network to denoise and refine low-sampling-rate ghost images, opening the door to reconstruction methods that now routinely outperform handcrafted algorithms.

Why go to all this trouble when cameras are everywhere? The answer, the review argues, is that conventional multi-pixel detectors such as CMOS and CCD sensors are optimized for narrow wavelength ranges and degrade badly outside them. In the extreme ultraviolet, at terahertz frequencies, and in much of the mid-infrared, fast high-resolution detector arrays are expensive, scarce, or simply nonexistent. A single-pixel detector, by contrast, can be chosen for peak sensitivity at almost any wavelength, and the simplicity of the setup reduces cost and complexity. Ghost imaging therefore thrives precisely where traditional imaging hardware struggles—decoupling illumination from detection and letting computation do the heavy lifting.

The technique has even crossed from photons to matter. In 2016, Khakimov and colleagues produced correlated pairs of metastable helium atoms by colliding a Bose–Einstein condensate, achieving micron-scale ghost images of a mask with massive particles. Follow-up work showed that higher-order correlations among ultracold atoms can boost image visibility without sacrificing resolution. Neutron ghost imaging, demonstrated on a reactor beamline by Kingston and colleagues, exploits the penetrating power of thermal neutrons, which pass through dense metals yet highlight light elements such as hydrogen—complementing X-rays in nondestructive materials evaluation while reducing dose. Electron ghost imaging, realized by Li and co-workers using patterned electron beams driven by a digital micromirror device, promises to cut acquisition time and specimen damage by orders of magnitude relative to conventional electron microscopy, a boon for radiation-sensitive samples.

X-ray ghost imaging may be the most consequential branch for medicine and materials science. X-rays penetrate soft tissue far more readily than bone or metal, but they also ionize, making radiation dose a critical constraint. In 2016, two groups independently achieved X-ray ghost imaging: Yu and colleagues performed lensless Fourier-transform ghost imaging with pseudo-thermal hard X-rays, while Pelliccia and colleagues split a synchrotron beam in Laue diffraction geometry and exploited natural speckle in the source. By 2018, Zhang and colleagues had demonstrated tabletop X-ray ghost imaging with dramatically reduced radiation, and a follow-up study combined Hadamard-patterned illumination with a multi-level wavelet convolutional neural network to reach roughly 10-micrometer resolution using only 18.75 percent of the Nyquist sampling rate—conditions the authors suggest could suit early-stage cancer detection. Polycapillary optics have since tripled resolution by shrinking the illumination speckle from 166 to 55 micrometers, and ghost tomography has extended the method to three-dimensional volumetric reconstruction. At free-electron lasers, ghost imaging has reached the extreme ultraviolet, enabling nanometer-scale imaging of radiation-sensitive samples.

In the visible and near-infrared, where cameras are cheap and excellent, ghost imaging finds its niche in hostile environments. Underwater, blue and violet light between 400 and 500 nanometers penetrates best, and compressive computational ghost imaging with Hadamard patterns and wavelet-domain enhancement has recovered images through turbid water; temporal ghost imaging has even carried error-free optical data across meter-scale underwater channels. Structured beams such as pseudo-Bessel rings and Lorentz beams, which self-heal after obstruction, improve contrast at depth. Deep learning has transformed the visible band: self-supervised dual networks now extract task-relevant features from raw speckle without labeled training data, block-wise networks iteratively refine multi-scale sub-images using only the bucket signal as supervision, and multi-input mutual-supervision frameworks co-train random-pattern and bucket-signal branches to correct artifacts. Multi-polarization fusion networks capture bucket signals under several polarization states and exploit their statistical differences to reconstruct scenes through dynamic scattering media.

The infrared story is equally striking. Because infrared light rides atmospheric windows, it sees through fog, smoke and dust while avoiding photo-damage—ideal for surveillance, remote sensing and diagnostics. Radwell and colleagues replaced the focal-plane array with a single indium-gallium-arsenide detector in a dual-band microscope, then pushed to 10-hertz real-time video through smoke and tinted glass, and later captured video-rate images of methane gas leaks at the 1.65-micrometer absorption line. Near-infrared ghost-imaging LiDAR has produced centimeter-resolution depth maps from a moving aircraft, and broadband single-pixel hyperspectral cameras classify concealed chemicals across 900 to 1700 nanometers at milliwatt illumination. In the mid-infrared, where digital micromirror devices fail and fast detectors are rare, Wu and colleagues used difference-frequency generation to transfer random patterns from a near-infrared signal to a mid-infrared idler between 3.2 and 4.3 micrometers, enabling ultrafast temporal ghost imaging without high-speed electronics. Graphene metasurface modulators now steer centimeter-scale beams at gigahertz rates, and passive thermal ghost imaging reconstructs hot objects in complete darkness from their self-emitted radiation.

Terahertz ghost imaging completes the spectrum. Terahertz waves pass through textiles, polymers and paper while carrying molecular-fingerprint contrast, making them attractive for security screening, pharmaceutical inspection and cultural heritage diagnostics. Chan and colleagues inaugurated the field by replacing pixelated arrays with a single photoconductive antenna and random printed masks; spinning disks, optically controlled silicon modulators, electrically tunable metamaterials and spintronic emitter arrays have since pushed acquisition toward video rate and achieved deep-sub-wavelength near-field resolution. Nonlinear ghost imaging schemes now fuse terahertz generation with time-resolved field sampling, targeting hyperspectral micro-volumetry of semitransparent samples. Water-vapor absorption, scarce modulators and heavy computational loads remain hurdles, but reconfigurable metamaterials, low-noise detectors, wavefront shaping and physics-informed neural networks are converging toward compact, real-time systems. Taken together, the review concludes, multi-wavelength ghost imaging has become a powerful low-dose alternative wherever conventional focal-plane arrays fall short—imaging with light, and with matter, in places no camera can go.

Subject of Research: Multi-wavelength ghost imaging using single-pixel correlation-based reconstruction across the electromagnetic spectrum and matter waves

Article Title: Multi-wavelength ghost imaging: a review

Article References: Tian, T., Oh, S., & Spielmann, C. (2025). Multi-wavelength ghost imaging: a review. Vicinagearth, 2(1), Article 4. https://doi.org/10.1007/s44336-025-00013-0

Image Credits: AI Generated

DOI: 10.1007/s44336-025-00013-0

Keywords: ghost imaging, single-pixel imaging, computational ghost imaging, X-ray imaging, terahertz imaging, infrared imaging, compressive sensing, deep learning, quantum imaging, matter waves, low-dose imaging, correlation imaging