Jen-Hsun "Jensen" Huang (Chinese: 黃仁勳; Wade–Giles: Huáng Jén-hsūn; Tâi-lô: N̂g Jîn-hun; born February 17, 1963) is a Taiwanese and American business executive and electrical engineer who is the founder, president, and CEO of Nvidia, the world's most valuable company. As of 2026, Forbes estimates his net worth at over US$200 billion, making him the seventh-wealthiest individual in the world. The son of Taiwanese immigrants, Huang spent his childhood in Taiwan and Thailand before moving to the United States, where he was a student in Kentucky and Oregon. After earning a master's degree from Stanford University, Huang launched Nvidia in 1993 from a Denny's restaurant in San Jose, California, at age 30 and has remained its president and CEO ever since. He led the company out of near-bankruptcy during the 1990s and oversaw its expansion into GPU production, high-performance computing, and artificial intelligence (AI). Under Huang, Nvidia experienced rapid growth during the AI boom, becoming the first company to reach a market capitalization of over $5 trillion in October 2025. In 2021 and 2024, Time magazine included Huang in their list of the most influential people. In 2025, he was named as one of the "Architects of AI" for Time's Person of the Year. == Early life and education == Huang was born in Taipei, Taiwan, on February 17, 1963, and moved to the southern city of Tainan as a child. He is the younger of two sons of Huang Hsing-tai, a chemical engineer at an oil refinery, and Lo Tsai-hsiu, a schoolteacher. They were a middle-class Taiwanese family that relocated often, and were native speakers of Taiwanese Hokkien. Each day, Jensen's mother randomly selected 10 words from the dictionary to teach her sons English. When he was five years old, Huang's family moved to Thailand to support his father's refinery career and remained there for approximately four years. He attended Ruamrudee International School while in Bangkok. In the late 1960s, Hsing-tai traveled from Taiwan to New York City to train under an air conditioning company and, after returning home, resolved to send his sons to the United States. At age nine, Jensen, despite not yet being able to speak English fluently, was sent by his parents to live in the United States. He and his older brother moved in 1973 to live with an uncle in Tacoma, Washington, escaping widespread social unrest in Thailand. Both Huang's aunt and uncle were recent immigrants to Washington state; they accidentally enrolled him and his brother in the Oneida Baptist Institute, a religious reform academy in Kentucky for troubled youth, mistakenly believing it to be a prestigious boarding school. In order to afford the academy's tuition, Jensen's parents sold nearly all their possessions. When he was 10 years old, Huang lived with his older brother in the Oneida boys' dormitory. Each student was expected to work every day, and his brother was assigned to perform manual labor on a nearby tobacco farm. Because he was too young to attend classes at the reform academy, Huang was educated at a separate public school—the Oneida Elementary school in Oneida, Kentucky—arriving as "an undersized Asian immigrant with long hair and heavily accented English" and was frequently bullied and beaten. In Oneida, Huang cleaned toilets every day, learned to play table-tennis, joined the swimming team, and appeared in Sports Illustrated at age 14. He taught his illiterate roommate, a "17-year-old covered in tattoos and knife scars," how to read in exchange for being taught how to bench press. In 2002, Huang said he remembered his life in Kentucky "more vividly than just about any other". Two years after Huang arrived in Oneida, his parents moved to the United States and settled in Beaverton, Oregon, after which the brothers withdrew from school in Kentucky to live back with them. As a teenager, Huang attended Aloha High School in Aloha, Oregon, where he excelled academically. He skipped two grades, graduated at age 16, and became a nationally ranked table-tennis player in addition to being a member of its mathematics, computer, and science clubs. In 1977, the school purchased an Apple II computer. Huang used the machine to play Super Star Trek, a text-based game, and to program in BASIC, creating his own version of Snake. Beginning at age 15, Huang got his first job working the graveyard shift at a local Denny's restaurant as a dishwasher, busboy, and waiter from 1978 to 1983. After high school, he chose to enroll at Oregon State University due to its low in-state tuition. He studied electrical engineering and graduated in 1984 with a bachelor's degree with highest honors. Huang later recalled, "I was the youngest kid in school, in class" and the only student who "looked like a child". Years later, while working as a microchip designer in Silicon Valley, he concurrently pursued graduate night classes at Stanford University, where he earned a master's degree in electrical engineering in 1992. == AMD and LSI Logic == After graduating from college, Huang was a microchip designer in Silicon Valley. He was recruited for positions at Texas Instruments, Advanced Micro Devices (AMD), and LSI Logic, ultimately choosing the California-based AMD due to already being familiar with the company. Huang designed AMD microprocessors while simultaneously attending Stanford and raising his two children. However, when he heard of new chip design processes at LSI Logic, Huang left AMD to assume a role as a technical officer at the LSI Corporation, working under a startup company, Sun Microsystems, where he met engineers Chris Malachowsky and Curtis Priem. LSI was in contract with Sun Microsystems and had introduced Huang to Malachowsky and Priem, who were working on a new graphics accelerator card. While the three produced the card's manufacturing process, the relationship between Malachowsky and Priem became strained as the two disputed the chip's design, leading to infighting; according to Malachowsky, they "broke every tool that LSI Logic had in their standard portfolio". In 1989, Huang, Malachowsky, and Priem finalized the accelerator, which they called the "GX graphics engine". GX was a widespread financial success; the sales of the graphics engine contributed to Sun Microsystem's revenue increasing from $262 million in 1987 to $656 million in 1990, and Huang was promoted to be the director of LSI's CoreWare, a division that manufactured chips for hardware vendors. == Nvidia == === Founding (1993) === When business began to slow for Sun Microsystems after 1990, Huang, along with Priem and Malachowsky, each resigned their jobs to pursue a venture together in making graphics chips for PC games. They initially named their new company "NVision" until Huang suggested that the company be named "Nvidia" based on the Latin word invidia, as Priem wanted competitors to turn "green with envy". They eventually dropped the "i" to honor the NV1 chip that they were then developing. The three met frequently in 1992 at a Denny's roadside diner in East San Jose to formulate a business plan. Huang chose for them to meet at Denny's due to his prior work experience at the restaurant chain and because it was "quieter than home and had cheap coffee". The three founded the company during one meeting at a breakfast booth at the diner. To formally incorporate the company, Huang found a lawyer, James Gaither of Cooley Godward, who demanded the $200 in cash in Huang's pockets to capitalize the company. After that meeting, Huang went back to Priem and Malachowsky to ask each of them for $200 for their respective shares of the company, which meant that Nvidia's initial capital was $600. On April 5, 1993, Huang personally signed Nvidia's original articles of incorporation into effect. Although he left LSI, Huang remained in good standing with the company and was able to secure funding for Nvidia from LSI's CEO, Wilfred Corrigan, who introduced Huang to venture capitalist Don Valentine. An account cited how Huang's presentation pitch went badly. Valentine, the leader of Sequoia Capital, chose to invest in Nvidia through Corrigan's support, as did Sutter Hill Ventures. The funding enabled Nvidia to begin development efforts toward its first chip and to begin paying wages for its employees. By the first day of operation, Huang was made Nvidia's president and CEO. Even though Huang, at age 30, was younger than Priem and Malachowsky, both Priem and Malachowsky believed that he was prepared to be CEO. According to Priem, "we basically deferred to Jensen on day one" and told Huang, "you're in charge of running the company—all the stuff Chris and I don't know how to do". === President and CEO (1993–present) === As of 2024, Huang has been Nvidia's chief executive for over three decades, a tenure described by The Wall Street Journal as "almost unheard of in fast-moving Silicon Valley". He owns 3.6% of Nvidia's stock, which went public in 1999. He earned US$24.6 million as CEO i
PropBank
PropBank is a corpus that is annotated with verbal propositions and their arguments—a "proposition bank". Although "PropBank" refers to a specific corpus produced by Martha Palmer et al., the term propbank is also coming to be used as a common noun referring to any corpus that has been annotated with propositions and their arguments. The PropBank project has played a role in research in natural language processing, and has been used in semantic role labelling. == Comparison == PropBank differs from FrameNet, the resource to which it is most frequently compared, in several ways. PropBank is a verb-oriented resource, while FrameNet is centered on the more abstract notion of frames, which generalizes descriptions across similar verbs (e.g. "describe" and "characterize") as well as nouns and other words (e.g. "description"). PropBank does not annotate events or states of affairs described using nouns. PropBank commits to annotating all verbs in a corpus, whereas the FrameNet project chooses sets of example sentences from a large corpus and only in a few cases has annotated longer continuous stretches of text. PropBank-style annotations often remain close to the syntactic level, while FrameNet-style annotations are sometimes more semantically motivated. From the start, PropBank was developed with the idea of serving as training data for machine learning-based semantic role labeling systems in mind. It requires that all arguments to a verb be syntactic constituents and different senses of a word are only distinguished if the differences bear on the arguments. Due to such differences, semantic role labeling with respect to PropBank is often a somewhat easier task than producing FrameNet-style annotations.
Mountain car problem
Mountain Car, a standard testing domain in Reinforcement learning, is a problem in which an under-powered car must drive up a steep hill. Since gravity is stronger than the car's engine, even at full throttle, the car cannot simply accelerate up the steep slope. The car is situated in a valley and must learn to leverage potential energy by driving up the opposite hill before the car is able to make it to the goal at the top of the rightmost hill. The domain has been used as a test bed in various reinforcement learning papers. == Introduction == The mountain car problem, although fairly simple, is commonly applied because it requires a reinforcement learning agent to learn on two continuous variables: position and velocity. For any given state (position and velocity) of the car, the agent is given the possibility of driving left, driving right, or not using the engine at all. In the standard version of the problem, the agent receives a negative reward at every time step when the goal is not reached; the agent has no information about the goal until an initial success. == History == The mountain car problem appeared first in Andrew Moore's PhD thesis (1990). It was later more strictly defined in Singh and Sutton's reinforcement learning paper with eligibility traces. The problem became more widely studied when Sutton and Barto added it to their book Reinforcement Learning: An Introduction (1998). Throughout the years many versions of the problem have been used, such as those which modify the reward function, termination condition, and the start state. == Techniques used to solve mountain car == Q-learning and similar techniques for mapping discrete states to discrete actions need to be extended to be able to deal with the continuous state space of the problem. Approaches often fall into one of two categories, state space discretization or function approximation. === Discretization === In this approach, two continuous state variables are pushed into discrete states by bucketing each continuous variable into multiple discrete states. This approach works with properly tuned parameters but a disadvantage is information gathered from one state is not used to evaluate another state. Tile coding can be used to improve discretization and involves continuous variables mapping into sets of buckets offset from one another. Each step of training has a wider impact on the value function approximation because when the offset grids are summed, the information is diffused. === Function approximation === Function approximation is another way to solve the mountain car. By choosing a set of basis functions beforehand, or by generating them as the car drives, the agent can approximate the value function at each state. Unlike the step-wise version of the value function created with discretization, function approximation can more cleanly estimate the true smooth function of the mountain car domain. === Eligibility traces === One aspect of the problem involves the delay of actual reward. The agent is not able to learn about the goal until a successful completion. Given a naive approach for each trial the car can only backup the reward of the goal slightly. This is a problem for naive discretization because each discrete state will only be backed up once, taking a larger number of episodes to learn the problem. This problem can be alleviated via the mechanism of eligibility traces, which will automatically backup the reward given to states before, dramatically increasing the speed of learning. Eligibility traces can be viewed as a bridge from temporal difference learning methods to Monte Carlo methods. == Technical details == The mountain car problem has undergone many iterations. This section focuses on the standard well-defined version from Sutton (2008). === State variables === Two-dimensional continuous state space. V e l o c i t y = ( − 0.07 , 0.07 ) {\displaystyle Velocity=(-0.07,0.07)} P o s i t i o n = ( − 1.2 , 0.6 ) {\displaystyle Position=(-1.2,0.6)} === Actions === One-dimensional discrete action space. m o t o r = ( l e f t , n e u t r a l , r i g h t ) {\displaystyle motor=(left,neutral,right)} === Reward === For every time step: r e w a r d = − 1 {\displaystyle reward=-1} === Update function === For every time step: A c t i o n = [ − 1 , 0 , 1 ] {\displaystyle Action=[-1,0,1]} V e l o c i t y = V e l o c i t y + ( A c t i o n ) ∗ 0.001 + cos ( 3 ∗ P o s i t i o n ) ∗ ( − 0.0025 ) {\displaystyle Velocity=Velocity+(Action)0.001+\cos(3Position)(-0.0025)} P o s i t i o n = P o s i t i o n + V e l o c i t y {\displaystyle Position=Position+Velocity} === Starting condition === Optionally, many implementations include randomness in both parameters to show better generalized learning. P o s i t i o n = − 0.5 {\displaystyle Position=-0.5} V e l o c i t y = 0.0 {\displaystyle Velocity=0.0} === Termination condition === End the simulation when: P o s i t i o n ≥ 0.6 {\displaystyle Position\geq 0.6} == Variations == There are many versions of the mountain car which deviate in different ways from the standard model. Variables that vary include but are not limited to changing the constants (gravity and steepness) of the problem so specific tuning for specific policies become irrelevant and altering the reward function to affect the agent's ability to learn in a different manner. An example is changing the reward to be equal to the distance from the goal, or changing the reward to zero everywhere and one at the goal. Additionally, a 3D mountain car can be used, with a 4D continuous state space.
Hierarchical control system
A hierarchical control system (HCS) is a form of control system in which a set of devices and governing software is arranged in a hierarchical tree. When the links in the tree are implemented by a computer network, then that hierarchical control system is also a form of networked control system. == Overview == A human-built system with complex behavior is often organized as a hierarchy. For example, a command hierarchy has among its notable features the organizational chart of superiors, subordinates, and lines of organizational communication. Hierarchical control systems are organized similarly to divide the decision making responsibility. Each element of the hierarchy is a linked node in the tree. Commands, tasks and goals to be achieved flow down the tree from superior nodes to subordinate nodes, whereas sensations and command results flow up the tree from subordinate to superior nodes. Nodes may also exchange messages with their siblings. The two distinguishing features of a hierarchical control system are related to its layers. Each higher layer of the tree operates with a longer interval of planning and execution time than its immediately lower layer. The lower layers have local tasks, goals, and sensations, and their activities are planned and coordinated by higher layers which do not generally override their decisions. The layers form a hybrid intelligent system in which the lowest, reactive layers are sub-symbolic. The higher layers, having relaxed time constraints, are capable of reasoning from an abstract world model and performing planning. A hierarchical task network is a good fit for planning in a hierarchical control system. Besides artificial systems, an animal's control systems are proposed to be organized as a hierarchy. In perceptual control theory, which postulates that an organism's behavior is a means of controlling its perceptions, the organism's control systems are suggested to be organized in a hierarchical pattern as their perceptions are constructed so. == Control system structure == The accompanying diagram is a general hierarchical model which shows functional manufacturing levels using computerised control of an industrial control system. Referring to the diagram; Level 0 contains the field devices such as flow and temperature sensors, and final control elements, such as control valves Level 1 contains the industrialised Input/Output (I/O) modules, and their associated distributed electronic processors. Level 2 contains the supervisory computers, which collate information from processor nodes on the system, and provide the operator control screens. Level 3 is the production control level, which does not directly control the process, but is concerned with monitoring production and monitoring targets Level 4 is the production scheduling level. == Applications == === Manufacturing, robotics and vehicles === Among the robotic paradigms is the hierarchical paradigm in which a robot operates in a top-down fashion, heavy on planning, especially motion planning. Computer-aided production engineering has been a research focus at NIST since the 1980s. Its Automated Manufacturing Research Facility was used to develop a five layer production control model. In the early 1990s DARPA sponsored research to develop distributed (i.e. networked) intelligent control systems for applications such as military command and control systems. NIST built on earlier research to develop its Real-Time Control System (RCS) and Real-time Control System Software which is a generic hierarchical control system that has been used to operate a manufacturing cell, a robot crane, and an automated vehicle. In November 2007, DARPA held the Urban Challenge. The winning entry, Tartan Racing employed a hierarchical control system, with layered mission planning, motion planning, behavior generation, perception, world modelling, and mechatronics. === Artificial intelligence === Subsumption architecture is a methodology for developing artificial intelligence that is heavily associated with behavior based robotics. This architecture is a way of decomposing complicated intelligent behavior into many "simple" behavior modules, which are in turn organized into layers. Each layer implements a particular goal of the software agent (i.e. system as a whole), and higher layers are increasingly more abstract. Each layer's goal subsumes that of the underlying layers, e.g. the decision to move forward by the eat-food layer takes into account the decision of the lowest obstacle-avoidance layer. Behavior need not be planned by a superior layer, rather behaviors may be triggered by sensory inputs and so are only active under circumstances where they might be appropriate. Reinforcement learning has been used to acquire behavior in a hierarchical control system in which each node can learn to improve its behavior with experience. James Albus, while at NIST, developed a theory for intelligent system design named the Reference Model Architecture (RMA), which is a hierarchical control system inspired by RCS. Albus defines each node to contain these components. Behavior generation is responsible for executing tasks received from the superior, parent node. It also plans for, and issues tasks to, the subordinate nodes. Sensory perception is responsible for receiving sensations from the subordinate nodes, then grouping, filtering, and otherwise processing them into higher level abstractions that update the local state and which form sensations that are sent to the superior node. Value judgment is responsible for evaluating the updated situation and evaluating alternative plans. World Model is the local state that provides a model for the controlled system, controlled process, or environment at the abstraction level of the subordinate nodes. At its lowest levels, the RMA can be implemented as a subsumption architecture, in which the world model is mapped directly to the controlled process or real world, avoiding the need for a mathematical abstraction, and in which time-constrained reactive planning can be implemented as a finite-state machine. Higher levels of the RMA however, may have sophisticated mathematical world models and behavior implemented by automated planning and scheduling. Planning is required when certain behaviors cannot be triggered by current sensations, but rather by predicted or anticipated sensations, especially those that come about as result of the node's actions.
Intelligent control
Intelligent control is a class of control techniques that use various artificial intelligence computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation and genetic algorithms. == Overview == Intelligent control can be divided into the following major sub-domains: Neural network control Machine learning control Reinforcement learning Bayesian control Fuzzy control Neuro-fuzzy control Expert Systems Genetic control New control techniques are created continuously as new models of intelligent behavior are created and computational methods developed to support them. === Neural network controller === Neural networks have been used to solve problems in almost all spheres of science and technology. Neural network control basically involves two steps: System identification Control It has been shown that a feedforward network with nonlinear, continuous and differentiable activation functions have universal approximation capability. Recurrent networks have also been used for system identification. Given, a set of input-output data pairs, system identification aims to form a mapping among these data pairs. Such a network is supposed to capture the dynamics of a system. For the control part, deep reinforcement learning has shown its ability to control complex systems. === Bayesian controllers === Bayesian probability has produced a number of algorithms that are in common use in many advanced control systems, serving as state space estimators of some variables that are used in the controller. The Kalman filter and the Particle filter are two examples of popular Bayesian control components. The Bayesian approach to controller design often requires an important effort in deriving the so-called system model and measurement model, which are the mathematical relationships linking the state variables to the sensor measurements available in the controlled system. In this respect, it is very closely linked to the system-theoretic approach to control design.
Pixlr
Pixlr is a group of SaaS creative tools including Pixlr.com, Designs.ai and Vectr.com. Pixlr.com is a cloud-based set of image editing tools and utilities, including AI image generation and enhancements. The Pixlr suite targets users who require subjectively simple, or more advanced, photo editing as well as graphic design. It features a freemium business model with subscription plans—Plus, Premium and Teams. The platform can be used on desktop and also smartphones and tablets. Pixlr is compatible with various image formats such as JPEG, PNG, WEBP, GIF, PSD (Photoshop Document) and PXZ (native Pixlr document format). Designs.ai lets users create content using AI, with a goal of being within two minutes, across different media types including videos, text, banners and audio. Vectr.com was acquired in 2017 before being spun out into Pixlr Group in 2023. == History == Pixlr was founded in 2008 and built on Macromedia Flash. On 19 July 2011, Autodesk announced that they had acquired the Pixlr suite. In 2013, Time listed Pixlr as one of the top 50 websites of the year. In 2017, Pixlr was acquired from Autodesk. It was subsequently rebuilt and relaunched in HTML5 in 2019. In September 2023, Pixlr was awarded as the Top 13 GenAi Web Product by the world's top venture firm Andreessen Horowitz. In November 2023, Pixlr, Designs.ai and Vectr were combined as a new business group named Pixlr Group focusing on generative AI and creative software solutions. In May 2024, Pixlr was featured as one of the top 18 progressive web applications highlighted on Google I/O. == Versions == Pixlr.com rebranded itself as a full creative suite in 2019 by introducing Pixlr X, Pixlr E and Pixlr M. The platform introduced more features in December 2021 with a new logo and added tools which included: Brushes, the 'Heal tool', Animation, and Batch upload. The brush feature enables the creation of hand-drawn effects. The Heal tool allows users to remove unwanted objects from their images whereas the Animation feature can be used to include movements into their edits. Users can also utilize Batch upload to edit up to 50 images simultaneously. In November 2022, Pixlr 2023 was launched, adding more tools such as "AI smart resize", colorization, text wrapping and other additional effects. In November 2023, Pixlr 2024 was launched with Pixlr Designer and new AI-powered updates which includes AI image generation, AI infill, AI inpainting and more.
Lynda Soderholm
Lynda Soderholm is a physical chemist at the U.S. Department of Energy's (DOE) Argonne National Laboratory with a specialty in f-block elements. She is a senior scientist and the lead of the Actinide, Geochemistry & Separation Sciences Theme within Argonne's Chemical Sciences and Engineering Division. Her specific role is the Separation Science group leader within Heavy Element Chemistry and Separation Science (HESS), directing basic research focused on low-energy methods for isolating lanthanide and actinide elements from complex mixtures. She has made fundamental contributions to understanding f-block chemistry and characterizing f-block elements. Soderholm became a Fellow of the American Association for the Advancement of Science (AAAS) in 2013, and is also an Argonne Distinguished Fellow. == Early life and education == Soderholm was awarded her PhD in 1982 by McMaster University under the direction of Prof John Greedan. Her dissertation focused on characterizing the structural and magnetic properties of a series of ternary f-ion oxides. After graduating, she was awarded a NATO postdoctoral fellow at the Centre national de la recherche scientifique in France from 1982 until 1985. After a short postdoctoral appointment as an Argonne postdoctoral fellow she was promoted to staff scientist the same year. Over several years, she moved up the ranks, becoming a senior chemist in 2001. She was also an adjunct professor at the University of Notre Dame from 2003 until 2007. In 2021, Soderholm was appointed interim Division Director for the Chemical Sciences and Engineering Division. == Career and research == === Uncovering structure of Yttrium-123 Superconductor === Early in her career, Soderholm focused on the characterizing the magnetic and electronic behavior of compounds containing f-ions (lanthanides and actinides) with a focus on high-Tc materials, compounds that are superconducting under usually high temperatures. She was part of the research group that first determined the structure of YBa2Cu3O7. Their discovery formed the foundation for the further developments in the broad field of superconductivity. === Understanding f-ion speciation in solution === Continuing her interest in the f-elements, Soderholm shifted her focus from solid-state materials to nanoparticles and solutions, taking advantage of advances in X-ray structural probes made available by synchrotron facilities. Building on her earlier work using neutron scattering, her team became the first to discover that plutonium exists in solution as tiny, well-defined nanoparticles. This work solved a longstanding problem in understanding transport of plutonium in the environment and resulted in the development of a new, patented approach to separating plutonium during nuclear reprocessing. === Using machine learning to evaluate molecular structures === Soderholm's more recent projects use machine learning to understand the influence of complex molecular structuring in solutions, in connection with low-energy processes for separation of f-block elements from complex mixtures. == Awards and honors == University of Chicago Board of Governors' Distinguished Performance Award, 2009. Fellow of the American Association for the Advancement of Science, 2013. Argonne Distinguished Fellow, 2016 DOE materials sciences research competition for Outstanding Scientific Accomplishments in Solid State Physics, 1987. == Select publications == Beno, M. A.; Soderholm, L.; Capone, D. W., II; Hinks, D. G.; Jorgensen, J. D.; Grace, J. D.; Schuller, I. K.; Segre, C. U.; Zhang, K., Structure of the single-phase high-temperature superconductor yttrium barium copper oxide (YBa2Cu3O7−δ). Appl. Phys. Lett. 1987, 51 (1), 57–9. Soderholm, L.; Zhang, K.; Hinks, D. G.; Beno, M. A.; Jorgensen, J. D.; Segre, C. U.; Schuller, I. K., Incorporation of praseodymium in YBa2Cu3O7−δ: electronic effects on superconductivity. Nature (London) 1987, 328 (6131), 604–5. Antonio, M. R.; Williams, C. W.; Soderholm, L., Berkelium redox speciation. Radiochim. Acta 2002, 90 (12), 851–856. Soderholm, L.; Skanthakumar, S.; Neuefeind, J., Determination of actinide speciation in solution using high-energy X-ray scattering. Anal. Bioanal. Chem. 2005, 383 (1), 48–55. Forbes, T. Z.; Burns, P. C.; Skanthakumar, S.; Soderholm, L., Synthesis, structure, and magnetism of Np2O5. J. Am. Chem. Soc. 2007, 129 (10), 2760–2761. Soderholm, L.; Almond, P. M.; Skanthakumar, S.; Wilson, R. E.; Burns, P. C., The structure of the plutonium oxide nanocluster [Pu38O56Cl54(H2O)8]14-. Angew. Chem., Int. Ed. 2008, 47 (2), 298–302. Jensen, M. P.; Gorman-Lewis, D.; Aryal, B.; Paunesku, T.; Vogt, S.; Rickert, P. G.; Seifert, S.; Lai, B.; Woloschak, G. E.; Soderholm, L., An iron-dependent and transferrin-mediated cellular uptake pathway for plutonium. Nat. Chem. Biol. 2011, 7 (8), 560–565. Wilson, R. E.; Skanthakumar, S.; Soderholm, L., Separation of Plutonium Oxide Nanoparticles and Colloids. Angew. Chem., Int. Ed. 2011, 50 (47), 11234–11237. Knope, K. E.; Soderholm, L., Solution and solid-state structural chemistry of actinide hydrates and their hydrolysis and condensation products. Chem. Rev. 2013, 113 (2), 944–994. Luo, G.; Bu, W.; Mihaylov, M.; Kuzmenko, I.; Schlossman, M. L.; Soderholm, L., X-ray reflectivity reveals a nonmonotonic ion-density profile perpendicular to the surface of ErCl3 aqueous solutions. J. Phys. Chem. C 2013, 117 (37), 19082–19090. Jin, G. B.; Lin, J.; Estes, S. L.; Skanthakumar, S.; Soderholm, L., Influence of countercation hydration enthalpies on the formation of molecular complexes: A thorium-nitrate example. J. Am. Chem. Soc. 2017, 139 (49), 18003–18008. == Patents == Solvent extraction system for plutonium colloids and other oxide nano-particles, (2016).