About

Pierre Baldi is a distinguished professor recognized for his remarkable contributions to the engineering of neural networks, applications of machine learning, and related sciences. His work has been acknowledged with the 2023 INNS Dennis Gabor Award by the International Neural Network Society, which honors outstanding individuals in the field of neural networks and machine learning. As a prominent figure in the Center for Machine Learning and Intelligent Systems at the University of California, Irvine, he has significantly advanced the understanding and development of neural network technologies and their applications.

Research topics

• Computer Science
• Artificial Intelligence
• Physics
• Psychology
• Nuclear physics
• Psychiatry
• Neuroscience
• Algorithm
• Particle physics
• Astrophysics
• Machine Learning
• Optics
• Bioinformatics
• Biology
• Mathematics
• Developmental psychology
• Genetics
• Astronomy
• Theoretical computer science
• Chemistry
• Engineering
• Clinical psychology
• Mathematical optimization
• Aerospace engineering

Selected publications

• The nBAF complex subunit CREST/SS18L1 regulates hippocampal memory processes via tyrosine 397 and histone acetyltransferase CBP

  Cell Reports · 2026-03-25

  articleOpen access

  Chromatin-modifying and -remodeling machineries are important for learning-induced transcriptional activity, yet it remains unclear how they coordinate to drive de novo gene expression for memory formation. Here, we examine the transcription factor known as calcium-responsive transactivator (CREST) in memory formation, synaptic plasticity, and learning-induced gene expression. CREST is known to bind major chromatin-modifying and -remodeling machineries via interaction with CREB-binding protein (CBP) and brahma-related gene 1 (BRG1), respectively. In silico modeling of CREST identified tyrosine 397 (Y397) within the CBP-binding domain. Expression of a CREST Y397F point mutant impairs long-term potentiation and memory. Conversely, expression of a CREST Y397D point mutant enhances memory in a CBP-dependent manner. Differential gene expression analysis reveals distinct CREST Y397-regulated signatures during memory consolidation. CBP acts through CREB and post-translation modifications to affect memory, but the findings of this study argue for consideration of the CREST-CBP interaction and Y397 accessibility as factors in memory processes.

  PublisherDOI

• Adapting vision-language models for neutrino event classification in high-energy physics

  Communications Physics · 2026-05-20

  preprintOpen accessSenior author

  Abstract Recent advances in machine learning, particularly in multimodal models, have created new opportunities for analyzing complex data in high-energy physics, where accurate identification of particle interactions is critical for scientific discovery. However, existing approaches rely heavily on convolutional neural networks, which lack interpretability and do not fully leverage multimodal reasoning capabilities. Here we show that a fine-tuned Vision Language Model (VLM) based on LLaMA 3.2 can effectively identify neutrino interactions in pixelated detector data, outperforming both a state-of-the-art convolutional neural network and a Vision Transformer baseline in classification accuracy and robustness. In addition, the VLM provides improved explainability through reasoning-based, interpretable predictions and supports integration of auxiliary semantic information. These results demonstrate the potential of multimodal transformer architectures as general-purpose tools for physics event classification, paving the way for more transparent, flexible, and scalable analysis methods in future high-energy physics experiments.

  PublisherOA PDFDOI

• Circadian reprogramming by timed sodium intake reveals transcriptional pathways of daily salt handling in the colon

  Science Advances · 2026-03-13

  articleOpen access

  Circadian misalignment of the feeding behavior and the terrestrial cycle is associated with obesity and metabolic perturbations. However, it remains unclear whether the quantity and timing of dietary salt intake influence temporal sodium handling and blood pressure regulation. Here, we demonstrate that the colonic mineralocorticoid receptor (MR) and peripheral clock affect the daily sodium absorption and blood pressure variations. Genes related to sodium handling display diurnal rhythms in synchrony with the daily rhythms of aldosterone and the colonic circadian clock. Cistromic analysis substantiated the overlap of occupancy between the MR and brain and muscle ARNT-like 1 (BMAL1). Diurnal oscillation of aldosterone and peripheral clocks, as well as blood pressure, was robustly driven by nighttime feeding of a low-salt diet but markedly disrupted by daytime feeding of a high-salt diet in nocturnal mice. These findings delineate the colonic temporal sensing of dietary sodium abundance and highlight the transcriptional mechanisms of daily salt handling and blood pressure variations.

  PublisherDOI

• The Liver Clock Tunes Transcriptional Rhythms in Skeletal Muscle to Regulate Mitochondrial Function

  Journal of Biological Rhythms · 2026-01-04 · 2 citations

  articleOpen access

  Circadian clocks present throughout the brain and body coordinate diverse physiological processes to support daily homeostasis, yet the specific interorgan signaling axes involved are not well defined. We previously demonstrated that the skeletal muscle clock controls transcript oscillations of genes involved in fatty acid metabolism in the liver, yet the impact of the liver clock on the muscle remained unknown. Here, we use male hepatocyte-specific Bmal1 KO mice (Bmal1 hep–/– ) to reveal that approximately one-third of transcript rhythms in skeletal muscle are influenced by the liver clock in vivo. Treatment of myotubes with serum harvested from Bmal1 hep–/– mice inhibits expression of genes involved in metabolic pathways, including oxidative phosphorylation. Only small transcriptional changes were induced by liver clock-driven endocrine communication in vitro, leading us to surmise that the liver clock acts to fine-tune metabolic gene expression in muscle. Consistent with functional tuning, treatment of myotubes with serum collected from Bmal1 hep–/– mice during the dark phase lowers mitochondrial ATP production compared with serum from wild-type mice. Overall, our results reveal communication between the liver clock and skeletal muscle, uncovering a bidirectional endocrine communication pathway that may contribute to the metabolic phenotypes of circadian disruption.

  PublisherOA PDFDOI

• Chronic intermittent hypoxia reshapes circadian metabolic architecture in a model of sleep apnea

  Science Advances · 2026-02-25 · 1 citations

  articleOpen access

  Obstructive sleep apnea (OSA), characterized by chronic intermittent hypoxia (IH) during sleep, is increasingly recognized as a driver of metabolic dysfunction. However, its impact on circadian metabolic regulation remains poorly understood. Here, we investigated how chronic IH reshapes 24-hour hepatic and systemic metabolic programs in a mouse model mimicking OSA-associated chronic hypoxia. Through integrated circadian transcriptomic, metabolomic, and physiological 24-hour profiling, we show that 4 weeks of rest phase-restricted IH reprograms hepatic and systemic metabolism in a time-specific manner. This reorganization involves the coordinated circadian regulation of glucose, lipid, and redox pathways, with a shift away from oxidative metabolism toward oxygen-sparing processes such as gluconeogenesis, glycogen turnover, and lipid mobilization. These changes align with the hypoxic phase exposure and coincide with reshaped hepatic metabolite oscillations and systemic autonomic rhythms, supporting a functional adaptation to intermittent oxygen availability. Mechanistically, we identify the cAMP-CREB1 pathway as a driver of circadian transcriptional remodeling in the liver and a central integrator of IH-dependent adrenergic stress. Our findings establish chronic IH as a potent metabolic zeitgeber that rewires hepatic transcriptional and metabolic programs, revealing a circadian dimension to the metabolic consequences of sleep-disordered breathing.

  PublisherDOI

• Particle hit clustering and identification using point set transformers in liquid argon time projection chambers

  Journal of Instrumentation · 2025-07-01

  articleOpen accessSenior author

  Abstract Liquid argon time projection chambers are often used in neutrino physics and dark-matter searches because of their high spatial resolution. The images generated by these detectors are extremely sparse, as the energy values detected by most of the detector are equal to 0, meaning that despite their high resolution, most of the detector is unused in a particular interaction. Instead of representing all of the empty detections, the interaction is usually stored as a sparse matrix, a list of detection locations paired with their energy values. Traditional machine learning methods that have been applied to particle reconstruction such as convolutional neural networks (CNNs), however, cannot operate over data stored in this way and therefore must have the matrix fully instantiated as a dense matrix. Operating on dense matrices requires a lot of memory and computation time, in contrast to directly operating on the sparse matrix. We propose a machine learning model using a point set neural network that operates over a sparse matrix, greatly improving both processing speed and accuracy over methods that instantiate the dense matrix, as well as over other methods that operate over sparse matrices. Compared to competing state-of-the-art methods, our method improves classification performance by 14%, segmentation performance by more than 22%, while taking 80% less time and using 66% less memory. Compared to state-of-the-art CNN methods, our method improves classification performance by more than 86%, segmentation performance by more than 71%, while reducing runtime by 91% and reducing memory usage by 61%.

  PublisherDOI

• Domain Knowledge Inclusive Monotonic Neural Network Guides Patient-Specific Induction of General Anesthesia Dosing

  A&A Practice · 2025-08-01

  articleOpen access

  BACKGROUND: Postinduction hypotension is a well-known risk factor for adverse postoperative outcomes. Anesthesiologists estimate anesthetic dosages based on a patient's chart and domain knowledge. Machine learning is increasingly applied in predicting postinduction hypotension, with neural networks providing a robust and accurate approach to model complex relationships. This study aims to use machine learning to suggest anesthetic doses, both generalized to an average patient population and personalized for specific patients, incorporating domain knowledge into the modeling process. METHODS: In this study, postinduction hypotension is defined as a mean arterial pressure (<65 mm Hg) occurring during the first 10 minutes after anesthesia induction. The dataset includes 201,000 patient records, after exclusion criteria, containing clinical data, medication history, procedure descriptions, and anesthetic dosages for fentanyl and propofol. Several classification algorithms were implemented to model postinduction hypotension, and likelihood calculations were made by fixing values of fentanyl and propofol dosages to assess patient risk. RESULTS: Gradient boosting and neural network models demonstrated the highest performance. However, these models did not account for domain experts' knowledge that anesthetic dosage and postinduction hypotension have a monotonically increasing relationship. To address this limitation, we developed a monotonic neural network (MNN), which integrates this domain knowledge. The models' results are presented through heatmaps, illustrating the likelihood of postinduction hypotension for both average and specific patients, with the MNN generating smoother, more plausible predictions compared to traditional models. CONCLUSIONS: We successfully predicted postinduction hypotension using the MNN, achieving performance comparable to existing methods. This model, by encoding clinically relevant monotonic relationships, provides anesthesiologists with a tool to assist in patient-specific fentanyl and propofol dosages, improving both the interpretability and clinical relevance of anesthetic dosing strategies.

  PublisherDOI

• Unraveling the molecular magic: AI explains the formation of the most stretchable hydrogel

  Reaction Chemistry & Engineering · 2025-11-05

  articleSenior authorCorresponding

  The reaction predictor expands and searches the synthesis pathway tree through a series of exponentially growing predictions that can ultimately explain the reasons behind the ultra-stretchability of our hydrogel.

  PublisherDOI

• Evaluating the Intelligence of large language models: A comparative study using verbal and visual IQ tests

  Computers in Human Behavior Artificial Humans · 2025-06-18 · 1 citations

  articleOpen accessSenior authorCorresponding

  Large language models (LLMs) excel on many specialised benchmarks, yet their general-reasoning ability remains opaque. We therefore test 18 models—including GPT-4, Claude 3 and Gemini Pro—on a 14-section IQ suite spanning verbal, numerical and visual puzzles and add a ”multi-agent reflection” variant in which one model answers while others critique and revise. Results replicate known patterns: a strong bias towards verbal vs numerical reasoning (GPT-4: 79 % vs 53 % accuracy), a pronounced modality gap (text-IQ ≈ 125 vs visual-IQ ≈ 103), and persistent failure on abstract arithmetic ( ≤ 20 % on missing-number tasks). Scaling lifts mean IQ from 89 (tiny models) to 131 (large models), but gains are non-uniform, and reflection yields only modest extra points for frontier systems. Our contributions include: (1) proposing an evaluation framework for LLM ”intelligence” using both verbal and visual IQ tasks, (2) analyzing how multi-agent setups with varying actor and critic sizes affect problem-solving performance; (3) analyzing how model size and multi-modality affect performance across diverse reasoning tasks; and (4) highlighting the value of IQ tests as a standardized, human-referenced benchmark that enables longitudinal comparison of LLMs’ cognitive abilities relative to human norms. We further discuss the limitations of IQ tests as an AI benchmark and outline directions for more comprehensive evaluation of LLM reasoning capabilities. • Evaluated cognitive performance of popular LLMs using verbal and visual IQ tests. • Found a positive correlation between LLM size and cognitive performance across tasks. • Significant performance variability across problem types suggests nuanced differences in reasoning. • LLMs showed high proficiency in text-based reasoning but struggled with image-based tasks. • Established a new benchmark for multi-modal assessment, highlighting gaps in spatial reasoning.

  PublisherDOI

• Spatial and temporal evaluations of the liquid argon purity in ProtoDUNE-SP

  Journal of Instrumentation · 2025-09-01 · 3 citations

  articleOpen accessCorresponding

  Abstract Liquid argon time projection chambers (LArTPCs) rely on highly pure argon to ensure that ionization electrons produced by charged particles reach readout arrays. ProtoDUNE Single-Phase (ProtoDUNE-SP) was an approximately 700-ton liquid argon detector intended to prototype the Deep Underground Neutrino Experiment (DUNE) Far Detector Horizontal Drift module. It contains two drift volumes bisected by the cathode plane assembly, which is biased to create an almost uniform electric field in both volumes. The DUNE Far Detector modules must have robust cryogenic systems capable of filtering argon and supplying the TPC with clean liquid. This paper will explore comparisons of the argon purity measured by the purity monitors with those measured using muons in the TPC from October 2018 to November 2018. A new method is introduced to measure the liquid argon purity in the TPC using muons crossing both drift volumes of ProtoDUNE-SP. For extended periods on the timescale of weeks, the drift electron lifetime was measured to be above 30 ms using both systems. A particular focus will be placed on the measured purity of argon as a function of position in the detector.

  PublisherDOI

Recent grants

• UCI BIT Undergraduate Summer Research (BIT-SR) Program

  NIH · $381k · 2011–2016

• NIH Grant R01LM010235

  NIH · $507k · 2014

• Mining Structured Data with Applications in Chemistry and Biology

  NSF · $311k · 2005–2009

• EAGER: A Theory of Local Learning

  NSF · $150k · 2015–2017

• CRCNS: Collaborative Research: Characterizing Bayesian Surprise in Humans and Monkeys

  NSF · $171k · 2005–2008

Frequent coauthors

• Siwei Chen

  Third Hospital of Nanchang

  95 shared

• B. Rebel

  Fermi National Accelerator Laboratory

  84 shared

• M. Lokajı́ček

  Czech Academy of Sciences, Institute of Physics

  83 shared

• P. Vahle

  82 shared

• J. Zálešâk

  76 shared

• Marcelo A. Wood

  University of California, Irvine

  74 shared

• J. S. Réal

  Laboratoire de Physique Subatomique et de Cosmologie

  73 shared

• Dina P. Matheos

  University of California, Irvine

  72 shared

Education

• Ph.D., Computer Science

  University of California, Santa Barbara

  1986

• M.S., Computer Science

  University of California, Santa Barbara

  1982

• B.S., Computer Science

  University of Paris VI (Pierre et Marie Curie)

  1980

Awards & honors

• INNS Dennis Gabor Award (2023)