Current Fellows

Arka Daw, an Alvin M. Weinberg Fellow, earned his PhD from Virginia Tech. His dissertation focused on developing uncertainty quantification techniques for machine learning models, especially focusing on scientific applications such as solving partial differential equations, and lake modeling. His work led to the development of three different methodologies for improving uncertainty quantification techniques for the emerging field of scientific knowledge-guided machine learning (KGML), where the goal is to infuse scientific knowledge with deep learning models to improve its overall generalizability. His dissertation provided a way of explicitly enforcing physics priors such as monotonicity constraints in neural networks for meaningful uncertainty quantification; presented a more general framework for quantifying uncertainty with KGML for generic physics supervision; and studied the limitations of the commonly used physics-based loss formulation in the context of physics-informed neural networks (PINNs) and provides strategies to mitigate them. Arka’s mentors are Edmon Begoli, the founding director of the Center for AI Security Research (CAISER) under the Cyber Resilience and Intelligence Division, and Amir Sadovnik, CAISER’s research lead. 

Arka will focus his fellowship research on understanding the underlying causes behind adversarial vulnerabilities in deep learning models, and ultimately develop novel deep learning architectures and training methodologies that enhance their robustness, reliability, and trustworthiness. His project is expected to contribute significantly to the field by enhancing our understanding of the current limitations in artificial intelligence (AI) security and risk associated with state-of-the-art deep learning models. Building on this deeper understanding, the project aims to develop innovative deep learning frameworks and training methodologies to improve robustness, reliability, and trustworthiness, thus making these models suitable for deployment in critical infrastructures. Arka’s ongoing research interests include developing robust and trustworthy AI systems, specifically exploring their generalizability on out-of-distribution samples, enhancing their robustness against attacks like adversarial examples and data poisoning, and augmenting their reliability in terms of risk assessment. Arka also has a keen interest in tackling interdisciplinary problems in AI for science. 

Philip Dee, a Eugene P. Wigner Fellow, earned his PhD from the University of Tennessee, Knoxville. His dissertation explored the interplay of quantum phases in materials, such as superconductivity and charge order, by simulating model Hamiltonians. The models he researched capture general qualitative behaviors that manifest in many materials and provide valuable intuition for interpreting experiments. Philip’s mentor is Thomas Maier, Advanced Computing Methods section head in the Computational Sciences and Engineering Division. 

Philip’s fellowship research will focus on developing better computational methods for simulating models of quantum materials, mainly using physics-informed machine learning approaches. This work will contribute novel methods to efficiently simulate effective models for quantum materials research, which will help the field overcome challenges associated with the high degree of algorithmic complexity that hinders simulations of these models. Philip’s ongoing research interests lie at the intersection of condensed matter theory, computational physics, and artificial intelligence. He particularly enjoys how these fields cross-pollinate ideas and spur innovations.  

Matthias Heinz, a Eugene P. Wigner Fellow, earned his PhD from the Institute for Nuclear Physics at Technische Universität Darmstadt, Germany. His dissertation explored ab initio, or first-principles, nuclear structure calculations. This work led to development of high-precision ab initio nuclear structure calculations using the IMSRG(3) method and highlighted the importance of nuclear structure calculations to interpret the signals measured in ongoing searches for physics beyond the standard model. Matthias’ mentors are Gustav Jansen, a computational scientist in the National Center for Computational Sciences, and Gaute Hagen, R&D staff member in the Theoretical and Computational Physics Group.  

Matthias will focus his fellowship research on nuclear structure predictions for ongoing and planned new physics searches including neutrinoless double-beta decay, coherent elastic neutrino-nucleus scattering, and permanent electric dipoles in radioactive molecules. His work will develop ORNL’s leadership computing capacities in ab initio calculations to tackle these heavy, deformed nuclei to refine our understanding of how complex phenomena arise from basic nuclear forces. An improved understanding of heavy nuclei will enable us to address open questions on the synthesis of heavy elements in astrophysical environments. Matthias’ ongoing research interests include computational many-body physics, the workhorse of ab initio calculations, and also nuclear forces and uncertainty quantification. Matthias likes to work at the interface of theoretical developments, high-performance computing, and statistical methods, where a lot of exciting work can be done. 

Paul Kairys, an Alvin M. Weinberg Fellow, earned his PhD from University of Tennessee–Knoxville’s Bredesen Center. His dissertation focused primarily on developing approaches to utilize controllable quantum devices to simulate real-world quantum mechanical phenomena, a task known as quantum simulation. His dissertation research laid out, and rigorously verified, best methods for programming quantum devices from an analog perspective to ensure highly efficient, application-specific quantum simulation. This work included demonstrations on actual quantum hardware as well as theory and computations. Paul’s mentors are Ryan Bennink, Quantum Computational Science group leader, and Matthew Stone, neutron scattering scientist in the Direct Geometry group. 

Paul will focus his fellowship research on integrating quantum sciences and neutron sciences. His research will develop quantum algorithms for neutron scattering and experimental design as well as assess the value of entanglement between neutrons. His ongoing research interests include the development and application of unconventional computational approaches to accelerate scientific discovery in domains like materials science, chemistry, and physics. He particularly likes to address challenging, interdisciplinary science problems.

Nishanth, an Alvin M. Weinberg Fellow, earned his PhD from University of Wisconsin–Madison. His dissertation focused on developing torque-dense and power-dense axial flux electric machines to electrify off-highway vehicles. His work led to development of one of the first high-power, high-speed integrated electro-hydraulic machines; a multi-physics optimization technique for co-design of the electro-hydraulic machine; one of the earliest additively manufactured electric machine stators with complex lamination-emulating geometry and 6.5% silicon steel, including performance characterization; and a technique to design electric machine windings capable of controlling any spatial harmonics in the airgap, including demonstration of the torque density improvements using these controlled harmonics. Nishanth’s mentor is Burak Ozpineci, Vehicle and Mobility systems section head. 

Nishanth will focus his fellowship research on investigating and developing methods to realize high-performance electric machines without critical rare-earth materials, due to their scarcity and sensitivity to geopolitical issues. In addition to demonstrating a promising pathway to high-performance electric machines free from critical rare-earth materials, Nishanth’s project is expected to quantify the performance capabilities of a range of machine topologies free from these materials and develop methods to design, optimize, manufacture, and control the machines. Nishanth’s ongoing research interests address the broad area of high-performance and sustainable electro-mechanical energy conversion. He enjoys investigating and developing devices and systems that efficiently and sustainably convert energy between electrical and mechanical forms. His research can be applied to electric cars and trucks, off-highway vehicles, airplanes and ships, manufacturing industries, HVAC equipment, semiconductor manufacturing, energy storage using flywheels, and power generation. 

Bhartendu Pandey, an Alvin M. Weinberg Fellow, earned his PhD from Yale School of the Environment. His dissertation focused on measuring and forecasting urban inequalities, as well as assessing their human health impacts while also contemplating broader implications for global sustainable development. His dissertation research developed novel empirical approaches to study urban inequalities at scale, using detailed satellite remote sensing and census datasets. It further emphasized how infrastructure inequalities are intertwined with urbanization—and are a challenge to urban sustainability as well as global sustainable development—and highlighted the human health benefits and disbenefits of reducing some forms of urban infrastructure inequalities. Bhartendu’s mentor is Supriya Chinthavali, who leads the Critical Infrastructure Resilience group within the Geospatial Science and Human Security Division (GSHSD). Within GSHSD, he also receives mentorship from Dalton Lunga, GeoAI group leader. His cross-disciplinary mentoring panel, which also includes senior R&D staff member David McCollum (Buildings and Transportation Science Division), Plant–Soil Interactions group leader Colleen Iversen (Environmental Sciences Division), and Computational Earth Sciences group leader Forrest Hoffman (Computer Sciences and Engineering Division), offers Bhartendu a unique opportunity to engage with a diverse set of ideas and perspectives. 

Bhartendu will focus his fellowship research on advancing the science and decision-making capabilities towards equitable urban transitions under climate change, with a goal to transform our ability to advance human well-being, considering the unprecedented impacts of urbanization and climate change. His project is expected to deliver a novel understanding of generalizable aspects of spatial and social inequalities including boundary conditions, dimension-specific constraints, and future expectations, all of which are necessary to inform equitable urban transitions—and decision making surrounding these transitions—considering urbanization and climate change. Bhartendu’s ongoing research interests include urbanization and global environment change, remote sensing, geographic information, and complex systems sciences.

Muhammad Mominur Rahman, an Alvin M. Weinberg Fellow, earned his PhD from Virginia Tech. His dissertation focused on characterizing the depth dependent structural and chemical processes in layered oxide cathodes for Na-ion and Li-ion batteries through utilizing synchrotron characterizations and formulating the design principles of stable layered oxide cathodes for these batteries. He worked to untangle the multiscale processes taking place in battery cathodes during operation and reveal the design principles of battery cathodes operating under extreme conditions such as in outer space and nuclear reactors. Mominur’s mentor is Ilias Belharouak, Electrification section head in the Electrification and Energy Infrastructure Division. 

Mominur’s fellowship will focus on materials development aided by advanced characterizations for beyond Li-ion batteries with a focus on sulfur cathodes and lithium-metal anodes. His research will tackle the fundamental issues facing these batteries such as polysulfide dissolution and lithium-metal anode reactivity, taking advantage of the state-of-the-art characterization facilities and battery manufacturing facilities available at ORNL. The project will contribute to the development of next-generation high-energy batteries utilizing cheap and abundant sulfur cathodes and high-energy lithium-metal anodes to secure the supply chain of battery manufacturing and ensure the widespread application of batteries in electric vehicles. Mominur’s ongoing research interests include electrochemistry and materials chemistry for electrochemical energy storage devices as well as advanced synchrotron characterization of battery materials. 

Daryl Yang, a Liane B. Russell Fellow, earned his PhD from Stony Brook University. His dissertation focused on the development and use of novel, multiscale remote-sensing technologies to understand the spatial and temporal complexity of high-latitude ecosystems and their response to climate change. Daryl’s research led to the design of novel remote-sensing platforms, including drones and time-lapse cameras with advanced sensing technologies (e.g., optical, hyperspectral, and thermal), to enable accurate and autonomous measurements of vegetation distribution, function, and seasonality from leaf-to-landscape scales and development of computational tools to scale them up to large airborne and satellite platforms. New, cross-scale understandings of the fundamental mechanisms and processes that drive vegetation dynamics and change in the Arctic and its high spatial and temporal variability across the landscape resulted from his work. Daryl’s mentor is Colleen Iversen, Plant–Soil Interactions group leader in the Environmental Sciences Division and principal investigator for DOE’s NGEE Arctic (Next Generation Ecosystem Experiment Arctic) project. 

Daryl’s fellowship research will focus on combining multiscale remote-sensing, fundamental ecology, and ecosystem models to understand fire-driven ecosystem transition and its impacts on soil–land–atmosphere  interactions across Arctic and boreal ecosystems. His work is expected to improve our ability to monitor and model fire-impacted ecosystems in the circumpolar Arctic. Daryl’s ongoing research interests include integrating novel Earth observations, fundamental ecological theories, and process models to advance our understanding of the interconnections between ecosystem dynamics and climate change. 

Fehmi Yasin, an Alvin M. Weinberg Fellow, earned his PhD from the University of Oregon. His dissertation focused on the development of a new kind of electron interferometer within a scanning transmission electron microscope (STEM) capable of quantitatively measuring the full specimen transmission function, amplitude and phase. His dissertation enabled the measurement of both amplitude and phase of electrostatic and magnetic materials, which is critical for characterizing materials and functional devices at atomic resolution. His work utilized inexpensive commercially available silicon nitride membranes as amplitude-dividing electron beam-splitters in order to decrease the coherence requirements of the electron source and enable interferometric imaging in any commercially available STEM. Fehmi’s mentor is Andrew Lupini, Scanning Transmission Electron Microscopy Group leader in the Center for Nanophase Materials Sciences.  

Fehmi's research will aim to expand ORNL’s world-class electron microscopy research to include magnetic imaging at temperatures ranging from room temperature down to liquid helium temperatures. He will focus on imaging the magnetic states in quantum materials that host emergent, topologically nontrivial real space spin textures (e.g., magnetic skyrmions), as well as their dynamics under external stimuli such as electric and heat currents. Fehmi’s ongoing research interests include using electron microscopy to help solve material science’s biggest mysteries as well as to identify new materials hosting properties that may help improve the human quality of life. Techniques he likes to explore include (S)TEM and electron interferometry technique development, in-situ electron microscopy imaging at cryogenic temperatures, transport measurements, focused ion beam fabrication of novel device geometries, and simulation techniques for both electron optical imaging and micromagnetics. 

Luke Bertels, a Eugene P. Wigner Fellow, earned his PhD from University of California–Berkeley. His dissertation focused on theoretical chemistry with a specialization in molecular electronic structure theory. His work explored the role of zeroth-order representations for correlated wavefunction calculations. Luke’s mentor is Ryan Bennink, Quantum Computational Science group leader in the Computational Sciences and Engineering Division.

Luke will focus his fellowship research on the development of adaptive classical and quantum machine learning approaches for studying quantum chemistry. The work will provide new, efficient methods to extend the reach of both classical and quantum simulation towards the study of strongly correlated molecules. His ongoing research interests include quantum algorithms for physical simulation and electronic structure theory of molecules and materials.

KC Cushman, a Liane B. Russell Fellow, earned her PhD from Brown University. Her dissertation explored the use of novel remote-sensing tools for measuring 3D structure and carbon dynamics in forests. KC's work demonstrated the value of drone technology for complementing traditional field- and satellite-based measurements of forests. Using drones for targeted, landscape-scale data collection allowed her to demonstrate that optical satellite data may underestimate tropical forest disturbance frequency and to explore how new estimates of forest biomass from spaceborne lidar (light detection and ranging) can be made robust to seasonal patterns of leaf production. KC’s mentor is Anthony Walker, Ecosystems Processes group leader in the Environmental Sciences Division. 

KC will leverage ground observations, near-surface remote-sensing data, and satellite platforms to develop innovative approaches to study ecosystems across spatial and temporal scales. Her work will explore the use of emerging SAR (synthetic aperture radar) data to study ecosystem structure. Results from this project will allow scientists to better monitor, understand, and predict the effects of disturbances on natural systems. KC’s research interests include studying variation in forest structure and function across space and time; understanding and predicting global cycles of carbon, water, and nutrients through forest science; and combining remote-sensing measurements and field-based observations to understand how organismal mechanisms affect landscape-scale processes. 

Huan Zhao, a Eugene P. Wigner Fellow, earned his PhD from the University of Southern California. His dissertation focused on the fundamental properties and device applications of 2D layered materials. His work led to the invention of the world’s most energy-efficient resistive memory device and the discovery of a material with the largest broadband optical birefringence. Huan’s mentors are Scanning Tunneling Microscopy Group Leader An-Ping Li; Benjamin Lawrie, a research scientist in the Quantum Heterostructures Group; and Nanomaterials Characterization Section Head Stephen Jesse. 

Huan will focus his fellowship research on quantum state transduction and quantum sensing. This work aims to bridge the gap between innovative quantum materials and quantum information technologies. Huan’s fellowship will enable development of key elements essential for building a quantum network. His ongoing research interests include quantum technologies, which have the potential to revolutionize our methods of communication, computing, and sensing. 

JungHyun Bae, a Eugene P. Wigner Fellow, earned his PhD from Purdue University. His dissertation focused on development of a muon spectrometer using multilayer pressurized Cherenkov gas radiators for muon tomography applications. His work delivered a new concept for measuring muon momentum in the field, resulting in improving the utility of cosmic ray muons in their applications, which have emerged as a promising nonconventional radiation probe to monitor dense and large objects, (e.g., spent nuclear fuel casks, nuclear reactor core, and magma chamber underneath volcanos). JungHyun’s mentor is Rose Montgomery, Used Fuel and Nuclear Material Disposition group leader in the Nuclear Energy and Fuel Cycle Division.

JungHyun will focus his fellowship research on designing and building a prototype of the Cherenkov muon spectrometer and momentum integrated muon tomography system to advance utility of cosmic ray muons in many engineering applications. This approach will show highly efficient, safe, and high-resolution reconstructed images of spent nuclear fuel casks. His ongoing research interests include developing an advanced muon detector in the Underground Research Laboratory to monitor long-term nuclear wastes as well as a radiation detector, nuclear security, and nuclear material management.

Jeff Foster, an Alvin M. Weinberg Fellow, earned his PhD from Virginia Tech. His dissertation focused on developing a methodology to leverage gaseotransmitters, specifically hydrogen sulfide, for human therapy. His work showed that hydrogen sulfide exhibits selective anticancer activity and may represent a promising alternative cancer therapy. Jeff’s mentor is Tomonori Saito, a chemist in the Chemical Sciences Division.

Jeff will focus his fellowship research on developing homogeneous, stimuli-responsive catalysts for precision polymer synthesis. His methodology will enable kinetic control over polymer sequence, providing a tool to create polymers with intentionally designed sequences. Fundamental sequence–structure–property relationships discovered during Jeff’s fellowship work will provide a framework for the design of future sustainable materials for packaging, construction, energy storage, and medicine. His ongoing research interests leverage a framework of synthetic methodology, homogeneous catalysis, and organic material science to uncover structure–property relationships, create novel materials with emergent functionality, and develop efficient and sustainable manufacturing processes.

Brenden Ortiz, a Eugene P. Wigner Fellow, earned his PhD from the Colorado School of Mines. His dissertation focused on accelerating the discovery and optimization of thermoelectric materials by developing techniques that aimed to accelerate both the theoretical and experimental aspects of material science. Brenden’s work resulted in the discovery of a new family of metals, AV3Sb5 (A: K, Rb, Cs) materials, which show a unique new quasi-2D kagome lattice and exhibit superconductivity, a charge density wave, and potential nontrivial topology. The combination of these properties together on the kagome lattice had never been seen before. His study renewed interest in the examination of kagome metals by researchers around the world, with over 350 additional manuscripts being published on this family in the past 2 years. Brenden’s mentors are Michael McGuire, Correlated Electron Materials group leader, and R&D staff member Andrew May, both in the Materials Science and Technology Division.

Brenden will focus his fellowship research on developing methods to control and predict the emergence of electronic instabilities in correlated metals. His project will facilitate the design of the next generation of quantum materials through a better understanding of the connection between the electronic structure of materials, the high-dimensional chemical space, and the emergence of correlated electron properties, such as superconductivity and charge density waves. Brenden’s ongoing research interests include the connection between chemistry and thermodynamics in complex materials. He is also interested in high-dimensional chemical spaces and how the influence over alloys, dopants, and defects can radically alter material properties.

Yue Yuan, an Alvin M. Weinberg Fellow, earned her PhD from North Carolina State University. Her dissertation focused on the challenges existing in global management of carbon dioxide emissions and recent research on applying biocatalysts, as an alternative to high-energy and high-cost traditional liquid solvents in carbon dioxide scrubbing processes. Her work introduced a new category of material that has hierarchical structure and biocatalytic function. Her study also uncovered the mechanism of enhanced catalyzed reactions at liquid–gas–solid interfaces. Yue Yuan’s mentor is Dr. Rigoberto Advincula, Macromolecular Nanomaterials Group Leader at the Center for Nanophase Materials Sciences.

Yue Yuan will focus her fellowship research on renewable macromolecular nanomaterials, particularly how their charge and hydrophobicity impact their reassembly with additive manufacturing techniques, outside the biological system. Her ongoing research interests include working on advanced functional materials, particularly bioderived and bioinspired materials, and focusing on bridging fundamental bioscience discoveries with advanced materials manufacturing through revealing the mechanisms behind the phenomena we observed in material formation.