Yuan Tian is a Visiting Assistant Professor in the Department of Computer Science at the University of Virginia. His research focuses on security and privacy, with a particular emphasis on cyber-physical systems, machine learning applications in vulnerability detection, and human-computer interaction challenges in privacy preservation. He has collaborated with industry leaders such as Google, Facebook, Microsoft, Samsung, and others to address real-world security and privacy issues in mobile systems and IoT devices. His work spans theoretical frameworks for automated vulnerability repair to practical tools like AuthSaber for OAuth security verification. Key research areas include: Development of LLM-powered systems for vulnerability detection (e.g., VulBinLLM) Post-fuzzing analysis of firmware (FirmRCA) Privacy perception studies in smart environments (e.g., WiFi services and commercial buildings) Hardware/software co-design security for IoT His recent work bridges machine learning and cybersecurity, addressing threats in smart contracts, federated learning, and adversarial attacks. Notable contributions include frameworks for privacy-preserving inference in edge networks and sonar-based authentication systems for smart speakers. Awards and recognition: No specific awards listed, though his work has been widely adopted by industry partners. His research has been published in top-tier venues, including S&P, CCS, and IEEE S&P, reflecting its technical impact.
Taylor Berg-Kirkpatrick is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego. They lead the BergLab, focusing on natural language processing (NLP), machine learning, and unsupervised methods for analyzing diverse data types, including historical documents, music, and early modern books. Their work bridges computational techniques with interdisciplinary domains such as music information retrieval, paleography, and cultural heritage analysis. Taylor has been awarded an NSF CAREER grant for research on language evolution via probabilistic models. The BergLab develops tools like Klavier (music transcription), Ocular (historical document recognition), and Puck (constituency parsing). Taylor's research emphasizes deciphering hidden structures in human data, from handwritten texts to musical patterns. Their teaching includes courses on algorithms for NLP at CMU and UC Berkeley. Taylor advises numerous PhD students, many of whom have contributed to impactful projects such as compositor attribution in Shakespeare's works, music separation algorithms, and climate text understanding benchmarks like ClimaBench. Their work is featured in top venues like ACL, NeurIPS, and ICML, with applications spanning from improving AI safety to enhancing cultural preservation efforts.
Anna Rogers is a leading researcher in Natural Language Processing (NLP), known for her foundational and critical work on language model interpretability, dataset quality, and the ethics of AI systems. She has published extensively at top-tier venues including ACL, EMNLP, and NAACL, often collaborating with prominent researchers like Anna Rumshisky and Isabelle Augenstein. Her research interests center on data curation , model generalization , responsible data use , and transparency in large language models . She has conducted influential surveys on community perspectives regarding intelligence in AI and has contributed to improving peer review practices in NLP conferences. Her work on BERTology, including papers like A Primer in BERTology and Revealing the Dark Secrets of BERT , has become key reading in the field. Recent publications highlight her focus on data transparency (e.g., the ROOTS Search Tool), synthetic content detection , and temporal annotation frameworks like NarrativeTime. She also investigates the robustness of models, probing techniques, and the limitations of current benchmarks. Her work consistently emphasizes methodological rigor and ethical considerations in NLP research. She has advocated for better data practices and more thoughtful evaluation paradigms across the community. Natural Language Processing Computational Linguistics Language Model Interpretability Dataset Curation and Quality Ethics in NLP Peer Review in Academic Conferences Anna Rogers has played a key role in shaping meta-discussions in the NLP field, including peer review reform and the societal impact of language technologies. She has co-organized workshops on negative results and contributed to tutorials on reviewing and data use. Her work bridges technical depth with critical reflection on the direction of the field. She has advised or collaborated with numerous researchers, though specific students are not listed in the available data. She has not received any explicitly mentioned scientific awards in the provided text. Anna Rogers leads or contributes to several open-source initiatives and shared resources, including the DECAF framework, the ROOTS Search Tool, and various datasets such as TimeBankNT and RuSentiment. Her work often includes released code and tools to promote reproducibility and further research.
Youngjin Kwon is an Associate Professor at the School of Computing, KAIST, and a member of the Computer Architecture and Systems Lab (CASYS). His work bridges systems research with practical applications in memory management, concurrency debugging, and energy-efficient computing. Recipient of Best Paper Awards at ACM SOSP'24, SOSP'21, and USENIX ATC'18 Honored with KAIST's EWON Endowed Chair Professor (2021) and Soo-Young Lee Teaching Innovation Award (2021) Awarded KAIST Breakthrough (2023) and Technology Innovation Award (2023) Research focuses on: Processing-in-Memory (PIM) and DRAM architectures Concurrency bugs in kernel and hypervisor systems Energy-efficient frameworks for large language models Memory disaggregation and tiered memory systems Trusted Execution Environments (TEE) for secure computing SmartNIC offloading and eBPF-based optimizations His recent publications highlight innovations in speculative decoding, memory safety, and system scalability. Awards reflect both technical excellence and pedagogical impact. He actively mentors graduate and undergraduate students in his lab, emphasizing hands-on research in cutting-edge system design.
Mayur Naik is the Misra Family Professor in the Department of Computer and Information Science at the University of Pennsylvania’s School of Engineering and Applied Science. His research lies at the intersection of programming languages and artificial intelligence, with a current focus on neurosymbolic programming, trustworthy AI for healthcare, and AI-enabled software engineering tools. Education & Career: Ph.D. in Computer Science, Stanford University (2008) – advisor Alex Aiken M.S. in Computer Science, Purdue University (2003) – advisor Jens Palsberg B.E. in Computer Science, BITS Pilani (1999) Former faculty at Georgia Institute of Technology and researcher at Intel Labs, Berkeley Research Interests: Naik’s group develops languages, algorithms, and compilers for neurosymbolic programming, an emerging paradigm that unites symbolic reasoning with data-driven learning. Their flagship system is the open-source Scallop language and toolchain, applied to computer vision, cybersecurity, medicine, and bioinformatics. He also investigates AI-assisted programming tools that boost productivity and software quality by marrying traditional program analysis with modern machine learning. Recent Highlights: In 2024 he was named Misra Family Professor; his former student Elizabeth Dinella received the 2025 ACM SIGSOFT Outstanding Dissertation Award; his team released IRIS , an LLM-assisted static analysis framework for security vulnerabilities, and published the first comprehensive book on Neurosymbolic Programming in Scallop . Teaching: He regularly teaches CIS 5470 (Software Analysis) every Fall and CIS 5500 (Database Systems) every Spring, both of which are also delivered in Penn’s MCIT Online and Georgia Tech’s OMSCS programs. Advising & Service: Naik has graduated 8 Ph.D. students and mentored numerous postdocs and undergraduates; many alumni now hold faculty or research positions worldwide. He has served on organizing, program, and steering committees for premier venues such as PLDI, POPL, OOPSLA, SPLASH, ESEC/FSE, ISSTA, SAS, and others.
Prateek Mittal is a Professor in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. His research spans multiple critical areas at the intersection of security, privacy, and machine learning, with a particular focus on developing robust and privacy-preserving AI systems. Dr. Mittal's research interests center on machine learning security and privacy, with specific expertise in adversarial machine learning, differential privacy, backdoor attacks and defenses, and network security. His work addresses fundamental challenges in ensuring that AI systems remain secure against sophisticated attacks while preserving user privacy. He has made significant contributions to certifiable defenses against adversarial examples, privacy-preserving machine learning techniques, and security mechanisms for large language models. His recent publications demonstrate a strong trend toward addressing emerging security challenges in large language models and foundation models, including privacy auditing, safety alignment, and robustness against novel attack vectors. His work shows increasing focus on practical applications of theoretical security concepts to real-world AI systems. Dr. Mittal has mentored numerous PhD students who have become active contributors to the security and machine learning research community. His lab has received significant research funding from various sources to support their innovative work at the security-privacy-ML intersection. He leads research efforts in multiple labs and collaborative projects focused on building trustworthy AI systems, with strong connections to both theoretical computer science and practical security applications. His team regularly publishes in top-tier venues including IEEE S&P, USENIX Security, NeurIPS, ICML, and ICLR.
Isaac Kohane is a Research Professor at Boston Children's Hospital , leading the Computational Health Informatics Program. His work bridges genomics, biomedical informatics, and machine learning to diagnose rare diseases and advance precision medicine. He co-leads large-scale collaborations like the Undiagnosed Diseases Network and 4CE Consortium , focusing on electronic health record analysis and neurodevelopmental disorders . Research Interests include: Genomic discovery of rare neurodevelopmental syndromes (e.g., variants in PPFIA3 , SDHA , ZBTB47 ) Machine learning for clinical risk prediction (e.g., suicide attempt modeling, type 2 diabetes subtyping) Ethical challenges in genomic research and AI implementation Health disparities in genetic variant interpretation International multi-institutional data harmonization Recent Publications emphasize genotype-phenotype mapping , AI-driven biomarker discovery , and repurposing real-world data for long COVID and autoimmune disease studies. His work highlights cross-modal data integration and multi-omics approaches in rare disease diagnostics.
Yifan Yu is an Assistant Professor at the Department of Information, Risk, and Operations Management, McCombs School of Business, The University of Texas at Austin. He holds a Ph.D. in Information Systems from the University of Washington and bachelor's/master's degrees in Management Science and Engineering from Tsinghua University. Research Focus: Economics of AI/ML, unstructured data analytics (text/images/video), and data-driven sustainable operations. Methodologies: Machine/deep learning, network analysis, econometric/game-theoretical modeling, and experiments. Publications: Appeared in top journals like Management Science , MIS Quarterly , and Information Systems Research . Awards: Best paper recognitions at INFORMS, CIST, WITS, and teaching awards from University of Washington.
Jiawei Chen is a "Hundred Talent" Research Fellow at Zhejiang University's College of Computer Science and Technology, with over 60 publications in top-tier venues including WWW, SIGIR, KDD, and NeurIPS. His research focuses on advancing recommender systems through innovative approaches to debiasing, graph-based learning, and large language model integration. His educational background includes: Ph.D. in Computer Science, Zhejiang University (2017-2020) Master's in Computer Science, Zhejiang University (2014-2017) Bachelor's in Micro-electronic, University of Electronic Science and Technology of China (2010-2014) Chen's research centers on solving fundamental challenges in recommender systems, particularly addressing popularity bias through spectral analysis and causal inference. His work bridges graph mining, knowledge representation, and large language models to develop robust recommendation frameworks. Recent contributions include pioneering graph transformers for ranking optimization and counterfactual reasoning to burst filter bubbles. His influential surveys on recommendation debiasing and deep clustering have established foundational taxonomies for the field. Analysis of his 2023-2025 publications reveals three dominant trends: (1) Deepening causal approaches to bias mitigation through counterfactual interventions and distributionally robust optimization, (2) Advancing graph-based architectures with sign-aware transformers and uncertainty-aware structure learning, and (3) Integrating large language models through knowledge distillation techniques to enhance sequential recommendation. His work consistently targets high-impact solutions to popularity bias while maintaining strong theoretical grounding. His research excellence has been recognized with: Best Paper Award at WSDM 2025 for spectral analysis of popularity bias amplification Best Paper Honorable Mention at SIGIR 2023 for offline reinforcement learning in recommendation Chen actively mentors future researchers and seeks self-motivated graduate students for projects in recommendation systems, LLMs, and graph mining. His extensive service as program committee member for WWW, AAAI, KDD, and SIGIR—along with reviewing for IEEE TNNLS, TKDE, and TOIS—demonstrates significant community leadership. He has released valuable community resources including the KuaiRec and KuaiRand datasets for unbiased recommendation research. While specific lab affiliations aren't detailed, Chen maintains strong collaborative ties with researchers across institutions, particularly with Prof. Xiangnan He's group. His work frequently involves large-scale industrial datasets and open-source tools like EasyRL4Rec, indicating leadership in practical recommendation system development and community resource sharing.
Alexander Nikitin is a Postdoctoral Researcher in the Department of Computer Science at Aalto University, working within the Probabilistic Machine Learning group under Professor P. Marttinen. His research integrates human expertise with machine learning systems to solve complex real-world problems, particularly in predictive maintenance and uncertainty quantification. He holds a Master's degree in Engineering and Technology from the State University Higher School of Economics, awarded in 2019, and completed his doctoral thesis at Aalto University in 2024. Nikitin's work centers on Human-in-the-Loop systems where human feedback enhances AI performance, predictive maintenance using graph-based models for industrial applications, and uncertainty quantification in large language models. His approach combines probabilistic methods with deep learning to address challenges in spatiotemporal data analysis, domain adaptation, and non-separable systems. Recent publications demonstrate his focus on practical implementations where machine learning directly impacts operational efficiency. Analysis of his 2021-2025 publications reveals a clear trajectory toward industrial AI applications, with increasing emphasis on human-AI collaboration frameworks. His work bridges theoretical machine learning advances (like kernel entropy methods for LLMs) with concrete use cases in workstation monitoring and synthetic time series generation, frequently appearing in top-tier venues like NeurIPS and KDD. He actively contributes to major research initiatives including the FCAI Flagship 2 project (2022-2026), which develops next-generation AI systems through collaboration between Aalto University, the University of Helsinki, and industry partners like Elisa. His earlier work on error log analysis for predictive maintenance (2020-2023) established foundations for current human-in-the-loop approaches. Nikitin operates within Aalto's Probabilistic Machine Learning ecosystem and the Finnish Center for Artificial Intelligence (FCAI), where he collaborates with leading researchers including Sami Kaski and P. Marttinen on projects spanning generative modeling, workstation maintenance, and large-scale AI deployment.
Professor Nigel Collier is a leading academic in Natural Language Processing at the University of Cambridge, holding positions as Professor of Natural Language Processing, Fellow of the Alan Turing Institute, Co-Director of the Language Technology Lab, and Professorial Fellow of Murray Edwards College. He serves within the Faculty of Modern and Medieval Languages and Linguistics, Department of Theoretical and Applied Linguistics. His educational background includes a BSc in Computer Science from the University of Leeds (1992), MSc in Machine Translation (1994), and PhD in Computational Linguistics (1996) from the University of Manchester. His doctoral research focused on English-Japanese Lexical Transfer using Hopfield Neural Networks. Professor Collier's research spans core machine learning for NLP with particular expertise in Information Extraction, Text Mining, Social Media Analysis, Textual Inference, and Generation. His work integrates text with knowledge graphs, addresses fact verification challenges, and explores applications in biomedicine, epidemiology, and public health. Recent research focuses on LLM evaluations including adversarial attacks, policy violations, uncertainty modeling, and synthetic personalities. His publication record demonstrates consistent contributions to top-tier venues including ACL, EMNLP, and CoNLL, with research themes evolving from early biomedical text mining systems like BioCaster to contemporary large language model research. His work shows strong interdisciplinary connections between linguistics, computer science, and healthcare applications. Fellowship, Alan Turing Institute for data science and artificial intelligence (2017) EPSRC Experienced Research Fellow (2014) Marie Curie International Research Fellowship (2012) Japan Science and Technology Agency Research Fellowship (2008) Japan Society for the Promotion of Science Visiting Fellowship (2002) Toshiba Corporation Research Fellowship (1996) Professor Collier actively supervises PhD students and has mentored numerous researchers who now hold prominent positions at institutions including Google DeepMind, Cohere, Amazon Alexa, and academic posts worldwide. His research has been funded by major agencies including EPSRC, ESRC, MRC, EU FP7, and JST. He co-founded Trismik, a spinout company launched in May 2025, serving as Chief Scientist. The Language Technology Lab, which he co-directs, serves as the primary research hub for his team's work in computational linguistics and NLP. The lab maintains strong connections with the Alan Turing Institute and focuses on both theoretical advances and real-world applications of language technology.
Tom McCoy is an Assistant Professor in the Department of Linguistics at Yale University, with additional affiliations in the Department of Computer Science and the Wu Tsai Institute. His interdisciplinary research bridges computational linguistics, cognitive science, and artificial intelligence to understand the computational principles underlying human language. Dr. McCoy completed his PhD in Cognitive Science at Johns Hopkins University in 2021, where his graduate training emphasized interdisciplinary approaches to studying language acquisition from multiple theoretical and methodological perspectives. McCoy's research centers on two primary strands: language learning mechanisms investigating how humans acquire language from minimal data and how these capabilities can be replicated in machines, and linguistic representations exploring what computational architectures can effectively model language structure. His work frequently employs neural network language models while maintaining strong connections to linguistic theory and cognitive science principles. He has published influential work on hierarchical inductive biases in sequence-to-sequence networks and generalization capabilities of neural networks with respect to center embeddings. His recent publications reveal a research trajectory increasingly focused on the intersection of neural networks and cognitive science, examining inductive biases, Bayesian approaches to language learning, and the symbolic-subsymbolic debate in AI. His work appears in top venues including Nature Communications, Proceedings of the National Academy of Sciences, and leading AI conferences. McCoy actively mentors the next generation of researchers, currently considering PhD applicants for Fall 2026 and postdoctoral researchers for Fall 2026. He maintains a selective research group focused on computational linguistics and cognitive AI, prioritizing quality mentorship and methodological rigor. Though specific lab names aren't publicized, McCoy's research operates at the intersection of multiple departments at Yale, suggesting a collaborative environment where linguists, computer scientists, and cognitive scientists work together to tackle fundamental questions about language processing in both humans and machines.
Jon Kleinberg is the Tisch University Professor at Cornell University , with affiliations in the Department of Computer Science and Department of Information Science . His research explores the intersection of algorithms, networks, and societal systems, supported by grants from NSF, ONR, MacArthur Foundation, Packard Foundation, Simons Foundation, Sloan Foundation, and Vannevar Bush Faculty Fellowship. Education : Not explicitly detailed in the provided text. Research Interests : Algorithms, network theory, human-AI collaboration, computational social science, fairness in AI, and societal implications of large-scale information systems. Recent Publications : Focus on algorithmic fairness, human-AI delegation, generative model limitations, social network polarization, and technical aspects of decentralized decision-making. Key venues include NeurIPS , ICML , AAAI , EC , and FAccT . Scientific Recognition : NSF CAREER Award ONR Young Investigator Award MacArthur and Packard Fellowships Simons Investigator Vannevar Bush Faculty Fellowship Member of National Academy of Sciences, National Academy of Engineering, American Academy of Arts and Sciences, and American Philosophical Society Advising and Grants : Mentored numerous PhD students and postdocs, including Katherine Van Koevering, Katy Blumer, and Harini Suresh. Collaborated with institutions like Microsoft Research, Google, and Facebook on projects involving network analysis, algorithmic fairness, and social dynamics. Labs and Teams : Leads research groups focused on network theory, algorithmic fairness, and human-AI interaction, with collaborations across computational social science and machine learning communities.
Tri Dao serves as Assistant Professor of Computer Science at Princeton University and Chief Scientist at Together AI, where he bridges machine learning theory with systems optimization. His work focuses on developing efficient algorithms for deep learning training and inference, particularly in hardware-aware computation and sequence modeling. Education Ph.D. in Computer Science, Stanford University (2023) Research Focus : Dao pioneers techniques for long-range memory in sequence models and structured matrices for compact deep learning , evidenced by breakthroughs like FlashAttention and Mamba. His research integrates theoretical insights with practical systems constraints, targeting memory efficiency, computational speed, and scalability in large language models. Key contributions span attention mechanisms, state space duality, and hardware-aware algorithm design. Publication Impact : Recent work (2022-2025) reveals a trajectory from foundational theory (e.g., state space duality in Mamba-2) to deployable systems (FlashAttention-3), addressing critical bottlenecks in LLM training and inference. Publications consistently emphasize hardware-aware optimizations, memory efficiency, and linear-complexity alternatives to attention mechanisms. Scientific Recognition Outstanding Paper Honorable Mention (MLSys 2025) Outstanding Paper Honorable Mention (NeurIPS 2024) Outstanding Paper (COLM 2023) Best Paper award (ICML Workshop 2022) Stanford Open Source Software Prize (2024) Outstanding Paper runner-up (ICML 2022) Mentorship and Funding : Dao actively advises PhD students Ted Zadouri, Berlin Chen, and Wentao Guo on sequence modeling and systems research. While specific grants aren't detailed, his dual Princeton/Together AI roles indicate sustained funding for open-source LLM development and hardware-aware ML research. Research Ecosystem : Leading Princeton's machine learning systems efforts while driving Together AI's open-source initiatives, Dao operates at the academic-industry nexus. His work directly informs real-world LLM deployment through tools like FlashAttention (adopted by major frameworks) and Mamba (enabling efficient long-context modeling).
Xiaorui Liu is an Assistant Professor in the Department of Computer Science at North Carolina State University's College of Engineering, where he joined the faculty in August 2022. He also holds a courtesy appointment in the Department of Electrical and Computer Engineering. His research focuses on large-scale machine learning, trustworthy artificial intelligence, and deep learning on graphs, with applications across various domains including networking, cybersecurity, manufacturing, biology, and healthcare. He has established himself as a leading researcher in graph neural networks and scalable machine learning systems. Dr. Liu's educational background includes: Ph.D. in Computer Science from Michigan State University (2022) M.S. in Computer Science from South China University of Technology (2017) B.S. in Computer Science from South China University of Technology (2015) Dr. Liu's research interests span several cutting-edge areas in artificial intelligence and machine learning. His primary focus is on developing scalable and trustworthy machine learning systems , with particular emphasis on graph neural networks, large language models, and robust AI. His work addresses fundamental challenges in large-scale optimization , distributed machine learning , and adversarial robustness . He explores how to make AI systems more reliable, efficient, and applicable to real-world problems across diverse domains including social networks, biological systems, and industrial applications. His research group is actively investigating how to integrate graph learning with generative AI, enhance model robustness against attacks, and develop efficient training methods for massive datasets. His recent publications demonstrate a clear trend toward integrating traditional graph machine learning with emerging AI paradigms, particularly large language models. His work spans both theoretical foundations and practical applications, with increasing focus on real-world deployment challenges. The research covers diverse subfields including robustness certification, efficient model training, and application-specific adaptations for domains like manufacturing, healthcare, and cybersecurity. Dr. Liu has received numerous prestigious awards recognizing his research excellence: NSF CAREER Award (2025) AAAI-2025 New Faculty Highlights National AI Research Resource Pilot Award (2024) ACM SIGKDD Outstanding Dissertation Award (Runner-up, 2023) Amazon Research Award (2023) NCSU Data Science Academy Award (2023) NCSU Faculty Research and Professional Development Award (2023) Chinese Government Award for Outstanding Students Abroad (2022) Best Paper Honorable Mention Award at ICHI (2019) MSU Cloud Computing Fellowship (2021) MSU Engineering Distinguished Fellowship (2017) Dr. Liu actively mentors students at all levels, currently advising multiple PhD and Master's students including Zhichao Hou, Weizhi Gao, Xingyue Shi, and Daniel Buchanan. His research is supported by significant funding from organizations including NSF, Amazon Research, Snap Research, and internal university grants such as the NCSU Data Science Academy seed grant and the Faculty Research and Professional Development Program. He is expanding his lab to address emerging challenges in AI safety, large-scale graph learning, and trustworthy foundation models, with plans to recruit additional PhD and Master's students for Fall 2025 and 2026. Dr. Liu leads the Network and Data Science research group at NC State, focusing on large-scale graph neural networks and trustworthy AI. The group collaborates with institutions including Oak Ridge National Laboratory and industry partners like Amazon. They have developed innovative approaches such as LazyGNN for efficient large-scale graph learning and ProTransformer for enhancing transformer robustness. The lab maintains strong connections with the broader research community through tutorials at major conferences and active participation in standard-setting research venues.
