The Alfred P. Sloan Foundation, renowned for its mission to advance scientific knowledge, awards these fellowships to early-stage researchers who have demonstrated exceptional capacity and creativity in their respective disciplines. Each fellow receives a grant of $75,000 distributed over two years to expedite their innovative investigations. Since 1955, the Sloan Research Fellowships have nurtured scientific careers that have profoundly influenced modern science, with an impressive alumnus including Nobel Laureates, Fields Medalists, and National Medal of Science recipients.

Danique Jeurissen’s research tackles one of the most intricate puzzles in neuroscience: the brain’s resilience and adaptability following injury. Her lab utilizes advanced behavioral paradigms coupled with causal manipulation technologies and electrophysiology to discern how the brain reroutes information flow when primary cortical pathways are disrupted. This research aims to unravel compensatory neural mechanisms that could elucidate natural recovery processes post-stroke or traumatic brain injury, potentially guiding the development of therapeutic interventions that align with the brain’s endogenous repair systems.

In the realm of chemistry, Marvin Parasram focuses on streamlining the integration of heteroatoms—atoms other than hydrogen or carbon—into complex organic structures, which are central to the development of pharmaceuticals. His innovative approach leverages light-activated 1,3-dipoles, unique reactive intermediates characterized by delocalized charges, to efficiently introduce heteroatoms into molecular frameworks. This methodology avoids traditional challenges such as harsh reaction conditions and limited selectivity, offering a sustainable and versatile paradigm for synthesizing medicinal compounds with enhanced efficacy and tailored biological activity.

Theoretical and computational sciences find two new representatives among the NYU fellows in Florian Schäfer and Joseph Tassarotti. Schäfer’s work bridges numerical computation with statistical inference, a synergistic approach that holds promise for industries ranging from aerospace engineering to computer graphics. His current research on information geometric mechanics revisits the statistical underpinnings of computational mechanics to formulate algorithms that integrate physical simulations with probabilistic models, potentially revolutionizing optimization and reliability in simulated environments.

Joseph Tassarotti’s investigations lie at the frontier of formal verification in computer science, addressing the complexity of highly non-deterministic programs—systems where unpredictability arises due to randomness or distributed execution across multiple computational units. These programs underpin critical applications in security and large-scale cloud computing but pose substantial challenges to conventional debugging techniques. His team devises robust program logic frameworks that provide rigorous mathematical proofs ensuring software correctness, thereby enhancing trustworthiness in critical computational infrastructures.

NYU’s selection of four Sloan Fellows in a single year underscores the institution’s role as a nexus for cutting-edge research and academic excellence. The Courant Institute School of Mathematics, Computing, and Data Science, recently established as a testament to NYU’s commitment to interdisciplinary scholarship, exemplifies this ethos by integrating applied and pure mathematics with data science and computer science under one academic umbrella. This environment not only fosters collaboration across traditional disciplinary boundaries but also propels innovation at the intersection of theory and application.

The Sloan Fellowship’s legacy is further cemented by its distinguished alumni: of the hundreds of recipients over decades, 59 have gone on to receive Nobel Prizes, including John Clarke, the physics laureate named just last year. The fellowship’s distinctiveness lies in its emphasis on both recognizing early promise and providing flexible support that allows researchers to explore bold scientific questions with autonomy. This model has proven successful in cultivating leaders who redefine their fields and expand humanity’s scientific horizons.

The scientific pursuits of this year’s NYU fellows also highlight the importance of interdisciplinary approaches in tackling complex problems. From understanding the plasticity of the brain’s neural networks and advancing medicinal chemistry through innovative synthetic methods to refining computational tools that assure software reliability and enhance physical simulations, their collective expertise cuts across biology, chemistry, mathematics, and computer science.

Danique Jeurissen’s focus on how neural activity reconfigures following injury could transform rehabilitation strategies by pinpointing new therapeutic targets that capitalize on the brain’s inherent flexibility. Her work has profound implications for millions affected by strokes and traumatic brain injuries, conditions that impose immense societal and healthcare burdens. By elucidating alternative pathways for information processing, her experiments could lead to treatments that not only repair but also improve functional recovery through a deeper understanding of neuroplasticity.

Similarly, Marvin Parasram’s advances in chemical biology propose a paradigm shift in how heteroatoms are utilized in drug design. The traditional synthesis of complex molecules often demands multiple steps and harsh conditions that hinder efficiency and scalability. By harnessing photochemical activation of 1,3-dipoles, Parasram’s approach promises a more streamlined, environmentally friendly process that enhances the ability to create diverse bioactive compounds, potentially accelerating the drug discovery pipeline.

The emergence of innovative computational models by Florian Schäfer integrates geometric principles from statistics with mechanical systems, potentially allowing more accurate predictive modeling in engineering disciplines. Information geometric mechanics could offer new insights into the behavior of materials and structures under various conditions, optimizing design and reducing experimental costs. These methodologies exemplify how mathematical abstractions translate into practical tools for industry.

Joseph Tassarotti’s contributions lie in the realm of software verification, particularly focused on stochastic and distributed systems where traditional testing methodologies fail to guarantee correctness. By developing program logics tailored to these paradigms, his research enhances software reliability, which is critical in contexts such as cybersecurity, cloud infrastructure, and privacy-preserving technologies. This foundational work provides a framework for building safer and more resilient software systems in an increasingly digital world.

The collective achievements and trajectories of these NYU Sloan Fellows reflect broader trends in contemporary science: the merging of disciplines, the prioritization of translational impact, and the embrace of novel methodologies—be it leveraging light in chemical reactions or geometric statistics in simulation. Their work not only advances theoretical understanding but also paves the way for applications that resonate beyond academic boundaries.

Furthermore, the Alfred P. Sloan Foundation’s support extends beyond funding, nurturing a vibrant community of scholars who push boundaries in STEM fields and economics. The fellows’ network facilitates collaboration, mentorship, and dissemination of knowledge, amplifying individual research impact across institutions and disciplines. This holistic support system contributes to sustained scientific progress and innovation.

As we look ahead, the breakthroughs anticipated from these NYU scientists have the potential to redefine their respective domains and inspire new lines of inquiry. The fellowship’s backing enables them to pursue high-risk, high-reward projects that might otherwise lack support, fostering an environment where novel ideas can flourish into transformative discoveries. The scientific community and society at large stand to benefit profoundly from their endeavors.

New York University, with its global reach and commitment to research excellence, continues to cultivate talent that advances frontiers of knowledge. The integration of diverse disciplines within its academic paradigm, as exemplified by the newly formed Courant Institute School of Mathematics, Computing, and Data Science, ensures that such distinguished researchers operate within a fertile intellectual ecosystem. This synergy is vital for sustaining the pipeline of innovation critical to addressing complex challenges of the modern world.

In summary, the 2026 cohort of NYU Sloan Fellows exemplifies the pinnacle of early-career scientific achievement, epitomizing both intellectual rigor and creative exploration. Their work spans fundamental neuroscience, synthetic chemistry, computational mechanics, and software verification, demonstrating the expansive scope and interdisciplinary nature of contemporary scientific inquiry. Their promising research trajectories inspire anticipation for future discoveries that will reshape our understanding and capabilities across multiple fields.

Subject of Research: Neuroscience, Chemistry, Computational Mechanics, and Software Verification

Article Title: NYU’s 2026 Sloan Foundation Fellows: Pioneering Innovations Across Neuroscience, Chemistry, and Computational Sciences

News Publication Date: 2026

Web References:

• NYU Faculty – Danique Jeurissen
• NYU Faculty – Marvin Parasram
• NYU News on Florian Schäfer
• Joseph Tassarotti’s NYU Homepage
• Sloan Fellows Database
• Alfred P. Sloan Foundation

Image Credits: Photos courtesy of New York University

Keywords: Early career scientists, Brain injuries, Neuroscience, Chemical biology, Chemistry, Computer science, Software, Computational mechanics, Mathematical modeling, Statistics

Neural oscillations provide an essential temporal scaffold for brain function, coordinating the timing of neuronal firing within and across diverse brain areas. Among the broad spectrum of frequencies, beta oscillations (ranging roughly from 13 to 30 Hz) have long been associated with sensorimotor processing and cognitive control. However, their role before sensory input—the “pre-stimulus” phase—has remained enigmatic. The new study harnesses cutting-edge electrophysiological techniques combined with sophisticated behavioral paradigms to systematically probe how fluctuations in beta power modulate perceptual decisions and biases.

The researchers employed a multimodal approach, bridging high-density electroencephalography (EEG) recordings with rigorous psychophysical testing in human participants. Subjects were asked to perform tasks where their perceptual judgments were prone to subtle biases that could be overt and consciously reportable (explicit biases) or unconscious and automatic (implicit biases). By analyzing the brain activity immediately preceding stimulus presentation, the investigators could causally link variations in beta oscillatory power with subsequent perceptual outcomes, elucidating the neural substrates that condition perception prior to sensory input.

Intriguingly, the study found that beta power in distinct cortical areas mediated different forms of bias. In particular, elevated beta power in prefrontal regions corresponded with explicit perceptual biases that the participants could consciously access and report. Conversely, fluctuations in beta power localized primarily to posterior parietal and occipital cortices aligned with implicit biases that shaped perception below conscious awareness. This spatial dissociation highlights the anatomical specificity through which neural oscillations govern the layering of conscious and unconscious perceptual processes.

Such differentiation in the cortical origins of explicit versus implicit biases provides important clues about the hierarchical architecture of perception and cognition. The prefrontal cortex, known for its role in executive functions and metacognition, likely exerts top-down influences on how sensory information is interpreted and selectively weighted, thus giving rise to explicit biases. Meanwhile, occipital and parietal regions, classically at the heart of sensory processing and spatial attention, appear to embed implicit biases directly into the initial stages of sensory coding. Beta oscillations, acting as a temporal organizing principle, orchestrate these distinct biasing mechanisms.

Furthermore, this study sheds new light on the dynamic interplay between ongoing brain states and external sensory input, a topic that has only recently gained traction. It underscores that perceptions are not passive reflections of the external world but active constructions influenced by the brain’s preparatory state. Pre-stimulus beta activity effectively acts as a neural gatekeeper, modulating how forthcoming sensory evidence is weighed, integrated, and ultimately experienced—sometimes even before the sensory input itself arrives.

Technologically, the study leveraged advances in EEG source localization methods, enabling the precise mapping of oscillatory activity to distinct cortical substrates. These methodological innovations allowed the authors to disentangle the overlapping beta signals and reveal the spatially segregated networks governing explicit and implicit biases. Combined with experimental designs that systematically manipulated stimulus uncertainty and participant expectations, the research achieved a fine-grained resolution of perceptual bias mechanisms hitherto inaccessible.

The ramifications of these findings extend beyond theoretical neuroscience into clinical and applied domains. Perceptual biases underpin numerous psychiatric and neurological disorders, such as schizophrenia, autism spectrum disorders, and anxiety, where altered pre-stimulus neural dynamics could skew sensory interpretation. Understanding how beta oscillations shape explicit and implicit biases paves the way for novel neuromodulatory interventions—targeting beta rhythms via transcranial stimulation techniques—to recalibrate maladaptive perceptual tendencies and restore balanced sensory processing.

Moreover, this research transforms our conception of the brain from a reactive organ to a predictive machine continually forecasting future events based on endogenous rhythmic states. Beta oscillations emerge as critical markers of the brain’s anticipatory set, aligning internal cognitive states with expected sensory contingencies, thus optimizing perception under uncertainty. Such insights dovetail with Bayesian and predictive coding frameworks that consider perception as inferential and dynamically biased by prior information and brain states.

The study also opens exciting avenues for future research to explore how beta power interacts with other oscillatory frequencies, such as alpha and gamma bands known for their roles in attention and sensory binding. Investigating cross-frequency coupling patterns could reveal richer patterns of temporal coordination that integrate multiple levels of perception—from unconscious processing to explicit awareness. Additionally, longitudinal studies could ascertain how experience, learning, and development shape the beta-related biasing mechanisms and their stability over time.

Importantly, the work challenges the widely held notion that perceptual biases always represent errors or noise in the sensory system. Instead, biases mediated by pre-stimulus beta activity could be adaptive, reflecting an optimized tuning of perception based on context and prior knowledge. By pre-activating specific neural ensembles in relevant cortical regions, the brain effectively sets perceptual priorities that enhance interpretation efficiency and behavioral relevance, highlighting the constructive nature of perception.

The delineation of distinct cortical substrates for explicit versus implicit biases also carries profound philosophical and cognitive implications. It offers a neurophysiological basis to the subjective experience of bias—why some biases enter conscious awareness while others remain hidden yet influence judgments. The findings support a layered model of consciousness, wherein neural oscillations gate the access of perceptual content to awareness, potentially bridging the explanatory gap in understanding conscious versus unconscious cognitive processes.

From an experimental perspective, the ability to predict perceptual bias based on pre-stimulus beta power advances brain-computer interface (BCI) technologies. By decoding ongoing beta rhythms, future BCIs could anticipate users’ perceptual inclinations and modify sensory presentations to align with or counteract these biases in real time. This capability could transform human-machine interactions and augment sensory rehabilitation for populations with perceptual impairments.

The interdisciplinary essence of this research—merging cognitive neuroscience, neurophysiology, psychology, and computational modeling—exemplifies the power of integrative approaches in unraveling brain-behavior relationships. It invigorates the long-standing scientific quest to decipher the neural code of perception and enriches our grasp of how temporal dynamics within brain circuits sculpt lived experience.

In summary, the pioneering work by Forster and colleagues provides compelling evidence that pre-stimulus beta power is a fundamental neural mechanism that mediates explicit and implicit perceptual biases via distinct cortical circuits. This elegant demonstration of the brain’s anticipatory orchestration of perception not only deepens our understanding of consciousness and cognition but also sets the stage for innovative therapeutic and technological applications harnessing the rhythmic nature of brain activity to modulate perception.

As neuroscience continues to map the intricate patterns of neural oscillations, studies like this illuminate the profound truth that perception emerges from a continuous dialogue between external reality and internal brain states—woven together seamlessly through the language of beta rhythms. The reverberations of such discoveries promise to resonate far beyond laboratory settings, reshaping how we conceive the mind, consciousness, and the very act of seeing the world.

Subject of Research: Pre-stimulus beta oscillations and their role in mediating explicit and implicit perceptual biases in distinct cortical areas

Article Title: Pre-stimulus beta power mediates explicit and implicit perceptual biases in distinct cortical areas

Article References:
Forster, C., Stephani, T., Grund, M. et al. Pre-stimulus beta power mediates explicit and implicit perceptual biases in distinct cortical areas. Commun Psychol 3, 93 (2025). https://doi.org/10.1038/s44271-025-00265-y

Image Credits: AI Generated

Reinforcement learning, a fundamental principle by which both natural and artificial agents optimize their actions through rewards and punishments, traditionally assumes a single exponential discounting factor. This discount factor governs how future rewards are valued in the present, typically emphasizing more immediate gains over distant ones. However, this classical model struggles to account for the nuanced and heterogeneous ways animals—including humans—evaluate rewards spread over time. The new study overturns this simplistic view by demonstrating that distinct dopaminergic neurons exhibit a broad spectrum of discounting behaviors, each encoding reward prediction errors over different temporal scales.

The researchers employed sophisticated behavioral paradigms involving mice engaged in two separate tasks, designed to probe the neural encoding of reward prediction errors. By recording neuronal activity in the midbrain’s dopaminergic cells, they observed that individual neurons did not conform to a uniform discounting pattern. Instead, these neurons exhibited unique time constants, reflecting varying degrees of sensitivity to delayed rewards. This heterogeneity suggests that the brain integrates multiple temporal discount factors simultaneously, allowing for a more flexible and adaptive learning strategy in fluctuating environments.

Interestingly, the study went beyond simple observation by introducing a computational model that could replicate these diverse temporal sensitivities within reinforcement learning frameworks. The model posits that learning at multiple timescales is not merely a biological idiosyncrasy but a crucial computational advantage. Agents that process reward signals through various discount factors can optimize their decisions with higher robustness, particularly in settings where reward contingencies change unpredictably or span long time horizons.

Another striking discovery lies in the relationship between transient cue-evoked responses and slower dopamine fluctuations, termed “ramps.” The researchers found that the temporal discount factors inferred from fast, phasic dopamine bursts correlated strongly with those derived from the slower ramps within the same neurons. This implies that the cell-specific discounting property manifests across different temporal dynamics of dopaminergic signaling, highlighting an intrinsic and stable feature of individual neurons.

These findings provide a mechanistic explanation for long-standing behavioral observations: humans and animals frequently display non-exponential discounting patterns in decision-making, often captured by hyperbolic or quasi-hyperbolic models. Such discounting behavior has puzzled scientists for decades, as it diverges qualitatively from predictions derived from classical reinforcement learning theories. By linking cellular heterogeneity directly to computational models, this work bridges a crucial gap between neurophysiology and behavioral economics.

The implications of this research are profound, both for neuroscience and artificial intelligence. From a biological perspective, the presence of multi-timescale reinforcement learning underscores the brain’s capacity for sophisticated resource allocation, enabling organisms to weigh immediate and delayed outcomes flexibly. This adaptability is vital for survival in dynamic environments where the valuation of outcomes must adjust to shifting contexts.

In the realm of artificial intelligence, these results inspire new algorithmic architectures that mimic the brain’s multiplicity of discount factors. Conventional reinforcement learning algorithms often rely on a single discount parameter, which can limit their ability to navigate tasks involving varying temporal structures. Incorporating multiple discount factors could lead to agents with enhanced learning efficiency and resilience, especially in domains such as robotics, autonomous systems, and complex game playing.

The experimental design itself showcases cutting-edge techniques integrating electrophysiological recordings with behavioral tasks that vary reward schedules systematically. The rigor and precision enable the detection of subtle neuronal differences often masked in population-level analyses. Moreover, the consistency of discount factors across different tasks for individual neurons suggests possible intrinsic molecular or genetic determinants, opening new avenues for research into the cellular basis of reinforcement learning heterogeneity.

Beyond the neural substrates, this work enhances our understanding of dopamine’s multifaceted roles. Dopamine has long been implicated as a key neuromodulator in reward processing, motivation, and decision-making. By revealing the fine-grained temporal dynamics of dopaminergic signaling, the study refines our conception of how reward prediction errors are computed and utilized across timescales, shaping ongoing behavior and learning.

Furthermore, the findings have potential clinical relevance. Disorders such as addiction, depression, and Parkinson’s disease involve dysregulation of dopaminergic systems. A better grasp of how temporal discounting is encoded at the neuronal level could inform therapeutic strategies aimed at recalibrating reward valuation mechanisms and improving behavioral interventions.

Importantly, this research embodies a paradigm shift towards viewing functional heterogeneity within neural populations not as noise, but as an essential computational feature. The brain’s ability to distribute learning computations across neurons with diverse temporal properties aligns with emerging theories emphasizing the importance of heterogeneity for robust cognitive function.

The study also invites reconsideration of classical economic models of intertemporal choice. While traditional economic theory often presupposes exponential discounting as normative, the biological evidence supports a richer, more nuanced framework where multiple discounting processes coexist. This concordance between biological data and behavioral economics enhances the ecological validity of models designed to capture real-world decision-making.

Future research inspired by these findings may delve deeper into the mechanisms governing the establishment and regulation of multiple discount factors within individual neurons. For example, synaptic plasticity rules, receptor subtypes, and intracellular signaling cascades could modulate the observed temporal diversity. Additionally, exploring how different brain regions interact to integrate these multiple timescales may reveal hierarchical or network-level architectures that further refine adaptive learning.

In sum, the discovery of multi-timescale reinforcement learning in the brain marks a significant advance in our understanding of neural computations underlying adaptive behavior. By elucidating how dopaminergic neurons encode reward prediction errors across various temporal windows, this research not only challenges classical theories but also lays the groundwork for innovations in both neuroscience and machine learning.

Subject of Research: Multi-timescale reinforcement learning mechanisms within dopaminergic neurons and their computational and behavioral implications.

Article Title: Multi-timescale reinforcement learning in the brain

Article References:
Masset, P., Tano, P., Kim, H.R. et al. Multi-timescale reinforcement learning in the brain. Nature (2025). https://doi.org/10.1038/s41586-025-08929-9

Image Credits: AI Generated