Quantum Experimentalist Postdoctoral Fellow

Harpur College welcomes Simons Empire Faculty Fellows in mathematics and physics. The four assistant professors' research is at the intersection of quantum materials and artificial intelligence. Left to right: Simons Empire Faculty Fellows Lebing Chen, Sammy Luo, Yahong Yang, and Kunyan Zhang. Image Credit: Provided photos. By Jennifer Micale August 20, 2026 This semester, Harpur College of Arts and Sciences welcomes a quartet of mathematicians and physicists, who will help establish Binghamton University as a major research center at the intersection of quantum science and artificial intelligence. The assistant professors are among the first class of Simons Empire Faculty Fellows in the SUNY system. Mathematicians Yahong Yang and Sammy Luo bring expertise in deep learning theory for partial differential equations and discrete mathematics, with applications for machine learning and optimization. Meanwhile, physicists Lebing Chen and Kunyan Zhang bring cutting-edge experimental capabilities in quantum materials discovery and ultrafast spectroscopy of light-matter interactions. Altogether, their publication record includes prestigious journals such as Science Advances, Nature Communications, and Physical Review X, as well as premier machine-learning venues. The Simons Foundation and Simons Foundation International established the fellowship program to stimulate faculty hiring in mathematics and the sciences across New York state. Four tenure-track faculty members were hired at each of SUNY's University Centers, which include Binghamton, the State University of New York at Albany, Buffalo, and Stony Brook. Simons Foundation International provides funding to participating institutions, while the Simons Foundation administers the program. "I thank the Simons Foundation and Simons Foundation International for investing in SUNY's early-career faculty whose research shows promise in vital and emerging fields," said Binghamton University President Anne D'Alleva. "At Binghamton, our faculty are international leaders in topics including AI, quantum materials and sensing, and mathematics. The Simons Empire Faculty Fellows program provides essential support for their work as they make pathbreaking discoveries that shape our world." Earlier this year, Governor Kathy Hochul announced that the New York Center for AI Responsibility and Research, the first-ever independent artificial intelligence (AI) research center at any public university in the United States, will be established at Binghamton. The center will be used to develop technical tools that make AI safe to use in daily life and build upon the research of Binghamton faculty, who are using machine learning and data science to solve real-world challenges, from delivering better healthcare to improving information security. Previously a visiting assistant professor at the Georgia Institute of Technology, Yang investigates the mathematical foundations of AI, focusing on neural network approximation, generalization analysis, and symmetry-informed learning models. "I am very excited to join Binghamton University and honored to be a Simons Faculty Fellow. I was especially inspired by the University's vision for AI and interdisciplinary collaboration," Yang said. "My research focuses on using mathematics to better understand AI and to help develop AI methods for applications in other fields, so I am excited to connect with researchers across campus and develop new collaborations through this fellowship." Zhang comes to Binghamton from the Chemistry Department at the University of California, Berkeley. Her research focuses on exploring the fundamental physics of nanoscale quantum systems using ultrafast and multidimensional spectroscopy. By integrating advanced spectroscopic technologies, quantum materials, and machine learning, she aims to develop energy-efficient technologies and next-generation sensing platforms. "I am excited to develop my research in the vibrant community at Binghamton University, and I look forward to opportunities to connect and collaborate with other Simons Empire Faculty Fellows at the frontiers of AI and quantum science," Zhang said. Chen comes to Binghamton from a postdoctoral appointment at the University of California, Berkeley, and the Lawrence Berkeley National Laboratory. He is an experimental condensed matter physicist whose research centers on the design, synthesis, and spectroscopic study of quantum materials, which exhibit properties typically not observed in simple materials or at room temperature, such as superconductivity. Prior to Binghamton, Luo was a National Science Foundation Postdoctoral Fellow at the Massachusetts Institute of Technology. He works in combinatorics and graph theory on the discrete structures that underlie modern learning systems, including frameworks relevant to quantum circuits and error correction. All four are part of a quantum-AI research cluster. Chen anchors the materials side of the experimental program, to which Zhang provides the optical, ultrafast, and photonics components. Yang anchors the AI program in theory, while Luo provides the discrete mathematical layer that connects the experimental and theoretical sides. "We are grateful to the Simons Foundation for making it possible for us to recruit four fantastic scholars. The ability to hire a cluster of faculty who address overlapping questions through very different approaches and perspectives has enabled Harpur College to build research strength with both depth and breadth," said Harpur College Dean Celia Klin. "These scholars are exciting additions to the departments of Physics and Mathematics and will enhance the education of the students who take their courses and work with them on their research."

Automating interconnection decision-making: Nira & Doral at ESIG's i2X STITCH series. How much financial risk should a developer accept on a project before they actually know what the network upgrade costs will look like? That question sits at the center of nearly every interconnection decision - and it's what Nira's Jenny Erwin (Senior Director, Customer Engagement) and Doral's Shawn Welch (VP, Interconnection & Transmission) addressed in their joint presentation at ESIG's i2X STITCH Meeting 4: Automation and Data Harmonization. The i2X STITCH initiative - facilitated by Berkeley Lab in collaboration with ESIG and Elevate Energy Consulting as part of the DOE's Interconnection Innovation e-Xchange - brings together industry stakeholders to explore interconnection study practices across U.S. regions and identify opportunities for harmonization. This session focused specifically on how automation is reshaping study workflows and decision-making throughout the interconnection process. Why This Matters: The Decision Window Problem Developers typically have a narrow window to make go/no-go calls and commit financial security payments on projects still working through the interconnection queue. Historically, that decision has relied on manual studies that can only cover a handful of scenarios before the deadline. Automation changes the math: instead of evaluating 1-2 scenarios, teams can run dozens of sensitivity studies across a portfolio of queue positions before a decision point closes. The Takeaway As Doral and Nira's presentation put it: without automation, some of these projects would have been dropped - projects that ultimately reached commercial operation because teams had the tooling to forecast outcomes and make informed decisions under time pressure, not just react after the fact.

Berkeley Lab: building the computational mind for the 'swiss army knife' of microscopes. August 18, 2026 Press play to listen to this content Aug. 18, 2026 - Biology doesn't happen at one scale. Molecular interactions unfold in milliseconds and nanometers, while disease-associated change such as in Alzheimer's spreads across millimeters of brain tissue. Understanding complex biological systems requires scientists to watch both - ideally at the same time and with the same sample. Historically, this has meant shuttling samples between specialized instruments, often damaging biological context and slowing results. There's also a common crux across microscopes: the closer you look at living tissue, the more the image blurs, and the more detail you capture, the more overwhelming the resulting data becomes. Five imaging modes provide complementary views of the same dividing human retinal pigment epithelial cell (hTERT-RPE1). From top left, clockwise: Lattice Light-Sheet Structured Illumination Microscopy (LLS-SIM), widefield, 3D-SIM, oblique illumination, lattice light sheet. This shows how different microscopy techniques compare when imaging the same cell, giving them a more complete picture of what's happening inside. (Credit: Fu, Liu, Milkie, Ruan et al., Nature Methods, 2026) A new instrument aims to address these problems - and has revealed a third, arguably harder challenge that Berkeley Lab is uniquely positioned to address. Researchers at Lawrence Berkeley National Laboratory (Berkeley Lab) and collaborating institutions have developed the Multimodal Optical Scope with Adaptive Imaging Correction (MOSAIC) - a reconfigurable microscope that consolidates more than ten imaging techniques into one compact instrument. It processes its massive datasets using computational tools developed at Berkeley Lab, funded by a Laboratory Directed Research and Development (LDRD) award and supported by the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC). Featured on the cover of Nature Methods, MOSAIC allows scientists to track biological processes across scales and compare imaging methods on the same sample. It generates data at a pace that is pushing the boundaries of what biology can discover. "MOSAIC can generate up to four terabytes of data per hour - far beyond what conventional processing workflows or human inspection can handle," said Srigokul "Gokul" Upadhyayula, a faculty scientist in the Molecular Biophysics and Integrated Bioimaging Division and co-corresponding author. "The microscope is only as useful as our ability to process those data and extract biological meaning from them. Berkeley Lab's expertise in high-performance computing and large-scale data analysis is essential to closing that gap." How MOSAIC Works MOSAIC grew from the adaptive-optical lattice light-sheet microscope reported by Nobel laureate Eric Betzig, Upadhyayula, and their colleagues in 2018. That earlier system delivered exceptional performance but was so large it occupied a 10-foot by 4-foot optical table. As demand from the broader research community grew, the team designed the MOSAIC to retain and expand those capabilities while reducing the instrument's footprint. "MOSAIC has been built over a dozen times in different places with over 50 research licenses already shared. We also created comprehensive documentation on how to build this instrument - think an IKEA-style instruction set geared towards a scientist who has no deep optical expertise but is willing to learn." MOSAIC's main innovation is that it can be quickly reconfigured in two to five seconds to switch between a dozen distinct imaging modes. The team designed a smart modular system where the same lasers, mirrors, cameras, and computational hardware serve multiple imaging functions through a custom optical switching system. Critically, every one of those modes is enhanced with adaptive optics: a technology borrowed from astronomers who developed it to sharpen images of distant stars blurred by Earth's atmosphere. This corrects blurring caused by aberrations in the living tissue itself. "Sample-induced aberrations distort and redirect light, reducing both signal and resolution," said Upadhyayula. "Adaptive optics measures those distortions and corrects them. It is like turning on the windshield wipers while driving in the rain: the information is present all along, just obscured." MOSAIC also relies on fluorescent molecules that allow biologists to mark specific cellular structures and molecular activities in living cells, fast and gentle light-sheet imaging that captures cellular dynamics with minimal stress or damage, and high-speed data transfer infrastructure capable of moving and processing massive imaging datasets. What Becomes Visible When You Clear the Windshield? MOSAIC's ability to image with minimal invasiveness at large scales over long durations has already enabled several experiments: tracking single molecules in living cells, observing organelle dynamics in developing zebrafish embryos, mapping neuronal architecture in expanded human brain tissue from a person with Alzheimer's, and imaging neural activity in live mouse brains. In that last application, adaptive optics correction revealed roughly 2.5 times more detectable neural calcium events than imaging without it - suggesting conventional microscopy has been quietly undercounting brain activity. MOSAIC is also the instrument that powered a related study on Volumetric Imaging via Photochemical Sectioning (VIPS), published in Science in 2025. That project used MOSAIC and the computational tools developed at Berkeley Lab to image two complete adult mouse olfactory bulbs at nanoscale resolution, generating roughly a petabyte of data in approximately two weeks. Analyzing it took two years: an illustration of the gap between what these instruments can see and what researchers can currently process. "The bottleneck is no longer our ability to acquire the data," said Upadhyayula. "These microscopes can generate massive datasets at staggering rates. The key bottleneck is turning dense five-dimensional observations into biological understanding." Berkeley Lab's contribution helps to target this gap. Round-the-Clock Data Collection for Biological AI Supported by an LDRD award, Eric Betzig and Upadhyayula's group developed PetaKit5D, an open-source software toolkit that can handle MOSAIC's terabyte-per-hour output in real time and cuts processing costs by more than an order of magnitude compared to previous approaches. The team also secured computing allocations on the Perlmutter supercomputer at NERSC to process and visualize portions of the largest datasets. But processing data efficiently is only half the equation. The other half is generating enough of it - consistently, at scale, and of sufficient quality - to train the kind of AI model that could one day make sense of it all. At UC Berkeley, two MOSAIC instruments now run around the clock, capturing the five-dimensional data - three spatial dimensions, time, and molecular identity - that will be needed to train a new state-of-the-art AI model. The data flowing from those instruments already represents a fundamental shift in how biology can be practiced. For the first time, researchers can watch in vivo biochemistry unfold inside cells living within their native tissues, inside a living organism. What comes next, he believes, could be transformative: a vision language model that reasons natively over biology, connecting what it sees with molecular identity, experimental context, and prior biological knowledge to determine which observations matter and which experiments should come next. "Connected to automated microscopes, sample handling, and perturbation systems, that capability could provide the foundation for self-driving biological laboratories - and fundamentally change the rate at which we can make discoveries," said Upadhyayula. Deep Origin this month announced that its AI drug discovery framework delivered nearly a 31%... AI models are getting better at a rapid pace. They are now able to reason,... 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