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Lixiao HuangView profile

Academic

• Human-AI-Robot Teaming
• Human Factors and Applied Cognition
• Emotional Attachment to Technology
• +3 more

Dr. Lixiao Huang serves as a Research Assistant Professor at Arizona State University's Center for Human, Artificial Intelligence, and Robot Teaming (CHART) within the Global Security Initiative, operating under the Ira A. Fulton Schools of Engineering. Her research integrates human factors principles with robotics and AI development to create effective human-technology systems. Education: Ph.D. in Human Factors and Applied Cognition, NC State University M.S. in Industrial/Organizational Psychology, Emporia State University M.Ed. in Applied Psychology with concentration in Human Resources Development, Beijing Normal University B.S. in Applied Psychology, Shanxi University Dr. Huang's research focuses on three interconnected domains: (1) Human-AI-Robot Teaming effectiveness through Distributed Dynamic Team Trust (D2T2) frameworks; (2) Modeling human emotional, behavioral, and cognitive responses to autonomous systems, particularly examining emotional attachment, intrinsic motivation, and trust dynamics; (3) Applying human factors methods to design user-friendly, safe, and trustworthy human-robot interfaces. Her work bridges robotics engineering with cognitive science to optimize team performance in complex environments. Analysis of her recent publications reveals a strong emphasis on dynamical systems approaches to human-automation interaction, with increasing focus on physiological metrics for team performance prediction and the development of standardized evaluation frameworks for artificial social intelligence. Her research increasingly incorporates machine learning techniques for real-time system adaptation. As founding chair of the Human-AI-Robot Teaming (HART) technical group at the Human Factors and Ergonomics Society, Dr. Huang has established herself as a leader in her field. She has served as research lead on multiple high-impact defense projects funded by ARL, ONR, and DARPA, focusing on next-generation combat vehicle systems and search-and-rescue applications. Dr. Huang directs the Human-Robot Coevolution Laboratory (HuRoCo), which develops experimental testbeds for studying human-technology teaming dynamics. Her lab emphasizes translational research that bridges theoretical human factors principles with practical robotics applications, particularly in safety-critical domains requiring high levels of human-automation coordination.

• Formal Methods
• Artificial Intelligence Safety
• Software Verification
• +7 more

Corina Pasareanu is a Principal Scientist at Carnegie Mellon University's CyLab Security and Privacy Institute and serves as a Technical Professional Leader for Data Science at NASA Ames Research Center through KBR. She holds a PhD in Computer Science from Kansas State University (2001), an MS (1995) and BS (1994) from the University Politehnica of Bucharest. Her research focuses on formal methods for trustworthy AI , including model checking, symbolic execution, compositional verification, and probabilistic software analysis. She pioneers techniques for verifying autonomous systems, neural networks, and cryptographic applications, with emphasis on safety-critical domains like autonomous vehicles and aerospace systems. Recent publications demonstrate strong focus on AI safety verification , including adversarial robustness of large language models, vision-based autonomous systems, and neural network interpretability. Her work integrates formal methods with machine learning to address security challenges in emerging AI technologies. Awards and honors: ACM Fellow (2023) IEEE ASE Fellow ETAPS Test of Time Award (2021) ASE Most Influential Paper Award (2018) ESEC/FSE Test of Time Award (2018) ISSTA Retrospective Impact Paper Award (2018) She leads major projects funded by DARPA, NSF, AWS, and NASA including: Trinity: Neurosymbolic Learning and Reasoning (DARPA) Proving Timing Side Channel Absence (AWS) Safety of Shared Control in Autonomous Driving (AAIP) Verifiable Federated Learning (CyLab) She advises PhD students at CMU and co-leads the CyLab Security and Privacy Institute's research initiatives.

• Database Systems
• Software Testing
• Program Analysis
• +7 more

Manuel Rigger is an Assistant Professor at the National University of Singapore's School of Computing, Department of Computer Science. He leads the Trustworthy Engineering of Software Technologies (TEST) Lab, focusing on improving data-centric systems' reliability. Prior to joining NUS, he was a postdoctoral researcher at ETH Zurich and completed his PhD at Johannes Kepler University Linz. His research centers on database systems reliability, software testing, and program analysis. Rigger's work has uncovered over 1,000 unique bugs in critical systems, significantly enhancing real-world software reliability. His research spans automated testing techniques, logic bug detection, query optimization, and compiler technologies, with strong practical impact through widely adopted tools like SQLancer. Rigger's publication record shows consistent high-impact contributions, with recent work focusing on database isolation levels, cardinality estimation testing, and query plan guidance. His research bridges theoretical foundations with practical applications, addressing fundamental challenges in data management systems. The TEST Lab's approach combines traditional testing methods with innovative learning-based techniques to automate database validation at scale. He has received numerous accolades including the EuroSys 2024 Best Paper Award, ICSE 2023 Distinguished Paper Award, and multiple Distinguished Artifact Awards. His work has been recognized with prestigious awards from Amazon Research, Google, and the Singapore government. EuroSys 2024 Best Paper Award for 'Validating Database System Isolation Level Implementations with Version Certificate Recovery' ICSE 2023 ACM SIGSOFT Distinguished Paper Award ASPLOS 2022, OSDI 2020, and OOPSLA 2020 Distinguished Artifact Awards AWS Database Services Amazon Research Award 2024 Singapore Open Research Award 2024 Rigger actively mentors students at all levels, with numerous Master's and Bachelor's theses supervised. His teaching includes courses on software engineering foundations and advanced software testing. He serves on program committees for major conferences including PLDI, ISSTA, and ASPLOS, and organizes workshops like the Fuzzing and Software Security Summer School. His research has practical industry impact with tools like SQLancer being widely adopted in the database community.

Omar MontasserView profile

Assistant Professor

• Machine Learning Theory
• Adversarial Robustness
• Distribution Shifts
• +2 more

Omar Montasser is an Assistant Professor in the Department of Statistics and Data Science at Yale University. His research focuses on theoretical foundations of machine learning with emphasis on adversarial robustness and distribution shifts, addressing critical challenges in modern AI reliability. His educational background includes: PhD in Computer Science from Toyota Technological Institute at Chicago (advised by Nathan Srebro) Combined BS/MS in Computer Science and Engineering from Penn State University (worked with Daniel Kifer and Sean Hallgren) Montasser's research program centers on developing theoretical frameworks for robust machine learning. He investigates PAC learnability under adversarial perturbations, transformation invariances, and distribution shifts, bridging gaps between theoretical guarantees and practical robustness. His work provides foundational insights into why deep learning models fail under distributional changes and how to design provably robust systems. Analysis of his publications from 2017-2025 reveals consistent focus on adversarial robustness theory. Key themes include minimax optimal learners for adversarial settings, OOD generalization guarantees, and computational aspects of robust learning. His most influential contributions characterize conditions for adversarially robust learnability and develop efficient algorithms for robust classification. Scientific recognition includes: FODSI-Simons Postdoctoral Fellowship COLT 2019 Best Student Paper Award Multiple oral presentations at NeurIPS (2022) Spotlight presentation at NeurIPS 2020 He actively recruits PhD students for his research group, emphasizing theoretical machine learning. Teaching experience includes Information and Coding Theory (Fall 2022) and Learning Theory (TA, Fall 2020). While specific grants aren't detailed, his publication record in top venues (NeurIPS, COLT, ICML) suggests substantial research funding. He maintains active collaborations with leading researchers including Avrim Blum and Nathan Srebro. Montasser leads a research group at Yale focused on theoretical machine learning, with current projects examining relaxed benchmarks in online classification and score matching in diffusion models. The group operates within Yale's Department of Statistics and Data Science, leveraging university resources for theoretical and computational research.

Narayana Prasad SanthanamView profile

Professor

• Information Theory
• Statistical Learning
• Signal Processing
• +11 more

Narayana Prasad Santhanam is a Professor in the Department of Electrical Engineering at the University of Hawaii, College of Engineering. His research spans theoretical and practical aspects of information theory, statistical learning, and signal processing, with particular focus on high-dimensional and complex problems that cannot be addressed by traditional statistical methods. Santhanam maintains an active research program funded by the National Science Foundation and teaches courses including Probability and Statistics, Linear Algebra and Machine Learning, and Information Theory. Santhanam's research interests center on the intersection of statistical learning and information theory, particularly in high-dimensional settings. His work addresses fundamental questions about when learning is possible, how to characterize non-uniform learning, and how to interpret data from complex sources like slow mixing Markov processes. He has made significant contributions to understanding the limitations of statistical methods in large alphabet scenarios, where traditional approaches fail. His research has important applications in diverse fields including genetic data analysis, risk management, smart grids, and text processing. His recent publications reveal a consistent focus on theoretical foundations with practical applications. Santhanam's work often explores the connections between seemingly disparate fields, bringing combinatorial and probabilistic approaches to bear on complex problems. A notable trend is his development of frameworks for pointwise convergence rather than uniform convergence in statistical estimation, which has significant implications for handling large alphabet problems where traditional methods break down. 2006 IEEE Information Theory Society Best Paper Award 2003 Capocelli Prize Santhanam has successfully secured significant research funding as Principal Investigator on an NSF award of approximately $1.1 million to examine the interplay of statistics and information theory, with applications to document classification and genetic analysis. He has also served as co-PI on multiple NSF awards totaling roughly $800,000 for research on channels with memory and smart grid organization. His research group includes students M. Asadi, A. Esraghi, A. Lee, M. Hosseini, R. Paravi, and G. Tobin, who contribute to projects spanning statistical learning, information theory, and their applications to biological and engineering problems. Santhanam has co-organized three major workshops on large alphabet information theory and statistics, bringing together over 80 researchers from diverse disciplines including biology, computer science, economics, information theory, mathematics, networking, and statistics.

Joshua TenenbaumView profile

Professor

• Computational Cognitive Science
• Artificial Intelligence
• Machine Learning
• +4 more

Joshua Tenenbaum is a Professor of Computational Cognitive Science in the Department of Brain and Cognitive Sciences at MIT, affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Center for Brains, Minds and Machines (CBMM). His research bridges human cognition and machine learning, focusing on perception, learning, and reasoning. He develops computational models inspired by Bayesian statistics and probability theory to explain human inductive reasoning, including causal inference, object perception, and language acquisition. Key projects include 3D object modeling from single images, neuro-symbolic AI frameworks, and theory-of-mind reasoning systems. His algorithms are widely used globally in science and engineering. Tenenbaum leads the Computational Cognitive Science Group at MIT, emphasizing human-like intelligence in machines and bidirectional insights between cognitive science and AI. His work spans visual computing, embodied intelligence, and cognitive AI, with applications in robotics, healthcare, and education. Recent research advances include understanding intuitive physics in humans and machines, moral judgment modeling, and multi-agent reasoning systems.

David EvansView profile

Professor

• Computer Science
• Machine Learning
• Cybersecurity
• +6 more

David Evans is a Professor of Computer Science at the University of Virginia, affiliated with the School of Engineering and Applied Science. His research focuses on the trustworthiness of machine learning, including privacy, security, fairness, and censorship. He co-teaches a joint Computer Science and Law course and has pioneered work in secure multi-party computation. Evans has won numerous teaching awards, including the Harold Morton Jr. SEAS Award (2003-4) and the Outstanding Faculty Award (2009). He is a co-author of influential textbooks like A Pragmatic Introduction to Secure Multi-Party Computation and Introduction to Computing . Education and Background: Evans earned his PhD, SM, and SB degrees from MIT. His academic genealogy traces back to Gottfried Wilhelm Leibniz. Research Interests: Evans leads the Security Research Group, focusing on adversarial machine learning, cryptography, and privacy-preserving technologies. His work includes developing defenses against adversarial examples, membership inference attacks, and secure protocols for distributed learning. He collaborates with institutions like the NSF Frontier Center for Trustworthy Machine Learning and the NSF AI Institute for Agent-based Cyber Threat Intelligence. Teaching and Outreach: Evans advocates for open education, making teaching materials freely available. He developed Udacity’s cs101: Building a Search Engine and cs387: Applied Cryptography , which have enrolled hundreds of thousands of students. His courses at UVA include Computational Biology , Theory of Computation , and Discrete Mathematics . Grants and Funding: His research is primarily funded by the National Science Foundation, with additional support from Lockheed Martin, Oracle, Google, and Microsoft. He leads projects exploring secure computation, privacy-preserving AI, and robust machine learning systems. Labs and Teams: Evans’ group is part of the NSF Frontier Center and the AI Institute for Cyber Threat Intelligence. His team includes graduate and undergraduate students working on cutting-edge problems in security and privacy.

Jacob AndreasView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Jacob Andreas is an Associate Professor of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), affiliated with the MIT-IBM Watson AI Lab and the School of Engineering. His research focuses on advancing artificial intelligence, machine learning, and natural language processing, with particular emphasis on neurosymbolic methods, language model reasoning, and interdisciplinary applications such as AI governance and bioacoustic analysis of sperm whale communication. He explores the intersection of language models and robotics, developing techniques for scalable system-level reasoning, code generation, and efficient computation allocation. His work also addresses societal challenges, including mitigating misinformation through generative AI and informing policy through white papers on AI governance. Andreas’s contributions span foundational ML theory (e.g., scaling laws) and applied systems (e.g., WhaleLM for bioacoustic analysis). He collaborates across disciplines, integrating cognitive science insights into model design and advocating for ethically aligned AI development. His research has been recognized in MIT’s EECS Faculty Awards and contributes to MIT’s broader mission of impactful technological innovation. While no specific awards are listed, his active role in high-impact projects underscores his academic leadership. His advising and grant work drive collaborative research in AI, with a focus on training next-generation researchers through the MIT-IBM Watson AI Lab and interdisciplinary initiatives.

Kamran DibaView profile

Associate Professor

• Neuroscience
• Neural Engineering
• Biomedical Computation and Modeling
• +5 more

Kamran Diba, Ph.D., is an Associate Professor in the Department of Anesthesiology at the University of Michigan Medical School. He is an affiliate faculty member in the Neuroscience Graduate Program and the Biomedical Engineering (BME) program. His research focuses on understanding how neuronal networks in the hippocampus generate oscillations and spike patterns that underlie memory formation and retrieval. The Diba Lab employs multi-channel electrophysiological recordings, optogenetics, and advanced computational methods to study neural circuits in freely moving rodents. Key research interests include the role of theta oscillations (5-12 Hz) and sharp-wave ripples (100-250 Hz) in hippocampal function, the encoding of spatial information via phase precession, and the impact of sleep and anesthesia on neural activity. The lab combines machine learning with experimental techniques to analyze high-dimensional spike train data and perturb neural circuits using optogenetics and chemogenetics. Recent work highlights the lab's exploration of how sleep loss disrupts hippocampal reactivation, the stabilization of neural networks under optogenetic manipulation, and the development of novel tools like 3D-printed microdrive systems for electrophysiological recordings. Dr. Diba's interdisciplinary approach bridges neuroscience, engineering, and computational modeling to uncover fundamental principles of memory processing. His lab is located at 3311 Med Sci I, with a primary website at diba-lab.github.io . Ongoing projects investigate synaptic plasticity resilience, hippocampal-prefrontal connectivity, and the dynamics of spike transmission between principal cells and interneurons.

• Tax Law
• Computational Law
• Legal Informatics

Sarah Lawsky is the Stanford Clinton Sr. and Zylpha Kilbride Clinton Research Professor of Law at Northwestern Pritzker School of Law. She leads research on computational approaches to tax law, notably co-developing Catala—a programming language for formalizing statutes—which was recognized as official software by the French government. Awarded the Inria International Chair (2024-2028), she collaborates with France's National Institute for Research in Digital Science on tax algorithm development. Her research explores tax law formalization, computational jurisprudence, and AI applications in legal reasoning. Recent publications focus on bridging statutory law with computational logic through tools like Catala, enabling transparent tax code implementation and government accountability. Publications demonstrate consistent focus on tax law computation, AI integration, and epistemic frameworks. Key themes include LLM capabilities in legal tasks, statute formalization methodologies, and interdisciplinary approaches to legal reasoning. Awards include the Inria International Chair for advancing computational tax law. Her work with Catala supports French government initiatives to modernize income tax and social benefit algorithms.

Junfeng YangView profile

Professor

• Reliable Systems
• Computer Security
• Software Engineering
• +3 more

Junfeng Yang is a Professor in the Department of Computer Science at Columbia University and co-director of the Software Systems Lab. His research focuses on reliable and secure systems, including tools for software protection, cloud programming systems, and machine learning robustness. He earned his BS from Tsinghua University and PhD from Stanford University, where he developed eXplode, a system for detecting storage system errors. Yang has held roles at Microsoft Research and co-founded NimbleDroid, a Columbia spin-off focused on app performance analysis. Key awards include the Sloan Research Fellowship (2012), AFOSR YIP Award (2012), and NSF CAREER Award (2011). His work has been featured in venues like ACM Tech News and Linux Weekly News. He advises numerous PhD students and has contributed to projects such as DeepXplore (best paper at SOSP '17) and Chablis (best paper at CIDR '24). His teaching includes courses on software engineering, blockchain systems, and reliable software design. Yang’s contributions span academic grants from NSF, ONR, and DARPA, alongside industry collaborations with Google, Facebook, and Amazon. His lab develops systems for concurrency, security, and AI-driven software tools, reflecting a commitment to bridging theory and practical system implementation.

Gillian SmithView profile

Associate Professor

• Computational Creativity
• Game Design
• Computer Science Education
• +3 more

Gillian Smith is an Associate Professor in the Computer Science department at Worcester Polytechnic Institute (WPI) and Director of the Interactive Media & Game Development program since 2021. Her interdisciplinary research bridges AI, human-computer interaction, and traditional crafts to address social justice and education equity. PhD, MS, and BS in Computer Science from University of California Santa Cruz and University of Virginia Her research focuses on generative design, computational creativity, and feminist approaches to game development. She leads the NSF-funded Code Crafters project, which teaches computational thinking through quilting, and co-created games like Threadsteading (embroidery machine gameplay) and eBee (electricity education via quilting). She also explores ungrading pedagogy to shift assessment paradigms in STEM education. Recent scholarly work emphasizes applications of procedural content generation in games and crafts. Articles like “My Brain Does Not Function That Way” (2022) analyze intersections between computing education and textile arts, while her 2016 Threadsteading project redefines interaction with fabrication devices. Key Awards: 2016 Indiecade award for Threadsteading WPI Board of Trustees’ Outstanding Academic Advising award (2020) PIT-UN network participant (2021) Smith advises projects across WPI’s labs, including the Crafting Computational Creativity Research Group and Interaction Lab. She has secured over $300K in NSF funding for computational thinking initiatives and collaborates with Disney Research Pittsburgh. Her work aligns with UN SDGs for Quality Education (SDG 4), Gender Equality (SDG 5), and Reducing Inequality (SDG 10).

Michael CarbinView profile

Associate Professor

• Programming Languages
• Software Engineering
• Probabilistic Programming
• +5 more

Michael Carbin is an Associate Professor at the Massachusetts Institute of Technology in the Department of Electrical Engineering and Computer Science, where he leads the Programming Systems Group at the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research spans programming languages, software engineering, systems, and security with a focus on language-driven systems that operate in uncertain environments including perception, neural networks, and unreliable hardware. Carbin's research interests center on programming languages and systems that operate effectively in uncertain environments. His work spans probabilistic programming, quantum computing, neural network optimization, and approximate computing. He investigates how to design language-driven systems that can handle uncertainty in perception, implementation (neural networks or approximate transformations), and execution (unreliable hardware). His research bridges theoretical foundations with practical systems implementation, often developing novel programming models, compilers, and runtime systems. His publication record shows consistent contributions across multiple domains including programming languages (PLDI, POPL), machine learning (ICLR, NeurIPS), and systems (ASPLOS, OSDI). Recent work focuses on neural network pruning, quantum programming, probabilistic inference, and compiling programs to neural networks. His research demonstrates strong interdisciplinary connections between programming languages, machine learning, and systems. Carbin has received numerous prestigious awards including the Sloan Research Fellowship (2020), NSF CAREER Award (2018), and multiple best paper awards at top conferences including ICLR (2019), OOPSLA (2014, 2013), and ICFP (2019). He has also been recognized with the MIT Frank E. Perkins Award for Excellence in Graduate Advising (2020). Carbin actively serves the academic community as a program committee member for major conferences including MLSys (as Program Co-Chair in 2023), POPL, PLDI, and NeurIPS. He has advised numerous graduate students who have gone on to positions at leading technology companies and research institutions. His Programming Systems Group at CSAIL focuses on creating robust, efficient systems that can handle uncertainty across multiple dimensions of computing.

Yao MaView profile

Assistant Professor

• Machine Learning
• Graph Neural Networks
• Natural Language Processing
• +3 more

Yao Ma is an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). His research bridges graph machine learning, natural language processing, and trustworthy AI, with a focus on scalable and robust computational frameworks. He directs the Graph Machine Learning Lab at RPI, advancing methodologies for graph condensation, neural architecture robustness, and foundation models. Research Interests: Dr. Ma's work spans graph neural networks (GNNs), adversarial robustness, language model efficiency, and data-centric AI. Key innovations include techniques for graph condensation, trustworthiness in LLMs, and multi-agent learning systems. His recent surveys systematize advancements in small language models and graph foundation models, highlighting scalability and transferability challenges. Publication Trends: His 2024–2025 articles emphasize graph-based NLP, efficient data valuation, and adversarial defenses. Dominant themes include graph condensation (6 papers), LLM/GNN integration (4 papers), and robustness benchmarking (3 papers), reflecting a cohesive agenda in scalable, reliable graph AI. Awards & Advising: No awards or students are noted in available sources. Lab & Team: Leads the Graph Machine Learning Lab at RPI, focusing on theoretical and applied graph AI. Collaborative projects include cross-departmental initiatives in quantum computing and NLP.

Sanaz Rahimi MoosaviView profile

Assistant Professor

• Blockchain
• Internet of Things
• Lightweight Cryptography
• +4 more

Dr. Sanaz Rahimi Moosavi is an Assistant Professor of Computer Science at California State University Dominguez Hills, specializing in IoT security and blockchain applications. She holds a PhD in Information and Communication Technology from the University of Turku, Finland, and previously served as a Postdoctoral Researcher at UC Irvine. Her research focuses on lightweight security solutions for IoT systems, with applications in healthcare technology, cryptographic key management, and privacy-preserving AI systems. Current projects include NSF-funded work on physiological biometric deidentification and privacy-preserving tutoring systems. Dr. Moosavi mentors numerous graduate and undergraduate researchers, with students working on projects spanning blockchain healthcare systems, network intrusion detection, and privacy-preserving machine learning. She has received multiple awards including Nokia Foundation Research Excellence grants and NSF collaborative research funding. NSF SaTC Award (2024-2027) NSF CISE-MSI Award (2022-2025) Nokia Foundation Awards (2014, 2016)