Shrikanth NarayananNiki and Max Nikias Chair in Engineering and University Professor of Electrical and Computer Engineering, Computer Science, Linguistics, Psychology, Pediatrics, and Otolaryngology
Leads SAIL on speech and multimodal signal processing, behavioral signal analysis and human-centered machine intelligence.
Speech recognition & spoken language
Speech enhancement & signal processing
Multimodal & grounded language
2026A Long-Form Single-Speaker Real-Time MRI Speech Dataset and BenchmarkICASSP
2026A Point Process Model of Skin Conductance Responses in a Stroop Task for Predicting Depression and Suicidal IdeationICASSP
Studies robustness, interpretability and evaluation of NLP models and large language models.
Interpretability, evaluation & safety of LMs
Large language models
Trustworthy & safe ML
2026Cancer-Myth: Evaluating Large Language Models on Patient Questions with False PresuppositionsICLR
2026EPSVec: Efficient and Private Synthetic Data Generation via Dataset VectorsICML
25 T1 · 44 papers · 7 members · 10 alumni
USUSCComputer Science
Vatsal SharanAssistant Professor of Computer Science
Studies foundations of machine learning, including learning theory and memory-sample tradeoffs.
Learning theory & optimization
2026A Unified Approach to Memory-Sample Tradeoffs for Detecting Planted StructuresSTOC
2026An External Fairness Evaluation of LinkedIn Talent SearchAAAI
25 T1 · 31 papers · 7 members · 6 alumni
USUSCDepartment of Industrial and Systems Engineering
Meisam RazaviyaynAndrew and Erna Viterbi Early Career Chair and Associate Professor Industrial and Systems Engineering, Computer Science, and Electrical and Computer Engineering
Researches optimization for machine learning, including nonconvex min-max problems and fair learning.
Learning theory & optimization
Trustworthy & safe ML
2026It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online OptimizationICLR
2026TNT: Improving Chunkwise Training for Test-Time MemorizationICLR
21 T1 · 32 papers · 6 members · 10 alumni
USUSCComputer Science
Sai Praneeth KarimireddyAssistant Professor of Computer Science and Electrical and Computer Engineering
Works on optimization and federated, collaborative learning, including incentives for data sharing.
Learning theory & optimization
Trustworthy & safe ML
Human-AI collaboration & trust
2026Beyond the Trade-off: Unifying Fairness and Performance in Federated LearningICML
2026Beyond URLs: Metadata Diversity and Position for Efficient LLM PretrainingICLR
Works on machine learning for decision making, Bayesian optimization and generative AI.
Reinforcement learning
Learning theory & optimization
2026Auditing Black-Box LLM APIs with a Rank-Based Uniformity TestICLR
2026Demystifying and Enhancing the Efficiency of Large Language Model Based Search AgentsICLR
18 T1 · 20 papers · Group size not listed
USUSCComputer Science
Jieyu ZhaoWiSE Gabilan Assistant Professor of Computer Science
Studies fairness, accountability and trustworthiness of NLP models and LLMs.
Interpretability, evaluation & safety of LMs
Large language models
Trustworthy & safe ML
2026CoAct-1: Computer-using Agents with Coding as ActionsICLR
2026Detecting and Filtering Unsafe Training Data via Data Attribution with Denoised RepresentationICML
17 T1 · 37 papers · 7 members · 19 alumni
USUSCElectrical and Computer Engineering
Mahdi SoltanolkotabiAndrew and Erna Viterbi Early Career Chair and Professor of Electrical and Computer Engineering, Computer Science, and Industrial and Systems Engineering
Researches theory of deep learning, optimization and AI foundations for science.
Learning theory & optimization
2026FoNE: Precise Single-Token Number Embeddings via Fourier FeaturesICLR
2026Learning to Recall with Transformers Beyond Orthogonal EmbeddingsICLR
Leads the DILL lab on datasets, interpretability and evaluation of language models.
Interpretability, evaluation & safety of LMs
Large language models
Trustworthy & safe ML
2026Are We Automating the Joy Out of Work? Designing AI to Augment Work, Not MeaningCHI
2026Believing without Seeing: Quality Scores for Contextualizing Vision-Language Model ExplanationsACL
16 T1 · 35 papers · 10 members · 13 alumni
USUSCComputer Science
Yan LiuProfessor of Computer Science, Electrical and Computer Engineering, and Biomedical Sciences, and Fletcher Jones Foundation Chair in Computer Science
Leads the Melady lab on time-series machine learning, spatiotemporal forecasting and healthcare AI.
2026"Someone Hid It!": Query-Agnostic Black-Box Attacks on LLM-Based RetrievalICML
2026Adaptive Collaboration with Humans: Metacognitive Policy Optimization for Multi-Agent LLMs with Continual LearningICLR
16 T1 · 31 papers · 6 members · 17 alumni
USUSCComputer Science
Ibrahim SabekAssistant Professor of Computer Science and Spatial Sciences
Builds ML-enhanced data systems, such as learned indexes and query optimizers, in the NexDIG group.
Data management & big data
2026How Can Quantum Computing and Databases Meet “in Practice”? From Algorithms to SystemsVLDB
2026Is Quantum Computing Ready for Real-Time Database Optimization?ICDE
15 T1 · 31 papers · 6 members · 17 alumni
USUSCComputer Science
Maja MataricChan Soon-Shiong Chair and Distinguished Professor of Computer Science, Neuroscience, and Pediatrics
Develops socially assistive robots for therapy, rehabilitation and education in the Interaction Lab.
Human-robot interaction & assistive robots
Language & society
Learning & education technology
2026Can LLMs Truly Embody Human Personality? Analyzing AI and Human Behavior Alignment in Dispute ResolutionAAAI
2026Can Large Language Models Infer Human Actions and Motives? Evaluation in Social Prediction and Inspection GamesFindings of ACL
15 T1 · 44 papers · Group size not listed
USUSCElectrical and Computer Engineering
Priyadarshini PandaLloyd F. Hunt Early Career Chair of Electrical & Computer Engineering and Associate Professor of Electrical & Computer Engineering
Designs efficient AI hardware and algorithms, including spiking neural networks and neuromorphic computing.
Accelerators, chips & architecture
Efficient training & inference
2026MD-SNN: Membrane Potential-aware Distillation on Quantized Spiking Neural NetworkDATE
2025Assortment of attention heads: Accelerating federated PEFT with head pruning and strategic client selectionTMLR
15 T1 · 39 papers · 7 members · 11 alumni
USUSCElectrical and Computer Engineering
Stephen TuAssistant Professor of Electrical and Computer Engineering and Computer Science
Studies learning for control, imitation learning and statistical learning with dynamical systems.
Learning theory & optimization
Manipulation & robot learning
2026CLAM: Continuous Latent Action Models for Robot Learning from Unlabeled DemonstrationsIROS
2026CrispEdit: Low-Curvature Projections for Scalable Non-Destructive LLM EditingICML